Handbook of Combustion, Volume 3 (Gaseous and Liquid Fuels)

j1 1 Overview of Gaseous Fuels Anuradda Ganesh 1.1 Introduction Gaseous fuels are popular and have distinct advantages...

Author: Maximilian Lackne | Franz Winter

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1 Overview of Gaseous Fuels Anuradda Ganesh 1.1 Introduction

Gaseous fuels are popular and have distinct advantages over solid and liquid fuels. They are easy and convenient to handle, generally free of any mineral impurities, require low or negligible maintenance of burners and result in good combustion efﬁciencies. Generally, in a highly populated industrial area, a distribution network is used to deliver gaseous fuels on an on tap basis, or some industries store gaseous fuels in gas holders. A few industries also produce the fuel on-site for their use. Gaseous fuels are either extracted from naturally occurring resources or are manufactured, and are composed mostly of one or a mixture of hydrocarbons (methane, propane, butane), carbon monoxide and hydrogen. Methane, is one of the most common constituents of gaseous fuels. The biogenic and thermogenic degradation of organic materials (be it fossils or waste) leads to the formation of methane. Depending on the number of years over which the degradation has occurred, the environmental conditions to which it has been subjected, and the composition of the precursors, the percentage of methane is seen to vary. Natural gas, coal bed methane, methane hydrates, biogas, and landﬁll gas all have methane as the main combustible component. Most of the synthesized gaseous fuels, however, mainly have carbon monoxide and hydrogen contributing to the caloriﬁc value. Hydrocarbons such as propane and butane are popular in the form of liqueﬁed petroleum gas (LPG), used as a domestic or transportation fuel.

1.2 Classification of Gaseous Fuels

Gaseous fuels are mainly classiﬁed based on their mode of occurrence and can be grouped as follows: .

Naturally occurring gases (the gases maybe extracted directly, or obtained as a product of another process like reﬁning).

Handbook of Combustion Vol.3: Gaseous and Liquid Fuels Edited by Maximilian Lackner, Franz Winter, and Avinash K. Agarwal Copyright Ó 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32449-1

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. .

Synthesized gases (which are manufactured from other sources such as solid/ liquid fuel or water). By-products (these gases are by-products of speciﬁc coal processing or utilization reactors).

1.2.1 Naturally Occurring Gases 1.2.1.1 Natural Gas Natural gas in its raw form consists primarily of methane but is a mixture of hydrocarbons such as ethane, propane, butane and pentane. It is found in gas wells and as dissolved gas in oil wells. The natural gas that comes from oil wells is therefore also called associated gas [1]. In some locations, natural gas contains large amounts of nitrogen and carbon dioxide. In some places, small yet recoverable amounts of helium are also found. Similarly, hydrogen sulﬁde is also present, which may be treated for production of elemental sulfur [1]. Natural gas is considered as a fossil fuel, as scientists believe that it is formed by decay of sea plants and animals which died many hundred million years ago. It is believed that these dead plants and animals sank to the bottom of the oceans and were buried under layers of sedimentary rocks. With time, the rocks grew in thickness to thousands of meters, thereby subjecting the dead plants and animals to extreme pressure and temperature conditions. The petroleum and natural gas thus formed were trapped in the rock layers. Natural gas processing comprises separation of various condensates, hydrocarbons other than methane (small quantities of ethane, propane and butane are left behind), and impurities. It broadly involves the following four steps: . . . .

oil and condensate removal water removal separation of natural gas liquids (NGLs) sulfur and carbon dioxide removal.

The ﬁnal product is a dry, pipeable, sweet (absence of hydrogen sulﬁde) gas. The byproducts are oil condensates (when raw natural gas is sourced from oil wells and exists as dissolved natural gas) and natural gas liquids (when raw natural gas is coming directly from gas wells) and are sent for further processing as they have commercial value. 1.2.1.2 Coal Bed Methane (CBM) Coal bed methane (CBM), similarly to natural gas, has a high percentages of methane and is found in seams of coal, and therefore is also called coal seam methane. Until recently, it was considered as a hazard and explosions due to lack of proper ventilation in mines occurred [2]. To reduce hazards and explosions, ventilation was provided, gases diluted and the mixture released to the atmosphere. Both to avoid release of a potent greenhouse gas and to tap the natural reserves of a gaseous fuel, it is now considered as a valuable source of gaseous fuel.

1.2 Classification of Gaseous Fuels

The difference between natural gas and CBM is the way in which the gas is actually trapped within the rock. In conventional reservoirs, natural gas is contained in the pore spaces between the sand grains, and in coal methane it is actually absorbed on the surface of the rock. Gas is part of the coal and is produced when coal is actually formed. Often, a coal seam is saturated with water and methane and is held in the coal by the pressure of water. CBM exists in areas where a coal seam is buried deeply enough to maintain sufﬁcient water pressure to hold the gas in place. A coal seam has favorable reserves if it produces 1–2 m3 per tonne of coal. It is reported that CBM extraction is economical at >1 m3 t1 of coal when a coal seam is 6 m or more thick [3]. 1.2.1.3 Methane Clathrates Clathrates are large molecules forming cage-like structures and when the molecules are water they are called hydrates. Hydrates are unstable and tend to dissociate rapidly due to the presence of a large, empty cavity at the core of their structure. When methane is added into the cavity, it stabilizes the hydrate structure and therefore the product is called a gas hydrate; it is also called a methane clathrate [4, 5]. Methane is by far the most commonly encountered guest in naturally occurring clathrates. Methane clathrates are also called natural gas clathrates. Cages are arranged in a body-centered type of packing wherein a unit cell contains 46 molecules of water and up to eight molecules of methane. Generally, not all cages are occupied. If all were occupied by methane, then 1 m3 of solid hydrate could contain 170.7 m3 of methane at standard temperature and pressure (STP). However, in Nature 1 m3 contains up to 164 m3 of methane [4]. Methane hydrate, when either warmed or depressurized, reverts back to water and natural gas. Gas hydrates require a cold temperature at moderate pressure or a warm temperature at higher pressure. They are reported to be stable at water depths below 500 m, where temperature and pressure are favorable for gas hydrate stability. Gas hydrates have been found at depths of over 4000 m within the ocean. Hydrate deposits may be several hundred meters thick and generally occur in two types of settings: under Arctic permafrost and beneath the ocean ﬂoor. Global estimates, although they vary, indicate that the energy content of methane as hydrates exceed the combined energy content of all other known fossil fuels. It is reported that as much as 1019 g of carbon is trapped, mostly as CH4, within solid gas hydrates [4, 5]. 1.2.1.4 Liquefied Petroleum Gas Liqueﬁed petroleum gas (LPG) is one of the ﬁrst end products upon reﬁning crude petroleum. LPG consists of C3 and C4 compounds and is liqueﬁed at room temperature by application of moderate pressure. LPG is available as commercial propane and also as commercial butane in various countries. When a mixture of two hydrocarbons is sold, then the LPG normally has higher proportions of C4 compounds [1]. It is also known as bottled gas. LPG products are high caloriﬁc value gases and are commonly used as domestic fuel. Since LPG is odorless, odorants such as thiols or sulﬁdes are added to detect leakage. LPG burners are available for domestic and commercial use [1].

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1.2.2 Synthesis Gases 1.2.2.1 Biogas and Landfill Gas Biogas is the gas produced by anaerobic biological process (popularly called anaerobic digestion) of organic materials and is composed mainly of methane, carbon dioxide, and water vapor, along with small amounts of hydrogen sulﬁde, nitrogen, and hydrogen. The gas formed from the treatment of sewage and industry waste water is also called biogas. Biogas production takes place mainly in two stages – acidogenesis and methanogenesis. Acidogenesis involves breaking down of complex organic molecules into simple organic acids by microbes called acidogenic bacteria. These acids are later converted in the methanogenesis step into methane and carbon dioxide by the methanogenic bacteria. In the acidogenesis stage, hydrogen gas is also evolved; however, hydrogen is not seen in signiﬁcant amounts in the ﬁnal biogas because it is used up by the methanogenic bacteria in making methane. The digestible organic material is called the substrate. The yield and composition of the biogas depends on the nature of substrate. The percentage of ﬁxed solid (FS), total solid (TS), and volatile solid (VS) is important for the yields; it is expected that a substrate having a large percentage of VS will give lot of biogas. Compounds having a high lignin content, however, behave stubbornly. The carbon to nitrogen (C/N) ratio is also important. In case of non-lignin substrate, a C/N ratio of 25–30 is considered good. In cases where the lignin content is higher, for example in woody material, a C/N of 40–50 is considered good [6]. The biogas production is sensitive also to pH and temperature. The acidogenic bacteria which produce fatty acids can tolerate low pH, whereas the methanogenic bacteria cannot survive below pH 5.5. It is generally preferable to maintain the pH between 6.5 and 8.5. There are two commonly known ranges of temperatures in which anaerobic bacteria survive – mesophilic (21–40 C) and thermophilic (40–60 C). Parameters such as loading rate (weight of volatile solids loaded each day divided by volumeofthedigester)andhydraulicretentiontime(HRT),whichistheaveragenumber of days a unit volume of the substrate stays in the digester, are also important [6, 7]. In addition to all these, agitation or mixing is advantageous as this helps control scum formation, maintains a uniform temperature throughout the digester, eliminates passive pockets, and so on. Large amounts of ammonia, urea, pesticides, herbicides, antibiotics, heavy metals, and synthetic detergents act as toxins and are harmful for the bacteria. Another source of methane is landﬁll gas. Similarly to anaerobic digestion in the biogas plant, the organic matter in the garbage dumped into landﬁlls also biodegrades and methane is evolved. Although this gas contains methane, the percentages are appreciably lower than those in natural gas and biogas and are in the range 30–50%, the remainder mainly being carbon dioxide. Landﬁll gas also contains varying amounts of nitrogen, oxygen, sulfur, and so on. Mercury is also known to be present in landﬁll gas and sometimes the radioactive contaminant tritium has also been reported. Many toxic chemicals such as benzene, toluene, chloroform, vinyl chloride, and carbon tetrachloride have also been reported [8].

1.2 Classification of Gaseous Fuels

Landﬁll gas is obtained by carefully installing gas collection systems which include a series of wells and a ﬂare system installed in the landﬁlls during construction. The gas thus formed is diverted to a central point where it is processed and treated in accordance with its ultimate usage – to be ﬂared off or used to fuel a generator. 1.2.2.2 Producer Gas, Synthesis Gas and, Blue Gas Producer gas is a product of partial combustion of biomass or coal with air or a mixture of air and steam. This process is called gasiﬁcation. The reactor wherein this process occurs is called a gasiﬁer. Urban waste and natural gas are also used as precursors. Carbon monoxide and hydrogen are the two principle combustible components of producer gas. Carbon dioxide and nitrogen are also present in relatively large quantities and consequently the resultant gas is of low caloriﬁc value. For speciﬁc application (to eliminate nitrogen in the gases), oxygen is used instead of air [7, 9]. Apart from the initial combustion reaction which takes place due to the presence of sub-stoichiometric amounts of oxygen, the other important reactions are as follows: Boudard reaction: CO2 þ C > 2CO DH ¼ 172:6 kJ mol1

Water gas reaction (steam-carbon reaction): C þ H2 O > CO þ H2 DH ¼ 131:4 kJ mol1 C þ 2H2 O > CO2 þ 2H2 DH ¼ 88:0 kJ mol1

ð1:1Þ

ð1:2Þ ð1:3Þ

Water gas shift reaction: CO þ H2 O > CO2 þ H2 DH ¼ 41:2 kJ mol1

ð1:4Þ

C þ 2H2 > CH4 DH ¼ 75:0 kJ mol1

ð1:5Þ

These reactions take place at high temperatures of the order of 800–1000 C. The methane-forming reaction is favored at lower temperatures and higher pressures [10]. Many design variations exist in the gasiﬁers. The most popular types of gasiﬁers are based on the ﬂow conditions and are the moving bed, the entrained ﬂow, and the ﬂuidized bed reactors. Recently, underground coal gasiﬁcation (UCG) has been looked into as an alternate to convert unminable coal deposits into synthesis gas. This technology is also expected to be advantageous for gasiﬁcation of high-ash coal, where problems with surface gasiﬁcation are still being resolved. In most UCG trials the gases have been used for power generation. Recently, syngas production has also been investigated [11]. When the producer gas consists of varying amounts of hydrogen and carbon monoxide, and is used as intermediate for producing synthetic natural gas, methanol, or synthetic petroleum, then the gas is called synthesis gas. Fischer–Tropsch conversion is one such well-known process for conversion of synthesis gas to synthetic liquid fuels. Normally, synthesis gas has a higher caloriﬁc value than

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producer gas because of the absence of nitrogen. Oxygen instead of air is generally used as oxidant in such cases [12]. When superheated steam is used for the gasiﬁcation reaction involving mainly the water gas reaction (Equation 1.2), then water gas is generated [1]. Theoretically, carbon monoxide and hydrogen should be produced in equal proportions. However, in practice it is impossible to exclude the other reactions (Equations ) and Boudards reaction (Equation 1.1), due to which carbon dioxide is also present. Owing to the high carbon monoxide content, this gas gives a blue ﬂame and is therefore called blue water gas or blue gas. 1.2.2.3 Hydrogen Hydrogen is today considered to be an attractive source for replacement of conventional fossil fuels. Since hydrogen is a fuel which generates no pollutants upon combustion, it is considered to be a clean energy source. Hydrogen can be used as a fuel directly in internal combustion engines, and can be used to power a vehicle via fuel cells. Hydrogen for fuel cells can be generated either on-board through catalytic steam reforming of hydrogen or by storing it as hydrates or in carbon nanotubes as an intermediate step. Natural gas and naphtha have been the main source of commercial hydrogen. Gasiﬁcation of coal and electrolysis of water are other industrial methods used for hydrogen production. Recently, alternative resources such as biomass are being exploited for production of biomass. For hydrogen production from biomass, various routes are being explored which include biomass gasiﬁcation coupled with the water gas shift reaction; fast pyrolysis followed by reforming of the resultant oil is also being investigated [13–15]. Microbial conversion of biomass is being studied extensively and is fast gaining popularity. Various hydrogen-producing microorganisms have been used to produce hydrogen selectively upon breaking up of biomass carbohydrates at various stages. Molecular hydrogen can also be recovered as a product or co-product of the anaerobic fermentation of biomass. It is reported that photosynthetic algae are capable of generating molecular hydrogen by bio-photolysis. However, the microbial generation of hydrogen is still far from achieving commercial success [7, 15]. 1.2.3 By-Product Gases 1.2.3.1 Blast Furnace Gas When the combustion gases in the blast furnace move upwards through the descending mix of coke, iron ore and ﬂux, the carbon dioxide is reduced to carbon monoxide, and the steam also decomposes to hydrogen and carbon monoxide. The reduction of iron oxide is achieved mostly by carbon dioxide and marginally by hydrogen. The gases leave the furnace top at temperatures of about 200 C. These gases are rich in carbon monoxide and poor in hydrogen content. The carbon dioxide content is also high. The gases also have a high dust content and have to be cleaned before any use [1].

1.3 Properties of Gaseous Fuels

1.2.3.2 Coke Oven Gas Coke oven gases are a by-product of the coal carbonization process. They are a result of secondary cracking reaction of the primary tar vapors at the high temperature in the coke oven. Methane and hydrogen form the principle combustible components of this gas. Table 1.1 gives the representative compositions of various gaseous fuels [1, 4, 6, 7, 9, 10, 16]. It must be noted that most of these gases are produced through various reactions either in Nature or by humans and are a mixture of gases. The composition may spread beyond the range given.

1.3 Properties of Gaseous Fuels

Gaseous fuels are characterized by their composition and the constituent gases contribute to the fuel-related properties. These properties are also dependent on many physical and chemical variables. The thermal and transport properties of the gaseous fuels are important for simulation and understanding of the combustion process. These include density, speciﬁc heat, viscosity, thermal conductivity and binary mass diffusivity. In most cases, the ideal gas laws are applicable. However, since most of the gaseous fuels are a mixture of various species, it is important to use the appropriate mixing rules for evaluating these properties, especially for fundamental studies such as aimed at understanding the combustion process. The subject had dealt with in detail in the book by Kuo [17]. Speciﬁc fuel-related properties are discussed here and below: Fuel properties such as caloriﬁc value, adiabatic ﬂame temperatures, Weaver ﬂame speed, ﬂammability limits, Wobbe number, and methane number are speciﬁc to each gaseous fuel, depend on its composition, and play an important role in classifying the gaseous fuels for their application and interchangeability. 1.3.1 Calorific Value

Gaseous fuels are often classiﬁed as high, medium and low caloriﬁc value gases. High caloriﬁc value gases are also sometimes called rich gases and the low caloriﬁc value gases are called lean gases. The typical ranges of caloriﬁc values of gaseous fuels are shown in Table 1.1. 1.3.2 Wobbe Number (Wo)

The Wobbe number is an important factor when determining the interchangeability of the gases in a burner, that is, for the same burner, the pressure drop (DP) across the

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a)

—

— — — — — — — — 100

— —

— — — — — — — — — — — 0–70 —

Acetylene (%)

Butane (%)

—

— — — <0.2 0.4 <0.5 — — —

0–0.4 —

Oxygen (%)

If Oxygen is used instead of air for its production, nitrogen will be absent, correspondingly increasing the other percentages.

—

—

— 100

— — — — — — — — — 30–100 —

— — — — — — — — —

0–0.8 <0.1 25–35 10–20 50–54 35–50 0–3 100 —

Propane (%) 1–3 —

Ethane (%) 0–16 0–6

Hydrogen (%) 0–2 —

Natural gas Coal bed methane Methane clathrates Biogas (digester gas) Landﬁll gas Syngas (reformed gas)a) Producer gas Coke oven gas Water gas Blast furnace gas Hydrogen Acetylene LPG Carbon monoxide

Carbon monoxide (%)

80–96 0–0.5 93–95 — Methane : water ¼ 1 : 6 60–80 — 35–50 <0.1 1–3 12–16 1–3 20–30 28–32 5–7 10–15 30–40 — 25–30 — — — —

Methane (%)

Representative composition of various gaseous fuels.

Gaseous fuel

Table 1.1

—

0–2 3–4 40–45 45–50 5–6 4–6 50–60 — —

0.5–8.5 0.5–5

Nitrogen (%)

—

0–0.8 <0.1 — — — — — — —

0–0.02 —

Hydrogen sulfide (%)

—

0–0.8 0–0.8 0.1–5 35–40 45–60 3–4 6–12 2–6 4–6 8–12 — —

Carbon dioxide (%)

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j 1 Overview of Gaseous Fuels

1.3 Properties of Gaseous Fuels

burner oriﬁce is the same, the area of the oriﬁce (A) is the same, and when heat release rates are similar, then the gases may be used interchangeably. The heat release rate Q is given by Q ¼ CV V

ð1:6Þ

The volumetric ﬂow rate (V) is given by Au

where u is the velocity of the gases, which in turn is given by sﬃﬃﬃﬃﬃﬃﬃﬃﬃ 2DP u¼ r

ð1:7Þ

ð1:8Þ

To permit interchangeability of fuel gas in the same burner, the heat release rates for the two gases (1 and 2) of interest need to be equal, Q1 ¼ Q2, that is, sﬃﬃﬃﬃﬃﬃﬃﬃﬃ sﬃﬃﬃﬃﬃﬃﬃﬃﬃ 2DP 2DP CV1 A ¼ CV2 A r1 r2

ð1:9Þ

Therefore, for interchangeability of two gases: CV1 CV2 pﬃﬃﬃﬃﬃ ¼ pﬃﬃﬃﬃﬃ r1 r2

ð1:10Þ

Thus we see that the heat release rate across a burner is dependent on both caloriﬁc value and density of gases. This number, which is derived from the caloriﬁc value and the speciﬁc gravity (in practice, the speciﬁc gravity with respect to air is used instead pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ of density) is called the Wobbe number (Wo) and is given by CV= sp:gr: . This is a dimensional number and thus depends on the units chosen [1, 18]. Table 1.2 gives the Wobbe numbers of various gaseous fuels. 1.3.3 Laminar Flame Speed

The speed of combustion of a gaseous fuel is largely determined by the ﬂame velocity, deﬁned as velocity of the unburned gases through the combustion wave in the direction normal to the wave surface. The ratio between the laminar ﬂame speeds of the gas of interest and hydrogen is called the Weaver ﬂame speed factor. This is used to deﬁne the propensity of the gas to react. Hydrogen has a Weaver ﬂame speed factor of 100 [18]. The lower the number, the lower is the ﬂame speed. The Weaver speed factor is greatly inﬂuenced by the amount of hydrogen in the mixture. Inert gases, such as nitrogen and carbon dioxide, and carbon monoxide reduce the ﬂame speed and also the Wobbe number. Hydrogen does the reverse, by increasing both the ﬂame

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Table 1.2 Calorific values and Wobbe numbers for various gases.

Gas Natural gas Coal bed methane Biogas Landﬁll gas Synthesis gas (with N2) Producer gas Coke oven gas Water gas Blast furnace gas LPG Hydrogen Acetylene Carbon monoxide

Calorific value (MJ m3) 35–38 36–40 22–26 10–16 5–10 5–7 16–22 13–16 3–5 105–115 12.7 51.5 11.7

Wobbe number 26–28 27–30 26–28 22–24 24–26 8–10 28–30 28–30 16–20 30 35 50 28

speed and the Wobbe number. Hydrocarbons reduce the ﬂame speed but increase the Wobbe number [17, 18]. Flame speed has been reported to be inﬂuenced by many physical and chemical variables. Thermal diffusivity, speciﬁc heat, initial temperature, pressure, additives, fuel molecular structure, and so on have all been found to have varied effects on ﬂame speeds. (It may be noted that in the absence of nitrogen, i.e., when oxygen instead of air is the oxidant, the ﬂame speed for the mixtures increases 5–10-fold). It is reported that ﬂame speed varies as the square of the absolute temperature and inversely as the fourth root of the pressure [18]. When the ﬂame propagates through a tube where the surface to volume ratios are very high, then the cooling effects, also called the quenching effects, can be high enough to slow the propagation drastically and ﬁnally even terminate the combustion process [17, 18]. 1.3.4 Flammability Limits

Flame speed is inﬂuenced by the temperature of the mixture, which in term depends on the fuel to oxidant ratio. The mixture with the maximum ﬂame temperature is also the mixture with maximum ﬂame speed; for example, for hydrocarbon fuels, the maximum ﬂame temperature occurs at near stoichiometric conditions and this point corresponds to the peak of the ﬂame speed. On both sides of the stoichiometric mixture, that is, on the richer and on the leaner sides, the ﬂame speed decreases. There would be a critical ﬂame speed below which ﬂame propagation is not possible. When the mixture is very rich, then there is too little oxidant for a stable ﬂame to propagate, and when the mixture is too rich, there is too little fuel for a stable ﬂame to propagate. In both cases there will be heat loss, which will not be compensated by combustion. A mixture below the critical ﬂame speed may be ignited but will not be able to sustain

1.3 Properties of Gaseous Fuels

the ﬂame and propagate. Since this critical ﬂame speed will exist on both sides, that is, the rich and lean sides, there exists lower and upper ﬂammability limits [18]. Like ﬂame speed, ﬂammability limits are inﬂuenced by many variables. An increase in pressure widens the range, raising the upper limit and lowering the lower limit. There exists a critical pressure below which the mixture ceases to be ﬂammable at any concentration [17, 18]. A high initial temperature of the mixture helps in widening the limits due to higher ﬂame speeds and therefore the propagation of the ﬂame through even difﬁcult compositions. This also leads to the fact that the higher the maximum ﬂame velocity, the wider are the ﬂammability limits in air. Carbon monoxide is an exception, with wide ﬂammability limits (12.5–74%) despite a very low ﬂame velocities of 0.5 m s1 [18]. 1.3.5 Methane Number (MN)

The octane number represents the knocking tendency of a liquid fuel and is critical in spark-ignited combustion engines. With the use of alternative gaseous fuels in such engines, the knocking tendency of the gaseous fuels also needs to be measured and represented. The knock measurement for gaseous fuels was standardized in 1930 and the ASTM established a standard for gaseous fuel knock testing in terms of the motor octane number (MON) method. The MON method has an upper limit of 120 and therefore is suitable for gases such as LPG. Since many alternative gaseous fuels were found to have MON values greater than 120, a new rating system extending the measurable range beyond MON was developed. The term methane number was coined and deﬁned as [16] The percentage by volume of methane blended with hydrogen that exactly matches the knock intensity of the unknown gas mixture under speciﬁed operating conditions in a knock testing engine. For the range beyond 100 MN, methane–carbon dioxide mixtures were used as reference mixtures. In this case in accordance with the deﬁnition, the MN is 100 þ the % CO2 by volume in the reference methane–carbon dioxide mixture. Methane numbers for various gaseous fuels are given in Table 1.3.

Table 1.3 Methane numbers for gaseous fuels [16].

Gaseous Fuel

Methane number (MN)

Biogas Landﬁll gas Water gas Synthetic gas

140 140 30 60

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Table 1.4 Mixture calorific values for various gases.

Mixture calorific value (MJ m3)

Gas Natural gas Coal bed methane Biogas Landﬁll gas Synthesis gas (with N2) Producer gas Coke oven gas Water gas Blast furnace gas LPG Hydrogen Acetylene Carbon monoxide

2.6–2.7 2.7–2.8 2.6–2.7 2.5–2.7 2.4–2.6 1–2 2.5–3 2.5–3 1.5–3 2.5–3.5 3.1 5.0 2.9

1.3.6 Mixture Calorific Value (Hmix)

The mixture caloriﬁc value (Hmix) is deﬁned as the heat output upon combustion of a unit volume of a stoichiometric air–fuel mixture. In the case of a liquid fuel such as diesel, Hmix would be the caloriﬁc value of diesel burned in a mixture with air since the volume occupied by the diesel is negligible in the said volume of air–fuel mixture. When a gaseous fuel is used, the volume occupied will include the volume of air plus fuel [10]. Therefore, in the case of gaseous fuels the mixture caloriﬁc value is given by Hmix MJ m3 ¼

CV ðMJ m3 Þ 1 þ ðair=fuel stoichiometric ratioÞ

ð1:11Þ

This property is of signiﬁcance today because gaseous fuels are being used in dedicated spark-ignited engines and therefore the relative performance of various available gaseous fuels needs to be predicted. A list of mixture caloriﬁc values is given in Table 1.4.

1.4 Conclusion

Gaseous fuels are composed of methane, hydrogen and carbon dioxide as the main combustible components. Carbon dioxide and nitrogen are the two inert gases that, although not contributing to the caloriﬁc value, contribute substantially to other properties such as ﬂame speed, Wobbe number, and methane number; although the caloriﬁc values of the gaseous fuels vary widely, the Wobbe number and the mixture

References

caloriﬁc value do not. Producer gas seems to be a distinct exception. This shows that there is a wide range of gaseous fuels which seem interchangeable for speciﬁc applications.

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Orient Longman, India. Narasimhan, K.S., Mukherjee, A.K., Sengupta, S., Singh, S.M., and Alam, M.M. (1998) Coal bed methane potential in India. Fuel, 77 (15), 1865–1866. Chand, S.K. (2001) Status of coal bed methane in India. TERI Newswire, VII (14), 16–31. Beauchamp, B. (2004) Natural gas hydrates: myths, facts and issues. Comptes Rendus Geosciences, 336 (9), 751–765. Chatti, I., Delahaye, A., Fournaison, L., and Petitet, J.-P. (2005) Beneﬁts and drawbacks of clathrate hydrates: a review of their areas of interest. Energy Conversion and Management, 46, 1333–1343. House, D. (2006) Biogas Handbook, Alternative House Information, USA. Klass, D.L. (2006) Biomass for Renewable Energy, Fuels and Chemicals, Academic Press (Elsevier), USA. Brosseau, J. and Heitz, M. (1994) Trace gas compound emissions from municipal landﬁll sanitary sites. Atmospheric Environment, 28 (2), 285–293. Beenackers, A.A.C.M. and Maniatis, K. (1997) Gasiﬁcation technologies for heat and power from biomass, in Biomass Gasiﬁcation and Pyrolysis – State of Art and Future Prospects (eds A.V. Bridgwater and M. Kaltschmitt), CPL Press, pp. 24–52.

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Gasiﬁcation of Biomass, IIT Bombay and DNES, India. Khadse, A., Qayyumi, M., Mahajani, S., and Aghalayam, P. (2007) Underground coal gasiﬁcation: a new clean coal utilization technique for India. Energy, 32, 2061. Srinivas, S., Khadse, A., Aghalayam, P., Ganesh, A., Malik, R.K., and Mahajani, S. (2007) Fischer–Tropsch synthesis of the UCG product gas. Proceedings of Pittsburgh Coal Conference (PCC) 2007, 10–14 September 2007, Johannesburg, South Africa. Czernik, S., Evans, R., and French, R. (2007) Hydrogen from biomassproduction by steam reforming of biomass pyrolysis oil. Catalysis Today, 129 (3–4), 265–268. Bleeker, M.F., Kersten, S.R.A., and Veringa, H.J. (2007) Pure hydrogen from pyrolysis oil using the steam-iron process. Catalysis Today, 127 (1–4), 278–290. Nath, K. and Das, D. (2003) Hydrogen from biomass. Current Science, 85, 265–271. Malenshek, M. and Olsen, D.B. (2009) Methane number testing of alternative gaseous fuels. Fuel, 88, 650–656. Kuo, K. K. (2005) Principles of Combustion, John Wiley & Sons, Inc., Hoboken, NJ. Mishra, D.P. (2008) Fundamentals of Combustion, Prentice-Hall of India, Delhi.

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2 Global Warming Implication of Natural Gas Combustion Jacob Ademola Sonibare 2.1 Introduction

Combustion of primary energy sources as required in energy delivery and hydrocarbon destruction in gas ﬂares in the oil and chemical industry may generate products which are known agents of global warming and a gradual increase in the Earths temperature, and currently attracting great attention in research due to its associated effects. Global warming is believed to induce gradual climate change with impacts such as the expansion of habitats of tropical insects [1], shifts in disease patterns [2], changes in ﬂuvial geomorphology [3], enhanced risk of suicide [4], and damage to crops [5], among others. Energy requirements in the daily activities of humans have resulted in the development of several ﬁelds of energy-related studies initially with the sole aim of efﬁcient energy delivery and utilization. However, having recognized the question of the globes capacity to sustain these activities and the general environmental problems associated with them, a number of international conferences, treaties, conventions, and protocols were embarked upon, starting in Stockholm in 1972 [6]. This is for the management of the Earths resources in an effort to ensure sustainable economic development. The clean energy requirement for environmental pollution control has now become another major issue attracting global attention. Economic and industrial growth call for more energy due to the associated increase in the number of energy-powered appliances. Energy plays a vital role in the socioeconomic development and raising of standards of humans [7–11]. Its consumption can be in the transportation, domestic, industrial, and service sectors, but with the levels varying from country to country (Figure 2.1). In the transportation sector, energy use includes the energy consumed in moving people and goods by road, rail, air, water, and pipelines [12]. The road transport component includes light-duty vehicles (e.g., automobiles, sports utility vehicles, minivans, small trucks, and motorbikes) and heavy-duty vehicles (including large trucks used for moving freight and buses for passenger travel). In the domestic sector, energy is consumed by households (e.g., cooking, heating, and cooling), excluding transportation uses.

Handbook of Combustion Vol.3: Gaseous and Liquid Fuels Edited by Maximilian Lackner, Franz Winter, and Avinash K. Agarwal Copyright Ó 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32449-1

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Others 7% Services 9%

Industry 23%

Services 14%

Industry 22%

Others 1% Services 3%

Industry 11% Transport 20%

Domestic 23%

Domestic 30% Transport 38% (a) Middle East

Transport 34% (b) Europe

Domestic 65% (c) Africa

Figure 2.1 Typical regional sectoral energy consumption.

Energy consumed in the industrial sector is represented by diverse industries, including manufacturing, agriculture, mining, and construction for a wide range of activities such as process and assembly uses, space conditioning, and lighting. The service sector, also referred to as the commercial sector, consisting of businesses, institutions, and organizations that provide services, also requires energy in order to function. 2.1.1 Energy Sources

Primarily, energy can be from fossil fuels, renewable sources, or nuclear sources. The fossil fuels include coal, natural gas, crude oil, and gas to liquids, while energy from renewable sources includes hydroelectric power, geothermal, solar, wind and biomass. Any of these can be an energy carrier because they contain energy in different forms, which can be converted into a usable energy when required [13]. However, electricity, another important energy carrier, is not a primary energy source. The choice of energy source or carrier is a function of several factors, among which are desires for greater convenience and cleanliness [14], affordability [15], government policies [16, 17], technological choices [18], and storage requirements [19]. Emotional and cultural considerations may even be applicable on some occasions [20]. For adequate planning, energy projection has been a reliable tool. Global energy projections include that from the Organization of the Petroleum Exporting Countries (OPEC), the Energy Information Administration of the Department of Energy (DOE) of the United States, and that of the Paris-based International Energy Agency (IEA). OPEC is a permanent, intergovernmental organization, established in Baghdad, Iraq, in September 1960 [21]. At present, it comprises 12 Members: Algeria, Angola, Indonesia, Iran, Iraq, Kuwait, Libya, Nigeria, Qatar, Saudi Arabia, United Arab Emirates, and Venezuela, with headquarters in Vienna, Austria. Its objectives are to coordinate and unify petroleum policies among Member Countries, in order to secure a steady income to the producing countries; an efﬁcient, economic and regular supply of petroleum to consuming nations; and a fair return on capital to those investing in the petroleum industry. Its global energy projection is contained in the World Oil Outlook [22].

2.1 Introduction

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The IEA is an intergovernmental organization which acts as energy policy advisor to 28 member countries of the Organization for Economic Cooperation and Development (OECD) in their effort to ensure reliable, affordable and clean energy for their citizens. It was founded in November 1974 with an initial role of coordinating measures in times of oil supply emergencies. Currently, its mandate has broadened to incorporate the Three Es of balanced energy policy making: energy security, economic development and environmental protection [23]. Since 1993, the IEA has provided medium- to long-term energy projections using a World Energy Model (WEM), a large-scale mathematical construct designed to replicate how energy markets function. It is used to generate detailed sector-by-sector and region-by region projections for both the Reference and Alternative Policy Scenarios. The energy demand is forecast by extrapolation of current economic development factors and geopolitical considerations [24]. Its global energy projection is reported in the World Energy Outlook [25]. The Energy Information Administration (EIA), created in 1977, is the body saddled with the responsibility of managing Ofﬁcial Energy Statistics from the United States Government. Its mission is to provide policy-neutral data, forecasts, and analyses to promote sound policy making, efﬁcient markets, and public understanding regarding energy and its interaction with the economy and the environment [26]. By law, EIA products are prepared independently of government administration policy considerations. It neither formulates nor advocates any policy conclusions. Its global energy projection is reported in International Energy Outlook [12]. Energy projections from these and other sources are reliable within the limits of their assumptions. The global energy consumption has been increasing in the last four decades and it is a commonly accepted projection that the increase may continue into the next two decades (Figure 2.2). However, this accepted level indicates that the global energy consumption within the period 2004–2030 will increase by about 1.6–1.7% [12, 22, 25]. Fossil fuels, which account for close to 83% of the overall increase in energy demand between 2004 and 2030, are projected to remain the 18000 16000

Demand (Mt)

14000 Gas

12000 10000 8000

Oil

6000 4000 Coal

2000 0 1960

Hydro/nuclear/renewables

1970

1980

1990

2000 Year

Figure 2.2 Energy demand by type [22].

2010

2020

2030

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18

Figure 2.3 World energy demand by fuel [25].

dominant sources of primary energy. Their share of world demand will edge up from 80% in 2004 to 81% in 2030. Within this period, coal will see the greatest increase in demand, closely followed by oil (Figure 2.3). If price is favorable, it is commonly accepted that gas demand may grow faster than coal demand, although it may not overtake it before 2030. Other renewable energy technologies, including wind, solar, geothermal, wave and tidal energy, will see the fastest increase in demand, but their share of total energy use will still reach only 1.7% by 2030 – up from 0.5% today. Regarding energy consumption, it is projected that the transport sector alone will consume over 20% of these projected energy levels (Figure 2.4), while Isaac and Vuuren [27] projected that the global energy demand for residential heating and cooling will also increase until 2030. Natural gas, as a cleaner burning source of fossil fuel than oil or coal, is now commonly believed to offer part of the solution to climate change and to problems associated with poor air quality [28]. It has a lower carbon content than coal and oil 30 25

2005

20

%

2010 2015

15

2020 10

2025 2030

5 0 North America

Europe

Asia

Middle East Africa

Region

Central & South America

Figure 2.4 Transport consumption of the global energy demand [12].

Average

2.1 Introduction

(about 50% lower than coal and 25% lower than oil), which makes it a favored fuel from an environmental perspective [29]. Similarly, unlike coal and oil, natural gas has a higher hydrogen/carbon ratio and emits less carbon dioxide for a given quantity of energy consumed. Its global consumption growth is propelled by the rapid growth of its use in electric power generation [30], which was driven by regulatory changes and the emergence of new technology [31]. Although having high and volatile prices in developed nations [32], the environmental regulation as experienced in China [33] always compels its use as an energy source. In addition to the environmental friendliness of natural gas, which makes it an attractive energy carrier of choice, the necessity for safe operation during production activities also demands its removal for storage or immediate combustion on-site in gas ﬂares. Flaring is a common method of disposal of ﬂammable waste gases in the upstream oil, gas, downstream reﬁning, and chemical processing industries [34]. When crude oil is extracted from the ground, natural gas associated with the oil is also produced at the surface and in areas of the world lacking infrastructure and markets, this associated gas is usually ﬂared or sometimes vented. The primary purpose of gas ﬂaring is to act as a safety device to protect vessels or pipes from over-pressuring due to unplanned upsets. Whenever the plant equipment items are over-pressured, pressure relief valves automatically releases gas (and sometimes also liquids). The released gases and/or liquids are burnt as they exit the ﬂare chimney. This is possible because a ﬂare is maintained at lower pressure than the facilities it serves [35]. 2.1.2 Hydrocarbon Destruction in Gas Flares

A ﬂare is an open-air ﬂame, usually at the tip of a long stack. The ﬂame is usually exposed to the weather, particularly winds, and it is commonly located far away from personnel and other structures in order to prevent damage. For air pollution regulatory purposes, it is classiﬁed as a stationary combustion source [36]. With the development of the crude oil extraction industry in the last decades of the nineteenth century, large amounts of associated gas, considered more often as a nuisance rather than an asset, were produced [37]. Globally, oil companies burn the gas associated with oil production in routine ﬂares because it is considered to be less proﬁtable, especially in countries that lack sufﬁcient regulation, infrastructure, and markets. Although operational ﬂares cannot be eliminated for safety reason, routine ﬂares can be discontinued for environmental and economic consideration. For this goal to be achieved, feasible alternatives are currently being advocated for improved handling of associated gas in oil ﬁelds [38]. Gas ﬂares are the choice disposal option for handling waste hydrocarbon gases because of their ability to burn efﬁciently [39]. In a ﬂare, complete combustion must occur within the available short residence time. Flame temperature is the primary variable in that combustion process [40]. A typical ﬂare system (Figure 2.5) consists of the ﬂare stack/boom and pipes that collect the gases to be ﬂared. The ﬂare tip at the end of the stack/boom is designed so as to promote the ingress of air into the ﬂare for improved efﬁciency. Seals installed in the stack prevent ﬂashback of the ﬂame. At the

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Figure 2.5 Schematic diagram of a typical flare system [34].

base of the stack is a knock-out drum to prevent liquid carryover into the ﬂare. Other ﬂare ancillaries are the auto-igniter, the wind deﬂector, which acts as a guard to prevent ﬂame blowout, the inlet bafﬂe, which prevents liquid accumulation, and steam, which prevents smoke formation. In order to keep the ﬂare system functional, a small amount of gas is continuously burnt, like a pilot light, so that the system is always ready for its primary purpose as an over-pressure safety system [41]. Usually the target heating value of the waste gas to be ﬂared is 11 MJ scm1 (300 Btu scf1). According to the US Environmental Protection Agency (EPA) [42], if the waste gas does not meet this minimum heating value, auxiliary fuel must be introduced in sufﬁcient quantity to make up the difference. The primary measure of ﬂare performance is combustion efﬁciency [43]. This is determined as the percentage of ﬂare emissions that are completely oxidized to CO2 and deﬁned mathematically as CEð%Þ ¼

CO2 100 CO2 þ CO þ THC

ð2:1Þ

where CE is the combustion efﬁciency, CO2 is parts per million by volume of carbon dioxide, CO is parts per million by volume of carbon monoxide, and THC is parts per million by volume of total hydrocarbon as methane. Globall, y about 150 billion m3 (or 5.3 trillion ft3) of natural gas are ﬂared and vented annually at different locations (Figure 2.6), although as at 2000 it was about

2.1 Introduction

Figure 2.6 Global flaring sites [44].

108 billion m3 with Africa being the largest region (Figure 2.7) and Nigeria the ﬁrst country as at 2007. The 108 billion m3 amount to about 4% of the worlds total marketed gas production (associated and non-associated) and to an estimated 20–30% of the associated gas production. These World Bank estimates of the annual amount of natural ﬂared or vented (unburnt) gas were equivalent to 30% of the European Unions gas consumption, 25% of the United States gas consumption, or 75% of Russias gas exports [44].

11%

34%

15%

Africa Asia-Oceanea Europe Former Soviet Union Central and South America Middle East

9%

North America

10% 18%

3%

Figure 2.7 World flared gas [89].

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Flaring

"No-action scenario

International markets

Domestic markets Site-use and re-injection optimistic scenario

2000

2020

Figure 2.8 World flared gas.

Although there are several ongoing efforts to ensure the total elimination of routine gas ﬂares, the present level contributes a signiﬁcant measure to greenhouse gases. One of these global efforts is the Global Gas Flaring Reduction Initiative, which was originally launched in November 2001 in Marrakech by the World Bank and the Government of Norway. Its aim is to support national governments, development agencies, and the petroleum industry in their efforts to reduce routine ﬂaring and venting of gas associated with the extraction of crude oil. The Initiative was successfully transformed into a Partnership at the World Summit on Sustainable Development (WSSD) in August 2002. However, even with all these efforts, some natural gas is expected to be ﬂared by 2020 (Figure 2.8), a reason why the study of the continuous contribution of emissions from gas ﬂares has to be sustained at its present level.

2.2 A Review of Global Warming Concept

Global warming, a gradual increase in the Earths temperature, can easily be described with the greenhouse effect. The term greenhouse effect was ﬁrst coined in 1827 by the mathematician and scientist Jean Baptiste Fourier [2], although the ﬁrst quantitative work on it was done by the Swedish Nobel Prize winning chemist Svante Arrhenius,. Arrhenius [45] developed a simple mathematical model for the transfer of radiant energy through the atmosphere–surface system, and solved it analytically to show that a doubling of the atmospheric CO2 concentration would lead to a warming of the surface by as much as about 5 C. In a greenhouse, the glass walls reduce airﬂow and increase the temperature of the air within. Analogously, certain gases in the atmosphere act like glass in a greenhouse, allowing sunlight through to heat the Earths surface but trapping the heat as it radiates back into space. As the greenhouse gases build up in the atmosphere, the Earth becomes hotter. As described by the IPCC [46], the Suns radiated energy at very short wavelengths, predominately in the visible or near-visible (e.g., ultraviolet) part of the spectrum,

2.2 A Review of Global Warming Concept

Figure 2.9 The greenhouse effect.

enters the Earth. Roughly one-third of this solar energy that reaches the top of the Earths atmosphere is reﬂected directly back to space while the remaining two-thirds is absorbed by the surface and, to a lesser extent, by the atmosphere. To balance the absorbed incoming energy, the Earth must, on average, radiate the same amount of energy back to space. However, because the Earth is much colder than the Sun, it radiates at much longer wavelengths, primarily in the infrared part of the spectrum (Figure 2.9). Much of this thermal radiation emitted by the land and ocean is absorbed by the atmosphere, including clouds, and re-radiated back to Earth. The greenhouse effect and global warming are currently controversial issues [47] and, as a result, models are being developed to describe the consequences of human activity on the temperature of the Earth. The models considered so far include the general circulation models (GCM) and model-based methods (MBM) dealing with historical data. Whereas the general circulation models approximate the conservation of mass, energy, and momentum with a numerical solution developed, the modelbased models study the relationship between temperature and forcing variables by analyzing time series with various mathematical and statistical tools. One of the latest studies on this is the inter-generational equity index approach adopted by Pan and Kao [48], where it is believed that the environment inherited by the current generation from the previous generation is characterized with a certain quality that might be responsible for this issue of global warming. The greenhouse gases include water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydroﬂuorocarbons (HFCs), and sulfur hexaﬂuoride (SF6) Whereas CO2, CH4, and N2O can occur naturally, HFCs [including perﬂuor-

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ocarbons (PFCs)], SF6 and their derivatives are artiﬁcial. A measure of how much a given mass of greenhouse gas is estimated to contribute to global warming is deﬁned as theglobal warming potential (GWP), which is the cumulative radiative forcing integrated over a period of time from the emission of a unit mass of gas relative to some reference gas [49]. It is a relative scale which compares the gas in question with that of the same mass of carbon dioxide whose GWP is 1. 2.2.1 Water Vapor

This is the most abundant and dominant greenhouse gas in the atmosphere, although it is neither long lived nor well mixed in the atmosphere, varying spatially from 0 to 2% [49]. In addition, atmospheric water can exist in several physical states, including gas, liquid, and solid. 2.2.2 Carbon Dioxide

Carbon dioxide is a naturally occurring gas, a by-product of burning fossil fuels and biomass, land-use changes and other industrial processes. It is the principal anthropogenic greenhouse gas that affects the Earths radiative balance. It is the reference gas against which other greenhouse gases are measured and therefore has a GWP of 1. As indicated earlier, its impact on global warming was discovered as far back as 1896. According to the IPCC [49], its duration in the atmosphere is 50–200 years and its 100-year global warming potential is 1. 2.2.3 Methane

This is another important greenhouse gas because its molecules survive for about 12 3 years in the atmosphere [49] and it is about 30 times more effective as a heat trap than CO2. Methane release into the atmosphere can be traced to natural sources because natural gas contains about 70–95% of methane [50]. Etiope [51] showed that geological emissions of methane are an important greenhouse-gas source. The work established that remarkable amounts of methane, estimated to be in the order of 40 1060 Tg yr1, are naturally released into the atmosphere from the Earths crust through faults and fractured rocks. Similarly, Saarnio et al. [52] reported methane release from wetlands and watercourses in Europe and some adjacent areas while Darling and Goody [53] conﬁrmed the presence of methane in non-polluted groundwater environments in England, including water supply aquifers, aquicludes, and thermal waters. In the energy sector, sources include leaks in natural gas transmission facilities such as pipelines and compressor stations [54], in coal mines [55], and in oil/gas ﬁelds [56]. Agricultural processes such as wetland rice cultivation, enteric fermentation in animals, and the decomposition of animal wastes

2.3 Fundamentals of Natural Gas Combustion

also emit CH4, as does the decomposition of municipal solid wastes [57]. If the methane present in natural gas does not participate in combustion either in energy extraction or in safe operation where associated gas is sent to ﬂares, methane emission will occur. 2.2.4 Nitrous Oxide

N2O contributes about 6% to the greenhouse effect at the moment. Naturally, it is formed in the oceanic environments mainly as a by-product during nitriﬁcation and as an intermediate during denitriﬁcation [58]. However, the spatial variation in its distribution and atmospheric ﬂuxes in the environment can be inﬂuenced by the quality of river waters received from the surroundings [59]. In addition, N2O is largely emitted in nitrogen-fertilized agricultural soils during denitriﬁcation [60] with oxygen concentration, nitrate concentration, and carbon availability being the main factors controlling its rate and the amount emitted [61]. Hynst et al. [62] reported cattle overwintering areas as important sources of N2O. The ﬁrst major ﬁnding on the relevance of nitrous oxide and methane as greenhouse gases was reported by Wang et al. [63]. The study used a one-dimensional radiative–convective model of the atmospheric thermal structure to compute the change in the surface temperature of the Earth for large assumed increases in the trace gas concentrations. It was found that doubling the N2O and CH4 concentrations could cause additive increases in the surface temperature of 0.7 and 0.3 K, respectively. If a temperature of about 600–1000 C is attained during combustion activities, as is usually the case in gas ﬂares, the formation of N2O will be encouraged [64], especially from HCN produced in intermediate reactions. In the atmosphere, it has a duration of about 120 years and a 100-year GWP of 310 [49]. 2.2.5 CFCs

CFCs are extremely effective greenhouse gases and one of the stratospheric ozonedepleting substances. Although there are lower concentrations of CFCs in the atmosphere than CO2, they trap more heat. A CFC molecule is about 10 000 times more effective in trapping heat than a CO2 molecule and their molecules survive for 110 years in the atmosphere.

2.3 Fundamentals of Natural Gas Combustion

Consumption of natural gas as a primary energy source requires combustion, a chemical reaction during which a fuel is oxidized for the release of chemical energy stored in it. Be it in residential use, industrial use, or power generation, once natural

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gas is delivered to end users, combustion activity is required for energy extraction. As reported by Sonibare and Akeredolu [65], natural gas combustion can be a curse or a blessing to society depending on the original intention for which it was meant and the nature of the attention given to its associated emissions. In domestic operations where heat energy is required, natural gas combustion is a great blessing. Cooking and heating of homes are two of its various domestic uses. In the industrial sector, natural gas combustion is useful in energy generation. It is employed in other useful ways and, globally, the numerous advantages of natural gas combustion are currently being enjoyed. Firing of utility boilers for steam generation [66] is a key requirement for industrial growth and natural gas combustion is involved. In the petroleum and allied sectors where natural gas is encountered (in the form of associated gas) without prior preparation for its positive use, its combustion (which takes place in ﬂares, through which they are being disposed of) has become a great problem because of its associated environmental problems in the form of gaseous emission. Akinbami et al. [67] reviewed the extent of environmental degradation by natural gas ﬂaring. Isichie and Sanford [68] highlighted the effects of gaseous emissions resulting from combustion of natural gas in ﬂares on vegetation, and acid rains resulting from gaseous compounds emanating from ﬂares had also been identiﬁed as a major problem [69]. Schwartz and White [70] reported that CO2 and H2O that are products of complete combustion from ﬂares contribute greatly to heat radiation experienced around ﬂares. The non-methane gaseous emissions resulting from natural gas combustion and which are of interest in global warming include the greenhouse gases water vapor, carbon dioxide, and nitrous oxide. In the absence of an efﬁcient combustion medium, methane is bound to be released as an additional greenhouse gas. Complete (Equation 2.2) and incomplete (Equation 2.3) combustion are anticipated in natural gas combustion. They always dictate the nature of the products and the greenhouse gaseous emissions, but with the ﬁrst reaction being more important. Cx Hy þ Ms ðO2 þ 3:76N2 Þ ! n1 CO2 þ n2 H2 O þ n3 N2

ð2:2Þ

Cx Hy þ Ms ðO2 þ 3:76N2 Þ ! n1 CO2 þ n2 H2 O þ n3 N2 þ n4 N2 O þ n5 CH4 þ . . .

ð2:3Þ

In the complete combustion (Equation 2.2), the carbon and hydrogen atoms from the hydrocarbon natural gas react with oxygen available for combustion from air to form CO2 and H2O, respectively, while nitrogen from the same air is released as one of the products without taking part in combustion reaction. Difﬁculty in attaining complete combustion may arise due to many factors that include slower chemistry in a highly turbulent environment (if in gas ﬂares, for instance) and inadequate mixing, which leads to rich pockets of fuel–air and consequently an inhomogeneous mixture [71]. Incomplete combustion leads to the formation of several products resulting from various reactions taking place. Li and Williams [72] gave several reactions that take place in the combustion of natural gas. In the incomplete reaction in Equation 2.3,

2.3 Fundamentals of Natural Gas Combustion

CO is formed as a result of formyl reactions where CHO radicals are either broken down in the presence of an external agent or react with H, O, or OH radicals. The reaction may even be with O2 present in the air made available for combustion. In addition, methylene reactions, where CH2 radicals react with H, O, OH, or CH2 radicals, can give CO as product. NOx are known to sensitize the oxidation of hydrocarbons to CO [73], among other products. Acetylene, ketyl, cyanoxy and isocyanic reactions also lead to CO products. In the presence of OH and HO2 radicals, the CO produced is oxidized to CO2. Also, CHO and O radicals combine to form CO2 while some oxygen available in the combustion react with CH2 to generate CO2. The same occurs in the presence of HC2O, where CHO radical is formed in addition to CO2. According to Li and Williams [72], an isocyanic acid reaction in which HNCO reacts with O radical also produces CO2. A high concentration of CO2 has combined effects of lowering the energy density (or heating value) of reactant gases [74], thus halting the combustion process. Although CO2 is part of the combustion products from natural gas, its production is typically about 32–45% less per unit of thermal output compared with coal and 30% less compared with fuel oil [75] used as alternative fuel in energy generation. To investigate the pattern of CO2 emission from natural gas combustion with nitrogen present in the air unconverted, Sonibare and Akeredolu [65] reported that Equations 2.4 and 2.5 can be used for sweet gas depending on the prevailing operating conditions in the combustion chamber, but for sour gas Equation 2.6 will be applicable. y y y Cx Hy þ x þ ðO2 þ 3:76N2 Þ ! xCO2 þ H2 O þ 3:76 x þ N2 ð2:4Þ 4 2 4 y x y x y Cx Hy þ x þ ðO2 þ 3:76N2 Þ ! CO2 þ H2 O þ CO þ H2 8 2 4 2 4 þ

x y O2 þ 3:76 x þ N2 4 8

ð2:5Þ

y x y x Cx Hy þ x þ ðO2 þ3:76N2 Þ þms H2 S ! CO2 þ H2 Oþ CO 8 2 4 2 0 1 y x y þ þ ms H2 þ @ 2ms AO2 þ 3:76 x þ N2 þms SO2 4 4 8 ð2:6Þ

Some 23 steps have the potential of generating H2O as end product in the combustion of natural gas. One of these reactions is due to the presence of ethane in the natural gas and another is due to the presence of ethene. An increase in H2O concentration can impact positively on the production rate of other combustion products such as N2O, which is also of great concern in global warming. In natural gas combustion, N2O is formed as one of the primary sources of NO (Equation 2.7), which is called the N2O intermediate although it contributes

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less than the other methods of its formation except in the lean premixed ﬂame branch [76]. N 2 þ O2 þ M N2 O þ O

> N2 O þ M > NO þ NO

ð2:7Þ

NO and NO2 are major products of natural gas combustion that are of utmost concern to environmentalists. Beer [77] gave the three principal sources of NOx in combustion of fossil fuel as the ﬁxation of atmospheric (molecular) nitrogen by atomic oxygen at high temperature in an oxidizing atmosphere, the formation of atmospheric (molecular) nitrogen by hydrocarbon fragments in a reducing atmosphere, and the oxidation of nitrogen components organically bound in the fuel (fuel N2). Temperature and CH radicals are very important in NOx formation [78] and control of these is expected in the control of NOx emissions from natural gas combustion. Thermal mechanism, cyanoxy reactions, amidogen reactions, nitroxyl hydride reactions, and nitrous oxide reactions constitute the various reaction mechanisms that are expected to produce NO in natural gas combustion. Radiant energy loss during combustion decreases the combustion temperature and this eventually reduces the rate of NO formation [79]. In the ammonia reaction, NH3 is reduced to NH2, which is eventually reduced to NH from which NO is formed. HNO may also be formed from HNH radical and may then be converted to NO. At times, NH3 may be oxidized to NO in the presence of CO [64]. NO2 is formed mainly due to oxidation of NO in the presence of HO2. This reaction is assumed to occur at not too high a temperature. Sonibare and Akeredolu [65] investigated the formation of NO in sweet natural gas combustion with the following combustion equation: y x y x y CO2 þ H2 O þ CO þ H2 þ Cx Hy þ x þ ðO2 þ 3:76N2 Þ ! 8 2 4 2 4 x y x x O2 þ 3:76 x þ N2 þ NO 8 8 8 4 ð2:8Þ

This reaction type is assumed to take place in a combustion chamber operating with an air to fuel ratio lower than the stoichiometric air to fuel ratio. Second, the reaction temperature is assumed to be 1200 K but <1600 K and about 50% of the nitrogen present in air is assumed to be converted to nitric oxide (NO) while the remaining 50% is released as free nitrogen (N2). When sour gas is involved in combustion with H2S assumed to be the only source of sulfur and under the same operating conditions, the formation of NO in the combustion activity can be investigated with the equation y x y x Cx Hy þ x þ ðO2 þ3:76N2 Þþms H2 S ! CO2 þ H2 Oþ CO 8 2 4 2 x y y þ þms H2 þ 2ms O2 þ3:76 x þ N2 þms SO2 4 4 8 ð2:9Þ

2.3 Fundamentals of Natural Gas Combustion

When the operating air to fuel ratio in the combustion chamber is less than the stoichiometric air to fuel ratio and its temperature is assumed to be 1600 K, the nitrogen present in air can be converted to both NO and NO2 with some released unconverted. For sweet gas, Sonibare and Akeredolu [65] reported that emissions from this combustion operation can be investigated with Equation 2.10 and for sour gas Equation 2.11 can be used: y x y x y x Cx Hy þ x þ ðO2 þ 3:76N2 Þ ! CO2 þ H2 O þ CO þ H2 þ O2 þ 8 2 4 2 4 8 y x x x N2 þ NO þ NO2 3:76 x þ 8 8 8 8 ð2:10Þ y y x Cx Hy þ x þ ðO2 þ 3:76N2 Þ þ ms H2 S ! CO2 þ H2 O þ CO 8 4 2 y x y x 2ms O2 þ 3:76 x þ N2 þ H2 þ 4 16 8 8 x x þ NO þ NO2 þ ms SO2 8 8 ð2:11Þ

For the conversion of both NO and NO2 formed above to N2O, Glarborg et al. [80] reported the operating reaction mechanism to be as shown in Equations 2.12 and 2.13, respectively. 9 NO þ N2 H2 ! N2 O þ NH2 > > = NO þ NH ! N2 O þ H NO þ HNO ! N2 O þ OH > > ; NO þ NCO ! N2 O þ CO

ð2:12Þ

9 NO2 þ N ! N2 O þ O = NO2 þ NH ! N2 O þ OH ; NO2 þ NH2 ! N2 O þ H2 O

ð2:13Þ

According to Skottene and Rian [81], another important route to N2O formation in hydrocarbon combustion is NNH conversion (2.14). However, this pathway appears to be important at ﬂame fronts and other areas where relatively high concentrations of H and O radicals are present. Similarly, HCN can be converted to N2O with the oxidation starting at lower temperatures but with the conversion being inhibited by an increase in initial H2O [82]. NNH þ O>N2 O þ H

ð2:14Þ

However, Lofﬂer et al. [83] showed that there are four circumstances that will favor the homogeneous decomposition of N2O, including temperature, reducing conditions,

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30

high H radical concentration, and long residence times. It has been shown that in a hydrocarbon ﬂame, N2O can be destroyed according to 9 N2 O þ M ! N2 þ O þ M > > = N2 O þ H ! N2 þ OH ð2:15Þ N2 O þ O ! N2 þ O2 > > ; N2 O þ OH ! N2 þ HO2

2.4 World Natural Gas Reserves, Production, Consumption and Destruction

For the reliable prediction of the global warming implications of natural gas combustion, it is required that the global trends in the reserves of natural gas, its production, and its consumption are appropriately captured. These three parameters are interdependent because production can only be obtained from the reserves in place while it is only what is produced that can be consumed. This relationship, however, depends on several factors, chief among which are economic, technological, and environmental aspects. 2.4.1 World Natural Gas Reserves

In oil and gas operations, reserves can be classiﬁed into three types: proven, probable, and possible reserves. These represent the certainty that some quantity of oil or gas exists based on the geological and engineering data with interpretation for a given location. Proven reserves are the estimated quantities of natural gas which geological and engineering data demonstrate with reasonable certainty to be recoverable in future years from known reservoirs under current economic and operating conditions. With probabilistic methods, there should be at least a 90% probability that the quantities actually recovered will equal or exceed the estimate. In proven reserves, developed, developed behind-pipe, and undeveloped reserves may be included. Proven developed producing reserves are only those reserves expected to be recovered from existing completion intervals in existing wells, whereas proved developed behind-pipe reserves are those reserves expected to be recovered from existing wells where a relatively minor capital expenditure is required for re-completion. Proved undeveloped reserves are those reserves expected to be recovered from new wells on undrilled acreage or from existing wells where a relatively major expenditure is required for re-completion. Probable reserves are estimates of unproved reserves which analysis of geological and engineering data suggests are more likely than not to be recoverable. For estimates of probable reserves based on probabilistic methods, there should be at least a 50% probability that the quantities of reserves actually recoverable will equal or exceed the sum of the estimated proven plus probable reserves. These reserves may include the following: reserves expected to be proved by normal step-out drilling where sub-

2.4 World Natural Gas Reserves, Production, Consumption and Destruction

surface control is inadequate to classify these reserves as proven; reserves in formations that appear to be productive based on well log characteristics but lack core data or deﬁnitive tests and which are not analogous to producing or proven reservoirs in the area; incremental reserves attributable to inﬁll drilling that could have been classiﬁed as proven if closer statutory spacing had been approved at the time of the estimate; reserves attributable to improved recovery methods that have been established by repeated commercially successful applications when a project or pilot is planned but not in operation and rock, ﬂuid, and reservoir characteristics appear favorable for commercial application; reserves in an area of the formation that appears to be separated from the proved area by faulting and the geological interpretation indicates that the subject area is structurally higher than the proven area; reserves attributable to a future work-over, treatment, re-treatment, change of equipment, or other mechanical procedures, where such procedure has not been proved successful in wells which exhibit similar behavior in analogous reservoirs; and incremental reserves in proven reservoirs where an alternative interpretation of performance or volumetric data indicates more reserves than can be classiﬁed as proven. Possible reserves are estimates of unproved reserves which analysis of geological and engineering data suggests are less likely to be recovered than probable reserves. For estimates of possible reserves based on probabilistic methods, there should be at least a 10% probability that the reserves actually recovered will equal or exceed the sum of the estimated proved plus probable plus possible reserves. These include the following: reserves which, based on geological interpretations, could possibly exist beyond areas classiﬁed as probable; reserves in formations that appear to be petroleum bearing based on log and core analysis but may not be productive at commercial rates; incremental reserves attributed to inﬁll drilling that are subject to technical uncertainty; reserves attributed to improved recovery methods when a project or pilot is planned but not in operation and rock, ﬂuid, and reservoir characteristics are such that a reasonable doubt exists that the project will be commercial; and reserves in an area of the formation that appears to be separated from the proven area by faulting and geological interpretation indicates that the subject area is structurally lower than the proven area. At present, the global proven reserves of natural gas range between 180 and 183 trillion m3. As at 1 January 2008, the EIA [12] reported a total of about 175.2 trillion m3 of global proven reserves of natural gas. About three-quarters of these are located in the Middle East and Eurasia (Figure 2.10), and Russia, Iran, and Qatar together account for about 57% of the reserves. The IEA [25] reported proven reserves amounting to 180 trillion m3 but with Russia, Iran and Qatar holding about 56% of them. Similarly, BP [84] reported a total of 181.5 trillion m3 world proven reserves of natural gas (Figure 2.11) whereas OPEC [22] reported a total of 183.1 trillion m3 for the same period (Figure 2.12). Several studies on the assessment of the long-term potential of worldwide petroleum resources (including natural gas) signiﬁed that a signiﬁcant volume of natural gas is yet to be discovered. Geologically estimated natural gas resources are 500 trillion m3 and with the advances in geological science an increase of estimated resources is expected [28].

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Reserves (trillion m3)

80 60 40 20 0

North America

Latin America

Europe and Euroasia

Middle East

Africa

Asia Pacific

Africa

Asia Pacific

Regions

Reserves (trillion m3)

Figure 2.10 World natural gas proven reserves [12]. 80 60 40 20 0

North America

South and Central America

Europe and Eurasia

Middle East

Regions Figure 2.11 World natural gas proven reserves [84].

2.4.2 World Natural Gas Production

Reserves (trillion m3)

Although natural gas production is primarily driven by consumption, there are several other factors that inﬂuence it. Technology and economic requirements are very important. Due to economic considerations, expansion of production in the lowest80 60 40 20 0

North America

South and Central America

Europe and Euroasia

Middle East

Regions Figure 2.12 World natural gas proven reserves [22].

Africa

Asia Pacific

2.4 World Natural Gas Reserves, Production, Consumption and Destruction

cost countries are always central to meeting the worlds needs at reasonable cost in the face of dwindling resources in most parts of the world and accelerating decline rates everywhere. Production of natural gas is deﬁnitely set to become more concentrated in the most resource-rich regions. Another common problem in natural gas production operations that need to be paid special attention include liquid loading of gas production wells, which reduces deliverability of gas wells. Blockage with gas hydrates of pipelines and equipment is another problem that reduces pipeline efﬁciency and this affects normal operations in production [76]. In addition, roughly 80% of proven gas reserves are associated with oil, thus subjecting natural gas production to the production logistics of the oil industry [85]. For instance, as at 2006, all of Kuwaits natural gas production was associated with crude oil, so that its availability was basically dependent on the level of oil output [86]. In 2007, the world natural gas production reached the range 2148–2970 billion m3 depending on the data source. While the IEA [25] reported 2148 billion m3 (Figure 2.13a), the EIA [12] reported 2885 billion m3 (Figure 2.13b) for the same year, but BP [84] and OPEC [22] reported production levels of 2940 billion m3 (Figure 2.13c) and 2971 billion m3 (Figure 2.13d), respectively. For resources planning and management, production projection is an important tool. Between 2006 and 2007, BP [84] and OPEC [22] recorded an increase in the rate of production with ranges of 0.1–4.9 and 1.1–6.4%, respectively. However, in their natural gas production projection from 2005 to 2030, the EIA [12] and IEA [25] used annual projection ranges of 0.2–3.9 and 0.4–4.5%, respectively, although these projections need to be treated with caution due to some uncertainties always related to both economic and physical constraints [87]. 2.4.3 World Natural Gas Consumption

As stated earlier, energy projection has been a reliable tool for global energy planning. Global energy projections include those from OPEC, the Energy Information Administration of the US DOE, and the IEA. These three bodies provide information on projections of global natural gas consumption with the current one being between 2005 and 2030. Projections are always correct within the limit of their reference scenarios. The basic assumptions in the OPEC reference scenario are concerned with economic growth, policies, and technologies [22]. Although it is assumed that there will be no signiﬁcant departure from current trends, announcements of policy goals and assessments of track records related to past objectives are taken into account. The benchmark crude price is assumed to remain in the range $50–60 per barrel in nominal terms for much of the projection period, rising further in the longer term with inﬂation. It is assumed that world population will grow by an average of 1% per annum (p.a.) over the years to 2030, although with a signiﬁcant fall in growth rates in developing countries. The age structure of these populations will change, with a knock-on effect for the size of the working age population (people aged between 15 and 64 years), while the output of the working age population is assumed, in the

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34

Figure 2.13 Global natural gas production by region (billion m3).

reference case, to follow paths broadly consistent with longer term trends. For OECD regions, this involves an initial productivity growth of close to 2% p.a., falling to around 1.5% p.a. by 2030. Developing countries, particularly in Asia, will experience growth at considerably higher rates, in line with recently observed activity, and although also set to decline over the medium and long term, these rates will remain high. It is assumed that South Asia will witness growth similar to that for much of the past decade, at 3–4% p.a., with China having slightly higher growth at around 5% in the longer term. The GDP growth rates are developed for the projection with the reference case seeing robust global economic growth averaging 3.5% p.a. at pur-

2.4 World Natural Gas Reserves, Production, Consumption and Destruction

Figure 2.14 OPEC world natural gas consumption projection [22].

chasing power parity (PPP) to 2030. Another factor assumed to shape the global future energy consumption is the global economy with elements including resource availability, the scope for efﬁciency gains, inter-fuel competition, and investment activity. In 2008 projections, the global natural gas consumption is expected to grow at fast rates (Figure 2.14). A major factor in the projections of the Energy Information Administration of US DOE is the assumptions for future world oil prices [12]. However, assumptions about regional economic growth – measured in terms of real GDP in 2000 US dollars at PPP rates – underlie its projections of regional energy demand. The time frame for its historical data begins at 1980 and extends to 2005, but with the projections extending to 2030. In its reference case scenario, the current laws and policies remain unchanged throughout the projection period with prices case in the medium term and relatively tight markets. For a better understanding of the global future energy needs, a high price case is used to quantify the uncertainly associated with long-term projections of future oil prices. The reference case assumes that OPEC producers will choose to maintain their market share of world liquids supply with incremental production capacity investment. The reference case further incorporates the improved prospects for world nuclear power. Since strong growth in income per capita may support the growth in transportation energy demand, the reference case anticipates that many of the worlds emerging economies will experience rapid modernization of their transportation systems, particularly in developing rural areas where economic growth often is achieved by increasing product exports. The projections assume that the pace of infrastructure expansion will not signiﬁcantly hinder economic growth in the rapidly expanding economies of non-OECD Asia, while the type of infrastructure developed will largely mirror the transportation infrastructure of todays developed economies. In its 2008 reference cases, natural gas consumption will increase from 2.9 trillion m3 in 2005 to 4.5 trillion m3 in 2030, and it is expected to replace oil wherever possible (Figure 2.15). To arrive at the projected values, the IEA [25] uses a reference scenario that takes account of those government policies and measures enacted or adopted by mid-2006 but with possible, potential, or even likely future policy actions ignored. Similarly, the global population is assumed to grow by 1% per year on average, from an estimated

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Consumption (trillion m3)

5

Central & South America Africa

Average 2005 - 2030 growth rate = 2.2 %

4 3

Middle East

2

Asia

1

Europe North America

0 2005

2010

2015

2020

2025

2030

Year Figure 2.15 EIA world natural gas consumption projection [12].

6.4 billion in 2004 to 8.1 billion in 2030, while the rate of growth in world GDP is assumed to average 3.4% per year over the period 2004–2030. Its crude oil import price is assumed to average slightly over $60 per barrel (in real year-2005 dollars) through 2007 – up from $51 in 2005 – and then decline to about $47 by 2012. It is assumed to rise again slowly thereafter, reaching $55 in 2030. Finally, it is assumed that energy-supply and end-use technologies will become steadily more efﬁcient, although at varying speeds for each fuel and each sector, depending on the potential for efﬁciency gains and the stage of technology development and commercialization. In the IEA reference scenario, primary gas consumption will increase in all regions over the period 2004–2030 from 2.8 trillion m3 in 2004 to 3.6 trillion m3 in 2015 and 4.7 trillion m3 in 2030, a global growth of an average of 2% p.a.. The power sector accounts for more than half of the increase in global primary gas demand. In aggregate, annual world gas production will expand by almost 1.9 trillion m3, or twothirds, between 2004 and 2030. Inter-regional gas trade will expand faster than output, thus the main gas-consuming regions will become increasingly dependent on imports (Figure 2.16).

Consumption (trillion m3)

5 4

Central & South America Africa

3

Middle East

2

Asia Pacific

1

Europe

Average 2004 - 2030 growth rate = 2.8 %

0

North America

2004

2010

2015

Year Figure 2.16 IEA world natural gas consumption projection [25].

2030

2.5 Global Warming Contribution from Natural Gas Combustion Table 2.1 Regional flared volumes, 2005–2007.

Reported flaring (billion m3)

Region

North America Europe Asia Middle East Africa Central and South America

2005a)

2006a)

2007b)

2.9 55.2 7.2 32.4 37.7 2.1

3.1 48.8 7.6 33.8 36.7 2.0

3.6 50 6.6 31.1 30.8 2.1

a) Source: GGFR [44]. b) Source: OPEC [88].

2.4.4 World Natural Gas Destruction

Owing to safety requirements in oil and gas production, natural gas released with oil (the associated gas) is removed and, in some places, sent to ﬂares for destruction. Some uncertainties surrounding the use of routine ﬂares globally make it a very difﬁcult task to project what the global amount of ﬂared gas will be even in the very near future. However, some organizations do make records of the global gas ﬂares. For instance, the Annual Statistical Bulletin of OPEC reports annual gas ﬂared in OPEC member countries with the latest released in 2007 [88]. Similarly, the Global Gas Flaring Reduction Initiative reports the annual ﬂared natural gas in its member countries with the latest reported in GGFR [44]. Table 2.1 summarizes the global current ﬂared gas levels with the least in 2005 in Central and South America and the maximum in Europe with 50 billion m3.

2.5 Global Warming Contribution from Natural Gas Combustion

Two major areas where natural gas combustion is being employed, as indicated earlier, are consumption for energy generation and destruction for safety purposes as applicable in gas ﬂares. The emissions of substances with different global warming potential from these sources are the reasons while they are identiﬁed as contributors to the global warming phenomenon. 2.5.1 Global Warming Contribution from Natural Gas Combustion in Energy Consumption

Using the 100-year global warming potential of important gases emitted in natural gas combustion, contributions of emissions from this source to global warming

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4 North America

3

Europe Asia

2

Middle East Africa

1

Central & South America

0 EIA IEA

EIA IEA EIA IEA EIA IEA EIA IEA EIA IEA

2005

2010

2015

2020

2025

2030

Figure 2.17 Calculated global warming potential from CO2 through natural gas combustion for energy, 2005–2030.

over the period 2005–2030 are as summarized in Figures 2.17–2.21 for carbon dioxide, nitrous oxide, carbon monoxide, methane, and oxides of nitrogen, respectively. Both EIA and IEA energy consumption projections for the period under consideration were used. During the period 2005–2030, the global warming potential of CO2 ranges between 0.17 106 and 3.39 106 Mt with the EIA energy consumption projection but between 0.15 106 and 3.32 106 Mt when the IEA energy consumption projection is used. With the EIA energy consumption projection, the calculated global warming potential in the period for the other emission products from natural gas combustion include: N2O, 92.69–1872.4 Mt; CO, 179.41–3624.00 Mt; CH4, 62.79–1268.40 Mt; and NOx, 2242.57–45 299.97 Mt. Similarly, the IEA energy consumption projection for the same period produces a global warming potential in the range: 82.93–1872.40 Mt from N2O, 179.41–1833.11 Mt from CO, 56.18–1241.78 Mt from CH4, and 2242.57–44 349.35 Mt from NOx. Since Africa and Europe are the projected

2000 Global warming (Mt)

Global warming (x 106 Mt)

38

1600

North America Europe

1200

Asia Middle East

800

Africa Central & South America

400 0 EIA IEA EIA IEA EIA IEA EIA IEA EIA IEA EIA IEA

2005

2010

2015

2020

2025

2030

Figure 2.18 Calculated global warming potential from N2O through natural gas combustion for energy (Mt), 2005–2030.

2.5 Global Warming Contribution from Natural Gas Combustion

j39

Global warming (Mt)

4000 North America

3000

Europe Asia

2000

Middle East Africa

1000

Central & South America

0 EIA IEA EIA IEA EIA IEA EIA IEA EIA IEA EIA IEA 2005

2010

2015

2020

2025

2030

Figure 2.19 Calculated global warming potential from CO through natural gas combustion for energy (Mt), 2005–2030.

Global warming (Mt)

1400 1200 North America

1000

Europe

800

Asia

600

Middle East Africa

400

Central & South America

200 0 EIA IEA EIA IEA EIA IEA EIA IEA EIA IEA EIA IEA 2005

2010

2015

2020

2025

2030

Figure 2.20 Calculated global warming potential from CH4 through natural gas combustion for energy (Mt), 2005–2030.

Global warming (Mt)

50000 40000

North America Europe

30000

Asia Middle East

20000

Africa Central & South America

10000 0

EIA IEA EIA IEA EIA IEA EIA IEA EIA IEA EIA IEA 2005

2010

2015

2020

2025

2030

Figure 2.21 Calculated global warming potential from NOX through natural gas combustion for energy (Mt), 2005–2030.

j 2 Global Warming Implication of Natural Gas Combustion

40

Global warming (x 103 Mt)

120 100 North America

80

Europe Asia

60

Middle East Africa

40

Central & South America

20 0 2005

2006

2007

Figure 2.22 Calculated global warming potential from CO2 through natural gas combustion in flares, 2005–2007.

least and highest energy consumers, respectively, in both the EIA and IEA energy consumption projections, the global warming potential from these sources also follows a similar pattern. For all the emission products parameters, the least global warming potential is calculated to be from Africa and the highest from Europe. 2.5.2 Global Warming Contribution from Natural Gas Combustion in Flaring Activities

The global warming potential from natural gas combustion in gas ﬂares is calculated with the available 3-year data. As shown in Figures 2.22–2.26, the calculated global warming potential of these emissions from combustion of gas ﬂares is: CO2, 3.9 103–98.7 103 Mt; N2O, 2.2–60.2 Mt; CO, 4.2–116.6 Mt; CH4, 1.5–40.8 Mt; and NOx, 52.8–1457.2 Mt. Unlike in combustion for energy, where the minimum global warming potential from natural gas combustion is contributed by Africa, the minimum global warming potential contribution from natural gas combustion in the

Global warming (Mt)

80

60

North America Europe Asia

40

Middle East Africa Central & South America

20

0 2005

2006

2007

Figure 2.23 Calculated global warming potential from N2O through natural gas combustion in flares, 2005–2030.

2.5 Global Warming Contribution from Natural Gas Combustion

j41

140 Global warming (Mt)

120 North America

100

Europe

80

Asia

60

Middle East Africa

40

Central & South America

20 0 2005

2006

2007

Figure 2.24 Calculated global warming potential from CO through natural gas combustion in flares, 2005–2030.

Global warming (Mt)

50 40

North America Europe

30

Asia Middle East

20

Africa Central & South America

10 0 2005

2006

2007

Figure 2.25 Calculated global warming potential from CH4 through natural gas combustion in flares, 2005–2030.

Global warming (Mt)

1600

1200

North America Europe Asia

800

Middle East Africa Central & South America

400

0 2005

2006

2007

Figure 2.26 Calculated global warming potential from NOX through natural gas combustion in flares, 2005–2030.

j 2 Global Warming Implication of Natural Gas Combustion

42

ﬂares is by Central and South America. However, the maximum contribution is still from Europe. The increase in global warming potential contribution from 2006 to 2007 conﬁrms the initial observation on the difﬁculties associated with natural gas ﬂaring projections.

2.6 Control Measures for Global Warming from Natural Gas Combustion

The possibility of emissions of air pollutants that play a major role in global warming as demonstrated in this study calls for urgent attention to ﬁrm control measures to reduce their release into the environment. Combustion of primary energy sources as required in energy delivery must be given adequate attention so as to ensure a drastic reduction in the release of air pollutants known to be agents of global warming. This can be done by paying special attention to the design, operation, and maintenance of all appliances where energy is extracted from natural gas. For instance, temperature and CH radicals are very important in NOx formation [78] and, to control their release in these appliances, they both must be controlled to minimize the release of NOx from natural gas combustion. If this is achieved, it implies that both nitrogen and oxygen present in combustion activities will be released unconverted, thus avoiding the burden of a global warming contribution through the supposed products. Similarly, the ultimate conversion of CO to CO2 will be desirable to reduce the possibility of CO emissions from natural gas combustion contributing to global warming. If the concept of energy conservation is embraced, both industrial and domestic sectors where the conversion of natural gas is required for energy delivery will minimize natural gas intake, thus indirectly reducing the formation of possible products and, ultimately, the global warming potential through this source. Since associated natural gas destruction in gas ﬂares in the oil and chemical industry may generate products which are known agents of global warming, there is an urgent need to intensify efforts at reducing routine ﬂares in all their locations. In taking appropriate action, there must be response in a focused way to control and reduce pollution while avoiding larger-scale damage to economic development. To decide whether action is necessary there is a need to know the state of the environment – that is, in evaluating whether the levels in the environment exceed those which will cause environmental harm. As established earlier, there is no controversy over the fact that a reduction in natural gas going to the ﬂares will reduce global warming potential, and elimination of routine ﬂares will deﬁnitely assist in achieving this. For example, although Nigeria planned to eliminate routine ﬂares as back as 1979 through the 1979 Associated Gas Re-Injection Act (AGRA) prohibiting ﬂaring of associated gas after 1 January 1984 except with permission from the Minister of Petroleum, this feat is yet to be achieved. In 1985, the Associated Gas ReInjection (Amendment) Decree No. 7 of 1985 was promulgated with effect from 20 April 1985 to deal with the problems arising from implementations of the 1979 Act, but as yet this has not been achieved. Although the new ﬂare out date in Nigeria has

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2.7 Conclusion

Energy extraction from natural gas in gas appliances and safety accomplishment through natural gas destruction in gas ﬂares require combustion activities. Either complete or incomplete, emission products from these combustion activities have far-reaching implications for global warming. Reduction of emissions from these sources is a major means through which global warming potential can be reduced.

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3 Theory, Modeling and Computation of Gas Explosion Phenomena Almerinda Di Benedetto and Valeria Di Sarli 3.1 Introduction

Gas explosions represent a signiﬁcant hazard, as witnessed by several accidents that have occurred worldwide. In reﬁnery and chemical plant operations, fuel losses through pipes and/or vessels may give rise to the formation of ﬂammable clouds the accidental ignition of which can lead, in turn, to serious injuries to personnel and damage to the environment. In this framework, the study of explosion phenomena for prevention and mitigation becomes a priority. Explosion is a combustion reaction that couples with heat and mass transport to give rise to a non-stationary chemical reactor: the ﬂame front traveling through the fresh gas. During its travel, the ﬂame interacts with the pre-existing and/or self-induced ﬂow ﬁeld that can strongly affect the ﬂame propagation itself. Chemical reactions involved in explosions spontaneously accelerate, releasing large amounts of energy. The kinetics of such reactions are based on the evolution of extremely reactive radicals. Explosions may be thermal, when they are the result of temperature increase due to the imbalance between heat generation rate and heat loss rate. Also, branched-chain explosions may occur, when chain-branching steps are present in the kinetic mechanism. Such steps involve active radicals whose concentrations grow at an exponential rate during explosions. In hydrogen combustion, the chain-branching step H. þ O2 ! OH. þ O. allows the production of OH. and O. radicals at an exponential rate, thus increasing the combustion rate. Unless free radicals are lost, the production rate of radicals remains extremely fast, sustaining the explosion process. Depending on the rates of the chainbranching steps, explosion may be favored or limited and controlled. The chainbranching reaction rates and, thus, the explosion limits are strong functions of temperature, pressure, and mixture composition. Fuel–air mixtures are non-ﬂammable, that is, not able to sustain ﬂame propagation after ignition, depending on their composition. The lower/upper ﬂammability limit (LFL/UFL) is deﬁned as the minimum/maximum fuel composition in air at which

Handbook of Combustion Vol.3: Gaseous and Liquid Fuels Edited by Maximilian Lackner, Franz Winter, and Avinash K. Agarwal Copyright Ó 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32449-1

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combustion can be sustained. In the literature, such limits have been described on an empirical/phenomenological basis, and a uniﬁed and satisfactory theory does not exist [1]. After local ignition of an explosive mixture, a reaction front starts to move from the burned to the unburned gas. Gradients of temperature and chemical species concentrations are established, leading to heat and mass ﬂuxes that, in turn, feed the combustion front. Within the ﬂame front, two zones may be distinguished: the pre-heat zone and the reaction zone. The velocity at which the ﬂame front propagates into the fresh gas is deﬁned as the ﬂame burning velocity. It can be seen as a measure of the combustion intensity. The ﬂame burning velocity is the result of the heat and mass transport in the preheat zone, chemical kinetics in the reaction zone, and overall interaction of the ﬂame front with the ﬂow ﬁeld into which the ﬂame propagation takes place. When an explosion occurs, the ﬂame propagating away from an ignition source may encounter objects along its path (walls, vessels, pipes, tanks, ﬂow cross-section variations, instrumentation, etc.). The unsteady coupling of the unburned mixture ﬂow, set in motion by thermal expansion, and the local blockage produces turbulent vortices of different intensity ahead of the ﬂame front. These vortices disturb the ﬂat propagation of the ﬂame, increasing its rate of progression through the reactants and the rate of pressure rise. In such conditions, the ﬂame experiences various combustion regimes. Initially, a weak turbulence, which is not able to affect the ﬂame propagation, develops. From this, the increasing turbulence level induced by the propagation itself allows the vortices formed ahead of the front to wrinkle the ﬂame, increasing the ﬂame surface area. Eventually, the vortices may also enter the ﬂame structure, enhancing the heat and mass transport in the pre-heat zone or disrupting/quenching the ﬂame. The transient ﬂame–turbulence interaction is the key process in the description of an explosive phenomenon. The understanding of the mechanisms and phenomena that underlay explosions and determine their severity at varying initial/operating conditions and geometric parameters is essential for effective and safe engineering practice (i.e., for selecting the key conditions and parameters at the design and operation stages). To achieve this goal, the use of computational ﬂuid dynamics (CFD) may be proﬁtable. In contrast to simple empirical or lumped-parameter models, CFD models take into account the interplay of chemical reaction, transport phenomena, ﬂow ﬁeld, and geometry occurring in explosions [2]. Thanks to the growing computational power and availability of distributed computing algorithms, advanced CFD based on large eddy simulation (LES) is emerging as a useful method for predicting turbulent reacting ﬂows [3–5]. The attraction of LES is that it offers an improved representation of turbulence, and the resulting ﬂame-turbulence interaction, with respect to classical approaches based on the Reynolds-averaged Navier–Stokes (RANS) equations. LES models have been successfully applied to steady-state problems such as those encountered in combustors and burners (see, e.g., [6–8]). CFD models for explosions, that is, safety computational ﬂuid dynamics (SCFD) models, are mainly based on the unsteady RANS (URANS) approach, which has

3.2 Modeling and Computation of Explosion Phenomena

been used at both small [9–15] and large [9, 16–21] scales. More recently, large eddy simulations of small-scale explosions have been run, showing the ability of LES to give more reliable predictions than URANS [22–27]. Examples of LES application to large-scale explosions can also be found in the literature [28, 29]. SCFD may take advantage of the progress made over the past 30 years in the development of models for steady turbulent premixed combustion. However, SCFD differs from models for combustors and burners, since it is faced with intrinsically unsteady phenomena. This means that CFD developed and validated for steady (or quasi-steady) turbulent combustion applications should not be simply applied to safety-related problems, but rather tested and possibly adapted.

3.2 Modeling and Computation of Explosion Phenomena

Modeling unsteady premixed ﬂame propagation in explosions has to capture the dynamic evolution of ﬂame and vortices and their coupling. Therefore, CFD models are needed to solve the unsteady Navier–Stokes equations for conservation of mass, momentum, energy, and chemical species. In this section, theoretical details on CFD modeling of explosion phenomena are given. 3.2.1 Overview of CFD Approaches

Turbulent reacting ﬂows consist of structures characterized by length scales and, thus, time scales that may differ by several orders of magnitude (turbulence spectrum). The largest length scale is the characteristic length scale of the mean ﬂow, that is, the characteristic length scale of the equipment (lt 1 mm–100 m, depending on the geometryscale). Thesmallestlengthscale isthecharacteristiclengthscale ofdissipative phenomena, that is, the Kolmogorov scale (lk 0.1–1 mm), and/or the scale of transport and reactive phenomena, that is, the ﬂame thickness (dF 0.1–1 mm). In CFD modeling, three techniques may be used to solve the Navier–Stokes equations. These techniques differ on the basis of the grid cell dimension (Dx): . . .

Dx min(lk, dF) – direct numerical simulation (DNS) Dx 30lk – large eddy simulation (LES) Dx lt – (unsteady) Reynolds-averaged Navier–Stokes [(U)RANS] equations.

With DNS, the grid cell dimension is smaller than the smallest physical scale involved. This approach is based on the direct numerical solution of the governing equations, thus simulating all turbulent scales without requiring any additional subgrid modeling. The (U)RANS approach solves the (unsteady) Reynolds-averaged equations, computing ensemble-averaged solutions. It needs sub-grid models for the whole spectrum of turbulent scales, including those related to the ﬂame itself.

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Modeled in (U)RANS DNS Computed in DNS

E (k)

Computed in LES

LES (U)RANS

Modeled in LES

(a)

(b)

kc

k

Time

Figure 3.1 Comparison among DNS, LES and (U)RANS: (a) turbulent energy spectrum as a function of the wavenumber; (b) time trend of a generic field variable.

LES solves the ﬁltered Navier–Stokes equations and represents a compromise between DNS and (U)RANS. It directly computes all of the large turbulent structures up to a cut-off length scale (that is linked to the grid cell size), and only models the small sub-grid eddies. The large structures represent the energy-containing regime of the turbulence spectrum. They are anisotropic, subjected to history and nonequilibrium effects, and strongly dependent on geometry and boundary conditions. For these reasons, they are more difﬁcult to model than the small eddies that exhibit a more universal behavior (they tend to be isotropic and, therefore, less dependent on both ﬂow and geometry). Unfortunately, in LES, the ﬂame remains a sub-grid phenomenon that has to be modeled. In Figure 3.1, the comparison among DNS, LES and (U)RANS is shown in terms of the turbulent energy spectrum as a function of the wavenumber, k (proportional to the inverse of the length scale) (a), and time trend of a generic ﬁeld variable (b). In DNS, the whole spectrum of turbulent scales is computed; in (U)RANS, it is completely modeled; in LES, the spectrum is resolved up to the cut-off length scale (kc is the cut-off wavenumber) and is modeled below (Figure 3.1a). As a consequence, LES and DNS allow computing the turbulence ﬂuctuations (Figure 3.1b). However, whereas DNS reproduces all frequencies, LES takes into account only the smallest ones. (U)RANS computes statistical mean ﬁelds (Figure 3.1b). Looking at the solution ﬁelds of DNS/LES and (U)RANS is like looking at photographs with different resolutions. The ﬂame photographs shown in Figure 3.2 were obtained at high and low pixel density, thus giving an idea about the better resolution of DNS/LES compared with (U)RANS. The choice of the CFD approach relies on the trade-off between accuracy and available computational resources. In spite of the growing computational power, DNS is still far from becoming a practical tool at both laboratory and industrial scales. DNS requires accurate numerics and very ﬁne time steps and grids. With 1024E þ 3 grid points, it possible to resolve a premixed ﬂame front in a DNS domain of only 25 25 25 mm [4].

3.2 Modeling and Computation of Explosion Phenomena

Figure 3.2 Qualitative representation of the flame resolution in DNS/LES and (U)RANS.

A computational grid condition for DNS of turbulent premixed combustion can be written in terms of the product of the turbulent Reynolds number, Ret, and the Damk€ohler number, Da (comparing turbulent, tt, and chemical, tc, time scales, Da ¼ tt/tc) [4]: Ret Da < ðN=Q Þ2

ð3:1Þ

where N is the number of grid points in each dimension and Q is the number of grid points required to resolve the inner structure of the ﬂame (it should be at least equal to 20). Assuming Ret ¼ 1000, N ¼ 1000, and Q ¼ 20, Da is bounded by Equation 3.1 to 2.5, which is too low compared with the typical Da values in turbulent combustion (Da 1). Nowadays, the use of DNS is limited to academic problems and is mainly devoted to the development of sub-grid models for both (U)RANS and LES. (U)RANS is the most widely used approach for industrial simulations. It requires a reduced computational cost, allowing the use of coarse grids and also the assumption of two-dimensionality and symmetry for the ﬂows being modeled. However, (U)RANS implies a strong modeling impact and the reliability of its predictions strictly depends on the sub-grid models used. Even when the sub-grid models for turbulence and combustion may be satisfactory in terms of overall quantitative results, (U)RANS is limited in that it can simulate only the averaged ﬁelds of velocity, pressure, temperature, and species concentrations. Compared with DNS, LES loses the resolution of the small scales, whereas, compared with (U)RANS, it directly simulates the instantaneous (exact) large-scale ﬂuctuations. Computing only the large eddies allows the use of much coarser grids and larger time steps in LES than in DNS. However, LES still requires substantially ﬁner grids and time steps than those typically used for (U)RANS calculations, given that a good LES has to resolve at least 80% of turbulence [30]. Moreover, large eddy simulations being by deﬁnition three-dimensional, the (U)RANS geometric simpliﬁcations cannot be applied in LES. In LES, the modeling inﬂuence is reduced with respect to (U)RANS. However, more accurate numerical schemes are needed to prevent numerics from dissipating the resolved eddies.

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3.2.2 LES for Explosion Phenomena

There are at least two main reasons that make LES a tool particularly ﬁt for explosion simulations. The ﬁrst is related to the fact that LES grasps the inherently unsteady nature of turbulent ﬂows and, hence, of transient combustion phenomena such as explosions. When performing (U)RANS, only the time dependence of statistical mean quantities is captured, missing all details about their unsteadiness. Furthermore, in both LES and (U)RANS, the ﬂame is not resolved on the numerical grid and its coupling with the turbulent ﬂow ﬁeld has to be taken into account by means of adequate sub-grid combustion models. However, in LES, the instantaneous large vortices are resolved and their effect on the ﬂame propagation is directly simulated (and not averaged). This implies a better description of the ﬂameturbulence interaction with respect to (U)RANS. In the following, attention is focused on LES for explosion phenomena and, in particular, on model equations, sub-grid models and numerical issues. 3.2.2.1 LES Model Equations Unsteady compressible ﬂows with premixed combustion are governed by the reactive Navier–Stokes equations, that is, the conservation equations for mass, momentum, energy, and species, joined to the constitutive and state equations. Since this type of ﬂow involves large changes in density, high velocities, and signiﬁcant dilatation, all terms in the equations have to be retained. The conservation equations for mass and momentum read as follows: qr q ruj þ ¼0 qt qxj

ð3:2Þ

qr ui q r ui uj qP q 1 ¼ þ 2n Sij dij Skk þ qxi qxj 3 qt qxj

ð3:3Þ

where ui is the velocity component in the i direction, P is pressure, r is density, n is dynamic viscosity, and Sij is the strain rate: Sij ¼

1 qui quj þ 2 qxj qxi

ð3:4Þ

The energy equation, formulated in terms of speciﬁc enthalpy, h, is written as quj qr h q r uj h qP 1 q n qh þ q_ c ¼ þ þ þ 2n Sij dij Skk : qxi qt qt 3 qxj Pr qxj qxj

ð3:5Þ

where the two terms on the left-hand side correspond to unsteady effects and convective ﬂuxes, and the ﬁrst three terms on the right-hand side correspond to contributions from pressure work, viscous dissipation, and ﬂow dilatation. Thermal

3.2 Modeling and Computation of Explosion Phenomena

dissipation is expressed in terms of the ﬂuid viscosity and a molecular Prandtl number, Pr. The ﬁnal term in Equation 3.5 is the heat generation rate, given by

q_ c ¼ Dhc v_ c

ð3:6Þ

where Dhc is the heat of combustion and v_ c the combustion rate. Pressure, temperature and density may be linked by the ideal gas equation: P ¼ rRT

ð3:7Þ

Under the assumptions of a ﬂamelet regime of combustion, a single-step irreversible chemical reaction, and unit Lewis number (Le ¼ 1), the species transport equation for premixed combustion systems may be recast in the form of a transport equation for the reaction progress variable, c, which is zero within fresh reactants and unity within burned products [31]: c ¼ 1

Yf Yf

ð3:8Þ

where Yf is the local fuel mass fraction and Yf is the fuel mass fraction in the unburned mixture. The transport equation for c reads as follows: qr c q r uj c q n qc þ þ v_ c ¼ ð3:9Þ qt qxj Sc qxj qxj where the two left-hand-side terms are the unsteady and convective terms, and the two right-hand-side terms correspond to molecular diffusion (Sc is the molecular Schmidt number, which, with Le ¼ 1, is equal to Pr) and combustion rate, respectively. The LES model equations are obtained by ﬁltering the governing equations. The ﬁltering process ﬁlters out the eddies whose length scales are smaller than the ﬁlter width so that the resulting equations govern the dynamics of the large eddies. A ﬁltered variable (denoted with an overbar) is deﬁned as ð f ðx Þ ¼ f ðx 0 Þ F ðxx0 Þ dx0 ð3:10Þ where F is the ﬁlter function determining the scale of the resolved eddies. The ﬁltered quantity f is resolved in the computations, whereas the difference f 0 ¼ f f represents the unresolved (sub-grid) part owing to the unresolved turbulent structures. F is normalized according to: ð FðxÞ dx ¼ 1 ð3:11Þ D

where D represents the ﬂuid domain. Different ﬁlters can be used in either the spectral or physical space. The ﬁltering operation in the spectral space eliminates the components greater than a given cut-off frequency:

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FðkÞ ¼

1 if k kc ¼ p=D

ð3:12Þ

0 otherwise

This ﬁlter cuts off the length scales smaller than 2D (where D is the ﬁlter size). The ﬁltering operation in the physical space corresponds to a weighted average over a given volume. A box ﬁlter in the physical space is deﬁned according to ( FðxÞ ¼ F ðx1 ; x2 ; x3 Þ ¼

1=D3

if jxi j D=2;

0

otherwise

i ¼ 1; 2; 3

ð3:13Þ

where (x1, x2, x3) are the spatial coordinates of the x position. With this ﬁlter, the quantity f is averaged over a cubic box whose linear size is equal to D. A Gaussian ﬁlter in the physical space is deﬁned as follows: FðxÞ ¼ F ðx1 ; x2 ; x3 Þ ¼

6 pD2

3 2

6 exp 2 x1 2 þ x2 2 þ x3 2 D

ð3:14Þ

For variable density problems, a Favre-ﬁltered variable (denoted with a tilde, ) can be obtained through the following operation (mass-weighted ﬁltering): ð ~f ðxÞ ¼ r f ðx0 Þ F ðxx 0 Þ dx 0 ¼ r f ðxÞ ð3:15Þ r D

The Favre ﬁltering of the model equations yields u ~j q r q r þ ¼0 qt qxj

ð3:16Þ

u ~i u ~j ~i q r q ru qP q ~i j 1 dij S ~kk qti j þ ¼ þ 2n S qxi qxj 3 qt qxj qx j

ð3:17Þ

u ~ j h~ uj q rh~ q r qP q ~kk : q~ ~ i j 1 dij S þ þ 2n S ¼ þ qt qt 3 qxj qxi qxj

n qh~ Pr qx j

! þ q_ c

qqSGS qx j ð3:18Þ

u ~ j~c q r~c q r q n q~c _ c qcSGS þ þv ¼ qt qxj Sc qxj qxj qxj

In Equation 3.17, the Favre-ﬁltered strain rate is expressed as follows: uj ui q~ ~ij ¼ 1 q~ þ S 2 qx j qxi

ð3:19Þ

ð3:20Þ

The Favre-ﬁltered ideal gas equation is written as ¼r RT~ P

ð3:21Þ

3.2 Modeling and Computation of Explosion Phenomena Table 3.1 Unknown terms in the LES Favre-filtered Navier–Stokes equations.

Favre-filtered equation Momentum (Equation 3.17) Energy (Equation 3.18) Reaction progress variable (Equation 3.19) Reaction progress variable (Equation 3.19)

Unknown term ug ~j ui u tij ¼ r i u j ~ uf u j h~ qSGS ¼ r j h~ uf u j~c cSGS ¼ r j c~ _ c v

Definition Sgs stress tensor Sgs enthalpy ﬂux Sgs reaction progress variable (i.e., species) ﬂux Sgs combustion rate

Owing to the non-linearity of the original Navier–Stokes equations, the ﬁltering process gives rise to unknown sub-grid scale (sgs) terms in Equations 3.17–3.19. As shown in Table 3.1, these terms include the sgs stress tensor, the sgs ﬂuxes of enthalpy and reaction progress variable (i.e., species), and the sgs combustion rate. To solve the LES model equations, these unknown terms have to be expressed as functions of known quantities. Modeling in LES means quantifying phenomena that occur at the unresolved sub-grid scales. It is worth saying that two further unclosed terms (not listed in Table 3.1) derive from ﬁltering Equations 3.5 and 3.9, which are related to the laminar diffusion terms of enthalpy and reaction progress variable. These sgs molecular ﬂuxes may be either modeled through a simple gradient assumption (see the fourth term on the righthand side of Equation 3.18 and the ﬁrst term on the right-hand side of Equation 3.19) or neglected [4]. 3.2.2.2 Sub-Grid Scale (sgs) Models for Stress Tensor and Scalar Fluxes The sgs stress tensor is closed by extending models developed for constant density and non-reacting ﬂows [32, 33] to combustion problems. For its very simple formulation, the most popular model is the Smagorinsky model [34]. This model retains the Boussinesq hypothesis, that is, the assumption of isotropic eddy viscosity [35, 36], and computes the sgs stress tensor according to 1 ~i j 1 di j S ~kk ð3:22Þ tij di j tkk ¼ 2nt S 3 3

where, based on dimensional arguments, the sgs viscosity, nt, is written as 2 S ~ CS D nt ¼ r ð3:23Þ ~ modulus of the resolved CS being the Smagorinsky constant (CS ¼ 0.1–0.2) and S strain rate: qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ S ~ ¼ 2S ~i j S ~i j ð3:24Þ The isotropic part of the sgs stress tensor, tkk , is either added to the ﬁltered pressure or modeled according to Yoshizawas relation [37]:

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2 2 S ~ tkk ¼ 2 rCI D

ð3:25Þ

with the model coefﬁcient, CI, of order 0.01. Despite its simplicity, the Smagorinsky model has some shortcomings that mainly derive from the model constant, CS. There is no single value of this constant that is universally applicable to a wide range of ﬂows. Furthermore, a constant value for CS is not applicable to transitional ﬂows, where the ﬂow is laminar either locally or intermittently (Equation 3.23 always gives a ﬁnite sgs viscosity, even in laminar regions, as long as there is velocity gradient). Finally, Equation 3.23 needs ad hoc damping in the near-wall regions. To resolve these problems, Germano et al. [38] and subsequently Lilly [39] proposed a procedure based on the scale-similarity assumption, that is, the unresolved stresses are mainly controlled by the largest unresolved vortices which are close to the smallest resolved vortices. CS is calculated dynamically during LES computations (i.e., on-the-ﬂy) using the information about the local instantaneous ﬂow conditions provided by the smaller scales of the resolved (known) ﬁeld. This allows the eddy viscosity to respond properly to the local ﬂow structures. To separate the smaller scales from the resolved ﬁeld, the dynamic procedure needs a so-called test ﬁlter having a width that is larger than the LES ﬁlter. The sgs ﬂuxes of heat and reaction progress variable (scalar ﬂuxes) appearing in the energy and species transport equations are usually modeled through the gradient hypothesis along with the sgs turbulent Prandtl (Prt) and Schmidt (Sct) numbers [4]: qSGS ¼

nt qh~ Prt qxj

ð3:26Þ

cSGS ¼

nt q~c Sct qxj

ð3:27Þ

In the dynamic models, Prt and Sct are obtained by applying the dynamic procedure proposed for the sgs stress tensor to the sgs scalar ﬂuxes. 3.2.2.3 Approaches for Sub-Grid Scale (sgs) Combustion Rate As shown in Figure 3.3, the thickness of the premixed ﬂame front (pre-heat zone plus reaction zone) is generally smaller than the grid size used in LES computations. Consequently, the ﬂame front (heat and mass transport plus combustion kinetics) and its coupling with turbulence remain sub-grid phenomena that have to be exclusively modeled. To this end, three main approaches have been proposed based on an artiﬁcially thickened ﬂame model, a ﬂame front tracking technique (G-equation) and a ﬂame surface density description [4]. In the thickened ﬂame (TF) approach, two steps are involved. In the ﬁrst step, the ﬂame is artiﬁcially thickened by increasing the gas diffusivity [40]. This allows a laminar ﬂame front to be obtained that is large enough to be resolved on the LES grid. In this step, the laminar burning velocity of the ﬂame is maintained by decreasing

3.2 Modeling and Computation of Explosion Phenomena Flame front

Burned gas

LES grid size (Dx)

Pre-heat Reaction zone zone

Unburned gas

Figure 3.3 Comparison between premixed flame thickness and LES grid size.

the pre-exponential factor of the Arrhenius reaction rate in which ﬁltered quantities are used. Owing to the increase in the diffusivity, the Damk€ ohler number, Da (i.e., the ratio between turbulent time scale and chemical time scale), decreases, and the ﬂame becomes less sensitive to turbulence [41–43]. Therefore, in a second step, the laminar burning velocity of the thickened ﬂame is increased by multiplying the preexponential factor by the so-called efﬁciency factor introduced to take into account the effect of the sgs vortices in wrinkling the ﬂame front. This procedure gives rise to a thickened ﬂame propagating with an sgs turbulent burning velocity. The TF approach offers the advantage of describing the combustion reaction on the computational grid as in DNS. Furthermore, since the combustion rate is expressed using an Arrhenius law, phenomena such as ﬂame ignition and stabilization and ﬂame–wall interactions can be captured without using ad hoc sub-grid models. Finally, this approach can be extended to the treatment of complex chemistry. According to the G-equation formalism, the turbulent premixed ﬂame is viewed as a surface (i.e., an inﬁnitely thin sheet separating burned and unburned gas) propagating normal to itself. The position of this surface is tracked by using a ﬁeld ~ deﬁned as given distance from the ﬂame front (to which the iso-surface variable, G, ~ ¼ 0 is assigned). In this approach, the resolution of the inner structure of the ﬂame G ~ is not required. Furthermore, the G-ﬁeld can be smoothed out to be resolved on the LES grid, given that it does not have to follow the gradients of temperature and species concentrations associated to the ﬂame. ~ The basic ﬁltered G-equation reads as follows: ~ ~ ~i G q rG q ru þ ¼ r0 St rG qt qxi

ð3:28Þ

where St is the sgs turbulent burning velocity (normal to the ﬂame surface) that has to be modeled. St depends on many parameters and there is no universal model for it.

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Moreover, when applying the G-equation approach, special attention has to be paid to the formation of cusps that naturally arise in the solution of Equation 3.28 [3]. In the ﬂame surface density (FSD) approach, the turbulent ﬂame is considered as an ensemble of wrinkled thin interfaces separating unburned and burned gas. These interfaces are called ﬂamelets and are assumed to retain the local structure of a laminar ﬂame. This ﬂamelet assumption is valid when the chemical time scale is shorter than the turbulent time scale (Da > 1). According to the FSD description, the ﬂame is identiﬁed as a surface and an sgs ﬂame surface density, S, is introduced to measure the ﬂame area available per unit _ c , is expressed according to volume at the sgs level. The sgs combustion rate, v _ c ¼ V S¼ V JD jrcj v ð3:29Þ s s is the mean combustion rate per unit ﬂame surface and JD the sgs ﬂame where V s wrinkling factor (i.e., the sgs ﬂame surface divided by the projection of the ﬂame surface in the propagating direction). The FSD approach requires ﬁlters larger than the grid cell size to resolve the ~ ﬁltered reaction progress variable, c , on the computational grid [44]. In Equation 3.29, V s is usually approximated by r0Sl [45], where r0 is the fresh gas density and Sl is the laminar burning velocity. Sgs models are then needed for S or JD. These models can be based on algebraic expressions [42–44], dynamic or similarity formulations [46–48], or balance equations [44, 49, 50]. The above-described approaches have been widely used in LES modeling of turbulent premixed combustion in combustors and burners (i.e., applications in which a stationary turbulent combustion regime is established), providing satisfactory results (see, e.g. [6–8, 49, 51–55]). Recently, Richard et al. [56] proposed the solution of a balance equation for the sgs ﬂame surface density to handle non-equilibrium situations in LES of unsteady combustion during spark ignition engine cycles. For explosion LES, the TF approach and the G-equation formalism have not yet been used. The TF model would be recommendable when ignition and ﬂame–wall interactions are crucial in the description of the explosive phenomenon. However, some doubts may arise concerning its applicability to unsteady problems, given that the key idea of this approach is based on a general property of one-dimensional steady-state convection/diffusion/reaction balance equations [4]. The G-equation could be tested for large-scale simulations, since it does not resolve the inner structure of the ﬂame. In the literature, all the LES models for explosions are based on the FSD approach [22–29]. The sgs wrinkling factor, JD, is either modeled as a function of the local ﬂow conditions [22–27, 29] or assumed to be constant [25, 28]. 3.2.2.4 Numerics Turbulent (reacting) ﬂows are not isotropic and they do not preserve any conditions of geometric symmetry. This means that such ﬂows, even if contained in a symmetric geometry, can be non-symmetric:

3.2 Modeling and Computation of Explosion Phenomena

symmetry in geometry 6¼ symmetry in turbulent ðreactingÞ flows

As a consequence, when simulating explosions with LES, two-dimensionality, periodicity, and symmetry cannot be assumed. Fully three-dimensional simulations are required. Concerning the grid, the LES equations are ﬁltered at a characteristic length scale that may be different from the grid cell size. However, ﬁlter size and cell size are strictly related, given that ﬁlters are often implicit. Typically, the size of an implicit ﬁlter is twice the grid cell size. Masri et al. [23] investigated the effects of grid cell size and (implicit) ﬁlter width on unsteady premixed ﬂamesthat, after ignition in an initially stagnant mixture propagate past obstacles in a vented enclosure. A coarse grid and a ﬁne grid were tested with the ratio of ﬁlter width to laminar fame thickness, D/dF, ranging from 22.7 to 25.5 and from 15.2 to 16, respectively. In Figure 3.4, the experimental time traces for overpressure, ﬂame location, and ﬂame speed are compared with the LES results. It can be seen that, thanks to the higher resolution of both turbulence and combustion-turbulence interaction, the predictions of the ﬁne grid are closer to the measurements than the predictions of the coarse grid. In several models, the cut-off length scale, determining the scale of the resolved eddies, is assumed to belong to the inertial range of the turbulence spectrum: the ﬁlter size has to be of the order of 30lk, where lk is the Kolmogorov scale (lk 0.1–1 mm). Grid optimization in LES should be performed reducing the cell dimension (and, thus, the ﬁlter size) until a converged solution is obtained. The procedure to follow is called adaptive LES. It is based on the evaluation of the M parameter, which measures the fraction of the total turbulent kinetic energy residing in the unresolved motions [30]. This parameter varies from 0 (DNS) to 1 (RANS). The procedure consists in reﬁning the grid where M exceeds 0.2. LES numerical schemes should be sufﬁciently accurate to avoid that the effects of numerical diffusion dissipate the resolved turbulence. Second-order or, better, higher-order schemes have to be used. Large eddy simulations are expensive in terms of computer resources. Nowadays, the computational power is sufﬁcient to handle LES in small-scale explosions with a good level of accuracy. To face large-scale explosions, the range of resolved scales has to be signiﬁcantly reduced, losing in physical and numerical accuracy. LES models have to be validated by comparing numerical predictions with experimental data. This comparison is a difﬁcult task since LES provides unsteady and spatially ﬁltered quantities. The comparison should be performed by extracting statistical quantities from LES variables (mean and variances) which is a difﬁcult step owing to the uncertain evaluation of statistical quantities at the sgs level. Alternatively, the experimental data could be ﬁltered as the computed data. However, this step requires the knowledge of the numerical ﬁlter used and the availability of threedimensional experimental maps of velocity, temperature, and species concentrations. The comparison between LES predictions and experimental data remains an open problem that should be addressed in the near future.

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Figure 3.4 Measured time traces for overpressure, flame location, and flame speed are compared with results of two LES runs that use a coarse mesh and a fine mesh (from [23]).

3.3 Applications in Research

3.3 Applications in Research

Safety CFD (SCFD) for research should have as its main goal to gain insight into the different mechanisms and phenomena coming into play during explosions. SCFD models allow correlation of the spatio-temporal evolution of the ﬂame structure with its speed and the pressure–time history. In addition, they allow the artiﬁcial suppression of one mechanism/phenomenon at a time, thus drawing conclusions about the relevance of the mechanisms and phenomena involved in explosions [14, 15, 25, 26]. In modeling gas explosions via SCFD, two different approaches have been used: URANS and LES. The URANS approach solves the time dependence of statistical mean quantities, but misses all details of the turbulence unsteadiness. Furthermore, in turbulent combustion problems, the ability of URANS to capture the mean ﬁelds relies strongly on the sub-grid models adopted for treating turbulence and its coupling with the ﬂame. In the literature, most of the SCFD models are based on the URANS approach [9–21]. Great effort has been devoted to the validation of URANS codes against experiments. Sensitivity analyses have also been performed to identify the values of model constants and parameters that best ﬁt the experimental data in terms of ﬂame speeds and pressure peaks. The LES technique grasps the intrinsically unsteady nature of ﬂame propagation in explosions. It offers a better representation of turbulence, and the resulting ﬂame– turbulence interaction, with respect to URANS. The key issue for research is to test the predictive ability of both LES and URANS, thus understanding what could/should be obtained with these two approaches in fundamental and applicative studies. Over the last decade, great progress has been made in LES modeling of steady or quasi-steady turbulent combustion systems (combustors, burners, etc.). The knowledge acquired in this ﬁeld is relevant; however, the passage to LES models for unsteady applications is not straightforward. The implementation of LES in SCFD models for simulating explosions is more recent [22–29]. By comparing some recent SCFD results, it can be demonstrated that LES outperforms URANS in predicting obstacle-aggravated explosions. In Figure 3.5, our simulation results for unsteady premixed ﬂame propagation around three repeated obstacles in a small-scale explosion chamber are shown. More precisely, the instantaneous images of the propagating ﬂame as obtained in the experiments by Patel et al. [13] (a) are compared with the corresponding LES (b) (from [25]) and URANS (c) maps of the reaction progress variable. Figure 3.5 shows the ﬂame as it propagates across the central plane of the combustion chamber at different time instants (also reported) after ignition/ initialization. As in the experiment, the LES ﬂame impinges on to the ﬁrst obstacle, with incomplete consumption of the fuel mixture in the upstream chamber zone. It then separates into two opposite ﬂames, one on each side of the obstacle. The ﬂames jet

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Figure 3.5 Time sequence of the instantaneous images of the propagating flame: experimental results by Patel et al. [13] (a), and LES (from [25]) (b) and URANS (c) calculations for the reaction progress variable maps. The images are taken at the central plane of the explosion chamber.

downstream of the obstruction and then curl towards the chamber centerline, thus expanding and reconnecting with each other. This same sequence is repeated, with ever greater velocities, as the ﬂames cross the second and third obstacles before venting out of the chamber. Also, the progressive intensiﬁcation of the ﬂame front wrinkling during the propagation through the obstacles is simulated by LES, together with the formation of ﬂame pockets leaving the main front when the ﬂame burns at the wake of the second and third obstructions. These changes of the ﬂame front structure are due to the interaction with the turbulent vortices induced behind the obstacles by the ﬂame propagation itself. Depending on the intensity of the ﬂame–vortex

3.3 Applications in Research

interaction, different ﬂame responses (i.e., different combustion regimes) have been found [25]. URANS is able to reproduce some features of the ﬂame propagation: the ﬂames jetting through the obstacles, reconnecting with each other between the obstacles, and venting. However, owing to the intrinsically smearing effect of RANS methods, the ﬂame shape is smooth during the entire propagation: no ﬂame wrinkling can be observed and the front remains continuous without any formation and separation of pockets from the main ﬂame. URANS is not able to predict the ﬂame turning behind each obstacle: the ﬂame crosses the three obstructions with a plug ﬂow-like behavior and, in exiting the chamber, it leaves a greater amount of fresh gas within the chamber compared to both experiment and LES. The intensity of the pressure peak is strongly linked to this amount of reactants accumulated inside the chamber. As a consequence, the pressure peak may be captured with URANS only after an ad hoc tuning of model constants/parameters. Different attempts to apply LES to small-scale explosions in the presence of obstacles have been made [22–27]. In all the proposed models, LES is based on the ﬂame surface density (FSD) approach for modeling the combustion rate at the sgs level. It has been shown that the identiﬁcation of the combustion regimes through which the ﬂame propagation evolves (from the laminar regime up to the thin-corrugated regime) is consistent with the ﬂamelet assumption on which the FSD approach relies [23, 25]. A number of studies by Masris group have been published [22–24, 27]. In most of these studies [22–24], the algebraic closure for the sgs ﬂame surface density of Boger et al. [44] was adopted without using any ﬁtting parameters. Although this sgs combustion model exhibits a weak dependence of the combustion rate on the unresolved vortices, the results obtained show satisfactory predictions in terms of ﬂame position, structure, and interactions with ﬂow and turbulence. The discrepancies found with regard to the pressure trend have been ascribed to the sgs combustion model implemented. Ibrahim et al. [27] obtained more accurate predictions using the dynamic ﬂame surface density formulation of Knikker et al. [48]. In our LES study of unsteady premixed ﬂame propagation around obstacles [25, 26], the sgs wrinkling factor (Equation 3.29) was treated according to the power-law ﬂame wrinkling model of Charlette et al. [43]. The numerical and experimental results agree well not only in terms of shape of the propagating ﬂame and ﬂame arrival times (as seen in Figure 3.5), but also in terms of spatial proﬁle of the ﬂame speed, pressure–time history, and velocity vector ﬁelds ahead of the ﬂame front [25]. Once validated, the LES model has been used to study the role of the large-scale vortices, in relation to that of the small sgs vortices, on the features of the ﬂame propagation [25]. To achieve this, large eddy simulations were also run with the effect of the sgs combustion model eliminated (i.e., by assuming the sgs wrinkling factor to be constant and equal to unity during the entire propagation). The results obtained demonstrate that the large-scale vortices play the dominant role in dictating all trends, including the evolution of the ﬂame structure along the path. Conversely, the sgs vortices do not affect the qualitative trends. However, it is essential to model their

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effects on the combustion rate to achieve quantitative predictions for both ﬂame speed and pressure peak. This methodology of implementing sgs combustion models developed for steady (or quasi-steady) turbulent combustion applications in SCFD codes seems to be successful, even if it has yet to be proven under various conditions and, mainly, at different geometry scales. The question of the optimal sgs model for combustion in explosions still remains open. Research effort is required to propose, develop, test, and compare sgs combustion models according to the criteria of level of description, completeness, cost and ease of use, range of applicability, and accuracy [33].

3.4 Applications in Industry

For industrial-scale explosions, modeling becomes much more important, since large-scale experiments are costly and often impractical. Scaling from small scales up to large scales is not an easy task, given that phenomena negligible at small scales may become predominant at large scales. Therefore, it would be better to speak about largiﬁcation rather than scale-up. In the literature, there is a lack of scaling laws able to link small-scale experiments and large-scale models. The only available laws come from Mercx et al. [57] and Puttock [58]. In order to describe large-scale explosions, we ﬁrst need to understand the evolution of the phenomena coming into play when largiﬁcation occurs. To look into the combustion–turbulence interaction, we may analyze the Borghi diagram (Figure 3.6) which shows the turbulent combustion regimes in 100

Distributed reaction zones

tc

=

tt

Well-stirred reactor

10 u' / Sl

tc = tk small

medium

large

Corrugated flamelets 1

Re

t

=

1

Laminar flame

Wrinkled flamelets

0.1 1

10

100 lt / dF

Figure 3.6 The Borghi diagram.

1000

3.4 Applications in Industry

the u0 /Sl–lt/dF plane (where u0 is the turbulent velocity ﬂuctuation). In this diagram, it is possible to localize the regions of small-, medium-, and large-scale explosions. It is shown that, when moving from small to large scales, the turbulent combustion regimes and, thus, the kind of interaction between ﬂame and vortices do not change. However, the strength of the interaction increases and, thus, the consequences associated with explosions (peak pressure and rate of pressure rise) are expected to become more severe. In SCFD modeling, this poses serious problems for URANS and, in particular, for the sgs combustion model. In this case, the key issue is to quantify the strength of the combustion-turbulence interaction and, thus, the sgs combustion models used at small scales cannot be directly implemented at large scales. Conversely, the scale-up of LES models is conceptually an easy task, given that LES resolves the large scales, requiring models for turbulence and combustion only at the sgs level. These models remain the same in the passage from small to large scales. Obviously, the main problem in performing LES at large scales is the computational power required. Hirano [59] has pointed out that, in large-scale enclosures, the ﬂammable gas scarcely becomes uniform. Therefore, in simulating gas explosions at large scales, non-uniformity effects should also be taken into account. Furthermore, it is well known that the ﬂame propagation generates pressure waves that may coalesce to give rise to a shock front, eventually leading to detonation. The study of the interaction between ﬂame and acoustic waves is essential in detonation and/or deﬂagration to detonation transition processes, which are, however, outside the scope of the present contribution. Nevertheless, in the framework of purely deﬂagrating processes, the ﬂame interaction with pressure waves propagating inside the enclosure is still a relevant issue, at least at medium/large scales. In medium- and large-scale explosions, the ﬂame interaction with rarefaction and acoustic waves, which can lead to Kelvin– Helmholtz and Rayleigh–Taylor instabilities, may be stronger than at small scales. Kumar et al. [60] detected severe Helmholtz-type oscillations for medium-scale lean hydrogen–air explosions vented through a duct. Furthermore, the diagnostics of large-scale experimental explosion tests reveal the presence of acoustic waves that interact with the ﬂame front. Starting from this point, Teerling et al. [61] numerically analyzed the response of a ﬂame to oscillatory waves to test and quantify the role of pressure disturbances on the ﬂame speed. Their results demonstrate that the interaction between ﬂame and acoustic waves may affect the burning velocity through effects on wrinkling and corrugation, leading to ﬂame acceleration. In particular, owing to the effect of the Rayleigh–Taylor instabilities, pressure waves of 800 Hz may have a magnifying effect on the ﬂame wrinkling, increasing the burning velocity. Therefore, for large-scale explosions, the sgs combustion model should be able to account for these effects of interaction between ﬂame front and pressure waves. In contrast to laboratory-scale equipment, the large-scale cases are characterized by very complex geometry.

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Process plants are an ensemble of parts and objects that render the geometry representation a difﬁcult task. The literature SCFD models devoted to large-scale explosions differ mainly in the level of detail in the geometry representation. One class of models represents congestion in the explosion scenario as a volume of homogeneous porosity and resistance, as ﬁrst proposed by Patankar and Spalding [62]. These models are based on the porosity distributed resistance (PDR) formulation, which couples the Darcy law (valid for ﬂows in porous systems) with the reactive Navier–Stokes equations. The PDR approach has been widely used to simulate explosions in large-scale conﬁned geometries (see, e.g., [16–19]). Reviews on this topic have been given by Lea and Ledin [63] and Cant et al. [64]. Another class of models directly represents the whole geometry. In these cases, the CFD model has to be coupled with computer-aided design (CAD) that allows for the representation of the actual geometry, giving the layout for grid generation. A detailed description of the CAD geometry generation and its interface with the CFD code has been given by Cant et al. [64, 65]. These SCFD codes allow an exact geometric representation of the explosion scenario, but they may be limited by the available computer memory. Most of the SCFD models developed for large-scale phenomena are based on URANS [9, 16–21]. Only recently, the LES approach has been proposed for large-scale explosions [28, 29]. The ﬂame interaction with pressure waves and acoustics was neglected. Furthermore, rather coarse grids were employed. This choice is the result of a trade-off between the need for applying LES at large scales and the computational cost. Makarov and Molkov [28] developed an LES model of gaseous deﬂagration in a closed spherical vessel (V 6.5 m3), with the sgs wrinkling factor assumed to be constant and equal to JD ¼ 1. Simulations were run for stoichiometric hydrogen–air premixed combustion initiated at the center of the vessel from quiescent conditions. The solution-adaptive mesh reﬁnement was used. The grid was adapted to the local gradient of reaction progress variable, providing a ﬁner resolution (average linear size of the adapted grid cell 35 mm) in the area around the ﬂame front with moderate central processing unit (CPU) time. The model reproduces the experimental pressure dynamics with an error smaller than 10%. In addition, as shown in Figure 3.7, it explicitly resolves the cellular structure of the spherically expanding ﬂame front. Molkov and Makarov [29] also published LES results of vented gas explosions in the SOLVEX enclosure (V 550 m3). In these very large eddy simulations, a grid cell dimension of around 0.7–0.8 m was chosen, which is much larger than the cell dimension required by LES (30lk 3–30 mm). The sgs wrinkling factor was assumed to be a function of the local turbulence conditions according to the model proposed by Yakhot [66]. The increase in the ﬂame surface density due to the anisotropic component of turbulence was taken into account by introducing an additional sgs wrinkling factor outside the enclosure, where more pronounced vortical structures arise. This additional JD was assumed to increase linearly from its initial value equal to 1 (when the ﬂame front touches the vent edge) up to around 2 in 100 ms, and to remain constant afterwards.

3.5 Outlook

Figure 3.7 Flame front profile across the vessel at different time instants after initialization: t ¼ 9.8, 20.1, 32.7, and 44.4 ms (a); resolved cellular structure of the flame front at t ¼ 44.4 ms (b) (from [28]).

Figure 3.8 Experimental and simulated pressure–time histories for explosions occurring in the SOLVEX enclosure: additional sgs wrinkling factor employed outside the chamber (a); no additional sgs wrinkling factor employed (b) (from [29]).

In Figure 3.8a, the experimental and simulated pressure–time histories are shown, which result in close agreement. LES computations were also performed without introducing the additional JD outside the enclosure. The resulting pressure–time history is compared with the experimental trend in Figure 3.8b. The peak pressure is strongly underpredicted, demonstrating the central role of the external vortical ﬂow anisotropy in the process of turbulent combustion intensiﬁcation and, thus, pressure development.

3.5 Outlook

An effective picture of turbulence may be given according to the energy cascade by Richardson [67]:

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Big whorls have little whorls, which feed on their velocity, and little whorls have lesser whorls, and so on to viscosity (in the molecular sense). The rationale behind LES of turbulent ﬂows can be depicted following Meneveau and Katz [32]: LES whorls have subgrid whorls, which feed on their velocity, but small whorls copy larger whorls, so we dont need viscosity (in the molecular sense). This statement is maybe too optimistic, but with the continuous progress in computing performance, it could become a realistic opportunity. Now, we need to add reaction in unsteady conditions for explosion phenomena: Big whorls wrinkle the ﬂame, little whorls copy larger whorls, but they also enter the ﬂame structure, and so on to intrinsic kinetics. The rationale behind LES of turbulent reacting ﬂows can be expressed according to: LES whorls wrinkle the ﬂame, subgrid whorls copy larger whorls, but they also enter the ﬂame structure, so we do need subgrid combustion models. The use of LES in gas explosions should be encouraged in the future. Thanks to LES, we are now able to describe fully the large whorls and their effect on the ﬂame surface, but we still need to obtain adequate models for treating the combustion–turbulence interaction at the sub-grid level.

3.6 Conclusions

The unsteady interaction of ﬂame propagation, turbulence, and geometry drives the mechanisms and phenomena determining the consequences of gas explosions. The understanding of such mechanisms and phenomena is a needed step for prevention and mitigation of unwanted events in reﬁnery and chemical plants. To achieve this goal, the use of Safety CFD (SCFD) models may be proﬁtable, owing to their ability to

References

simulate more physics in explosions than simple empirical or lumped-parameter models. LES explicitly resolves the large turbulent structures in a ﬂow ﬁeld, modeling only the small structures that, however, exhibit a more universal behavior. Conversely, the approach based on the URANS equations models the entire turbulence spectrum. For turbulent combustion problems, the improved representation of turbulence provided by LES also implies a better description of the ﬂame–turbulence interaction with respect to classical RANS methods. Moreover, LES grasps the inherently unsteady nature of turbulent ﬂows and, thus, of transient combustion phenomena such as explosions. When performing URANS, only the time dependence of statistical mean quantities is captured, and all details about their unsteadiness are lost. Unfortunately, LES is much more computationally demanding than URANS. Nowadays, computer power is sufﬁcient to handle LES at the laboratory scale with a good level of accuracy. In the ﬁeld of small-scale explosions, LES has shown its superiority with respect to URANS. LES can be seen as a truly predictive tool. In contrast, URANS is simply an a posteriori descriptive tool: in order to reproduce ﬂame speeds and pressure peaks, it needs experimental data against which to compare and validate numerical results by an ad hoc tuning of model constants/parameters. Therefore, results cannot be extrapolated outside their range of validation. Thanks to the continuing growth of computational power and the development of ever more robust distributed computing algorithms, it can be expected that LES will be extended to medium- and large-scale explosions in the near future. This poses a number of challenges for modelers (e.g., sgs combustion models for high turbulence intensity; non-uniformity of the ﬂammable gas; interaction between ﬂame front and pressure waves; complex geometry layout). In the meantime, since URANS still remains the only feasible methodology for modeling real-scale explosions, it should be used with the full awareness of its limitations.

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58 Puttock, J.S. (1999) Improvements in

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4 Eulerian (Field) Monte Carlo Methods for Solving PDF Transport Equations in Turbulent Reacting Flows Vladimir Sabelnikov and Olivier Soulard 4.1 Introduction

In turbulent ﬂames, phenomena of interest, such as pollutant production, soot formation or extinctions/ignitions, mainly arise from a conjunction of rare physical events (peak temperature, weak mixing conditions, etc.) and ﬁnite rate chemistry effects. Predicting these phenomena thus requires a precise knowledge of the onepoint statistics of the species concentrations and temperature, and also an accurate description of chemical reactions. Regarding both aspects, the one-point joint composition probability density function (PDF) is a relevant tool: it transports the detailed one-point statistical information of the turbulent scalars and allows chemical source terms to be treated exactly [1, 2]. These advantages are nonetheless counterbalanced by a severe numerical constraint: the composition PDF possesses a potentially high number of dimensions, which induces heavy computational costs. In particular, the ﬁnite methods traditionally employed in computational ﬂuid dynamics (CFD) cannot be used, as their cost increases exponentially with dimensionality. Monte Carlo methods, on the other hand, yield a linearly growing effort and are more adapted to solve PDF equations. So far, in the ﬁeld of turbulent combustion, Monte Carlo methods have mostly been considered in their Lagrangian form, following the impulse given by the seminal work of Pope [2]. Numerous publications document the convergence and accuracy of Lagrangian Monte Carlo (LMC) methods. They have been used in many complex calculations [including large eddy simulation (LES)], and for several years now they have been implemented in commercial CFD codes. However, the development and evaluation of an alternative Eulerian (Field) Monte Carlo (EMC) method is also useful and stimulating, since the competition between LMC and EMC methods could push both approaches forward. EMC methods are based on stochastic Eulerian ﬁelds, which evolve from stochastic partial differential equations (SPDEs) stochastically equivalent to the PDF equation. These SPDEs are the Eulerian counterpart of the stochastic ordinary differential equations (SODEs) used in LMC methods.

Handbook of Combustion Vol.3: Gaseous and Liquid Fuels Edited by Maximilian Lackner, Franz Winter, and Avinash K. Agarwal Copyright 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32449-1

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EMC methods have been extensively used in several domains (for instance, see [3] for shallow water applications, [4] for quantum mechanics, and [5] for applications of polynomial chaos expansions). However, their application to simulate the composition PDF in turbulent reactive ﬂows seems only to date back to Valiños work [6], in which the method is named Field Monte Carlo formulation. Sabelnikov and Soulard [7] proposed a new path to derive SPDEs for solving composition PDF equations. They also presented the details of the theoretical and numerical issues concerning EMC methods. Valiño [6] established a connection between the composition PDF equation and SPDEs with an Ito interpretation of the stochastic integrals [8, 9]. However, the SPDE derivation applied by Valiño [6] in order to obtain this connection suffers from several limitations, as pointed out by Sabelnikov and Soulard [7]. First, restrictive hypotheses are used. The connection between the PDF equation and the SPDEs is explicitly limited to smooth, twice differentiable in space stochastic ﬁelds. An argument is given suggesting that this restriction is not impairing and that discontinuities can be regarded as pathological: a mapping is introduced to show that the stochastic ﬁelds and the PDF have similar grades of spatial smoothness. However, this last argument only holds if the mapped ﬁelds are themselves smooth, which remains an hypothesis. In fact, it has been shown [7] that discontinuities are on the contrary likely to appear due to the presence of boundary conditions. Another drawback of the derivation used by Valiño [6] is that it does not allow one to gain any insight into the o SPDE obtained. It is erroneously interpreted [6] as a parabolic advection–diffusion–reaction equation. As shown by Sabelnikov and Soulard [7], it is in fact a hyperbolic advection–reaction equation. This consideration is crucial for building a numerical scheme. In this regard, the knowledge of the actual advection velocity, only apparent in the Stratonovitch formulation of stochastic integrals [8], is also crucial. Thus, despite yielding correct SPDEs (with Ito interpretation), the derivation proposed by Valiño [6] appears to be constrained by unnecessary hypotheses and to be without discussion of physical meaning. The path followed by Sabelnikov and Soulard [7] has its foundation in the rapidly decorrelating velocity ﬁeld model ﬁrst proposed by Kraichnan [10] and Kazantsev [11]. The Kraichnan–Kazantsev model describes the advection of a scalar by a solenoidal white-in-time Gaussian velocity ﬁeld, and leads to a Fokker–Planck composition PDF equation with a diffusion term in physical space. The SPDEs derived by Sabelnikov and Soulard [7] are semi-linear hyperbolic advection–reaction equations. Advection [10, 11] is performed by a smooth-in-space, white-in-time velocity ﬁeld. In the derived SPDEs, the stochastic advection term is interpreted in the Stratonovitch sense [8, 9]. These SPDEs are also treated in a generalized sense. Indeed, discontinuous stochastic scalar ﬁelds are likely to appear due to the inﬂuence of boundary conditions, even for continuous and differentiable initial solutions. The SPDEs proposed by Sabelnikov and Soulard [7] are written in a non-conservative form. A conservative formulation was later proposed [12]. Examples of recent applications of the scalar EMC method with SPDEs in nonconservative form have been reported [12–19].

4.1 Introduction

In [13], the EMC method is applied to the solution of the modeled evolution equation for the subgrid joint composition PDF in an LES of a pilot methane–air diffusion ﬂame (Sandia Flame D). A simple model for subgrid scale (SGS) stresses and ﬂuxes and a global four-step reduced mechanism for combustion are combined in the formulation. Eight stochastic ﬁelds were shown to be sufﬁcient to characterize the inﬂuence of SGS ﬂuctuations on the ﬁltered species formation rate with reasonable accuracy and at moderate computational cost. With the exceptions of H2 and CO, good agreement between measured and computed mean and root mean square (RMS) proﬁles of velocity, composition, and temperature was achieved. The discrepancies in H2 and CO concentrations are attributable to limitations in the global chemistry mechanism used in the LES. Overall, the results serve to highlight the potential of the scalar EMC method in LES. EMC–LES has been applied [14] to the auto-ignition of a hydrogen jet issuing into a turbulent co-ﬂowing air stream. A 19-step, nine-species detailed mechanism is used for reaction. The method is able to reproduce ignition lengths and different regimes observed experimentally without any adjustment or calibration of the model constants. EMC–LES has been applied [15] to the solution of the subgrid joint PDF of the reacting scalars in LES of a jet of hydrogen issuing into a co-ﬂow of vitiated air. The hot co-ﬂow induces auto-ignition of the mixture and a lifted ﬂame results downstream of the nozzle exit. The simulations were performed using a detailed H2–air reaction mechanism. The results were found to be sensitive to the co-ﬂow temperature even with temperatures varied within the experimental uncertainty. The results obtained were in excellent agreement with the experimental data, both quantitatively and qualitatively. The method was able to capture partially premixed and partially extinguish zones with a relatively small number (16) of stochastic ﬁelds. An EMC–Reynolds-averaged Navier–Stokes (RANS) method has been used for a reacting plume [16]. The method simulates macromixing with eddy diffusivity and micromixing by a random walk in scalar space with appropriate models for the scalar dissipation. The numerical technique used for the solution of the stochastic partial differential equation that arises from the stochastic ﬁelds method is discussed. The predictions are very close to experimental data for a plume of NO in an O3-doped turbulent air ﬂow for a range of Damk€ohler numbers. The EMC–RANS method was applied [17] to the calculation of a premixed methane–air ﬂame over a backward-facing step. Comparison with experimental data yielded qualitatively good agreement between mean temperatures and temperature variances. The EMC method is currently implemented into CEDRE, the industrial CFD code developed at ONERA. The EMC formulation has been solved [18] by a stabilized ﬁnite element method based on the variational multi-scale theory, which permits an accurate and stable treatment of the difﬁcult and stiff source terms employing iterative techniques. Extension of EMC methods to include velocity has been performed [12, 19]. These EMC methods are referred to as velocity-scalar EMC methods. By using the notion of stochastic characteristics, SPDEs equivalent to the SODEs used in LMC methods were established. The correspondence between Lagrangian and Eulerian one-point

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statistics led to the introduction of a stochastic density, and to consideration of statistics weighted by this stochastic density. As a result of these procedures, hyperbolic conservative SPDEs were obtained. The remainder of this chapter is organized as follows. First, in Section 4.2, we recall the reactive scalar PDF equation. Then, in Section 4.3, SPDEs stochastically equivalent to this PDF equation are derived, both in conservative and in non-conservative forms. Emphasis is placed on the advection properties of these SPDEs. Numerical aspects of the derived SPDEs are analyzed in Section 4.4. The special role played by boundary conditions is illustrated. Section 4.5 presents a hybrid EMC–RANS algorithm for solving the composition PDF. This algorithm is applied in Section 4.6 to simulate a premixed methane–air ﬂame over a backward-facing step. Finally, in Section 4.7, the velocity-scalar EMC method is described. In addition to the derivation of the method, numerical aspects and validation tests are presented.

4.2 PDF Equation of a Turbulent Reactive Scalar

Without loss of generality, the one-point composition PDF, and the subsequent derivation of the SPDEs allowing us to compute it, are detailed for only one turbulent reactive scalar c. This scalar evolves according to an advection–diffusion–reaction equation: qc qc 1 qJj ¼ þ SðcÞ þ Uj qt qxj r qxj

ð4:1Þ

The left-hand side describes the advection of the scalar ﬁeld by the turbulent velocity U and the two terms on the right-hand side describe the effects of molecular diffusion and chemical reaction, respectively; Jj represents the molecular diffusion ﬂux and SðcÞ is a chemical source term, depending on the scalar value. In this chapter, low Mach number ﬂows will be considered. It is well known (e.g., [2]) that the low Mach number assumption allows one to remove the coupling between the ﬂuid mechanical equations and the thermodynamic equations through the pressure. In such a case, the density is a function of the scalar c and of a uniform reference pressure P0 ðtÞ. For simplicity, we will only indicate the dependence of the density on the scalar: r ¼ rðcÞ. As a result, the velocity divergence is affected by heat release, molecular transport and the time derivative of P0 ðtÞ. The pressure which enters into the momentum equation is determined from a Poisson equation, which proceeds from the divergence of the momentum equation [20]. This procedure is similar to that followed in the incompressible case. For variable density ﬂows, working with density-weighted (Favre) statistics is a widely used technique. If f c is the one point Reynolds PDF of the scalar c, then the Favre one-point PDF ~f c is deﬁned by hri~f c ðc; x; tÞ ¼ rðcÞf c ðc; x; tÞ

ð4:2Þ

4.2 PDF Equation of a Turbulent Reactive Scalar

where r is the density. The Reynolds average of a quantity Q is denoted hQi. Its Favre ~ respectively. From ~ and Q 00 ¼ QQ, average and Favre ﬂuctuation are denoted Q the deﬁnition of the Favre PDF (Equation 4.2), we have the well-known relation ~ ¼ hrQi=hri. Q Using standard techniques [1, 2], the following transport equation is obtained for the Favre PDF ~f c : q ~ q q hri f c þ hriU~j ~f c ¼ hrihu00j jci~f c qt qxj qxj h i q 1 qJj q hri hriSðcÞ~f c jc ~f c qc r qxj qc

ð4:3Þ

where h jci denotes averages conditioned on the scalar value. The left-hand side of this equation represents the advection of ~f c by the Favre averaged velocity U~. The ﬁrst term on the right-hand side describes the effects of turbulent advection by the ﬂuctuating velocity u00 ¼ UU~, the second the effects of molecular mixing, and the third the effects of chemical reaction. While chemical reactions are treated exactly, the effects of molecular mixing and turbulent advection appear in an unclosed form and require modeling. D E qJ The conditional average of the divergence of the scalar ﬂux, r1 qxjj jc , is usually called the micromixing term and in the general case is modeled by an operator denoted M. In this work, the interaction by exchange with the mean (IEM) model [21] will be considered for simplicity. The IEM model is limited to speciﬁc applications [22]. Nevertheless, due to its simple mathematical form, the IEM model is widely used in applications. IEM model reads M~f c ¼ hvc iðc~cÞ~f c

ð4:4Þ

where hvc i is the mean mixing frequency. As for turbulent advection, it is usually modeled with an isotropic gradient diffusion assumption [2]: hu00j jci~f c ¼ CT

q~f c qxj

ð4:5Þ

where CT is a turbulent diffusion coefﬁcient. As a result, the following modeled transport equation is obtained for ~f c, the Favre one-point PDF of c: q ~ q q q~f hrif c þ hriCT c hriU~j ~f c ¼ qt qxj qxj qxj ð4:6Þ h i q q hriM~f c hriSðcÞ~f c qc qc In this equation, the turbulent diffusivity CT , the mean mixing frequency hvc i, and the Favre averaged velocity U~j are supposed to be known. For instance, they can be

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computed from a RANS solver [22]. As a consequence, this equation is closed. Note that if M~f c does not include derivatives in composition space (as with the IEM model), Equation 4.6 is parabolic in space and hyperbolic in composition space, so that it is a degenerate hypoelliptic Fokker–Planck equation.

4.3 EMC Method for the Scalar PDF: Derivation of SPDEs

The purpose of this section is to derive SPDEs stochastically equivalent to the PDF Equation 4.6. First, we consider a simpliﬁed version of Equation 4.6 in order to illustrate the main idea of the derivation, then we derive SPDEs for the full PDF equation. 4.3.1 Illustration of the Main Idea

To clarify the main idea of our approach, we ﬁrst consider the case of pure turbulent advection, that is, with zero mean velocity and no molecular diffusion or reaction: M ¼ 0, S ¼ 0. Since density is constant, we note fc ¼ f c ¼ ~f c . For the sake of simplicity, only one dimension is considered and a constant coefﬁcient CT ¼ C is chosen. Equation 4.6 then degenerates to a diffusion equation: qfc q2 fc ¼C 2 qt qx

ð4:7Þ

Two cases will be examined. In the ﬁrst case, the physical domain is chosen unbounded. This case allows us to gain more insight into the connection between PDF equations and hyperbolic SPDEs and to illustrate the advecting properties of SPDEs. In the second example, a ﬁnite domain is considered. This case reveals the impact of boundary conditions on the regularity of the solutions of hyperbolic SPDEs. 4.3.1.1 Turbulent Advection Acting Alone: Unbounded Domain Let the initial condition of Equation 4.7 be given by fc ðc; x; t ¼ 0Þ ¼ f0 ðc; xÞ

ð4:8Þ

The solution of the parabolic Equation 4.7 with initial condition (4.8) is then ð y2 1 fc ðc; x; tÞ ¼ f0 ðc; xyÞ pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ e4Ct dy ð4:9Þ 4pCt 1 Note that the function pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ exp y2 =4Ct is the PDF of the stochastic process 4pCt pﬃﬃﬃﬃﬃﬃ y ¼ 2CW. Here, WðtÞ is the Wiener process. The standard Wiener process is a stochastic Gaussian process with independent increments and continuous trajectories. It has the following properties [8]:

4.3 EMC Method for the Scalar PDF: Derivation of SPDEs

1) Wð0Þ ¼ 0, almost surely. 2) WðtÞWðsÞ is a Gaussian variable with zero mean and variance ts for all t s 0. 3) for all times 0 < t1 < t2 < < tn , the random variables Wðt1 Þ; Wðt2 Þ Wðt1 Þ; ; Wðtn ÞWðtn1 Þ are independent. Note in particular that hWðtÞi ¼ 0; hW 2 ðtÞi ¼ t for each time

t0

ð4:10Þ

The pﬃﬃﬃﬃﬃﬃon the right-hand side of Equation 4.9 can be interpreted as the mean

integral of f0 c; x 2CWðtÞ : D pﬃﬃﬃﬃﬃﬃ E fc ðc; x; tÞ ¼ f0 c; x 2CW ð4:11Þ W

where h iW denotes paveraging ﬃﬃﬃﬃﬃﬃ

over the Wiener process. Now, we identify the function g ¼ f0 c; x 2CWðtÞ as a ﬁrst integral of the following SODEs: dc ¼ 0 pﬃﬃﬃﬃﬃﬃ ð4:12Þ dx ¼ 2CdWðtÞ The term ﬁrst integral means that g is constant alongside the trajectory given by the SODEs (Equation 4.12). As a result, knowing the initial condition f0 and the trajectories given by Equation 4.12 allows us to compute the PDF fc , through Equation 4.11. In the deterministic case, the solutions of the ordinary differential equations (ODEs) similar to Equation 4.12 are named characteristic curves of hyperbolic advection PDEs. In the stochastic case, it is logical to name the solutions of the SODEs (Equation 4.12) stochastic characteristic curves of the following hyperbolic advection SPDE: pﬃﬃﬃﬃﬃﬃ qc qc dt þ 2C dW ¼ 0 qt qx

ð4:13Þ

Equation 4.13 describes the pure turbulent p stochastic advection of the concentration ﬃﬃﬃﬃﬃﬃ _ is the time ﬁeld by the white-in-time velocity ﬁeld 2CjðtÞ, where jðtÞ ¼ W derivative of the standard Wiener process W. Usually, jðtÞ is called Gaussian white noise [8]. To integrate Equation 4.13, we face the following problem. Even if the trajectories of WðtÞ are continuous, they are nowhere differentiable: even at small time intervals, WðtÞ ﬂuctuates enormously. As a result, the Wiener process is not of ﬁnite variation [8], so that the classical Riemann–Stieltjes integral calculus cannot be used for stochastic integrals. In the literature, there are two main interpretations of stochastic integrals, due to Ito and Stratonovitch [8]. The symbol in Equation 4.13 is used to denote the Stratonovitch interpretation pﬃﬃﬃﬃﬃof ﬃ the stochastic integral of the product of the Gaussian random velocity dV ¼ 2CdW with the derivative qc=qx, where c itself is a functional of dV. The time integration in the Stratonovitch deﬁnition is performed using the mid-point rule:

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ðt

X ðtÞ dW ¼ lim

Dt ! 0

0

N X 1 k¼0

2

½X ðtk þ 1 Þ þ X ðtk Þ½W ðtk þ 1 ÞW ðtk Þ

ð4:14Þ

where 0 < t1 < < tN ¼ t, Dt ¼ maxðDtk Þ, and XðtÞ is a functional of the Wiener process W. In the Ito deﬁnition of the stochastic integral, there is no symbol to denote the stochastic product. The time integration is performed using the left-end point rule: ðt

N X

X ðtÞdW ¼ lim

Dt ! 0

0

X ðtk Þ½W ðtk þ 1 ÞW ðtk Þ

ð4:15Þ

k¼0

Further discussions on the Ito and Stratonovitch deﬁnitions can be found elsewhere [8, 9, 23]. In particular, in these references, the relation between the Ito and Stratonovitch interpretations of the product of a functional X of the Wiener process W and the Wiener process itself is proven: X dW ¼ X dW þ

1 dX dW 2

ð4:16Þ

We conclude this discussion about the Ito and Stratonovitch integrals by summarizing the main advantages of each deﬁnition: .

Ito integral: simple equations: hdWi ¼ 0

.

hdW 2 i ¼ dt

ð4:17Þ

Stratonovitch integral: ordinary chain rule holds, classical differential calculus is preserved.

It has been shown [8, 9, 23, 24] that the Stratonovitch calculus corresponds to the limit case in which the velocity ﬁeld is Gaussian, with a correlation time tending to zero. The Stratonovitch interpretation in Equation 4.13 is crucial. If one erroneously chooses the Ito interpretation, then one obtains after averaging Equation 4.13 with the Ito interpretation: qhci ¼0 qt

ð4:18Þ

pﬃﬃﬃﬃﬃﬃ since for the Ito interpretation 2Cðqc=qx ÞdW ¼ 0 (as deduced from Equation 4.17). This result is incompatible with the equation for hci deduced from the PDF Equation 4.7: qhci q2 hci ¼C qt qx2

ð4:19Þ

pﬃﬃﬃﬃﬃﬃ With the Stratonovitch interpretation, the correlation 2C ðqc=qx ÞdW is not zero. It can be proved either by application of the Furutsu–Novikov formula [8, 24] or by Equation 4.16 between the Ito and Stratonovitch integrals. Both methods give pﬃﬃﬃﬃﬃﬃ qc q2 hci dW ¼ C 2C dt ð4:20Þ qx qx 2

4.3 EMC Method for the Scalar PDF: Derivation of SPDEs

This result yields the desired evolution for the averaged scalar Equation 4.19. Let us prove Equation 4.20 by applying Equation 4.16. In our case, X ¼ qc=qx. It follows from Equation 4.13 that pﬃﬃﬃﬃﬃﬃ q2 c qc dX ¼ d ð4:21Þ ¼ 2C 2 dW qx qx Thus, we obtain from Equations 4.16 and 4.21 pﬃﬃﬃﬃﬃﬃ qc pﬃﬃﬃﬃﬃﬃ qc q2 c dW ¼ 2C dWC 2 dt 2C qx qx qx

ð4:22Þ

Averaging Equation 4.22 and taking into account Equation 4.17, we obtain Equation 4.20. We note that using Equation 4.22, SPDE Equation 4.13 can be presented in Ito form as pﬃﬃﬃﬃﬃﬃ qc qc q2 c dt þ 2C dWC 2 dt ¼ 0 qt qx qx

ð4:23Þ

As the Stratonovitch interpretation preserves the classical differential calculus, Equation 4.13 is a hyperbolic advection equation as would be the case if the coefﬁcient in the advection term had a non-zero correlation time. The solution of Equation 4.13 is given by h pﬃﬃﬃﬃﬃﬃ i cðx; tÞ ¼ c0 x 2CWðtÞ ð4:24Þ where c0 ðxÞ is the initial condition of the stochastic ﬁeld c. Equation 4.24 is another way of expressing the fact that Equation 4.13 preserves the shape of the initial proﬁle and advects it along Wiener paths. In particular, even an initial discontinuous proﬁle such as the Heaviside function HðxÞ is transported without alteration. It can also be checked that the solution in Equation 4.24 yields correct evolutions for the moments. For instance, with a Heaviside initial condition c0 ðxÞ ¼ HðxÞ, and knowing

pﬃﬃﬃﬃﬃﬃﬃthat the PDF fW of W is a centered Gaussian of variance t fW ¼ 1= 2pt expðW 2 =2tÞ, one obtains for the scalar mean hciðx; tÞ ¼

pﬃﬃﬃﬃﬃﬃ 1 x H x 2CW fW dW ¼ 1 þ erf pﬃﬃﬃﬃﬃ 2 2 Ct ¥

ð¥

ð4:25Þ

This expression is also the one obtained directly from Equation 4.19. We conclude this section by noting that SPDE Equation 4.13 is stochastically equivalent to PDF Equation 4.7. It is proved using standard techniques [1, 2, 8, 24] and the Furutsu–Novikov formula [8, 24] or Equation 4.16. 4.3.1.2 Pure Turbulent Advection, Bounded Domain: Impact of Boundary Conditions Let us consider now a bounded domain in order to illustrate the impact of boundary conditions on the solution of Equation 4.13. The domain after proper normalization is ½0; 1 and the boundary conditions for the PDF fc are chosen to be fc ¼ dðcÞ at x ¼ 0

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and fc ¼ dðc1Þ at x ¼ 1. This corresponds for the stochastic ﬁeld of Equation 4.13 to the boundary conditions c ¼ 0 at x ¼ 0 and c ¼ 1 at x ¼ 1. However, if the simultaneous speciﬁcation of two boundary conditions is necessary for the diffusion equation of the PDF fc , it is not the same for the stochastic ﬁeld cðx; tÞ, due to the advective nature of Equation 4.13. For instance, at x ¼ 0, the c ¼ 0 inﬂow condition is only effective when dW is positive and it becomes an outﬂow condition when dW is negative, and reciprocally at x ¼ 1. What might seem more surprising is that with any arbitrary initial conditions, the limit when t ! ¥ of the solution of Equation 4.13 can be shown to be a step whose position is moved randomly by the Wiener process in interval ½0; 1. Thus, initial proﬁles, even continuous, are transformed into discontinuous ones due to the inﬂuence of boundary conditions. This process can be loosely explained as follows: when dW is positive, part of the initial proﬁle is advected beyond the x ¼ 1 boundary. When dW becomes negative, this initial information is lost, as it is replaced by the inﬂow value at the x ¼ 1 boundary. The same also happens at the x ¼ 0 boundary, where initial information is replaced by the inﬂow value at x ¼ 0. This process is then repeated at both boundaries until eventually, with probability one, the initial information is lost and only the information given by both boundaries remains. The evolution described above can be formalized by introducing two supplementary stochastic processes, W þ and W :

pﬃﬃﬃﬃﬃﬃ W þ ðtÞ ¼ maxs2½0;t 2CWðsÞ; WðsÞ > 0 ð4:26Þ

pﬃﬃﬃﬃﬃﬃ W ðtÞ ¼ mins2½0;t 2CWðsÞ; WðsÞ < 0 Without boundary conditions, W þ gives the maximum deviation of the initial proﬁle towards positive x and W gives the maximum deviation of the initial proﬁle towards negative x. When W þ < 1, W þ gives the maximum extent of the initial proﬁle that has crossed the x ¼ 1 limit. Identically, when W < 1, W gives the maximum extent of the initial proﬁle that has crossed the x ¼ 0 limit. Now, reintroducing boundary conditions, W þ gives the length of the initial proﬁle modiﬁed by the x ¼ 1 boundary, and W gives that modiﬁed by the x ¼ 0 boundary. This result is valid for times smaller than the time tS for which the initial condition is converted into a step. This happens when all the initial proﬁle has crossed at least one of the boundaries. tS is given by tS ¼ min fs; W þ ðsÞ þ W ðsÞ ¼ 1g s2½0; þ ¥

ð4:27Þ

pﬃﬃﬃﬃﬃﬃ With probability one, tS is ﬁnite, as for instance, 2CW will be greater than one with probability one. Then, for times t < tS , the solution is given by h pﬃﬃﬃﬃﬃﬃ i cðx; tÞ ¼ CW x 2CWðtÞ ð4:28Þ with

8 < c0 ðxÞ; CW ðxÞ ¼ 0; : 1;

W < x < 1W þ x < W x > 1W þ

ð4:29Þ

4.3 EMC Method for the Scalar PDF: Derivation of SPDEs t=0 t = 0.16 t = 0.4 t = 2.4 t = 3.2

1 0.9 0.8 0.7

C

0.6 0.5 0.4 0.3 0.2 0.1 0

0

0.25

0.5 x

0.75

1

Figure 4.1 Formation of discontinuities due to boundary conditions.

Figure 4.1 illustrates some moments of the conversion between an initial linear proﬁle and a step. This ﬁgure was obtained from Equation 4.28. A time step Dt ¼ 0:08 was chosen in order to compute the processes W þ and W . 4.3.2 Derivation of the SPDEs 4.3.2.1 Non-Conservative Formulation In this section, an SPDE stochastically equivalent to PDF Equation 4.6 is derived. This SPDE governs the evolution of a stochastic scalar ﬁeld hereafter denoted q. In devising such an SPDE, the major difﬁculty does not stem from the inﬂuence of mean advection, chemical reactions or micromixing. Mean advection and chemical reactions appear in an exact form in the PDF Equation 4.6 and will also be present in an exact form in the stochastic ﬁeld equation. As for micromixing, stochastic processes yielding a model M in the PDF Equation 4.6 have already been devised in the frame of LMC methods and can be readily applied to our case. These processes corresponding to the operator M are further denoted Mðq; x; tÞ, and are added as source terms in the stochastic ﬁeld equation. For the IEM model, M is deterministic and is deﬁned by Equation 4.4. The last and main question that now remains to be answered is: how can one account for the inﬂuence of turbulent advection on the scalar ﬁeld statistics? To try to solve this problem, an equation for the stochastic ﬁeld q is looked for in the following form:

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qq qq ¼ Fðq; x; tÞ ð4:30Þ þ uj qt qxj where Fðq; x; tÞ ¼ U~j qq=qxj þ Mðq; x; tÞ þ SðqÞ accounts for mean advection, micromixing, and chemical reaction as explained above. Equation 4.30 is a ﬁrst-order SPDE. In this equation, u ¼ (u1, u2, u3) is a stochastic velocity vector which needs to be made precise. It does not correspond to the Favre ﬂuctuating velocity u00 and in particular does not necessarily respect the continuity constraint and does not necessarily average to zero. The only feature which is of interest for our purpose is that u should yield, in the PDF equation derived from Equation 4.30, a diffusion term equal to that present in Equation 4.6. As was illustrated in the previous section, for the particular case of pure turbulent advection, such a diffusion arises from a from a white-in-time Gaussian velocity ﬁeld. Such velocity ﬁelds have been used by Kraichnan [10] and Kazantsev [11] to study the statistics of passive scalars in homogeneous turbulence. Thus, the key idea of our approach consists in modeling u as u ¼ ud þ ug , where ud is a deterministic component and ug is a Gaussian random component of the velocity. Then, in Equation 4.30, we let the correlation time of ug tend to zero, in the same way as Stratonovitch did to give a meaning to his stochastic integral [23]. As a result, we obtain the following SPDE with the Stratonovitch interpretation for the stochastic ﬁeld: qq qq qq g þ udj þ uj ¼ Fðq; xÞ qt qxj qxj

The velocity ug is delta correlated in time: D E g g ui ðx; tÞuj ðx 0 ; t0 Þ ¼ 2Aij ðx; x 0 Þdðtt0 Þ

ð4:31Þ

ð4:32Þ

As opposed to Kraichnan [10] and Kazantsev [11], the tensor Aij does not account for the spatial structure of the velocity ﬁeld. As shown later (see after Equation 4.34), Aij is instead linked with turbulent diffusion. It is essential to note that this equation is a hyperbolic advection–reaction equation. As the Stratonovitch calculus is identical with the classical calculus, the g term uj qq=qxj has the same physical advection properties as if ug was deterministic. In particular, if Fðq; xÞ ¼ 0 then the stochastic ﬁeld q is simply advected alongside a stochastic path. Except for the inﬂuence of boundary conditions, initial proﬁles are strictly preserved and do not undergo any kind of diffusion process. This advection property would be lost if an Ito interpretation was used (see the section above). The last step in the derivation of an equation for the stochastic ﬁeld consists in determining ud and ug so that the PDF of q is identical with fc . This can be achieved by expressing the PDF equation of the stochastic scalar ﬁeld q and by identifying it to the PDF Equation 4.6 of c. This procedure is not reproduced here for the sake of brevity, but is detailed in [7]. It yields the following constraints on ud and ug :

4.3 EMC Method for the Scalar PDF: Derivation of SPDEs

8 D E 1 g g > > du ðx; tÞdu ðx; tÞ ¼ CT dij dt > j i > > <2 * + g > 1 qdui 1 qhri > g d > ðx; tÞduj ðx; tÞ CT dt u dt ¼ > > : j 2 qxi hri qxj

ð4:33Þ

Solutions fulﬁlling these constraints are not unique. The simplest is given by 8 g pﬃﬃﬃﬃﬃﬃﬃﬃ _j u ¼ 2CT W > > < j 1 qCT 1 qhri ð4:34Þ > ud ¼ CT > : j 2 qxj hri qxj where the Wj are independent standard Wiener processes. Note that this choice pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ implies that Aij ðx; x 0 Þ ¼ CT ðxÞCT ðx 0 Þdij . With this solution, the following SPDE is obtained: pﬃﬃﬃﬃﬃﬃﬃﬃ qq qq 1 qCT 1 qhri qq dt þ U~j CT dt þ 2CT dWj ðtÞ qt 2 qxj hri qxj qxj qxj ð4:35Þ ¼ Mðq; x; tÞdt þ SðqÞdt

This equation is a hyperbolic advection–reaction equation, stochastically equivalent to the PDF Equation 4.6. In its derivation, as opposed to Valiño [6], no hypothesis on the smoothness and differentiability of the stochastic ﬁelds was required, so that it has a generalized sense. Further, the velocity advecting the stochastic ﬁeld is formed by mean quantities, so that its length scale is also that of a mean quantity. This, however, does not imply that the scalar ﬁeld also evolves on a mean length scale. Equation 4.35 is also driven by a chemical source term, which, in practice, possesses stiff gradients in composition space. These in turn can generate strong gradients in physical space for the stochastic ﬁelds. The stochastic advection term in Equation 4.35 is expressed with a Stratonovitch interpretation. It can also be recast with an Ito interpretation [8], using Equation 4.16 with X ¼ qc=qx: qq qq pﬃﬃﬃﬃﬃﬃﬃﬃ _ qq þ U~j þ 2CT W j qt qxj qxj 0 1 1 q @ qq A ¼ Mðq; x; tÞ þ SðqÞ hriCT hri qxj qxj

ð4:36Þ

This equation was ﬁrst derived by Valiño [6], but under a restrictive hypothesis on the smoothness of the scalar ﬁeld. It was also considered [6] as a parabolic SPDE. As was clariﬁed in the previous section, the presence of a second-order spatial operator in Equation 4.36 is deceptive. Because of the stochastic term with an Ito interpretation, it does not act as a diffusion term but, instead, contributes with the stochastic term to

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the advection of the scalar ﬁeld. As Equation 4.35, Equation 4.36 is an advection– reaction equation. For instance, in the case of no molecular mixing or chemical reaction, the scalar q is constant alongside Wiener paths. It has to be stressed that the stochastic Eulerian Equations 4.35 and 4.36 are written in such a form that unconditional averaging over the ensemble of realizations directly yields Favre statistics. We note that SPDE Equations 4.35 and 4.36 are in non-conservative form. As will be shown in the next section, it is possible to derive different SPDEs which are also stochastically equivalent to PDF Equation 4.6, but which are written in conservative form. Conservative SPDEs can simplify the integration of the EMC method into CFD codes, which are often based on the conservative form of the Navier–Stokes equations. 4.3.2.2 Conservative Formulation As has been shown [12, 19], in order to derive conservative SPDEs, stochastically equivalent to PDF Equation 4.6, one needs to introduce a stochastic density r; r differs from the physical density, but has the same mean value. Details can be found elsewhere [12, 19]. The ﬁnal system of conservative SPDEs is written as follows: U i ¼ U~i þ CT

1 qhri 1 qCT pﬃﬃﬃﬃﬃﬃﬃﬃ _ þ þ 2CT W i hri qxi 2 qxi

ð4:37Þ

qr q þ r U j ¼ 0 qt qxj

ð4:38Þ

i q q h ðrqÞ þ ðrqÞ U j ¼ rhvc iðqhqir Þ þ rS qt qxj

ð4:39Þ

This set of SPDEs can be shown to be stochastically equivalent to PDF Equation 4.6. However, this equivalence is not direct: the unweighted PDF of the stochastic ﬁelds is not the modeled Reynolds PDF. The PDF of the stochastic ﬁelds weighted by the physical density r is not the modeled Favre PDF. The equivalence between the stochastic ﬁelds and the modeled Reynolds and Favre PDFs is only achieved by making use of the stochastic density r. To make this equivalence explicit, we introduce h is as the average operator related to the stochastic ﬁelds Wi . Then, we denote hQir the average of a stochastic quantity Q weighted by the density r: hQir ¼

hrQis hris

ð4:40Þ

The corresponding unweighted and weighted PDFs are denoted by fs and fr , respectively, and are related by fr ðqÞ ¼

hrjqis fs ðqÞ hris

ð4:41Þ

4.4 Numerical Aspects

Then, it can be shown [12, 19] that the transport equation of fr is identical with Equation 4.6, provided that the following condition is veriﬁed: hris ¼ hri

ð4:42Þ

If consistency condition (4.42) is veriﬁed and if fr and ~f c have the same initial and boundary conditions, then ~f ¼ fr c

~ ¼ hQir and Q

ð4:43Þ

SPDEs (4.38)–(4.39) can also be recast in an Ito form, using Equation 4.16, with X ¼ r and X ¼ rq: pﬃﬃﬃﬃﬃﬃﬃﬃ qr q ~ r _ j ¼ q hriCT q þ ð4:44Þ rU j þ r 2CT W qt qxj qxj qxj hri pﬃﬃﬃﬃﬃﬃﬃﬃ q q ~ rq _ j ¼ q hriCT q ðrqÞ þ rqU j þ rq 2CT W qt qxj qxj qxj hri

ð4:45Þ

rhvc iðqhqirÞ þ rS

In contrast to Equations 4.35 and 4.36, Equations 4.37–4.39 and Equations 4.44– 4.45 are written in a divergent form.

4.4 Numerical Aspects 4.4.1 Numerical Scheme

We want to solve a hyperbolic SPDE, such as Equation 4.35, in which the velocity ﬁeld is white-in-time and is interpreted in the Stratonovitch sense. The stochastic product of the white-in-time velocity and of the concentration gradient induces a strong coupling between time and space discretizations. Because of these features, we cannot use standard numerical schemes straightforwardly. Indeed, to be consistent with the Stratonovitch interpretation, we must respect a mid-point rule in temporal integration [8]. For instance, fully explicit schemes are not consistent when used to discretize Equation 4.35 [25]. Another important point is that precision must be evaluated in a weak sense: we are concerned with the error made in solving the PDF equation, not the SPDE. Thus, we must adapt existing numerical schemes. First, we address the issue of temporal integration. We recast Equation 4.35 in an SODE form; this allows the use of traditional SODE techniques [8]. An explicit ﬁrst-order scheme is chosen, with a predictor–corrector procedure generalizing the Heun scheme [26]. As for spatial discretization, scalar ﬂuxes are interpolated with a second-order essentially non-oscillatory (ENO) scheme and a decentered procedure is used for the

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advection term. Decentering derivatives yields a correlation between the white noise and the discretization error [25]. As a result, despite being second-order accurate for the individual stochastic ﬁelds, the numerical scheme only discretizes the PDF equation with a ﬁrst order spatial accuracy [25]. Higher order numerical schemes can also be constructed [25]. The time step is set according to a Courant–Friedrichs–Levy(CFL) criterion, built on the stochastic velocity. As has been shown [25], this guarantees the linear stability of the overall scheme. Because the advection velocity is stochastic, the CFL criterion that we obtain is different from the usual one, and resembles an advection–diffusion stability criterion. For instance, for Equation 4.35, in one dimension, with a grid of size Dx, the time step Dt would be expressed as Dt ¼ CFL

Dx2 U~Dx þ 2CT

ð4:46Þ

where CFL is the CFL constant. 4.4.2 Boundary Conditions

The issue of boundary conditions is also crucial. It should be noted that a diffusion equation such as that for the PDF requires boundary conditions to be speciﬁed on all the domain frontiers, whereas a hyperbolic equation only requires boundary conditions to be speciﬁed on certain parts of the frontier. Furthermore, the stochastic advection term of SPDE Equations 4.35, 4.38 and 4.39 can change the speciﬁcation of boundary conditions in time. For instance, for the simpliﬁed case considered in Section 4.3.1, the boundaries alternatively become inﬂow boundaries, with a speciﬁed value of the stochastic ﬁeld or outﬂow boundaries, with a value of the stochastic ﬁeld computed from the interior of the domain. A more general discussion on boundary conditions has been given previously [7, 25]. Here, we present an additional simple example to illustrate this issue. Let us consider the case of a wall boundary condition. In many numerical simulations, the spatial resolution is not sufﬁcient to capture the boundary layer at a solid wall. As a result, the turbulent viscosity cannot be set to zero at this boundary. Instead, a reﬂective wall boundary condition is usually imposed. Following Gardiner [8], the reﬂective wall boundary condition for the PDF Equation 4.6 is given by q~f U~n ~f c CT c ¼ 0 for all c qn

ð4:47Þ

where n is the direction normal to the wall. This condition means that the spatial ﬂux of the PDF normal to the wall is set to zero. Integration over c shows that this condition is only possible if U~n ¼ 0. We will hereafter assume that this is true, so that the boundary condition becomes CT

q~f c ¼ 0 for all c qn

ð4:48Þ

4.4 Numerical Aspects

For LMC methods, condition (4.48) corresponds to making the Lagrangian particles bounce on the boundary. For EMC methods, imposing this condition is less obvious. The question is: how can this boundary condition for the PDF equation be set at the level of the stochastic ﬁelds qðx; tÞ and rðx; tÞ? Hereafter, we will consider the conservative formulation of the EMC method, that is, Equations 4.38 and 4.39. To answer this question, we recast the boundary condition (4.48) in order to make the unweighted PDF of qðx; tÞ, fs appear (see Equation 4.41). Then, we use the deﬁnition fs ðcÞ ¼ hd½cqðx; tÞis in order to transform the spatial derivative of the PDF into a scalar derivative. Equation 4.48 is then equivalently written as r qhri qr q qq CT c fs ðcÞ þ CT r c fs ðcÞ ¼ 0 CT ð4:49Þ hri qn qn qc qn s s The interest in this reformulation is to reveal the role played by the derivative of the stochastic ﬁelds and their unweighted statistics. Many different boundary conditions on qðx; tÞ and rðx; tÞ could allow Equation 4.49 to be respected. The simplest one is 1 qr 1 qhri ¼ r qn hri qn

and

qq ¼0 qn

ð4:50Þ

Finally, the low Mach assumption has to be taken into account. Since r depends explicitly on the scalar value, then its derivative at the wall is equal to zero and so is the derivative of the mean density: qhri=qn ¼ 0. Thus, we eventually obtain that the following boundary conditions allow Equation 4.48 to be respected: qr ¼ 0 and qn

qq ¼0 qn

ð4:51Þ

Equation 4.51 is only one part of the problem. Indeed, as CT is not zero at the pﬃﬃﬃﬃﬃﬃﬃﬃ _ i is not zero. As a consequence, the ﬂuxes boundary, the stochastic velocity 2CT W of r and q are not necessarily equal to zero at the boundary, even if the ﬂux of the PDF is indeed equal to zero. What is more, the ﬂuxes of r and q may be directed inside or outside the domain, depending on the direction of the characteristic velocity. When the characteristic velocity goes outside the domain, Equation 4.51 can be fulﬁlled numerically using the values of r and q inside the numerical domain. When the characteristic velocity is directed inside the domain, one should use values of r and q located outside the domain. In practice, this can be done by using ghost cells and ﬁlling them with the values of r and q at the boundary condition, at a previous time step. This introduces a ﬁctive ﬂux directed inside the domain. To illustrate the difﬁculty, let us consider a one-dimensional initial-boundary problem for the SPDE Equation 4.38 in conservative form and with wall boundaries. The problem is deﬁned on the interval 0 x 1, with U~ ¼ 0 and hri ¼ 1. SPDE Equation 4.38 simpliﬁes to pﬃﬃﬃﬃﬃﬃﬃﬃ qr q 1 qCT _ r ¼ 0 þ ð4:52Þ r þ 2CT W qt qx 2 qx

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rðx; t ¼ 0Þ ¼ 1

and

qr ¼ 0 at qx

x ¼ 0 and x ¼ 1

ð4:53Þ

CT ðx ¼ 0Þ and CT ðx ¼ 1Þ are not equal to zero. We stress again that the condition qr=qx ends of the interval does not imply that the total stochastic ﬂux ¼ 0 at both pﬃﬃﬃﬃﬃﬃﬃﬃ 1 qCT pﬃﬃﬃﬃﬃﬃﬃﬃ _ qr _ (Equation 4.16 was used) is also jr ¼ þ 2CT W r ¼ 2CT r WC t 2 qx qx zero. In fact, one has pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ _ 6¼ 0 jr ð0; tÞ ¼ 2CT ð0Þrð0; tÞW ð4:54Þ jr ð1; tÞ ¼

pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ _ 6¼ 0 2CT ð1Þrð1; tÞW

The mass contained in the domain is deﬁned by Ir ¼ given by dIr ¼ jr ð0; tÞjr ð1; tÞ dt

ð1

ð4:55Þ

rdx. Its evolution is 0

ð4:56Þ

In the general case, the stochastic mass Ir changes in time, except when CT ð0Þ ¼ CT ð1Þ. On the other hand, the mean value of Ir always remains constant hIr i ¼ constant ¼ 1 because h jr i ¼ 0 at x ¼ 0 and x ¼ 1. This difference in the behavior of the stochastic ﬂux and its mean value at the boundaries has to be taken into account when developing EMC solvers and formulating boundary conditions. For instance, if the ﬁctive ﬂux introduced above is not taken into account, then the stochastic density will accumulate in the vicinity of the wall. 4.4.3 Elementary Validation Tests

The efﬁciency and accuracy of the EMC method have been tested on a simpliﬁed one-dimensional version of the scalar PDF Equation 4.6, with constant density and constant mean velocity. Details of these calculations can be found in [7]. It is checked [7] that statistical and spatial convergence rates conform to the theoretical values. Further, the EMC method was also compared against its Lagrangian counterpart. It was shown that, for the tests studied in [7], both methods attain a given precision for an equivalent CPU time.

4.5 Hybrid EMC–RANS Algorithm 4.5.1 Governing Equations

In the expression of the PDF Equation 4.6, it was assumed that the mean density hri, the Favre averaged velocity U~j, the turbulent diffusion coefﬁcient CT and the

4.5 Hybrid EMC–RANS Algorithm

turbulent mixing frequency hvc i were known. A RANS solver can be used to compute these quantities. Namely, the Favre averaged continuity and momentum equations are solved and a standard ke model is used to compute the turbulent stresses, with k the turbulent kinetic energy and e the turbulent dissipation. Continuity:

qhri q þ hriU~j ¼ 0 qt qxj

Momentum:

hri

Turbulent kinetic energy:

Dk q nt qk ¼ hri þ Pk dk hri Dt qxj Prk qxj

Turbulent dissipation:

hri

DU~i qhri qsij þ ¼ Dt qxj qxj ð4:57Þ

De q nt qe ¼ hri þ Pe de Dt qxj Pre qxj

D q q is the substantial derivative, and stands for þ U~j . sij models the turbulent Dt qt qxj stresses with an eddy viscosity hypothesis: 2 qU~i qU~j 2 qU~k sij ¼ hrikdij þ hrint þ dij 3 qxj qxi 3 qxk

! ð4:58Þ

Pk and dk (Pe and de ) are the production and dissipation terms of the turbulent kinetic energy (dissipation). Standard expressions are used for these terms, as found for instance in [22]. The eddy viscosity is given by nt ¼ Cm ðk2 =eÞ. Standard values are chosen for the ke model constants Cm ; Prk ; Pre , as given in [22]. The statistics of species mass fractions Yk and total enthalpy ht are computed with an EMC solver using SPDEs derived with the procedure derived in non-conservative form (Equation 4.35): Mass fraction :

qYk 1 qCT 1 qhri qYk ~ dt þ U j CT dt qt qxj 2 qxj hri qxj pﬃﬃﬃﬃﬃﬃﬃﬃ qYk ~ k dt þ SðY; T Þdt dWj ðtÞ ¼ vY Yk Y þ 2CT qxj

Total enthalpy :

qht 1 qCT 1 qhri qht ~ CT dt dt þ U j qt qxj 2 qxj hri qxj pﬃﬃﬃﬃﬃﬃﬃﬃ qht 1 dP0 dWj ðtÞ ¼ vh ht h~t dt þ þ 2CT hri dt qxj ð4:59Þ

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or in conservative form (Equations 4.38 and 4.39): i q q h ~ k þ rSk ðY; TÞ rYk þ ¼ rvY Yk Y r Yk U~j þ U j qt qxj

ð4:60Þ

i dP qrht q h 0 ¼ r vh ht h~t þ þ r ht U~j þ U j qxj qt dt

ð4:61Þ

U j ¼ Ct

1 qhri 1 qCt pﬃﬃﬃﬃﬃﬃﬃ _ þ þ 2Ct W j hri qxj 2 qxj

ð4:62Þ

Equation 4.38 follows from Equation 4.60 since

X

Y k k

¼ 1 and

X

S k k

¼0

In the enthalpy equation, an assumption of a Lewis number of unity was made and acoustic interactions, viscous dissipation, and body forces were neglected under a low Mach number assumption. In these equations, the turbulent diffusivity is deﬁned by CT ¼ nt =Sct, where Sct is a turbulent Schmidt number, assumed here to be unity. The mixing frequencies are deﬁned by vY ¼ vh ¼ hvi ¼ Cw ðe=kÞ, where Cw is a constant assumed to be equal to 0.7. For the non-conservative formulation in Equation 4.59, the Favre averaged values of the species mass fraction and total enthalpy are estimated from the ﬁnite number X X ~ k ¼ ð1=NÞ Yk and h~t ¼ ð1=NÞ ht , of stochastic ﬁelds (samples) as Y real:

real:

where the sums are taken over N realizations of the stochastic ﬁelds. To simplify the notations, we use hereafter the same notations for the estimated mean values and their exact deﬁnitions as introduced in Section 4.2. For the conservative formulations in Equations 4.60–4.62, the estimated Favre averaged values of the species mass fraction and total enthalpy are computed by X X X X ~k ¼ rYk = r and h~t ¼ rht = r, where the sums are taken over Y real:

real:

real:

real:

the N realizations. Information is transmitted from the RANS solver to the EMC solver via hri, U~j , nt , and hvi. The inﬂuence of the EMC solver on the RANS solver is achieved in the mean momentum equation, through the gradient of the mean pressure, which is computed from the equation of state: hPi ¼ hriR0

X Yg kT k

Mk

ð4:63Þ

where R0 is the gas constant and Mk is the molecular weight of the species k. This way of coupling the EMC and RANS solvers worked directly for the nonconservative formulation. However, for the conservative formulation, this coupling does not allow satisfactory convergence rates to be obtained. The statistical ﬂuctuations from hris and Yg k T in Equation 4.63 are large and prevent the convergence. To overcome this difﬁculty, an equation is considered for the following quantity: Wc ¼

hPi hci1 ~ ~ þ UiUi hri 2

ð4:64Þ

4.6 Simulation of a Backward-Facing Step with a Hybrid EMC–RANS Algorithm

where c is the ratio of speciﬁc heats. With some approximation, the following RANS equation is used for Wc: X qhriWc qWc q q þ hriFc U~j ¼ hriCT hðc1Þhk Sk is ; qxj qxj qt qxj k hPi ð4:65Þ Fj ¼ Wj þ h ji1 hri where hk is the speciﬁc enthalpy of species k and c is its speciﬁc heat ratio. The source terms in Equation 4.65 are obtained from the EMC solver. This way of coupling allows the stability and the statistical convergence to be improved. 4.5.2 Correction Algorithms

The hybrid algorithm is consistent at the level of the governing Equation 4.42 hris ¼ hri). However, due to the limited number of stochastic ﬁelds and the accumulation of numerical errors, the condition (4.42) may not always be respected during the calculation. In order to limit the statistical error, a correction diffusion term is added to SPDE Equation 4.60: i q q h ~ k þ rSk ðY; TÞ rYk þ rYk U~j þ U j ¼ rvY Yk Y qt qxj ð4:66Þ q q hris hri þ hriCT Yk qxj qxj hri The correction diffusion term is equal to zero once hris ¼ hri. The second correction concerns the velocity U j . In theory, the Favre average of this velocity is equal to zero: D E D E r U j s U j ¼ ¼0 ð4:67Þ r hris In the calculations, due to stochastic and numerical errors, condition (4.67) is not satisﬁed. To respect condition (4.67), we correct U j by subtracting its Favre average, as done for LMC methods in [27]. The new stochastic velocity is X r U j real: Uj ¼ Uj X ð4:68Þ r real: To reduce statistical noise, we also applied a time-averaging technique similar to the one described in [27].

4.6 Simulation of a Backward-Facing Step with a Hybrid EMC–RANS Algorithm

The EMC–RANS solver described above was validated against experimental data [28] on a conﬁguration consisting in the turbulent combustion of a premixed

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stoichiometric methane–air mixture. The ﬂame was stabilized by a recirculation zone in a plane channel with a sudden expansion (backward-facing step). Another validation test was also performed. consisting in the ignition by a laser of a chamber ﬁlled by a co-axial H2–O2 injector. The results of this second test will be published elsewhere. 4.6.1 Configuration

The physical domain is L ¼ 1 m horizontally and H ¼ 0.1 m vertically. The height of the step, placed at the lower wall, is h ¼ 0.035 m, and its extremity is located at 0.2 m inside the computational domain. Positions will hereafter be given using the step extremity as the origin. At the inlet, a methane–air mixture is injected at U0 ¼ 58 m s1 and T0 ¼ 525 K, with a stoichiometric equivalence ratio w ¼ 1. The inlet values of the turbulent quantities are k0 ¼ 60 m2 s2 and e0 ¼ 800 m2 s3. At the outlet, the pressure is ﬁxed: Ps ¼ 1 bar. The geometry and inlet conditions correspond to the experimental values [28]. At the upper and lower wall, wall functions are applied for k and e, while the enthalpy and mass fractions are set by assuming that the walls are adiabatic. It should be noted that these are simpliﬁed boundary conditions. Indeed, the measurements of the temperature in [28] were done in a water-cooled combustion facility. Thus, in the experiment, there were wall heat ﬂuxes and the redistribution of the heat through metal walls by means of thermal conductivity. As a consequence, the approximation of adiabatic walls overestimates temperature at the lower wall and underestimates the temperature at the upper wall. This has to be taken into account when comparing computed and experimental data. Methane combustion in air was modeled by a global single-step chemical reaction, which describes complete combustion of methane, the resultant products being CO2 and H2O [29]. Although the single-step reaction offers only an approximate description of methane oxidation and, in particular, overpredicts the temperature T, it was used for preliminary testing of the hybrid EMC–RANS algorithm developed here. 4.6.2 Results and Discussion

The EMC method in non-conservative form (Equation 4.59) was applied to the calculation of the methane–air ﬂame described above [12, 25]. Comparison against experimental data yielded qualitatively good agreement between mean temperatures and temperature variances. Statistical and spatial errors were thoroughly analyzed in [12]. It was shown that a grid resolution of Dx¼ 102 m and Dy ¼ 2:4 103 m is sufﬁcient to capture the ﬂame structure. In this section, we present some results obtained with the EMC method in conservative form (Equations 4.60 and 4.61). This work was done in collaboration with the ONERA PhD student M. Ourliac.

4.6 Simulation of a Backward-Facing Step with a Hybrid EMC–RANS Algorithm

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Table 4.1 Grid resolution: x, horizontal direction; y, vertical direction; Nc ¼ Nx Ny .

Mesh 1 Mesh 2 Mesh 3 Mesh 4

Dxmin ðmÞ

Dymin ðmÞ

Nc

1:5 102 6 103 4 103 1:5 103

5 103 5 103 3 103 1 103

900 1600 4000 8000

SPDE Equations 4.60 and 4.61 in conservative form yield almost the same results as those published [12] with the non-conservative formulation. This is not surprising because the conservative and non-conservative formulations are statistically equivalent to the same PDF Equation 4.6. Table 4.1 recapitulates the resolution of the four grids we used. The grid reﬁnement was performed in the near step nose region. We ﬁrst analyze stochastic convergence. We perform ﬁve calculations on Mesh 3 with N ¼ 5, N ¼ 10, N ¼ 25, N ¼ 50, and N ¼ 100 stochastic ﬁelds. We compare the results once a statistical convergence is attained. This is the case for times on the order of 10L=U0 ¼ 0:15 s. Figure 4.2 shows the inﬂuence of the number of stochastic ﬁelds used in the simulation on the proﬁles of the mean (Figure 4.2a) and RMS temperature (Figure 4.2b). To characterize the statistical convergence better, we introduce two integrals: ð qﬃﬃﬃﬃﬃﬃﬃﬃ qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ ð g 00 2 dxdy T 00 2 Tg T ~ T~ ref dxdy ref E1 ¼ ð ð4:69Þ and E2 ¼ ð qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ 00 2 dxdy T~ ref dxdy Tg ref

800

2400 Temperature RMS (°C)

Mean temperature (°C)

2200 2000 1800 1600 1400 1200

N=5 N = 10 N = 25 N = 50 N = 100

1000 800

600

400 N=5 N = 10 N = 25 N = 50 N = 100

200

600 0.02

(a)

0.04

0.06 Y

0.08

0.02

(b)

0.04

0.06 Y

Figure 4.2 Influence of the number of stochastic fields N on the mean (a) and RMS temperature (b) vertical profiles at x ¼ 250 mm. Mesh 3 is used for these calculations.

0.08

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Figure 4.3 Evolution of the errors E1 and E2 against the number of stochastic fields N.

00 2 are computed from a high-resolution solution with N ¼ 100 ~ ref and Tg where T ref stochastic ﬁelds. Figure 4.3 shows the evolution of E1 and E2 against the number of stochastic ﬁelds. A convergence is indeed observed: E1 decays as N 0:8 and E2 decays as N 0:7 . These decay rates are slightly higher than the theoretical N 0:5 rate that we could expect. We now analyze spatial convergence. Calculations are done with N ¼ 50 stochastic ﬁelds, and the number of computational cells is varied from Mesh 1 to Mesh 4. The computation done with Mesh 4 is considered as a reference solution againstqwhich ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ 00 2 ~ and Tg other calculations are compared. The integrals E and E , with T 1

2

ref

ref

computed from Mesh 4, are used for this comparison. Figure 4.4 shows the evolution of E1 and E2 against the number of computational cells. Comparison of Figures 4.3 and 4.4 shows that for Nc ¼ 4000 cells the spatial error is on the order of the stochastic error obtained with N ¼ 50 ﬁelds. Thus, by choosing the most reﬁned mesh Nc ¼ 8000 cells for our calculations, we expect the spatial discretization error to be much smaller than the stochastic error. It then becomes useless to reﬁne the grid further without adding more stochastic ﬁelds. Therefore, in the remaining calculations, we will choose Mesh 3 and N ¼ 50 ﬁelds as a compromise between precision and calculation cost. Mean and RMS temperature vertical proﬁles at different locations are shown in Figure 4.5. They are compared against the experimental results obtained by Magre et al. [28]. The main point of this comparison is the satisfactory agreement found between calculation and experiment. In particular, the peak location of the calculated

4.7 Velocity-Scalar EMC Method

Figure 4.4 Evolution of the error E1 and E2 against the number of computational cells.

RMS temperature is close to the experimental one. The temperature PDFs were also computed and found to compare favorably with experimental data [28]. This comparison is not presented here and can be found elsewhere [12, 25]. In Figure 4.6, the effectiveness of the correction algorithm Equation 4.66 is hri hri . illustrated by the proﬁles of relative density difference Q s ¼ s hri

4.7 Velocity-Scalar EMC Method 4.7.1 Velocity-Scalar PDF

As in the scalar EMC method, we will restrict ourselves to low Mach number ﬂows. As a consequence, the physical density and the chemical source terms can be approximated by functions of the reactive scalars. From the Navier–Stokes equations, it is possible to derive [1, 2] an exact transport equation for the Favre velocity-scalar PDF ~f . In this equation, the advection and chemical reaction processes are treated exactly, whereas the effects of the pressure gradient, of the viscous stresses, and of the scalar diffusion are unclosed. We model the pressure and viscous terms with a generalized Langevin model (GLM) [22, 30]. As for the effects of the molecular diffusion of the scalars, we choose

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100

Expe EMC/RANS

800

Mean temperature (°C)

2200

EMC/RANS solver Experiment

2000

600

1800 1600 400

1400 1200 1000

200

800 600 0.02

0.04

0.06

0

0.08

0.02

Y (m) T profile at x = 210 mm

0.04

0.06

0.08

Y (m) TRMS profile at x = 210 mm 800

Mean temperature (°C)

2200 600

2000 1800 1600

400

1400 1200 200

EMC/RANS solver Experiment

1000 800

Expe EMC/RANS

0

600 0.02

0.04

0.06

0.08

0.02

0.04

Y (m) T profile at x = 250 mm

0.06

0.08

Y (m) TRMS profile at x = 250 mm 800 Expe EMC/RANS

Mean temperature (°C)

2400 2200 2000

600

1800 1600

400

1400 1200 1000

200

Experiment EMC/RANS solver

800 600 0

0.02

0.04

0.06

0.08

Y (m) T profile at x = 460 mm

Figure 4.5 Vertical profiles of T~ and TRMS .

0

0.02

0.04

0.06

0.08

Y (m) TRMS profile at x = 460 mm

4.7 Velocity-Scalar EMC Method

0.08 not corrected corrected

0.06

Q

0.04

0.02 0 -0.02 -0.04 0

0.02

0.04

0.06

0.08

0.1

Y Figure 4.6 Horizontal profiles of relative density difference with and without correction term.

to model them with the IEM model [21]. These standard models are further simpliﬁed for the purpose of this work, which is to study the numerical properties of EMC methods. We neglect rapid distortion effects in the GLM, and we assume in the validation tests (Section 4.7.4) a constant turbulent frequency. By applying these idealized models to the exact transport equation of the Favre onepoint velocity-scalar PDF ~f , one obtains the corresponding modeled PDF equation: q ~ q q hri f þ hriUj ~f ¼ qt qxj qUi

(

) 1 qhri ~ hri þ Gij Uj U j ~f r qxi

i 1 q2 ~f q h C0 hrie hriCh hvi ht h~t ~f 2 qUi qUi qht i q h ~ a ~f þ hriSa ~f hriCw hvi Ya Y qYa þ

ð4:70Þ

where P is the pressure, Sa is the chemical source term for species a, hvi is the turbulent frequency, hereafter denoted by v, e is the turbulent energy dissipation, Gij is a tensor deﬁning the GLM, and C0 , Ch and Cw are model constants. In this work, they are set to (except Section 4.7.4.3, where C0 ¼ 2) C0 ¼ Ch ¼ Cw ¼ 1

ð4:71Þ

The ﬁrst two terms on the right-hand side of Equation 4.70 correspond to the GLM. The following two terms on the right-hand side correspond to the IEM model. The last term corresponds to the contribution of chemical reactions.

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4.7.2 Velocity-Scalar SPDEs

SPDEs, which govern the evolution of a stochastic velocity ﬁeld U j , of stochastic mass fractions Y a , and of a stochastic total enthalpy H are, as follows from [12, 19]: qr q þ r Uj ¼ 0 qt qxj q q ðr U i Þ þ r U i U j þ hPidij ¼ qt qxj

ð4:72Þ pﬃﬃﬃﬃﬃﬃﬃﬃ r qhPi _i 1 þ r Gij U j U~j þ r C0 eW r qxi ð4:73Þ

r dP q q 0 ðrHÞ þ rU j H ¼ r v Hh~t þ qt qxj r dt

ð4:74Þ

q q ~ a þ rSa ðrY a Þ þ rU j Y a ¼ rv Y a Y qt qxj

ð4:75Þ

This set of SPDEs can be shown to be stochastically equivalent to PDF Equation 4.70. However, as in the scalar EMC method (see Equation 4.40 and following text), this equivalence is not direct: the equivalence between the stochastic ﬁelds and the modeled Reynolds and Favre PDFs is only achieved by making use of the stochastic density r. To avoid any confusion, we repeat with some minor modiﬁcations the presentation done for the scalar EMC method after Equations 4.38 and 4.39. We recall that h is denotes the average operator related to the stochastic ﬁelds Wi, and that hQir is the average of a stochastic quantity Q weighted by the density r : hQir ¼

hrQis hris

ð4:76Þ

The corresponding unweighted and weighted PDFs are denoted by fs and fr , respectively. They are related by fr ðU; Y a ; HÞ ¼

hrjU; Y a ; His fs ðU; Y a ; HÞ hris

ð4:77Þ

Then, it can be shown that the transport equation of fr is identical with Equation 4.6, provided that the following two conditions are veriﬁed: hris ¼ hri and

r ¼1 r s

ð4:78Þ

The ﬁrst condition guarantees consistency at the level of Favre statistics, while the second guarantees consistency at the level of Reynolds statistics. For the ﬁrst condition, since hris and hri have identical equations, it is sufﬁcient to take the

4.7 Velocity-Scalar EMC Method

same initial and boundary conditions for hris and hri in order to ensure the condition. As for the second condition, it is unconditionally veriﬁed by the deﬁnition of the physical density: r¼

R0 T

P0 ðtÞ X Yk =Mk

ð4:79Þ

k

We recall (see Section 4.2) that due to Equation 4.79, a local relation exists between the velocity divergence, heat release, molecular transport, and the time derivative of P0 ðtÞ. The pressure which enters the momentum equation is determined from a Poisson equation, which proceeds from the divergence of the momentum equation [20]. If both consistency conditions (4.78) are veriﬁed and if ~ f and fr have the same initial and boundary conditions, then ~f ¼ fr ¼ hrjU; Y a ; His fs hri

hri hrjU; Y a ; His fr ¼ fs and f ¼ r r

From these equalities, we deduce that, for a quantity Q: ~ ¼ hQi ¼ hrQis and hQi ¼ r Q Q r r hri s

ð4:80Þ

ð4:81Þ

With these deﬁnitions, one can see that mean pressure is deduced directly from SPDE Equations 4.72–4.75, without reference to the physical density. Indeed, we have * + X r P ¼ r R0 T hPi ¼ Yk =Mk ð4:82Þ r s k s

4.7.3 Numerical Scheme for the Velocity-Scalar EMC Method

In order to solve SPDE Equations 4.72–4.75, we use a ﬁnite volume method. We consider that the terms on the right-hand of the system of Equations 4.72–4.75 are advection terms, whereas the terms on the left-hand side are source terms. We deal with spatial and time discretization separately. As opposed to scalar EMC methods, the SPDEs used in velocity-scalar EMC methods do not have a zero time-correlated velocity. White noises (the time derivatives of the Wiener processes) only appear as multiplicative source terms. This greatly simpliﬁes the coupling between time and space discretizations. However, some issues remain. First, the spatial discretization should preserve monotonicity. This is made difﬁcult because of the complexity of the SPDE characteristic system. Second, the time discretization should be strong stability preserving (SSP). It should also allow for an implicit treatment of chemical source terms. To meet these requirements, the

j103

j 4 Eulerian (Field) Monte Carlo Methods for Solving PDF Transport Equations

104

following solutions are proposed. For spatial discretization, we use a second-order centered monotone scheme [31], which does not require detailed information about the characteristic system. For time integration, we deﬁne a new, weak, second-order stochastic Runge–Kutta scheme, which consists in a stochastic extension of an implicit–explicit (IMEX) SSP Runge–Kutta scheme proposed by Pareschi and Russo [32]. This extension is based on work by Tocino and Vigo-Aguiar [33, 34]. The details can be found elsewhere [19]. 4.7.4 Validation Tests

In order to validate this numerical scheme, we consider a one-dimensional turbulent reactive ﬂow. The simpliﬁed Langevin model (SLM), Gij ¼ C1 vdij ; C1 ¼ 1, is used to represent unclosed pressure and viscous effects in the PDF equation. Further, in 00 2 . Finally, the turbulent frequency is v this one-dimensional ﬂow, we set e ¼ vuf assumed to be constant. With these assumptions, we perform a ﬁrst test in order to check the monotonicity of the numerical scheme. It consists of a Riemann problem. At initial time, the domain is split into two parts having discontinuous states. The evolution of the discontinuity is then studied. In a second test, the return to gaussianity is examined. For this test only, the dissipation e in the coefﬁcient of the Wiener process is set constant. Then, starting from a Dirac distributed velocity at the inlet, we study the convergence of the solution towards a Gaussian. In a third test, we apply the numerical scheme to a problem of passive scalar transport in homogeneous stationary turbulence. Spatial and statistical convergence are checked. Finally, we consider an auto-ignition problem. At the inlet, a stoichiometric methane–air mixture at 1500 K is injected, with a Gaussian distributed velocity ﬁeld. The convergence of the solution is studied. 4.7.4.1 First Test: Riemann Problem The purpose of this test is to demonstrate the monotonicity of the numerical scheme, and to illustrate its ability to transport the mean and ﬂuctuating ﬁelds with different characteristic waves. Calculations are performed on an L ¼ 1 m domain. At initial time, the domain is divided into a left and a right state, denoted by subscripts L and R, respectively. More precisely, we set 0

U~L B f2 B u00 L for x < 0:5 mB B @ hriL hPiL

¼ 0 m s1 ¼ 50 m2 s2 ¼ 0:729 kg m3 ¼ 105 Pa

0

U~R B g2 B u00 R and for x > 0:5 m B B @ hriR hPiR

¼ 0 m s1 ¼ 0 m2 s2 ¼ 0:456 kg m3 ¼ 5 104 Pa

All other non-speciﬁed variances are set to zero. The velocity distribution is chosen as Gaussian. The turbulent frequency is taken equal to v ¼ 200 s1 . N ¼ 400 stochastic ﬁelds are used and the domain is discretized with Nx ¼ 320 cells.

4.7 Velocity-Scalar EMC Method

10

Stochastic velocity

Stochastic velocity Solution to the Riemann problem

120 100 Velocity (ms-1)

5 Velocity (m s-1)

j105

0

80 60 40 20

-5

0 -10

-20 0

25

50

75

100

0

x (cm)

25

50 x (cm)

(a)

(b)

Figure 4.7 First test: examples of stochastic velocity fields (a) at t ¼ 0 s and (b) at t ¼ 5 104 s.

Figure 4.7 shows the evolution of a sample of the stochastic velocities. At initial time (Figure 4.7a), N different velocity jumps are observed, since the variance of the left state is non-zero. However, at later times (Figure 4.7b), only one shock wave propagates to the right part of the domain. The velocity ﬂuctuations are not propagated along with this wave, but are transported by the mean velocity ﬁeld. Similarly, only one rarefaction wave propagates to the left of the domain. This can be seen by the fact that the N stochastic ﬁelds are reached simultaneously by this wave, and not at N different instants as would be the case if there were different waves. To illustrate this behavior further, we present in Figure 4.8a–c the mean proﬁles of density, pressure, and velocity at time t ¼ 5 104 s. These proﬁles are compared with the exact solution of the Riemann problem computed from the Euler equations. Good agreement is observed. The velocity variance is presented in Figure 4.8d at the same time. Again, it is seen that the velocity ﬂuctuations are not transported along the complex characteristic waves of the mean system. Note that monotonicity is well preserved. No spurious oscillation is observed in this test. 4.7.4.2 Second Test: Return to Gaussianity The purpose of this test is to check the ability of the numerical method to yield a stationary Gaussian solution. For this testﬃ only, we replace the coefﬁcient of the qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ 00 2 , by 2vs2; s2 is a constant and is taken as Wiener process in Equation 4.73, C vuf 0

equal to 1m2 s2 . Then, the velocity ﬁeld should tend to a Gaussian with variance s2 . At the left boundary, we impose deterministic conditions, that is, all stochastic ﬁelds have a Dirac d function, which is centered at U~ ¼ 10:4 m s1 ;

hri ¼ 0:146 kg m3 ;

hPi ¼ 105 Pa

ð4:83Þ

75

100

j 4 Eulerian (Field) Monte Carlo Methods for Solving PDF Transport Equations

106

0.75

110000

Computed mean density Solution to the Riemann problem

100000

0.65

Pressure (Pa)

Density (kg m–3)

0.7

0.6 0.55 0.5 0.45

Computed mean pressure Solution to the Riemann problem

90000 80000 70000 60000 50000 40000

0

25

50 x (cm)

75

100

0

25

50 x (cm)

75

100

(b)

(a)

50

Computed mean velocity Solution to the Riemann problem

Velocity variance (m2 s–2)

Velocity (m s–1)

100 80 60 40 20 0 (c)

0

25

50 x (cm)

75

40

30

20

10

0

100

0

25

50 x (cm)

75

100

(d)

Figure 4.8 First test: profiles of mean density (a), mean pressure(b), mean velocity (c), and velocity variance (d) at t ¼ 0.05 s. Mean profiles are compared against the exact solution to the Riemann problem.

At the initial time, the stochastic ﬁelds are initialized with the left boundary conditions. The domain has a length L ¼ 0:04 m and is discretized with Nx ¼ 40 cells. The turbulent frequency is taken as equal to v ¼ 2500 s1 . We perform calculations with a number of stochastic ﬁelds varying from N ¼ 5 to 1000. In addition to summation over stochastic ﬁelds, we perform time averages of the means, variances, and other moments we obtain. The period for time averaging is Tav ¼ 103 s, and calculations are led until t¼ 102 s. Figure 4.9a shows the time-averaged solution obtained for N ¼ 1000, for the variance, the skewness, the ﬂatness, and the hyper-ﬂatness of the velocity ﬁeld. These different moments have a zero value at the left boundary (because of the deterministic conditions) and tend to their Gaussian value at the right boundary (respectively

4.7 Velocity-Scalar EMC Method Variance Skewness Flatness Hyper flatness

14

12

10

Velocity

Moment value

15

j107

10

5

8

3 1 0

6 0

0

0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04

0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04

x (m)

x (m) (b)

(a)

Figure 4.9 Second test: (a) profiles of the velocity variance, skewness, flatness, and hyper-flatness; (b) stochastic velocity fields from which the moments are computed.

Stochastic error for the velocity hyper-flatness

1, 0, 3, and 15). Figure 4.9b shows some of the stochastic ﬁelds from which these moments are computed. We observe that the stochastic ﬁelds remain smooth throughout the calculation. To assess the convergence of the method, we compute the differences between the ﬂatness and the hyper-ﬂatness obtained at the right boundary of the domain and their respective theoretical Gaussian values. Figure 4.10 shows the evolution of these differences as a function of the number of stochastic ﬁelds. We see that the ﬂatness

Stochastic error for the velocity flatness

Stochastic error Best fit: 3.8 N

–0.72

0.1

10

100

1000

1

10

Number of stochastic fields N (a)

Stochastic error Best fit: 15.8 N –0.55

100 Number of stochastic fields N

(b)

Figure 4.10 Second test: stochastic convergence rate for the velocity flatness (a) and hyperflatness (b).

1000

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108

convergence rate (Figure 4.10a) is slightly higher than the N 0:5 theoretical rate, and the hyper-ﬂatness (Figure 4.10b) is close to it. 4.7.4.3 Third Test: Passive Scalar Transport We consider the transport of a passive scalar Y in homogeneous stationary turbulence. For times much greater than the turbulent time scale, the turbulent transport can be approximated by a diffusion solution. The purpose of this test is to check if this asymptotic behavior is obtained with the numerical scheme proposed in this work. In order to make the turbulence stationary, we set C0 ¼ 2, for this test only. The initial velocity proﬁles are constant in space and are randomly distributed. They follow a Gaussian law with mean U~ ¼ 0 m s1 and variance u~00 2 ¼ 1 m2 s2 . The turbulent frequency is set to v ¼ 20 s1 . The initial scalar proﬁles are Heaviside functions all centered at the middle of the domain. As a result, there is no initial scalar variance or scalar ﬂux. Pressure and temperature are set to P¼ 105 Pa and T ¼ 300 K. The domain has length L ¼ 1 m. Neumann boundary conditions are imposed at each end of the domain. The domain is discretized with Nx cells, with Nx varying from 25 to 200. The number of stochastic ﬁelds is varied from N ¼ 10 to 800. Calculations are performed until time vt ¼ 5. First, we check that the velocity ﬁeld remains statistically stationary. Figure 4.11 shows the time evolution of the velocity variance, ﬂatness, and hyper-ﬂatness for Nx ¼ 100 and N ¼ 800. Only small departures from the initial values can be observed.

25

Variance Flatness Hyperflatness

Velocity moments

20

15

10

5

0

0

0.5

1

1.5

2

2.5 wt

3

3.5

4

4.5

5

Figure 4.11 Third test: time evolution of the profiles of the variance, flatness, and hyper-flatness of the velocity.

4.7 Velocity-Scalar EMC Method

j109

In a second step, we assess the stochastic convergence of the numerical solution. We set Nx ¼ 100 and let N vary. We deﬁne the time-dependent coefﬁcient of turbulent diffusion n (it does not depend on x in our case) as 00 Y 00 = n ¼ ug

~ qY qx

The limit nd of n as t ! ¥ can be easily found from the stationary equations for the f 00 Y 00 and u 002 . It is given by ﬂux ug ~ qY 00 Y 00 j ug diff : ¼ nd qx

with

nd ¼

00 2 uf ; C1 ¼ C0 =2 Cw þ C1 v

ð4:84Þ

With the parameters given at the beginning of this subsection, the value of nd is nd ¼ 0:25. Figure 4.12a shows the temporal behavior of n at the center of the mixing zone. It is seen that n tends to its theoretical limit value nd after approximately ð vta ¼ 3. We then deﬁne a convergence criteria as En ¼ ð1=tta Þ

t

jnnd jdt.

ta

Figure 4.12b shows the evolution of En at time vt ¼ 5 against the number of ﬁelds. A convergence rate close to N 0:5 is observed. Finally, we look at spatial convergence. We consider N ¼ 40 ﬁelds and let the number of cells vary from 10 to 200. Figure 4.13 compares the spatial proﬁles of the mean (Figure 4.13a) and of the variance (Figure 4.13b) of the passive scalar with the theoretical diffusion solution at time vt ¼ 5. The theoretical solution are given by 1 x ~ ð4:85Þ Y ¼ 1 þ erf pﬃﬃﬃﬃﬃﬃ 2 2 nd t 1 x2 g 00 2 ¼ exp ð4:86Þ Y 4pCw vt 2nd t 0.04

0.025 0.02 0.015

Stochastic error

0.03

n

Stochastic error Best fit 0.015 N –0.5

N = 10 N = 40 N = 200 N = 800 nd

0.035

0.001

0.01 0.005 0 (a)

10

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 wt

100 N

(b)

Figure 4.12 Third test: (a) time evolution of n; (b) stochastic convergence rate for n.

j 4 Eulerian (Field) Monte Carlo Methods for Solving PDF Transport Equations

110

1

0.8

0.016

N x = 25 N x = 50 N x = 100 N x = 200 Exact

N x = 25 N x = 50 N x = 100 N x = 200 Exact

0.014 Scalar variance

Mean scalar

0.012 0.6

0.4

0.01 0.008 0.006 0.004

0.2 0.002 0

0 0

0.2

0.4 0.6 x (m)

0.8

(a)

1

0

0.2 0.4

0.6 x (m)

0.8

1

(b) Figure 4.13 Third test: (a) spatial profile of the mean scalar; (b) spatial profile of the scalar variance.

Figure 4.14 gives the evolution of the norm of the spatial error against the number of cells for the mean scalar (Figure 4.14a) and for the scalar variance (Figure 4.14b). Convergence rates of Nx1:8 and Nx1:6 are observed for the mean and variance, respectively.

Spatial error for mean scalar

Spatial error Best fit 100x –1.8

0.1

0.01

Spatial error for scalar variance

4.7.4.4 Fourth Test: Auto-Ignition We examine the properties of the numerical scheme on a test problem similar to the one proposed by Muradoglu et al. [35]. It consists of a one-dimensional auto-ignition ﬂow. At the inlet, a stoichiometric methane–air mixture is injected, with temperature

Spatial error Best fit 3x –1.6

0.01

0.001

100

100 Nx

Nx (a)

(b) Figure 4.14 Third test: (a) spatial error for the mean scalar; (b) spatial error for the scalar variance.

4.7 Velocity-Scalar EMC Method

Mean temperature (K)

3000

2500

2000

1500

0.005 0.01 0.015 0.02 0.025 0.03 0.035

t = 1 ms t = 1.5 ms t = 2 ms t = 3 ms

250000 200000 150000 100000 50000 0

0

x (m) (a)

300000

Temperature variance (K2)

t = 1 ms t = 1.5 ms t = 2 ms t = 3 ms

3500

j111

0

0.005 0.01 0.015 0.02 0.025 0.03 0.035

x (m) (b)

Figure 4.15 Fourth test: time evolution of the profiles of the mean (a) and variance (b) of the temperature.

Tin ¼ 1500 K and pressure Pin ¼ 105 Pa. The inlet velocity is random and follows 00 2 ¼ 1 m2 s2 . At the a Gaussian law with mean U~in ¼ 10:4 m s1 and variance ug in outlet, all quantity gradients are set to zero, except for the pressure, which is ﬁxed at Pin . The length of the one-dimensional domain is L ¼ 0:035 m. The methane–air chemistry is dealt with a simple one-step global reaction, taken from [29]. 4.7.4.4.1 Stochastic Convergence First, calculations are performed for a ﬁxed number of cells Nx ¼ 128. The number of stochastic ﬁelds is varied from N ¼ 5 to N ¼ 1000. The solution obtained with 1000 ﬁelds is taken as a reference solution against which other calculations are compared. The mean and the variance of the temperature obtained from this reference solution are represented in Figure 4.15a and b, respectively, at different times. No time averaging is performed here. We observe that the auto-ignition of the mixture is followed by a propagation of reactions due to mixing between hot and cold gases. The temperature variance has non-zero values in the reaction zone. It increases during auto-ignition and then propagates towards cold gases because of mixing. The mean and the variance of the velocity are presented in Figure 4.16a and b, respectively, at different times. Outside the reaction zone, the velocity variance decreases under the effects of turbulent dissipation. In the reaction zone, it rapidly increases, under the combined action of both velocity and pressure gradients. To compare the calculations against the N ¼ 1000 ﬁeld reference solution, we introduce the following errors. For any quantity Q, we deﬁne EQ1 ðNÞ and EQ2 ðNÞ as ð ð 1 ~ 1 g g 2 00 2 =s 00 dx ~ EQ1 ðNÞ ¼ Q Q 00N2 Q Q ref ref N Q ref =sQref dx and EQ ðNÞ ¼ L L ð4:87Þ

where sQref and sQ 00ref are the RMS values of Q and Q 00 2 computed from the reference solution. At a given time, EU1 ðNÞ, EU2 ðNÞ, ET1 ðNÞ, and ET2 ðNÞ are computed for several

j 4 Eulerian (Field) Monte Carlo Methods for Solving PDF Transport Equations

112

1

t = 1 ms t = 1.5 ms t = 2 ms t = 3 ms

Velocity variance (m2 s–2)

Mean velocity (m s–1)

30

25

20

15

0.8

0.6

0.4

0.2

10

0 0

0.005 0.01 0.015 0.02 0.025 0.03 0.035

0

0.005 0.01 0.015 0.02 0.025 0.03 0.035

x (m)

x (m) (a)

t = 1 ms t = 1.5 ms t = 2 ms t = 3 ms

(b) Figure 4.16 Fourth test: time evolution of the profiles of the mean (a) and variance (b) of the temperature.

values of N. The calculated points are best ﬁtted with the function f ðNÞ ¼ aN b . This operation is repeated at different times. Figure 4.17 shows the evolution of the convergence rates b and coefﬁcients a for the average (Figure 4.17a) and variance (Figure 4.17b) of the velocity and of the temperature. We observe that the convergence rates ﬂuctuate around the N 0:5 theoretical value. As for the convergence coefﬁcients, they ﬂuctuate between 0.1 and 10, with an average found between 2 and 3.

mean velocity convergence rate velocity variance convergence rate mean temperature convergence rate temperature variance convergence rate Theoretical

Convergence rate

-0.2

-0.4

-0.6

-0.8

mean velocity convergence coeff. velocity variance convergence coeff. mean temperature convergence coeff. temperature variance convergence coeff.

Convergence coefficient

0

10

1

-1 0.1

0.003 0.004 0.005 0.006 0.007 0.008 0.009 0.01

t (s) (a)

0.003 0.004 0.005 0.006 0.007 0.008 0.009 0.01

t (s) (b)

Figure 4.17 Fourth test: time evolution of the convergence rates (a) and coefficients (b) for the means and variances of velocity and temperature.

Spatial error for the mean velocity

Spatial error Best fit: 4.5 10^4 N_x^-2.1

1 100 Number of cells N x (a)

Spatial error for the velocity variance

4.8 Outlook

j113

Spatial error Best fit: 3.5 10^2 N_x^-1.65

0.1

100 Number of cells N x (b)

Figure 4.18 Fourth test: spatial convergence of the mean (a) and variance (b) of the velocity at t ¼ 2:5ms.

4.7.4.4.2 Spatial Convergence Calculations are now performed with a ﬁxed number of stochastic ﬁelds N ¼ 400. The number of grid cells is varied from Nx ¼ 64 to Nx ¼ 256. The solution obtained at Nx ¼ 256 is taken as a reference solution against which the other calculations are compared. We deﬁne errors to the reference solution as ð ð 1 ~ 1 g 4 00 2 dx ~ EQ3 ðNx Þ ¼ Q ð4:88Þ Q 00N2x Qg Nx Q ref dx and EQ ðNx Þ ¼ ref L L

Figure 4.18 shows the evolution of E3 and E4 for the mean velocity (Figure 4.18a) and velocity variance (Figure 4.18b) as a function of NX , at time t ¼ 2:5 ms. A second order convergence is indeed observed for the mean velocity, whereas for the velocity variance the convergence rate is equal to 1.65.

4.8 Outlook

In recent years, EMC methods have emerged as a promising alternative method for solving the composition and composition–velocity PDF transport equations in a pure Eulerian manner. In the EMC method, in contrast to the Lagrangian stochastic particle method, stochastic Eulerian species and velocity ﬁelds are used to represent the PDF. These ﬁelds evolve according to SPDEs, stochastically equivalent to the PDF equation. This fact predetermines the most outstanding feature of EMC methods: it allows the use of numerical methods (with some proper adaptation to stochastic terms in SPDEs) developed for Navier–Stokes solvers. The following advantages of EMC methods can be put forward: the statistical moments are calculated with ease,

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114

there are no sampling errors, and hybrid EMC–RANS and EMC–LES algorithms are very easy to construct. Another advantage is that the method appears to converge more rapidly than LMC methods, its Lagrangian counterpart. As LES is applied to increasingly complex problems, there is a need to develop new efﬁcient hybrid EMC–LES algorithms. Further work is needed to exploit the capabilities of the EMC method in complex geometric conﬁgurations (close to practical combustion-device geometries) in multi-block grids (both structured and non-structured). The Eulerian character of EMC methods makes it more suited (than LMC methods) for tackling these issues. The scalar EMC is just beginning to be employed for practical problems such as auto-ignition, lean blowout, reigniting, and extinction, issues of importance for gas turbine applications. Further research is required to explore the capabilities of velocity-scalar EMC for complex ﬂows. One can forecast that with the development of more efﬁcient numerical algorithms, EMC–LES may become the favored instrument for the gas turbine industry.

4.9 Conclusion

This chapter has reviewed some recent progress made in the ﬁeld of EMC methods, used for efﬁcient solving of one-point PDF transport equations in turbulent reacting ﬂows. The framework of this review includes both RANS and LES approaches, and both composition and velocity–composition PDFs. Instead of stochastic particles, as in LMC methods, stochastic Eulerian ﬁelds are used to represent the PDF in EMC methods. These ﬁelds evolve according to SPDEs, stochastically equivalent to the PDF equation. The connection between Eulerian and Lagrangian Monte Carlo approaches through the notion of stochastic characteristics is established. SPDEs are derived from Lagrangian SODEs in the same way as in classical hydrodynamics one transforms the Navier–Stokes equations written in Lagrangian variables into Eulerian variables. Boundary conditions for the stochastic ﬁelds are discussed. From a numerical point of view, recent publications give evidence that EMC methods converge more rapidly than LMC methods. Further, EMC methods offer an additional advantage over LMC methods: they allow typical Eulerian transport solvers to be employed, thus facilitating the coupling in hybrid EMC–RANS or EMC–LES algorithms. Numerical issues emerging in the solution of the SPDEs are discussed and numerical schemes are adapted. A hierarchy of numerical validation tests is performed. A novel hybrid composition EMC–ﬁnite volume algorithm is developed based on the conservative formulation of SPDEs. Correction techniques, timeaveraging procedures, and coupling strategy efﬁciency are evaluated. This algorithm is implemented into the industrial code ONERA CEDRE. Then, the hybrid EMC–RANS method is applied to simulate a turbulent premixed methane–air ﬂame stabilized by backward-facing step. Computation results are compared with experimental data. The results are found to compare favorably with experimental data. Examples of recent applications of scalar EMC–LES to auto-

4.9 Conclusion

ignition of hydrogen jet issuing into a co-ﬂow of vitiated hot air and EMC–RANS for an NO plume in the atmosphere are reviewed. Finally, potential future developments and areas of EMC use are highlighted. Acknowledgments

This work was supported by ONERA, in the framework of Olivier Soulards PhD thesis. We thank ONERA PhD student M. Ourliac for sharing his results with us, presented in Sections 4.5 and 4.6, before the defense of his thesis. Nomenclature

Aij c C0 Ch , Cw CW Cm dt dW de dk E1 , E 2 EQ1 , EQ2 En F Fj ~f ~f c f c fc fs fr Gij HðxÞ H hk ht Ji

tensor, cf. Equation 4.32 turbulent reactive scalar, cf. Equation 4.1 coefﬁcient in the Langevin Equation 4.73 model constants, cf. Equation 4.70 function deﬁned by Equation 4.29 model constant, cf. Section 4.5.1, Equation 4.57 time increment differential of standard Wiener process dissipation term of the turbulent dissipation, cf. Equation 4.57 dissipation term of the turbulent kinetic energy, cf. Equation 4.57 statistical errors, cf. Equation 4.69 statistical errors, cf. Equation 4.87 convergence rate, cf. Section 4.7.4.3 function, cf. Equation 4.30 function, cf. Equation 4.65 Favre one-point velocity-scalar PDF, cf. Equation 4.70 Favre one-point scalar PDF, cf. Section 4.2 Reynolds one-point PDF of the scalar, cf. Section 4.2 Reynolds one-point PDF for constant density case, cf. Equation 4.7 unweighted PDF related to the stochastic ﬁelds, cf. Equations 4.41 and 4.77 weighted PDF, related to the stochastic ﬁelds, cf. Equations 4.41 and 4.77 tensor deﬁning the generalized Langevin model (GLM), cf. Equation 4.70 Heaviside function stochastic total enthalpy, cf. Equation 4.74 speciﬁc enthalpy of species, cf. Equation 4.65 total enthalpy, cf. Equations 4.59 and 4.61 molecular diffusion ﬂux, cf. Equation 4.1

j115

j 4 Eulerian (Field) Monte Carlo Methods for Solving PDF Transport Equations

116

total stochastic ﬂux, cf. Equation 4.54 turbulent kinetic energy, cf. Equation 4.57 micromixing term, cf. Equation 4.35 micromixing operator, cf. Equation 4.4 molecular weight of the species k, cf. Equation 4.63 total number of stochastic ﬁelds, cf. Section 4.5.1 number of computational cells deﬁned in Table 4.1 number of grid cells in x direction deﬁned in Table 4.1 number of grid cells in y direction deﬁned in Table 4.1 direction normal to the wall, cf. Equation 4.47 pressure, cf. Equation 4.63 uniform reference pressure, cf. Section 4.2 production the turbulent kinetic energy, cf. Equation 4.57 production of the turbulent dissipation, cf. Equation 4.57 Prandtl number for turbulent energy, cf. Equation 4.57 Prandtl number for turbulent dissipation, cf. Equation 4.57 Q p ¼ ðhris hriÞ=hri relative density difference, cf. ﬁgure 4.6 hQi Reynolds average of a quantity Q, cf. Section 4.2 ~ ¼ hrQi=hri Q Favre average of a quantity Q, cf. Section 4.2 Reynolds ﬂuctuation of a quantity Q, cf. Section 4.2 Q0 Favre ﬂuctuation of a quantity Q, cf. Section 4.2 Q 00 average of a stochastic quantity weighted by the Q r ¼ hrQis =hris density r, cf. (4.40) absolute gas constant, cf. Equation 4.63 R0 r stochastic density, cf. Section 4.3.2.2 SðcÞ chemical source term, cf. Equation 4.1 chemical source term for species k, cf. Equation 4.59 Sk turbulent Schmidt number Sct t time T temperature, cf. Equation 4.63 u stochastic velocity, cf. Equation 4.30 U turbulent velocity, cf. Equation 4.1 deterministic velocity component, cf. Equation 4.31 ud Gaussian random velocity component, cf. Equation 4.31 ug stochastic velocity ﬁeld, cf. Equations 4.72–4.75 Ui stochastic velocity ﬁeld, cf. Equations 4.60–4.62, 4.66–4.68 U i WðtÞ standard Wiener process, cf. Section 4.3.1.1 W ðtÞ vector Wiener process, cf. Equations 4.37, 4.62, 4.73 stochastic process, cf. Equation 4.26 Wþ stochastic process, cf. Equation 4.26 W X ðtÞ function or functional of Wiener process, cf. Section 4.3.1.1 species mass fractions, cf. Equation 4.59 Yk stochastic mass fractions, cf. Equation 4.75 Ya jr k M M Mk N Nc Nx Ny n P P0 ðtÞ Pk Pe Prk Pre

4.9 Conclusion

Greek letters

Dx, Dy Dt dð Þ dij C CT c e q n nd nt jðtÞ r sij Wc v hvi hvc i vh vY

grid sizes in x and y directions, respectively, Table 4.1 time step Dirac delta function Kroneckers delta constant diffusion coefﬁcient, cf. Equation 4.7 turbulent diffusion coefﬁcient, cf. Equation 4.5 ratio of speciﬁc heats, cf. Equation 4.64 turbulent energy dissipation, cf. Equations 4.57 and 4.73 stochastic scalar ﬁeld, cf. Equation 4.30 time-dependent diffusion coefﬁcients, cf. Section 4.7.4.3 limit diffusion coefﬁcients, cf. Equation 4.84 eddy viscosity, cf. Equation 4.58 derivative of Wiener process, Gaussian white noise, cf. Section 4.3.1.1 density of ﬂuid turbulent stresses, cf. Equation 4.58 function deﬁned by equation, cf. Equation 4.64 mean turbulent frequency, cf. Sections 4.7.4.1–7.4.4 mean turbulent frequency, cf. Equation 4.70 mean mixing frequency, cf. Equation 4.4 mean mixing frequency for enthalpy, cf. Equation 4.59 mean mixing frequency for species, cf. Equation 4.59

Subscripts

0 L R ref

inlet or initial conditions boundary conditions at left side, cf. Section 4.7.4.1 boundary conditions at right state, cf. Section 4.7.4.1 reference solution, cf. Equation 4.69

Special notation

hi h jci h is h iW X ðtÞ*dW X ðtÞdW ¼ qtq þ U~k qxq k

D Dt

Reynolds ensemble averages, cf. Equation 4.3 averages conditioned on the scalar value, cf. Equation 4.3 average operator related to the stochastic ﬁelds, Wi, cf. Equation 4.40 averaging over the Wiener process, cf. Section 4.3.1.1 Stratonovitch interpretation of stochastic integral, cf. Equation 4.13 Ito interpretation of stochastic integral, cf. Equation 4.12 substantial derivative, cf. Equation 4.57

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References 1 Kuznetsov, V.R. and Sabelnikov, V.A.

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(1990) Turbulence and Combustion, Hemisphere, London. Pope, S.B. (1985) PDF methods for turbulent reactive ﬂows. Prog. Energy Combust., 27, 119–192. Osnes, H. and Langtangen, H.P. (1998) A study of some ﬁnite difference schemes for a unidirectional stochastic transport equation. SIAM J. Sci. Comput., 19 (3), 799–812. Werner, M.J. and Drummond, P.D. (1997) Robust algorithms for solving stochastic partial differential equations. J. Comput. Phys., 132, 312–326. Jardak, M., Su, C.-H., and Karniadakis, G.E. (2002) Spectral polynomial chaos solutions of the stochastic advection equation. J. Sci. Comput., 17, 319–338. Valiño, L. (1998) A ﬁeld Monte Carlo formulation for calculating the probability density function of a single scalar in a turbulent ﬂow. Flow Turbulence Combust., 60, 157–172. Sabelnikov, V.A. and Soulard, O. (2005) Rapidly decorrelating velocity ﬁeld model as a tool for solving Fokker–Planck PDF equations of turbulent reactive scalars. Phys. Rev. E, 72, 016301. Gardiner, C.W. (1985) Handbook of Stochastic Methods, 2nd edn, Springer, Berlin. Van Kampen, N.G. (2007) Stochastic Processes in Physics and Chemistry, Elsevier, Amsterdam. Kraichnan, R.H. (1968) Small-scale structure of a scalar ﬁeld convected by turbulence. Phys. Fluids, 11, 945. Kazantsev, A.P. (1031) Enhancement of a magnetic ﬁeld by a conducting ﬂuid. Sov. Phys. JETP, 26, 1968. Soulard, O. and Sabelnikov, V.A. (2006) Eulerian Monte Carlo method for the joint velocity and mass-fraction probability density function in turbulent reactive gas ﬂow. Combust. Explos. Shock Waves, 42 (6), 753–762. Mustata, R., Valiño, L., Jimenez, C., Jones, W.P., and Bondi, S. (2006) A probability density function eulerian monte carlo ﬁeld method for large eddy simulations:

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Application to a turbulent piloted methane–air diffusion ﬂame (Sandia D). Combust. Flame, 145, 88–104. Jones, W.P., Navarro-Martinez, S., and R€ohl, O. (2007) Large eddy simulation of hydrogen auto-ignition with a probability density function method. Proc. Combust. Inst., 31, 1765–1771. Jones, W.P. and Navarro-Martinez, S. (2007) Large eddy simulation of autoignition with a subgrid probability density function method. Combust. Flame, 150, 170–187. Garmory, A., Richardson, E.S., and Mastorakos, E. (2006) Micromixing effects in a reacting plume by the stochastic ﬁled method. Atmos. Environ., 40, 1078–1091. Sabelnikov, V.A. and Soulard, O. (2006) White in time scalar advection model as a tool for solving joint composition pdf equations: derivation and application. J. Flow Turbulence Combust., 77, 333–357. Hauke, G. and Valiño, L. (2004) Computing reactive ﬂows with a ﬁeld Monte Carlo formulation and multi-scale methods. Comput. Methods Appl. Mech. Eng., 193, 1455–1470. Soulard, O. and Sabelnikov, V. (2006) A new Eulerian Monte Carlo method for the joint velocity-scalar PDF equations in turbulent ﬂows. European Conference on Computational Fluid Dynamics ECCOMAS CFD, Tu Delft, The Netherlands, http://proceedings.fyper. com/eccomascfd2006/documents/ 602.pdf. Muller, B. (1999) Low Mach number asymptotics of the Navier–Stokes equations and numerical implications. Presented at the 30th Computational Fluid Dynamics Lecture Series, von Karman Institute for Fluid Dynamics, March 1999, http://user.it.uu.se/~bernd/ vki_ls_cfd_1999_1.pdf. Villermaux, J. and Devillon, J.C. (1972) Representation de la redistribution des domaines de segregation dans un ﬂuide par un modele dinteraction phenomenologique. 2nd International Symposium on Chemical Reaction

References

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Engineering, Amsterdam, vol. B-1-13. Pope, S.B. (2000) Turbulent Flows, Cambridge University Press, Cambridge. Stratonovitch, R.L. (1967) Topics in the Theory of Random Noise, Gordon and Breach, New York. Klyatskin, V.I. (2005) Stochastic Equations through the Eye of the Physicist, Elsevier, Amsterdam. Soulard, O. (2005) Approches PDF pour la combustion turbulente: Prise en compte dun spectre dechelles turbulentes dans la modelisation du micromelange et elaboration dune methode de Monte Carlo Eulerienne. PhD thesis, University of Rouen. Carrillo, O., Ibañes, M., Garcia-Ojalvo, J., Casademunt, J., and Sancho, J.M. (2003) Intrinsinc noise-induced phase transitions: beyond the noise interpretation. Phys. Rev. E, 67, 046110. Jenny, P., Pope, S.B., Muradoglu, M., and Caughey, D.A. (2001) A hybrid algorithm for the joint pdf equation of turbulent reactive ﬂows. J. Comput. Phys., 166, 218–252. Magre, P., Moreau, P., Collin, G., Borghi, R., and Pealat, M. (1988) Further studies by CARS of premixed turbulent

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combustion in a high velocity ﬂow. Combust. Flame, 71 (2), 147–168. Westbrook, C.K. and Dryer, F.L. (1984) Chemical kinetic modeling of hydrocarbon combustion. Prog. Energy Combust. Sci., 10, 1–57. Minier, J.-P. and Pozorski, J. (1997) Derivation of a pdf model for turbulent ﬂows based on principles from statistical physics. Phys. Fluids, 9 (6), 1748–1753. Toro, E.F. and Titarev, V.A. (2006) MUSTA ﬂuxes for systems of conservation laws. J. Comput. Phys., 216, 403–429. Pareschi, L. and Russo, G. (2005) Implicit–explicit Runge–Kutta schemes and applications to hyperbolic systems with relaxation. J. Sci. Comput., 25, 129–155. Tocino, A. and Vigo-Aguiar, J. (2002) Weak second order conditions for stochastic Runge–Kutta methods. SIAM J. Sci. Comput., 24, 507–523. Tocino, A. and Vigo-Aguiar, J. (2003) New Ito–Taylor expansions. J. Comput. Appl. Math., 158, 169–185. Muradoglu, M., Jenny, P., Pope, S.B., and Caughey, D.A. (1999) A consistent hybrid ﬁnite-volume/particle method for the PDF equations of turbulent reactive ﬂows. J. Comput. Phys., 154, 342–371.

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5 Flame Lift-Off and Blow-Out Stability Limits and Their Application in Gas Burners Yajue Wu 5.1 Introduction

Flame stability is associated with the phenomena observed as a result of a balance or mismatch of the local ﬂame speed and the local ﬂow velocity. These phenomena include ﬂame attachment to the burner rim, the ﬂame base lifting off the rim and the ﬂame being extinguished as a lifted ﬂame or an attached ﬂame. Flame stability is usually characterized by three terms, namely lift-off velocity, liftoff height, and blow-out velocity. The lift-off velocity is deﬁned as the mean jet velocity at which the ﬂame lifts above the burner rim. If the jet velocity is further increased, the ﬂame moves downstream to a new position where it stabilizes. Consequently, the distance between the base of the lifted ﬂame and the burner mouth is described as the lift-off height. A further increase in the jet velocity results in the ﬂame being pushed further downstream, where reaction may not be sustained and the ﬂame is extinguished. The velocity at which the ﬂame is extinguished is called the blow-out velocity. The features of a lifted ﬂame are illustrated in Figure 5.1 and an image of a lifted ﬂame is shown in Figure 5.2. Flame extinction could also occur in an attached ﬂame where the ﬂame goes out directly without the lift-off stage. This is sometimes called blow-off to differentiate it from blow-out. Reducing the fuel ﬂow rate could also cause stability problems. If the local ﬂame velocity exceeds the local ﬂow velocity, the ﬂame enters and propagates upstream through the burner tube or port without quenching. This phenomenon is called ﬂashback and it is generally a transient event, occurring as the fuel ﬂow is decreased or turned off. For the safety of the fuel system, a ﬁre arrest device is recommended to prevent ﬂash back and protect the fuel tank. Theseparametersdescribethestableandsafe operatingrangeforaparticularburner. Conditions for lift-off and blow-out are important in the development of burners for combustion and process applications. In some industrial applications, burners may be subject to strong cross-wind and therefore the stability and performance of those burners might be inﬂuenced by cross-wind. This chapter provides a comprehensive review of the main theories on the stabilization mechanisms for a turbulent jet ﬂame

Handbook of Combustion Vol.3: Gaseous and Liquid Fuels Edited by Maximilian Lackner, Franz Winter, and Avinash K. Agarwal Copyright Ó 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32449-1

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122

Lifted flame base Lift-off height

Air

Fuel Figure 5.1 Features of a lifted jet diffusion flame.

which have been proposed over the years. Discussions are presented on the most practical theories on the prediction of the stability limits of diffusion ﬂames in still air and also in cross-wind for hydrocarbon fuels, including hydrogen fuel.

5.2 Theoretical Analysis of Diffusion Flame Stability

The mechanisms of the lift-off and blow-out phenomena have been the subject of various studies. In general, the ﬂame stabilization models proposed can be classiﬁed into three categories: The premixed ﬂame propagation models, which consider sufﬁcient premixing ahead of the lifted ﬂame base. 2) The laminar ﬂamelet models, in which the ﬂame lift-off process is treated as the result of laminar ﬂamelets quenching. 3) The large-scale turbulent structural mixing models, which incorporate largescale turbulent eddies as the controlling stability mechanism. 1)

5.2.1 The Premixed Flame Propagation Model

The premixed ﬂame propagation models are based on the foundation of a classical ﬂame stabilization theory proposed by Vanquickenborne and van Tiggelen [1]. It

5.2 Theoretical Analysis of Diffusion Flame Stability

Figure 5.2 Image of a lifted methane flame in a 2 mm diameter burner.

suggested that a lifted diffusion ﬂame stabilizes at a position where the ﬂame base is anchored in a region of turbulent ﬂow with the combustible mixture concentration close to stoichiometric. At this anchoring position, the turbulent burning velocity, ST , is counterbalanced by the local velocity of the jet, Ug . The principles of this model are illustrated in Figure 5.3. Yst is the stoichiometric concentration. The model considers that the lift-off height between the burner mouth and of the ﬂame base is long enough for the fuel and the diffusing air to reach a premixed state. It also proposed that the blow-out occurs when the ﬂame lifts to a zone where the fuel concentration becomes too rich or too lean and hence its burning velocity can no longer counterbalance the gas velocity. Vanquickenborne and van Tiggelen postulated the premixed combustion model based on extensive experimental measurements of methane ﬂames having a range of jet exit diameters and velocities and also on some early experimental observations on ﬂame stability made by Wohl et al. [2] using butane gas ﬂames and by Scholeﬁeld and Garside [3] on ethylene (C2H4) diffusion ﬂames. In the last two decades, many studies have been carried out to examine the details of the ﬂow condition in the lifted ﬂames to verify the hypothesis of Vanquickenborne and van Tiggelens model.

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124

Figure 5.3 Illustration of the premixed combustion model assumptions.

Eickhoff et al. [4] carried out detailed measurements for the lift-off and blow-out phenomena in jet diffusion ﬂames. Single-point measurements of concentration and velocity proﬁles around the stabilization zone were taken and used to determine the burn-out and mixing rates. The results for two different types of natural gas jet ﬂames showed that nearly 40% of the fuel was already mixed upstream of the stabilization zone. Based on these measurements, sufﬁcient premixing takes place ahead of the ﬂame base to favor the premixed combustion model. In the experiments of Watson et al. [5], sufﬁcient fuel–air premixing was also found to occur ahead of the ﬂame base. In this study, a joint particle image velocimetry (PIV)–planar laser-induced ﬂuorescence (PLIF) technique was utilized to measure the velocity proﬁles and CH and OH radical concentrations in the lift-off zone of a lifted methane (CH4) jet diffusion ﬂame. The ﬂame base was located at distances where the turbulent burning velocity is approximately three times the laminar burning velocity, SL. However, in this study, the large-scale turbulent structures were thought to cause local extinction of the reaction zone. The earliest correlation for lift-off height was proposed by Kalghatgi [6]. A series of experiments were conducted for H2, CH4, C2H4, and C3H8 using a wide range of jet exit diameters, d0 and jet velocities, U0 . The measurements showed that that the liftoff height increases linearly with the jet exit velocity and is independent of burner diameter. For hydrocarbon fuels, the fuel–air mixture has its maximum laminar ﬂame speed, SL;max , near stoichiometric. Kalghatgis ﬁndings [6] show that the values of the lift-off height are inversely proportional to S2L;max . Assuming the ratio of the turbulent-to-laminar burning velocity, ST =SL , to be proportional to the square root of the local turbulence number based on the Taylor length scale (Rel) and through dimensional analysis, Kalghatgi then derived the following correlation for the lift-off height: ! rJet 1:5 n0 h ¼ Ch 2 U0 ð5:1Þ r1 SL;max where Ch is approximate 50, h is the lift-off height (m), U0 is the average jet velocity at the nozzle outlet (m s1), rJet is the density at the burner exit (kg m3), r1 is the air density (kg m3), and n0 is the fuel kinematic viscosity (m2 s1).

5.2 Theoretical Analysis of Diffusion Flame Stability

Based on the premixed combustion model assumption that the incoming gas velocity at the stabilization zone balances the local turbulent velocity ST, Kalghatgi [7] successfully derived an empirical equation that correlates the blow-out velocity, Ublow-out , for various fuel mixtures (CH4–air, CH4–CO2, C3H8–air, and C3H8–CO2) and burner diameters. The correlation for blow-out velocity is expressed as Ublow-out ¼ SL;max

r1 rJet

!1:5

0:017ReH 13:5 106 ReH

ð5:2Þ

where ReH is Reynolds number based on dimensionless height, H, which is obtained from the following equation: "

# Y0 rJet 0:5 H¼ 4 þ 5:8 d0 YST r1

ð5:3Þ

where Y0 is the fuel mass fraction at burner exit and YST is the stoichiometric mass fraction. 5.2.2 The Laminar Flamelet Model

In recent years, the classical premixed ﬂame propagation model has been challenged by various theories based on the principles that the lifted ﬂame results from the ﬂame extinction process taking place in the turbulent structures nearby the unignited ﬂow. Peters and Williams [8] and Peters [9, 10] argued that the lift-off heights which typically vary from 3 to 30 cm for exit velocities ranging from 10 to 60 m s1 are too short for molecular mixing in the turbulent jets to reach premixed combustion. They suggested that diffusion ﬂames and partially premixed ﬂames are an ensemble of laminar diffusion ﬂamelets (LDFs), which are highly distorted by the local turbulence. The ﬂamelets are essentially thin reaction sheets that are embedded within the turbulent ﬂow. The ﬂamelets are extinguished when the local turbulence-induced concentration gradients characterized by the time-averaged scalar dissipation are sufﬁcient to quench combustion. Flame stabilization occurs at the point where combustion extinction and propagation are in balance. These models for ﬂame stability are dependent on small turbulent structures and thus the local Reynolds number. There are some uncertainties [11] concerning the fundamental assumptions in the laminar ﬂamelet model. The model considers no substantial molecular premixing of fuel and air upstream the ﬂame front. This is not supported by detailed measurements [4, 5] and in a lifted turbulent propane–air jet ﬂame using the planar Rayleigh scattering imaging (PRSI) technique by Everest et al. [12]. Signiﬁcant premixing was found in the incoming fresh ﬂow upstream of the base of the lifted propane–air ﬂame. It becomes apparent that the laminar ﬂamelet model alone is not sufﬁcient to explain the physical processes responsible for the stabilization of lifted ﬂames.

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5.2.3 The Large-Scale Structural Mixing Model

Broadwell et al. [13] introduced a different ﬂame extinction model which proposes lifted ﬂames as a result of the ﬂame extinction in large-scale turbulent structures. The model is concerned primarily with blow-out and it is postulated that the re-entrainment of hot gases and the mixture of non-ﬂammable jet gases by the large-scale structures is responsible for the blow-out phenomena. These non-ﬂammable gases cool and quench the entrained hot products before the hot gases can ignite the ﬂammable mixture. Blow-out is viewed as resulting from competition between the large-scale mixing time td and the chemical reaction time tc . It is assumed that blowout of a diffusion ﬂame occurs when a critical value eB , as deﬁned below, is reached: eB ¼

td tc

ð5:4Þ

With this concept, Broadwell et al. [13] produced an expression for blow-out velocity as a function of burner diameter and maximum ﬂame speed for fuels as follows: eB ¼

d0 S2L y2 ðr0 =r1 Þ0:5 kUblow-out

ð5:5Þ

where y is the stoichiometric air-to-fuel ratio and k is the diffusivity. It is shown that the blow-out velocity increases linearly with the fuel nozzle diameter. The value of eB in this model was found to vary with the different fuels, as shown in Table 5.1, and on average it takes a value of 4.8. The model predictions for blow-out velocities were in good agreement with Kalghatgis data [7]. For the ﬂame lift-off height, Broadwell et al. [13] extended the concept of e to lift-off height and proposed, without making any comparisons with experimental results, the expression h¼

Ch0

" #0:5 U0 d0 ðr0 =r1 Þ0:5 S2b

ð5:6Þ

where C 0h is a constant and Sb is the ﬂame speed. Table 5.1 Values of blow-out parameter, eB [13].

Gas

eB

Methane Propane Ethylene Acetylene Hydrogen Butane Average value

4.6 5.6 5.3 3.9 4.4 4.8 4.8

5.2 Theoretical Analysis of Diffusion Flame Stability

5.2.4 Other Analysis

Both the premixed ﬂame propagation models and the large-scale structural mixing models pointed out that the isothermal ﬂow ﬁeld upstream of the ﬂame base could be used to explain and predict the lift-off height location. The importance of the isothermal mixing process of the jet was emphasized by Pitts [14–16]. Pitts suggested [16] that the similarity proﬁles for velocity and concentration established for isothermal diffusion jet ﬂow might be used to determine the local ﬂow conditions at the lift-off height. An empirical equation was proposed for the local ﬂow velocity: Ul ¼ Ch00 h2 S2L;max Yl 2 =re

ð5:7Þ

where C00h is the proportionality factor, Ul is the time-averaged axial velocity along the mass fraction Yl contour where the fuel–air mixture has its maximum laminar ﬂame speed, and re is the radius. Pitts [15] conducted multi-point velocity and concentration measurements in propane jets and found that the ﬂame stabilizes at a radial position where intermittent ﬂow characteristics were observed in the corresponding isothermal jet. The role of intermittence and large-scale eddies associated with premixed combustion was examined further by Burgess and Lawn [17]. Jet similarity laws for species mass fraction, velocity, and velocity ﬂuctuation modiﬁed to include jet intermittency effects were used to predict the local ﬂow velocity, U, at the lift-off height of the ﬂame. The turbulent burning velocity, ST, was calculated using the turbulent theories of Bray [18], Abdel-Gayed et al. [19], and G€ ulder [20] for wrinkled or corrugated ﬂame fronts. The value of ST/U was found to vary around 1.7 0.7 at the ﬂame base radial location. Since ST/U values diverge and do not equal to 1 for most of the ﬂames investigated, the study points out that the results are consistent with the large-scale turbulent structural mixing hypothesis. The large-scale vortical structures in lifted ﬂames have been subjected to extensive experimental studies [21, 22] using planar images of CH4, CH, and temperature. Schefer et al. [21, 22] identiﬁed that single-point, time-averaged measurements as conducted by Eickhoff et al. [4, 23] may not fully describe the characteristics of lifted turbulent diffusion ﬂames for the identiﬁcation of the large-scale turbulence. More sophisticated techniques such as Raman scattering imaging (RSI) would allow a useful insight into the ﬂame structure. Schefer et al. [21] used the RSI technique to study the mixing characteristics of reacting and non-reacting lifted CH4 jet ﬂames. This study showed that large-scale structures occur at the interface between the fuel jet and the surrounding air. The large eddies were larger in the case of the burning jet due to the volume expansion of these eddies in the ﬂame. The decay rate of CH4 was also lower in the lifted jet ﬂames than in the non-reacting jet, whereas the concentration ﬂuctuations were higher. This lower decay rate of CH4 was attributed to the reduction in mass entrained from the high-temperature ﬂame front. The large concentration ﬂuctuations were traced back to a combined effect of large turbulent structures and the high concentration of CH4 within the central jet. In a later study,

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Schefer et al. [22] used planar imaging measurements of CH, CH4, and temperature to evaluate ﬂame stabilization models. Captured images showed that at the stabilization height the fuel and air were premixed and within the CH4 ﬂammability limits. The ﬂame zone was found outside the region of high scalar dissipation rate. This study also showed that the large-scale motions from the fuel jet interact with the ﬂame zone. Based on these results, the authors supported both the premixed ﬂame propagation model and the large-scale turbulent structures model. Cheng and Chiou [24] conducted laser-induced ﬂuorescence (LIF)–PLIFmeasurements on a lifted H2 diffusion ﬂame that had an exit velocity of 680 m s1. Reported values included temperature and species concentration. In the lifted zone, combustion was found to occur in an intermittent ﬂame brush. In a study by Tacke et al. [25], the LIF–PLIF technique was employed to investigate the stabilization mechanism of lifted H2 diffusion ﬂames. The study revealed the existence of combustion products and also elevated temperatures upstream of the stabilization point. In view of this result, the dominant role of the large-scale structural mixing was further stressed. Brockhinke et al. [26] used a laser spectroscopic technique to measure the structural properties of lifted turbulent jet H2 and H2–N2 diffusion ﬂames. It was found that the fuel and air become mixed over a small lift-off distance and that the mixture is within the H2 ﬂammability limits. Large-scale structures were also observed in the captured images, particularly close to the lift-off zone. Pitts [14] critically examined the available ﬂame stabilization theories at that time, and concluded, based on real-time measurements of mixing behavior in turbulencefree unignited fuel jets, that none of them can satisfactorily describe the stabilization mechanism. It was suggested, however, that the premixed combustion model could be further improved by taking the presence of large-scale turbulent structures into account. Pitts also recommended further experimentation before identifying stabilization processes and before lift-off and blow-out prediction models are developed.

5.3 Jet Flame Stability in Cross-Wind

When jet ﬂames are subjected to cross wind, the stability exhibits different features for liftable ﬂames and non-lifting ﬂames. As deﬁned earlier, the liftable ﬂames could proceed from attached ﬂames to lifted, and then lead to blow-out as the jet velocity increases. The non-lifting ﬂames extinguish when the ﬂame remains attached. It could be considered that blow-out occurs immediately when the ﬂame lifts off the jet nozzle, and therefore a lifted ﬂame cannot form; this was referred to as blow-off in the previous section. The stability domain of the jet ﬂames in the presence of cross wind can be divided into sections as shown in Figure 5.4. A separation point divides the liftable ﬂames and the non-lifting ﬂames. For liftable ﬂames, there exist two upper stability limits corresponding to the jet-dominant region and the transit region, and together with a lower stability limit in the cross-ﬂow dominant region. The stability limits of jet ﬂames in the presence of cross-wind were tested experimentally by Huang and Chang [27], Kalghatgi [28] and Lee and Shin [29].

5.3 Jet Flame Stability in Cross-Wind

j129

110 100

Jet velocity U j (m s-1)

90 Blow-out region

Blow-out region

80

Up Tra per s nsi tab tio ility nr egi limi on t

it ty lim abili egion t s r r e Upp minant o Jet d

70 60 50

mit ty li bili minant a t s er do Low ss flow Cro Blow-out region

Stable region of liftable flame

40 30 20 10

Separation point

Stable region of non-lifting flame

0 0

1

2

3 4 Wind velocity U w (m s–1)

5

Figure 5.4 Typical stability domain of a propane jet flame in cross-wind. Data shown were measured in the 2.2 mm diameter burner [29].

The results [28, 29] showed that the ﬂame stability limits are inﬂuenced by the burner nozzle diameter. The effects of nozzle diameter on the stability limits are demonstrated in Figure 5.5. On increasing the burner diameter for a given cross-wind velocity, the upper limit increases and the lower limit decreases. Therefore, the stable ﬂame region for a liftable ﬂame widens with increasing nozzle diameter. In contrast,

Figure 5.5 Blow-out stability limits of propane jet flames using various nozzle diameters in crosswind [29].

6

j 5 Flame Lift-Off and Blow-Out Stability Limits and Their Application in Gas Burners

130

Figure 5.6 Common stability curve of a combusting propane jet flames in cross-flow [28].

the stability limit of the non-lifting ﬂame shows no dependence on the nozzle diameter, and it depends predominantly on the jet velocity and cross-ﬂow velocity. Kalghatgi [28] also showed that the stability limits can collapse on to a single curve using Uj =d0 and Uw =d0 in the data processing, as shown in Figure 5.6 for a propane ﬂame using a range of nozzle diameters. However, the similarity cannot apply to different fuels, as Kalghatgi showed that the respective curves for methane, ethylene and commercial butane are completely different from one another. Both Kalghatgi and Lee et al. gave explanations for the mechanism of the upper and lower limits. Kalghatgi proposed that the mechanism of blow-out is similar to that of a ﬂame in still air. He only ascertained that the upper limit is usually higher than without cross-wind and is explained with different intensities of turbulence. Lee et al. [29] applied the mixing time concept of the large-scale structural mixing model for the description of the blow-out mechanism in a cross-wind, which was ﬁrst proposed for still air by Broadwell et al. [13]. They modiﬁed the blow-out parameter in Equation 5.4 with a the velocity ratio R0 : e0 ¼

t0d ðd0 R0 Þ=Ublow-out ¼ tc kSb2

ð5:8Þ

where R0 ¼ ðr0 =rw Þ0:5 Uj =Uw

ð5:9Þ

Uj is the jet velocity and Uw is the cross-wind velocity. With this modiﬁcation, they produced a constant blow-out parameter e0 ¼ 49 for a turbulent non-premixed jet ﬂame.

5.4 Comparison of Experimental Data with Predictions

j131

5.4 Comparison of Experimental Data with Predictions

To verify the suitability and also the uncertainties of each practical correlation for the lift-off height and blow-out velocity identiﬁed in Section 5.2, a comparison of experimental measurements and predictions is carried in this section for a range of burner diameters. The discussions are focused on methane and hydrogen ﬂames. 5.4.1 Lift-Off Height

30

6

25

5

Lift-off height h (cm)

Lift-off height h (cm)

In Figure 5.7, the experimental measured lift-off heights for methane and hydrogen ﬂames [6] are compared with the predicted lift-off heights using expressions derived by Kalghatgi, Broadwell and Pitts. The constants used in the predictions are given in Table 5.2. The burner diameter d0 is 8.3 mm for methane ﬂames and 4.06 mm for hydrogen ﬂames. The predictions of Kalghatgi and Pitts give good agreement for methane ﬂames. Broadwells equation does not ﬁt so well with the experimental data. The prediction of the lift-off height with a hydrogen ﬂame seems to be more difﬁcult. None of the predictions give good agreement with experimental measurements. Especially at low jet velocities, the predictions are between three and four times too high.

20

15

10 Kalghatgi Broadwell Pitts d 0 = 8.3

5

(a)

20

40 60 80 Jet velocity U j (m s–1)

3

2 Kalghatgi Broadwell Pitts d 0 = 4.06

1

0 0

4

0

100

0 (b)

500 1000 1500 Jet velocity U j (m s–1)

Figure 5.7 Comparison of calculated lift-off heights with experimental measurements in still air. (a) Methane flame; (b) hydrogen flame.

2000

j 5 Flame Lift-Off and Blow-Out Stability Limits and Their Application in Gas Burners

132

Table 5.2 Properties of gases and constant parameters used.

Maximum laminar burning velocity Sb (m s1) Mass fraction of fuel–air mixture corresponding to maximum Y1 Fuel density (kg m3) Air density (20 C) (kg m3) Ch 1 C 0h for d0 ¼ 8.3 mm (cm s /2) 2 00 C h for d0 ¼ 8.3 mm (s cm ) k (air at 2000 K) (m s2)

Methane

Hydrogen

0.39 0.058

3.06 0.076

0.722 1.2 50 13.23 0.38 4.56 104

Source [6] [6]

0.0899 [6] [13] [21] [13]

13.02 0.51

5.4.2 Blow-Out Velocity

A comparison of experimentally measured blow-out velocities with the predicted values in still air by Kalghatgi and Broadwell and Pitts are shown in Figure 5.8. All three predictions show good agreement with measurements of the blow-out velocity of a methane-ﬁred ﬂame. The best prediction is provided by Pittss method. For hydrogen ﬂames, Broadwells predictions give good agreement with the experimental results.

6000 5000

200

blow-out velocity U j (m s–1)

blow-out velocity U j (m s–1)

250

150

100 Kalghatgi Broadwell Pitts CH4

50

3000 2000 Kalghatgi Broadwell H2

1000

0

0 0

(a)

4000

2

4 6 8 10 12 burner exit diameter d 0 (mm)

14

0

0,5 1 1,5 burner exit diameter d 0 (mm)

2

(b) Figure 5.8 Comparison of calculated blow-out velocities with experimental measurements for methane and hydrogen turbulent jet diffusion flames in still air. (a) Methane flame; (b) hydrogen flame.

5.4 Comparison of Experimental Data with Predictions

5.4.3 Hydrogen Flames

Discrepancies between the experimental and predicted values of lift-off heights and blow-out velocities for hydrogen ﬂames were observed by Pitts [14] and Wu et al. [30]. Pitts pointed out that the mass fraction contour for the maximum burning velocity Yl is almost superimposed on the stoichiometric contour Yst for most hydrocarbon fuels. For hydrocarbon fuels, the maximum burning velocity occurred near the stoichiometric value and the stabilization point of hydrocarbon fuels is on the stoichiometric contour. In contrast to a hydrocarbon fuel, hydrogens maximum ﬂame velocity occurs at an equivalence ratio of 1.8 and Yl is separated from the Yst contour. Therefore, it is not clear if the maximum laminar burning velocity is suitable for the hydrogen ﬂame in the empirical correlations of ﬂame stability limits. Tacke et al. [25] showed experimentally that the stabilization point of hydrogen ﬂame was on the lean side, not the rich side. Further work with detailed concentration measurements is needed to clarify the location of the stabilization point of the hydrogen ﬂame and diluted hydrogen ﬂames. One of uncertainties about the predictions for hydrogen jet ﬂames is that the jet similarity law adopted in the stability corrections is for a ﬂow ﬁeld in the intermittent region. Hydrocarbon fuels usually lifted in the intermittent region; however, the hydrogen lift-off height is usually within 10 times of the nuzzle diameter, and at this location the jet similarity law may not valid. There are also some uncertainties over the value of the maximum laminar burning velocity. Experimental studies of the laminar or fundamental ﬂame velocity of hydrogen–air mixtures and diluted hydrogen–air mixtures have been reported in a number of studies [31–36]; the reported value of the maximum laminar ﬂame velocity of hydrogen–air mixtures varied from 250 to 370 cm s1. The discrepancy was mainly due to whether the effect of the stretch rate over the laminar ﬂame velocity for spherically expanding ﬂames was taken into account in determining the values for the burning velocity. The ﬂame velocity without inclusion of the stretch effect was much higher. A comparison of experimental values of the laminar ﬂame velocity as a function of the equivalence ratio was made by Lamoureux et al. [31]. With consideration of the stretch effect, the maximum laminar ﬂame velocity of a hydrogen–air mixture can be taken as 260 cm s1, which is much smaller than the value of 3.06 m s1 used in predictions of the correlations of Kalghatgi, Broadwell and Pitts as shown in the Table 5.2. A comparison of measured hydrogen lift-off heights obtained by various researchers is shown in Figure 5.9. Adopting the nondimensional analysis of the lift-off height approach used by Kalghatgi [6], Wu et al. [30] correlated lift-off data for H2, H2–C3H8, H2–CO2, C3H8 and H2–Ar jet ﬂames tested in a 2 mm burner. The measured lift-off height is plotted against the jet exit velocity divided by the square of the maximum laminar ﬂame velocity in Figure 5.10. It was shown that the hydrogen line was separate from the propane line with data from mixtures scattered in between. Figure 5.11 shows the turbulence Reynolds number, ReSu ¼ hSu =njet , versus the jet exit velocity divided by the burning velocity and modiﬁed by jet-to-air density ratio, where h is ﬂame lift-off height, njet is

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j 5 Flame Lift-Off and Blow-Out Stability Limits and Their Application in Gas Burners

134

35 Wu et al. (2 mm)

25

Wu et al. (2 mm) Wu et al. (2 mm)

20

Kalghatgi (1.74 mm)

15

Cheng (2.9 mm) Cheng (1.8 mm)

10

Trend line Kalghatgi (1.7 mm )

5 0 500

700

900

1100

1300

1500

Jet exit velocity (m s–1) Figure 5.9 Comparison of experimentally measured lift-off height of pure hydrogen jet flames against the jet velocity.

the viscosity and Su is the laminar ﬂame velocity. Using the value of 260 cm s1 as the laminar burning velocity for hydrogen, the experimental data were correlated into a single line with a slope of 48, which is slightly lower than the 50 obtained by Kalghatgi [6] for undiluted CH4, C2H4, C3H8 and H2 ﬂames. The results show that it is possible to form a single empirical equation for the prediction of the lift-off height for hydrogen and hydrocarbon fuels.

90 80 70 Lift-off height (mm)

Lift-off height (mm)

30

60 H2; 2 mm burner

50 C3H8; 2 mm burner

40 30

H2-CO2; 2 mm burner

20

H2-C3H8; 2 mm burner

10 0

0

50

100

150

200

U e / S u2 Figure 5.10 Variation of measured lift off height with Ue =S2u .

250

5.5 Application in Gas Burners

j135

10000 H2; 2 mm burner 8000 C3H8; 2 mm burner ReSu

6000 H2-CO2; 2 mm burner 4000 C3H8-H2; 2 mm burner 2000 H2-Ar; 2 mm burner 0

0

40

80

120

160

200

240

U e / S u* (jet density / air density) Figure 5.11 Non-dimensional analysis of lift-off height.

5.5 Application in Gas Burners

The burner stability limits have direct implications for the design of industrial burners. As an example, the stability limits can be applied in the design of energyefﬁcient pilot burners. For the consideration of ﬂame stability, a large burner diameter is preferred in pilot burners. However, a large diameter usually leads to a higher fuel consumption, which is governed by 2 pd0 _ fuel ¼ rfuel Uj ð5:10Þ m 4 _ fuel is the mass ﬂow rate of the fuel and rfuel is the fuel density. To reduce the where m fuel consumption of the burner, it is very important to use a small nozzle diameter. However, the desired burner diameters are usually too small to provide the ﬂame stability against the cross-wind to which the pilot burners may be subjected. Therefore, it is desirable to improve the stability of small nozzle diameter jet ﬂames. One of the effective methods to improve the stability is to use a burner head that expands the jet diameter and also allows air to entrain into the jet to form a partially premixed jet ﬂame. Since the stability limits widen with increase in jet diameter, the expanded diameter usually provide much improved ﬂame stability. An experimental program was carried out by the present author to test the stability limits of three burners with and without expanding heads in the cross-wind situation. The details of the burners are shown in Figure 5.12 and Table 5.3; d1 is the burner nozzle diameter and d2 is the inner diameter of the head and is the diameter of the expanded jet. The results show that the burner c with the smallest nuzzle diameter offers the best energy performance. The data for the jet velocity Ue and the wind velocity Uw at

j 5 Flame Lift-Off and Blow-Out Stability Limits and Their Application in Gas Burners

136

Figure 5.12 Structures of the jet expansion heads used in the pilot burners listed in Table 5.3

blow-out are analyzed compared with the common stability curve for methane jet ﬂames (Figure 5.13) [28]. It is shown that the data for the partially premixed ﬂames ﬁt the common curve. The ﬂames conditions are all in the wind-dominant lower stability limits. Burner a with the highest d2 had the best stability performance. The analysis shows that stability mechanisms established for diffusion ﬂames can be 25000

20000

Ue / d2

15000

10000 Kalghatgi a b c

5000

0 0

100

200

300

400

500

600

700

Uw / d2 Figure 5.13 Comparison of stability data for expanded methane flames with the common stability curve for methane flames in cross-flow.

5.6 Outlook Table 5.3 Geometry of the pilot burners.

Burner a b c

Diameter d1 (mm)

Diameter d2 (mm)

Expansion ratio d2 =d1

2.76 1.54 1.00

31.88 23.53 18.90

11.23 15.28 18.90

applied to partially premixed ﬂames, which provides support for the premixed ﬂame propagation hypothesis for jet ﬂames.

5.6 Outlook

It has emerged that both the premixed ﬂame propagation model and the large-scale structural mixing model successfully explained the stability phenomena in diffusion jet ﬂames and the correlations for the lift-off heights and blow-out velocities have been tested against the experimental data with good agreement. The premixed propagation model contributed simple universal correlations for the lift-off height and it is successful in the prediction of ﬂame lift-off height and less good at the prediction of blow-out velocity. The large-scale structural mixing model is consistently good at prediction of the blow-out velocity, but it is less successful when the concept is extended to predictions of ﬂame lift-off height. Both models remained most relevant to the liftoff and blow-out mechanisms, but both are susceptible to criticism. One of the weaknesses of the large-scale structural mixing model is its dependence on an arbitral value e, which is determined from limited blow-out data. It is not clear how to determine a suitable e value for lifted ﬂames. The criticisms of the premixed ﬂame propagation model are often rooted in the assumption of totally premixed and ﬂame base anchoring on the stoichiometric line. This can be interpreted as the ﬂame base seeking the maximum burning, since hydrocarbon fuels have their maximum burning velocity near the stoichiometric value. Therefore, it is appropriate to use the maximum laminar burning velocity SL;max in the correlations for lift-off height. However, the stoichiometric assumption is challenged by hydrogen ﬂames, where the laminar burning velocity peaks away from the stoichiometric value. The complexity of the jet ﬂow ﬁeld also contributes to some of the uncertainties. It is easy to accept that the large-scale turbulent structures play the dominant role in the blow-out phenomena, as shown in Figure 5.14 with the image of a ﬂame at the moment of blow-out. The role of the large-scale turbulent structures in the stable lifted ﬂame is more difﬁcult to quantify as the blow-out ﬂame image in Figure 5.14 is very different from the lifted jet ﬂame in Figure 5.2. The jet similarity equations enjoyed success in the derivation of the stability equation when the ﬂame lift-off height is in the valid region of the similarity law; however, the limitation of the similarity equations is also reﬂected when the lift-off height is too short, which is demonstrated in hydrogen ﬂames.

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j 5 Flame Lift-Off and Blow-Out Stability Limits and Their Application in Gas Burners

138

Figure 5.14 Image of a methane flame at the moment of blow-out.

In conclusion, both the premixed ﬂame propagation model and large-scale structural mixing model have their respective limitations, and neither of them can explain fully the mechanisms of lift-off and blow-out alone. Future developments in the stabilization theory will depend on the use of sophisticated measurement tools to establish the ﬂow conditions in the ﬂame base and the ﬂame and to verify the hypotheses in the stabilization theories.

5.7 Conclusion

The stability theories to describe the physical mechanisms of the lift-off and blow-out behaviors of turbulent jet diffusion ﬂames have been classiﬁed into three categories. The main principles of the each theory were outlined. The practical correlations for

References

prediction of the stability parameters of non-premixed jet ﬂames are summarized and also are used to predict the lift-off height and blow-out velocity for ﬂames of hydrocarbon and hydrogen fuels. The uncertainties of the stabilization theories and empirical correlations are discussed in the comparison of the predicted and measured lift-off height and blow-out data for a range of fuels and burner diameters. The application and uncertainties of applying jet similarity proﬁles of concentrations and velocity ﬁelds of unignited jets in the equation for stability parameters are discussed. The issues with the ﬂame stabilization position and conditions are discussed through a case study of hydrogen jet ﬂames. The stability limits of turbulent jet diffusion ﬂames are important for operation of combustion systems and have safety implications for handling combustible fuels. This chapter has explored the application of the stability theory in the design of pilot gas burners. The application of the stability theories to the ﬂames subject to cross-wind has been discussed. The blow-out data from pilot burners were analyzed and the stability correlations were extended to partially premixed ﬂames from the gas burners.

References 1 Vanquickenborne, L. and van Tiggelen, A.

2

3

4

5

6

7

8

(1966) The stabilization mechanism of lifted diffusion ﬂames. Combust. Flame, 10, 59–69. Wohl, K., Kapp, N.M., and Gazley, C. (1949) Flame stabilization and quenching. Symposium (International) on Combustion, 3, pp. 3–21. Scholeﬁeld, D.A. and Garside, J.E. (1949) The structure and stability of diffusion ﬂames. Symposium (International) on Combustion, 3, pp. 102–110. Eickhoff, H., Lenze, B., and Leuckel, W. (1984) Experimental investigation on the stabilization mechanism of jet diffusion ﬂames. Symposium (International) on Combustion, 20, pp. 311–318. Watson, K.A., Lyons, K.M., Donbar, J.M., and Carter, C.D. (1999) Scalar and velocity ﬁeld measurements in a lifted CH4–air diffusion ﬂame. Combust. Flame, 117 (1–2), 257–271. Kalghatgi, G.T. (1984) Lift-off heights and visible lengths of vertical turbulent jet diffusion ﬂames in still air. Combust. Sci. Technol., 41, 17–29. Kalghatgi, G.T. (1981) Blow- out stability of gaseous jet diffusion ﬂames in still air. Combust. Sci. Technol., 26 (5–6), 233–239. Peters, N. and Williams, F.A. (1983) Liftoff characteristics of turbulent jet diffusion

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11 12

13

14

15

ﬂames. J. Am. Inst. Aeronaut. Astronaut., 21 (3), 423–429. Peters, N. (1984) Partially premixed ﬂamelets in non-premixed turbulent combustion. Symposium (International) on Combustion, 20, pp. 353–360. Peters, N. (1986) Laminar ﬂamelet concepts in turbulent combustion. Symposium (International) on Combustion, 21, pp. 1231–1250. Peters, N. (2000) Turbulent Combustion, Cambridge University Press, Cambridge. Everest, D.A., Feikema, D.A., and Driscoll, J.F. (1996) Images of the strained ﬂammable layer used to study the liftoff of turbulent jet ﬂames. Symposium (International) on Combustion, 26, pp. 129–136. Broadwell, J.E., Dahm, W.J.A., and Mungal, M.G. (1985) Blowout of turbulent diffusion ﬂames. Symposium (International) on Combustion, 20, pp. 303–310. Pitts, W.M. (1988) Assessment of theories for the behavior and blow-out of lifted turbulent jet diffusion ﬂames. Symposium (International) on Combustion, 22, pp. 809–816. Pitts, W.M. (1990) Large-scale turbulent structures and the stabilization of lifted turbulent jet diffusion ﬂames.

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26

Symposium (International) on Combustion, 23, pp. 661–688. Pitts, W.M. (1989) Importance of isothermal mixing processes to the understanding of lift-off and blowout of turbulent jet diffusion ﬂames. Combust. Flame, 76, 197–212. Burgess, C.P. and Lawn, C.J. (1999) The premixture model of turbulent burning to describe lifted jet ﬂames. Combust. Flame, 119, 95–108. Bray, K.N.C. (1990) Studies of the turbulent burning velocity. Proc. Roy Soc. Lond. A Math., 431, 315–335. Abdel-Gayed, R.G., Bradley, D., and Lawes, M. (1987) Turbulent burning velocities: a general correlation in terms of straining rates. Proc. Roy Soc. Lond. A Math., 414, 389–413. G€ ulder, O.L. (1990) Turbulent premixed ﬂame propagation models for different combustion regimes. Symposium (International) on Combustion, 23, pp. 743–750. Schefer, R.W., Namazian, M., and Kelly, J. (1988) Structural characteristics of lifted turbulent-jet ﬂames. Symposium (International) on Combustion, 22, pp. 838–318. Schefer, R.W., Namazian, M., and Kelly, J. (1994) Stabilization of lifted turbulent-jet ﬂames. Combust. Flame, 99, 75–86. Eickhoff, H., Winandy, A., and Natarajan, R. (1989) Azimuthal instability in jet diffysion ﬂames. Exp. Fluids, 7 (6), 420–421. Cheng, T.S. and Chiou, C.R. (1998) Experimental investigation on the characteristics of turbulent hydrogen jet ﬂames. Combust. Sci. Technol., 136 (1–6), 81–94. Tacke, M.M., Geyer, D., Hassel, E.P., and Janicka, J. (1998) A detailed investigation of the stabilization point of lifted turbulent diffusion ﬂames. Symposium (International) on Combustion, 27, pp. 1157–1165. Brockhinke, A., Haufe, S., and KohseH€ oinghaus, K. (2000) Structural properties of lifted hydrogen jet ﬂames measured by laser spectroscopic

27

28

29

30

31

32

33

34

35

36

techniques. Combust. Flame, 121 (1–2), 367–377. Huang, R.F. and Chang, J.M. (1994) The stability and visualized ﬂame and ﬂow structure of a combusting jet in cross ﬂow. Combust. Flame, 98, 267–278. Kalghatgi, G.T. (1981) Blow-out stability of gaseous jet diffusion ﬂames. Part II. Effect of cross wind. Combust. Sci. Technol., 26, 241–244. Lee, K.L. and Shin, H.D. (1997) A largescale structural mixing model applied to blow-out of turbulent non-premixed jet ﬂames in a cross air-ﬂow. J. Inst. Energy, 70, 128–140. Wu, Y., Al-Rahbi, I.S., Lu, Y., and Kalghatgi, G.T. (2007) The stability of turbulent hydrogen jet ﬂames with carbon dioxide and propane addition. Fuel, 86, 1840–1848. Lamoureux, N., Djeba€ıli-Chaumeix, N., and Paillard, C.-E. (2003) Laminar ﬂame velocity determination for H2–air–He–CO2 mixtures using the spherical bomb method. Exp. Thermal Fluid Sci., 27, 385–393. Huang, Z., Zhang, Y., Zengg, K., Liu B., Wang, Q., and Jiang, D. (2006) Measurements of laminar burning velocities for natural gas-hydrogen–air mixtures. Combust. Flame, 146, 302–311. Ilbas, M., Crayford, A.P., Yilmaz, Ï., Bown, P.J., and Syred, N. (2006) Laminar burning velocities of hydrogen–air and hydrogen–methane–air mixtures: an experimental study. Int. J. Hydrogen Energy, 31, 1768–1779. Liu, D.D.S. and MacFarlane, R. (1983) Laminar burning velocities of hydrogen–air and hydrogen–airstream ﬂames. Combust. Flame, 49, 59–71. Leason, B.D. (1953) The effect of gaseous additions on the burning velocity of propane-air mixtures. 1953 Symposium (International) on Combustion 4(1), pp. 369–375. Milton, B.E. and Keck, J.C. (1984) Laminar burning velocities in stoichiometric hydrogen and hydrogenhydrocarbon gas mixtures. Combust. Flame, 58, 13–22.

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6 The Combustion of Low Heating Value Gaseous Fuel Mixtures Ghazi A. Karim 6.1 Introduction

The composition of processed pipeline natural gas is usually made up primarily of methane with only small concentrations of a range of other gaseous components that may include some combustible higher hydrocarbons and the gases nitrogen and carbon dioxide. However, the compositions of raw natural gases vary widely worldwide depending on the source, nature and level of treatment they may have undergone. Some natural gases contain other components such as hydrogen sulﬁde and may have uncommonly high concentrations of non-combustible gases. Moreover, the increased practice of using some exhaust gas recirculation in combustion devices primarily for improved emissions and combustion controls will also result in gaseous fuels being combusted in association with such diluents. Their presence with methane can bring about signiﬁcant adverse changes to the combustion characteristics of the fuel mixture and may undermine its effective utilization altogether, depending on the composition and type of device used and associated operating conditions [1, 2]. There are numerous sources of potentially combustible gas mixtures besides natural gases that are made up mainly of methane together with varying concentrations of carbon dioxide, water vapor and/or nitrogen. Examples of these include mixtures associated with coal-bed methane, coal mine ventilation gases, landﬁll gases and biogases that were formed via the degradation of a wide range of organically based materials. There is also a very wide variety of gaseous fuel mixtures that become available from industrial processes not originating from natural sources or events. These gases can vary in composition and origin very widely. They can contain combustible components such as methane, hydrogen and carbon monoxide, often in association with relatively very high concentrations of the gases carbon dioxide and nitrogen. Some relatively small concentrations of higher hydrocarbons, hydrogen sulﬁde or ammonia may also be present. Generally, such gas mixtures have a much lower overall heating value than methane, which is commonly considered a typical representative of processed pipeline natural gas. On this basis, fuel gas mixtures

Handbook of Combustion Vol.3: Gaseous and Liquid Fuels Edited by Maximilian Lackner, Franz Winter, and Avinash K. Agarwal Copyright Ó 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32449-1

j 6 The Combustion of Low Heating Value Gaseous Fuel Mixtures

142

traditionally have been categorized loosely on the basis of their heating value relative to the heating value of methane as being medium or low heating value fuels. Also, the loose terms medium and low BTU gases, respectively, are commonly used. These alternative terms are gradually being phased out of the technical and scientiﬁc literature in preference to the former naming. In general, low heating value fuel gases have no precise deﬁnition but they are commonly understood to be associated with fuels having heating values of less than 20–25% of that of common pipeline processed natural gas [3, 4]. The reactivity, density and other key characteristics of these gaseous fuel mixtures will depend on their composition and are not necessarily, as sometimes assumed to be, dictated mainly by their speciﬁc heating values alone. For example, the presence of a small amount of hydrogen in a gaseous fuel mixture may not necessarily increase the heating value signiﬁcantly as the corresponding presence of a similar concentration of methane or propane. However, the combustion and heat transfer characteristics can be very different for the two sets of gas mixtures even when burned in the same device under identical conditions. There is always the need to deal with the impurities associated with raw biogases to render them suitable for utilization in conventional combustion systems; otherwise, serious operational problems will be encountered affecting their durability, safety, emissions and stable operation [5]. Biomass is a biodegradable material that can be fermented anaerobically to produce after puriﬁcation a mixed gaseous fuel composed mainly of methane and carbon dioxide commonly known as biogas. In principle, its organic origin renders its combustion not a signiﬁcant contributor to the increase in the net amount of the greenhouse gas carbon dioxide in the atmosphere. The composition of the biogases produced through the anaerobic fermentation of organic wastes depends on the type and quality of the raw materials processed and the associated operating conditions that include temperature, pressure, residence time, pH, water content, and presence of catalysts. The gases produced will contain (in addition to methane and carbon dioxide) small amounts of other gases such as hydrogen sulﬁde, nitrogen, oxygen, hydrogen, carbon monoxide, ammonia and trace amounts of saturated or halogenated hydrocarbons. The utilization of biogases as fuels has been a long established practice that goes back in history throughout many parts of the world [4, 6, 7]. It is clearly evident that the exploitations through combustion of low heating value gaseous fuel mixtures will become increasingly important as premium quality fuels are depleted and their cost and that of other energy sources increase.

6.2 Stoichiometric and Thermodynamic Considerations

The term stoichiometric mixture relates to a mixture that contains exactly the theoretical amount of oxidant needed that will permit complete combustion. In practice, however, there is no assurance that simply by providing the correct fuel-toair ratio combustion will be completed. The terms lean and rich mixtures need to be speciﬁed as whether they are meant to be leaner or richer in fuel or in air than the

6.2 Stoichiometric and Thermodynamic Considerations

corresponding stoichiometric amounts. The term equivalence ratio is widely used to indicate the ratio of the actual fuel-to-air ratio relative to the corresponding stoichiometric value. This is usually given the symbol K. However, in some ﬁelds of applications it is the corresponding air-to-fuel ratio that is used such as in some gas turbine applications, and is given the symbol l. On this basis, K is the inverse of l. The term excess air relates to the amount of air supplied beyond that needed ideally to oxidize the fuel completely. Similarly, excess fuel is the extra fuel supplied beyond the amount that can be oxidized completely by the available oxygen. The volumetric fraction of a diluent such as carbon dioxide or nitrogen in the diluent-fuel mixture is often given the term carbon dioxide index or nitrogen index of the fuel [8]. As an example, for a carbon dioxide–methane mixture in air, the carbon dioxide index a on a volumetric or molar basis is a ¼ ½CO2 =½CO2 þ CH4

ð6:1Þ

The molecular weight, Mfm, and hence the density, rfm, of the fuel mixture increase with increasing the amount of carbon dioxide relative to methane: Mfm ¼ 44a þ 16 ð1aÞ ¼ 16 þ 28a

ð6:2Þ

rfm ¼ rm ð1 þ 1:75aÞ

ð6:3Þ

that is,

Per mole of fuel mixture, a smaller amount of oxygen than for pure methane will be needed to carry out the combustion stoichiometrically. In general, a fuel–air mixture of equivalence ratio K is represented by Wð1aÞ½CH4 þ Wa½CO2 þ 2 ð½O2 þ 3:76½N2 Þ

ð6:4Þ

CH4 ð%Þ ¼ W ð1aÞ=ðW þ 9:52Þ

ð6:5Þ

that is

This indicates that the presence of carbon dioxide will displace some fuel and reduce its relative concentration by (1 a). Accordingly, the heating value per mole fuel-air mixture is reduced by a similar factor. In general, when the oxygen-to-nitrogen ratio is not that of air, for example due to the presence of excess oxygen or nitrogen, the reactant mixture will be correspondingly represented by Wð1aÞ½CH4 þ aW½CO2 þ 2 ð1aÞ f½O2 þ ð1bÞ=b½N2 g

ð6:6Þ

where K is the fuel-to-oxygen equivalence ratio and b is the oxygen-to-(oxygen plus nitrogen) ratio. It can be seen in Figure 6.1 that the non-fuel components in the reactants per mole of methane increase rapidly as the proportion of nitrogen relative to oxygen increases. It is evident then that the fuel potential energy will be shared increasingly with a greater number of molecules. This would lead to lowering of the combustion temperature and the associated reaction rate. Increases in the relative concentration

j143

j 6 The Combustion of Low Heating Value Gaseous Fuel Mixtures

144

N O2+N CO2 + N N2)/N CH4

16

Stoichiometric mixtures (CH4, O2, N2 and CO2)

12

N CO2 / N CO2 + N CH4) = 0.75

8

4

N CO = 0 2

0 0.0

0.2

0.4

0.6

0.8

1.0

N O2 / (N O2 + diluent), % Figure 6.1 Variation per mole of methane of the stoichiometric feed of oxygen, carbon dioxide, and nitrogen with changes in the feed oxygen index for two fuels.

Maximum burning velocity (m s-1)

of carbon dioxide in the fuel for the same oxygen-to-nitrogen ratio also increase the total moles that need to be heated, albeit to a lesser impact since the fuel concentration in the total mixture remains rather low. Variations in the maximum adiabatic temperature attained with the combustion of methane in oxygenated air or pure oxygen is very much higher than the corresponding values in air. The increased presence of carbon dioxide or methane with the oxygen brings about a rapid decrease in maximum value of the ﬁnal temperature to fairly low values. As shown in Figure 6.2, this is reﬂected in the maximum burning rate decreasing rapidly with the relative increase in the presence of the diluents. The corresponding reduction in the presence of carbon dioxide is signiﬁcantly greater than that for nitrogen addition [9]. It is common practice to refer to carbon dioxide, nitrogen, or water vapor when present with a fuel such as methane as diluents although they may not necessarily remain entirely neutral chemically, especially at high temperature, with carbon 4 3

gen

-oxy

en itrog

N

en

xyg

2

-o ide

x

n rbo

1 0 20

dio

Ca

30

40

50 60 70 80 O2 /(O2 + diluent) (%)

90

100

Figure 6.2 Variation of maximum values of the burning velocity of methane in oxygen–diluent mixtures with the oxygen index at ambient temperature and pressure [9].

6.2 Stoichiometric and Thermodynamic Considerations

2.0

M et han

C Pt /CP300

e

2.5

bon Car

1.5

ide

diox

Nitrogen

1.0 300

600

900 1200 Temperature (K)

1500

1800

Figure 6.3 Relative variation in Cp with temperature for methane, carbon dioxide, and nitrogen.

dioxide. The presence of diluents with the methane reduces proportionally the effective heating value of the resulting fuel mixture. The energy released through combustion of the fuel component will be shared with the diluents present [1, 2]. The relative fraction of this energy release taken by these diluents will increase as the temperature is increased since, as shown in Figure 6.3, the thermodynamic properties, especially the enthalpy and internal energy, increase rapidly with temperature. The rates of increase for carbon dioxide are greater than those for nitrogen or oxygen. Carbon dioxide, water vapor, and, to a much lesser extent, nitrogen also will tend to undergo increasingly endothermic dissociation reactions that increase rapidly in intensity as high combustion temperatures are approached. The net effect will be substantial reductions in the adiabatic ﬂame temperature through the presence of the diluents with the methane, as shown in Figure 6.4 [10]. The relative reduction is especially signiﬁcant for near-stoichiometric mixtures where the high temperatures are. For lean or rich mixtures containing substantial concentrations of diluents, the calculated resulting peak temperature may become so low that often it may not permit in reality ﬂame propagation and combustion to proceed at all, even when ignition sources of substantial energy are employed. Carbon dioxide, again, is the most effective of the diluents in reducing the calculated ﬁnal combustion temperature, followed by water vapor and then nitrogen. These processes will tend to modify the composition of the products and the available net energy release at high temperature, depressing the corresponding value of the adiabatic ﬂame temperature. The adiabatic ﬂame temperature with pure methane for mixtures with high initial temperature will be high (Figure 6.5) [11]. The concentration of carbon dioxide, as shown in Figure 6.6, decreases virtually linearly with increasing initial temperature as dissociated effects increase in intensity [12]. However, at relatively low initial preheat mixture temperatures and with high carbon dioxide concentrations with the methane, much of the gas appears in the products essentially intact and behaves largely as a diluent [13]. A most important consideration in the behavior of the combustion of methane– carbon dioxide mixtures in air is that the reaction rate under isothermal conditions will be little affected by the increased presence of diluents. It is the amount of energy

j145

j 6 The Combustion of Low Heating Value Gaseous Fuel Mixtures

146

Flame temperature (K)

2400

Methane and carbon dioxide T 0 = 300K % CH4 100

2000 1600

80 20

1200 800

10

400 0 0.0

0.5

1.0 1.5 2.0 Equivalence ratio

Flame temperature (K)

2400 2000 1600

20

2.5

3.0

Methane and nitrogen T 0 = 300K % CH4 100 80 50

1200 10

800 400 0 0.0

0.5

1.0 1.5 2.0 Equivalence ratio

2.5

3.0

Methane and water T 0 = 300K % CH4 100 80

2400 Flame temperature (K)

50

2000 1600

20

50

1200 10

800 400 0 0.0

0.5

1.0 1.5 2.0 Equivalence ratio

2.5

3.0

Figure 6.4 Calculated adiabatic flame temperature for methane containing the diluents carbon dioxide, water vapor, and nitrogen for a range of diluent indices as a function of equivalence ratio. T0 ¼ 300 K, P ¼ 1.0 bar [10].

release by the reaction and the consequent temperature rise with time that will be affected and reduced very signiﬁcantly by the increased presence of the diluents in the mixture. Accordingly, in practice, whenever the temperature varies, these factors are mainly responsible for bringing about the substantial reduction normally observed in the oxidation rates in combustion processes such as under constant

6.2 Stoichiometric and Thermodynamic Considerations

2000

Air inlet temperature K

Temperature rise (K)

1900

250 300 400 500 600 700 800 900 1000

1800 1700 1600 1500 1400 1300 1200 1100 0.03

0.04

0.05

0.06

0.07

0.08

Fuel:air ratio Figure 6.5 Calculated adiabatic temperature rise following the combustion of methane–air mixtures with equivalence ratio values for various mixture initial temperatures, at atmospheric pressure [11].

CO2 equilibrium concentration (% by volume)

pressure or volume conditions. The slowing of the overall conversion rate of the fuel and the reduction in the associated energy release will depend mainly on the relative concentrations of oxygen and the diluent involved, the temperature level, and the equivalence ratio. The heating value of the fuel mixture is not the sole factor that establishes the combustibility of the fuel mixture in air. The type and composition of the fuel mixture also need to be considered. A fuel mixture of methane and nitrogen would be more likely to permit combustion than a similar mixture of methane and carbon dioxide 35 a = 0.20

30 25

Methane-carbon dioxide

20

Stoichiometric adiabatic combustion P = 1.0 bar

15

a = 0.50 a = 0.80

10 5 a = 1.00

0 800

1000

1200

1400

1600

1800

2000

Initial temperature (K) Figure 6.6 Variation of the calculated volumetric concentrations of carbon dioxide at equilibrium for CH4–CO2–air stoichiometric mixtures at different initial temperatures at constant ambient pressure [13].

j147

j 6 The Combustion of Low Heating Value Gaseous Fuel Mixtures

148

having the same heating value. Moreover, it follows that the use of oxygen-enriched air and hence the presence of less nitrogen in the air will allow the combustion of fuel mixtures containing higher concentrations of diluents [9, 14, 15]. The displaced nitrogen in the air can then be viewed as being replaced by some of the diluent within the fuel mixture. Figure 6.6 shows typically the concentration of carbon dioxide in the products at equilibrium following the stoichiometric combustion of various fuel mixtures of methane and carbon dioxide in air for different initial mixture temperatures [13]. It can be seen that for pure methane, with high initial mixture temperatures, the resulting adiabatic ﬂame temperature will be very high and the concentration of CO2 in the products will be relatively low, largely due to dissociation effects. The concentration of carbon dioxide decreases virtually linearly with increase in the initial temperature as the combined effects of dissociation and reduced relative temperature rise are increased in intensity. At low initial temperatures for fuel mixtures that contain very high proportions of carbon dioxide, the concentrations of the gas in the products will be relatively high.

6.3 Chemical Kinetic Considerations

The property that has the most inﬂuence on the performance of fuel gas burners and the acceptance of increased fuel dilution effects is the combustion reaction rate. This determines key performance parameters such as the ﬂame speed, energy release rates, size of the ﬂame, ﬂammability limits, and emissions. The course of the combustion of fuel–air mixtures such methane–air involves the production and consumption of numerous transient species generated during the oxidation reaction together with the interaction of numerous reaction steps, each with its own different rate and species. The net effect of these reactions is to establish the parameters of the combustion process such as the rates of fuel and oxygen consumption, the energy release rate, and the production rates of the different reacting species, both stable and unstable. One very approximate simplifying approach has been to consider the reaction rate on a global overall basis while ignoring details of the reaction activity of the mixture. The results of experimental observations can then be employed to produce an optimized ﬁtted relationship for an apparent single-step reaction for the conversion of the fuel to products by combustion. The relevant key kinetic data in this simplistic approach, such as the activation energy, order of the reaction, and reaction rate constants, are obtained through a best ﬁt of the experimental data available [3]. Such a formulation has been traditionally represented typically as d½CH4 =dt ¼ k½CH4 a ½O2 b ½diluentc eE=RT

ð6:7Þ

where [ ] represents the molar concentration, often that of the initial mixtures, and k, a, b, c and E are speciﬁc constants for the reaction. Since the actual combustion reaction cannot be represented universally by such a relatively simple formulation, these constants can vary signiﬁcantly depending on the conditions under which the

6.3 Chemical Kinetic Considerations

experimental data were obtained. For common diluents with methane, including carbon dioxide, the index c in Equation 6.7 generally tends to be much smaller than unity [10, 16]. Hence the reaction rate under isothermal conditions will be affected only slightly by the presence of the diluent. As was stated earlier, it is the energy released by the reaction and the consequent temperature change with time that will be affected very signiﬁcantly by the increased presence of the diluents in the mixture. These factors are mainly responsible for bringing about the substantial reduction normally observed under non-isothermal conditions in the oxidation rates of combustion processes such as under constant pressure or volume conditions. The kinetic delay time is primarily dictated by the time required to build a sufﬁcient pool of OH, H, O, and HO2 radicals. The carbon monoxide produced during the course of the reaction does not react very signiﬁcantly until the hydrocarbon molecules are depleted sufﬁciently. Examination of details of the set of reactions that proceed during the oxidation of methane at constant temperature will indicate that the presence of diluents with methane will not affect signiﬁcantly the initiation reactions of the oxidation of methane. The presence of the diluents, however, can undermine somewhat the concentration of the pool of radicals available for propagating the reaction and encourage three-body recombination reactions [12]. These will lead to slowing of the overall conversion rate of the fuel and the associated energy release to an extent that will depend on variables such as the diluent involved, its concentrations, the temperature, and the equivalence ratio. By employing a fairly comprehensive detailed representative scheme for the oxidation of methane, the course of the oxidation reactions can be followed computationally over a wide range of initial composition temperature and pressure [13, 16, 17]. Extensively detailed and comprehensive information can be obtained about the chemical processes in the combustion of gaseous fuels. For example, such kinetic modeling shows that the presence of a diluent such as carbon dioxide with the methane reduces, as expected, the reaction rates for the same operating conditions (Figure 6.7) [16]. This would extend the time needed before acceleration of the reaction rate and the onset of ignition and also the time needed to complete the combustion to achieve essentially near-equilibrium conditions. During the course of the reaction, the concentrations of various species change very signiﬁcantly before approaching ultimately their expected equilibrium concentrations. The presence of a signiﬁcant amount of a diluent with the methane affects both the values of such maximum concentrations and the times at which their peak values may be obtained. The commencement of the exothermic reactions such as under constant pressure conditions will produce temperature increases that will have profound effects on the reaction rates and the concentrations of the reactive species. The rates will accelerate very rapidly as the temperature increases. The concentrations of the products eventually will be those expected at the very high ﬁnal temperatures reached. Almost all the methane is eventually consumed fully as very high ﬁnal temperatures are reached, even for the mixtures containing very signiﬁcant amounts of diluents. It can be noted that the reaction initiation time represents the bulk of the reaction time since much of the early part of the ignition delay time is associated with relatively low

j149

j 6 The Combustion of Low Heating Value Gaseous Fuel Mixtures CH4 concentration (% by volume)

150

10 Adiabatic constant pressure reaction Initial temperature = 1000 K Equivalence ratio (f) = 1.0 Various CH4/(CH4 + CO2) ratio

8 6 4

100%

2 0

50%

0

10

20%

20 30 Residence time (ms)

10%

40

50

Figure 6.7 Variation with time of the concentration of methane for four fuel mixtures with different carbon dioxide concentrations during constant-pressure combustion with an initial temperature of 1000 K [16].

Concentrtion (% by volume)

temperature levels. The dominant role played by the changing mixture temperature is evident throughout. The peak values for the concentrations of carbon monoxide and hydrogen during the adiabatic stoichiometric combustions at constant pressure with air for a number of fuel mixtures containing methane and carbon dioxide initially preheated to an initial temperature of 1000 K are shown typically in Figure 6.8 [13]. The corresponding equilibrium values are also shown. Signiﬁcant differences in the two sets of values are evident throughout. For the carbon dioxide, the values of the equilibrium concentrations are the maximum concentrations that would be encountered. It is also evident that interrupting the full course of the combustion reactions can produce higher concentrations of carbon monoxide and lower concentrations of carbon dioxide over the whole range of diluent concentrations in the fuel mixture. The

7.5 6.0 4.5 3.0 1.5 0.0 10

Methane-carbon dioxide-air mixture CO peak Initial temperature = 1000 K, P = 1.0 bar Stoichiometric mixture

H2 peak CO equil. H2 equil. CO2*10 CO2*10 init.

20 30 40 50 60 70 80 90 100 CH4 concentration (% by volume)

Figure 6.8 Calculated peak and equilibrium product concentrations during the transient reaction of a stoichiometric mixture of methane and air containing various concentrations of the diluent carbon dioxide at an initial mixture temperature of 1000 K [13].

6.3 Chemical Kinetic Considerations

20 18 16 14 12 10 8 6 4 2 0

24 22

Isothermal reaction 2000K

20

Average reaction rate

18 16

Ignition delay

14 12 10 Methane-air mixture

0.2

0.4

0.6 0.8 1.0 Equivalence ratio (f)

Ignition delay (ms)

Average rate of methane consumption (kmol kg-1 s-1 of mixture)

peak concentrations of carbon monoxide are not necessarily associated with fuel mixtures that contain very high concentrations of carbon dioxide with the methane. This is of great importance in relation to the production of exhaust emissions and reﬂects the important role of the resulting temperature level of the reactive mixture. For fuel-rich mixtures, the initial part of the reaction appears to be associated predominately with exothermic reactions that will result in temperatures that can slightly exceed eventual equilibrium values [13, 16, 18]. These high temperatures, after the lapse of some time, will accelerate the rates of the reforming reactions, which will result in the temperature of the adiabatic system decreasing eventually to the ﬁnal equilibrium value. Therefore, for rich mixtures with diluents, to retain these excess temperature values it may be desirable not to prolong the combustion process well beyond the time when this peak temperature is achieved. The rates of oxidation reactions of methane, and also to control the eventual combustion products, can be enhanced very signiﬁcantly through the homogeneous mixing with the reactants of small proportions of uncooled ﬁnal products [16]. Such mixing not only leads to a slight preheating of the reactive mixture, hence accelerating somewhat the relatively slow reactions, but also may provide small amounts of badly needed radicals and other active species leading to enhancement of the rates of the early stages of the reactions. The extent of this enhancement in the rates would depend largely on the composition of the mixture, the temperature, and the mass of recirculated gases employed. What is also signiﬁcant is that such improvements appear to be achievable even for fuel mixtures containing a very substantial amount of diluents. The time needed for autoignition is logarithmically dependent on temperature. Accordingly, high temperatures are needed to effect rapid reactions to ignition and completion of fuel conversion to products within the available residence time in a combustor. This is particularly needed when substantial concentrations of diluents are present. Moreover, the time needed for ignition under isothermal conditions is essentially linearly related to the equivalence ratio (Figure 6.9) [17]. This time

8 1.2

6

Figure 6.9 Variation of the average reaction rate and ignition delay under isothermal conditions at 2000 K with equivalence ratio for methane–air mixtures [17].

j151

j 6 The Combustion of Low Heating Value Gaseous Fuel Mixtures

152

2.5

100 % Methane

1.0 1.5 2.0 Log10 time (ms)

5% n-Heptane

2% n-Heptane

10% n-Heptane

1% n-Heptane 0.5% n-Heptane 0.1 % n-Heptane

1500

0.05% n-Heptane

20% n-Heptane

2000

80% n-Heptane

2500

50% n-Heptane

90% n-Heptane

100% n-Heptane

Temperature (K)

3000

1000 500 -0.5

0.0

0.5

3.0

Figure 6.10 Calculated temperature rise with time under constant pressure for a range of fuel mixtures with varying concentrations of methane and n-heptane in stoichiometric air at an initial temperature of 760 K [19].

increases rapidly as leaner mixtures or increased diluents become present. The instantaneous reaction rate during the course of the reaction is variable and reﬂects the corresponding changes of the many species concentrations and temperature. Thus, for fuel–diluent mixtures much longer reaction times are expected in order to complete the conversion of the fuel to products and the release of the fuel energy. It also points to the greater corresponding vulnerability of such fuel combustion to greater heat losses and being prone to incomplete reaction or even its failure. Obviously, the degree of complexity and completeness to be considered in the simulation of methane–diluent air combustion reactions will depend on the nature of the output information needed and the accuracy required. The presence of an extremely small amount of n-heptane, for example a fraction of 1%, with methane reduces the ignition delay signiﬁcantly without necessarily making a substantial change to the ﬁnal temperature, the energy released, or the product composition (Figure 6.10) [19]. As an example, it is the non-methane fuel components in a natural gas that react with the air far more rapidly than the methane to release energy and radicals earlier in the course of the reaction, speeding up the overall reaction and reducing the ignition delay as shown in Figure 6.11 [19]. This behavior represents a potential approach for improving the reactivity of fuel mixtures of methane with diluents, even those with low heating value.

6.4 Some Combustion Characteristics

The term lean, weak, or lower ﬂammability limit indicates the minimum concentration by volume of the fuel needed in air that will support continued ﬂame propagation from an adequate ignition source within quiescent homogeneous

CH4 concentration (% by volume)

6.4 Some Combustion Characteristics

1.1

1.0

0.9

0.8

0.7 0.0

Fuel mixture Adiabatic constant volume Natural gas; sample "9" T 1 = 760 K, P 1 = 62.6 bar, f = 0.583 Non-methane fuel components

0.1

0.2 0.3 Residence time (ms)

0.4

0.5

Figure 6.11 Variation of the normalized concentration of methane and the non-methane components in a natural gas with time for an equivalence ratio of 0.583 and an initial temperature of 760 K, at a constant pressure of 62.6 bar [19].

fuel–air mixtures under a speciﬁed set of operating conditions. The corresponding maximum fuel concentration is referred to as the rich, upper, or higher ﬂammability limit. Accordingly, the term ﬂammability limit is strictly restricted to apply only to a speciﬁc set of operating conditions. Similarly, the term ﬂame spread limit indicates the condition for a ﬂame to begin to spread under different prevailing conditions from an ignition source through the mixture. An increase in temperature widens the limits almost linearly, permitting leaner and richer mixtures to support ﬂame propagation. Also, an increase in pressure widens the limits. The relative changes in the rich limit values are usually greater than those for the lean limit values [20, 21]. The presence of diluents with fuels such as methane in ﬂammability-limiting mixtures affects primarily their thermodynamic and transport properties and consequently the mode of heat ﬂow from the ﬂame. The reaction kinetics are also affected but to a comparatively lesser extent because of the relatively low temperatures involved. Figure 6.12 shows the changes in the lean limit values with the presence of carbon dioxide and nitrogen with methane. The value of the limit appears to increase nearly linearly with the extent of fuel dilution. Carbon dioxide is more effective than nitrogen in impeding combustion [20, 21]. Mixtures of methane and air at the corresponding composition of the lean ﬂammability limits at various initial mixture temperatures tend to be associated with an approximately constant adiabatic ﬂame temperature value of around 1585 K [22, 23]. The presence of a diluent with the methane in lean limit mixtures can be viewed as a replacement of some of the excess air to bring the resulting mixture closer to the stoichiometric value. The maximum amount of diluent that can be tolerated and support ﬂame propagation will be approximately that which will be at a stoichiometric fuel-to-air ratio within the total mixture. Correspondingly, the increased presence of the diluent in rich limit mixtures can be viewed as a gradual replacement of some of the excess fuel by the diluent while providing approximately the same energy release levels. Accordingly, the ﬂammable mixture region will

j153

j 6 The Combustion of Low Heating Value Gaseous Fuel Mixtures

154

CO+ N2

50

CH4 +N2

6 8

H2 +CO2 CO+ CO2

60

10 Non-flammable region

40

12 14

CO

2

30 4+

20

Flammable zones of fuel -diluent mixtures in air

CH

Limits (% flammable gas plus diluents, by volume)

70

H2 +N2

16 18

10

Oxygen in mixture (% by volume)

4

80

20 0

0

2 4 6 8 10 12 14 16 Ratio inert gas:flammable gas (by volume)

18

Figure 6.12 Flammable mixture regions for gaseous fuels and diluent mixtures in air at ambient temperature and pressure conditions [20].

narrow gradually with the addition of increasing amounts of the diluent to the fuel. Preheating of mixtures using energy from sources other than the combustion of the fuel itself or reducing heat loses from the combustion zone to the surroundings would permit combustion with higher concentrations of diluents in the fuel. As an approximate assessment of the ﬂammability limit values for methane–diluent mixtures in air, the concept of a near constant adiabatic ﬂame temperature remains an effective practical tool for assessing the contribution to the limit values of changes in the concentration of diluents, initial mixture temperature and heat transfer effects. Using the concept of a near-constant ﬂame temperature associated with limiting mixtures of the same fuel, the effects of heat transfer to and from the mixture can also be estimated. Extremely lean mixtures can be burned when sufﬁcient heat is transferred. This is the principle behind approaches to destroy very low concentrations of toxic combustible waste, especially when preheated oxygen rather than air is employed as the oxidant. Moreover, sufﬁciently less lean mixtures may fail to propagate a ﬂame when excessive heat loss is present such as takes place in ﬂame quenching. The laminar burning velocity of methane is known to be lowered signiﬁcantly as the concentration of the carbon dioxide added to the methane is increased [24], as shown in Figure 6.13. This is mainly a consequence of the combined effects of the reduction in the reaction rates, ﬂame temperature, diffusivity, and transport properties of the mixture. Hence the presence of diluents with the methane will affect signiﬁcantly the ﬂame propagation within ﬂowing streams of turbulent homogeneous fuel–air mixtures. The ﬂammable range, established on the basis whether an ignition source of adequate energy such as an electric spark or pilot ﬂame, can initiate a propagating ﬂame or not, narrows signiﬁcantly with the increase in the mixture stream velocity [25], as shown typically in Figure 6.14. For sufﬁciently fast streams,

6.4 Some Combustion Characteristics

Burning velocity (m s-1)

0.5 0.4

77.2% CH4 22.8 N2

0.3 0.2

50% CH4 50% N2 41.5% CH4 58.5 N2

0.1 0.0 0.8 0.5

Burning velocity (m s-1)

100% CH4 0% N2

0.4

1.0

100% CH4 0% CO2

1.2 Equivalence ratio

1.4

1.6

1.4

1.6

87.6% CH4 12.4 CO2

0.3 0.2

75% CH4 25% CO2 67.9% CH4 32.1 CO2

0.1 0.0 0.8

1.0

1.2 Equivalence ratio

Figure 6.13 Variations in the laminar burning velocity with equivalence ratio for a number of methane–nitrogen and methane–carbon dioxide mixtures at ambient conditions [24].

ﬂame propagation cannot be achieved even with stoichiometric mixtures. The rate of dissipation of the energy release by the ﬂame is then much too fast to permit adequate time for the release of the chemical energy so as to propagate the reaction further. The presence of a diluent with the methane will restrict this ﬂammable range further. As can be seen in Figure 6.14, for any stream velocity, the ﬂammable mixture range narrows rapidly as the concentration of carbon dioxide in the methane–carbon dioxide fuel mixture increases. Very fast streams cannot tolerate the excessive presence of the diluent, which leads to ﬂame blowout. Throughout, the stoichiometric mixture, as expected, remains the most capable of supporting ﬂame propagation for the highest of carbon dioxide and/or turbulent velocities. As can be seen in Figure 6.14, the corresponding ﬂammable ranges when nitrogen is the diluent tend to be appreciably wider than those with carbon dioxide. For the same fuel composition, employing larger scales of turbulence tends to widen the ﬂammable limits. The presence of a diluent such as carbon dioxide with the methane will narrow the combustible mixture range and makes it more sensitive to the velocity level and turbulent characteristics of the mixture to be burned. Throughout, a much higher degree of tolerance for the presence of nitrogen with the methane in comparison with

j155

j 6 The Combustion of Low Heating Value Gaseous Fuel Mixtures

156

2.0

Equivalence ratio

1.8

Re Nitrogen-methane mixture Ambient conditions

0 2000 4000 6000 8000 10000 11000

1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.0

0.2

0.4

0.6

0.8

1.0

CO2 /(CO2 +CH4) (by volume)

2.0

Equivalence ratio

1.8

Re Carbon dioxide-methane mixture Ambient conditions

0 2000 4000 6000 8000 10000 11000

1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.0

0.2

0.4

0.6

0.8

1.0

CO2 /(CO2 +CH4) (by volume) Figure 6.14 Variation of the operational volumetric mixture limits of flowing streams of methane with nitrogen and with carbon dioxide mixtures within a circular pipe for a number of flow Reynolds numbers at ambient temperature and pressure [25].

the presence of carbon dioxide is evident. As shown typically in Figure 6.15, for a stream within a circular pipe ﬂowing with an average ﬂow value of Re ¼ 2000, the ﬂame spread rate is reduced, while the range of equivalence ratio over which the ﬂame can be made to propagate is narrowed very signiﬁcantly. With ﬂows of higher velocities, the relative reduction in the ﬂame propagation rates on the addition of carbon dioxide to the methane is even greater, leading to early ﬂame propagation failure. Moreover, the extent of random variation in the ﬂame initiation phase, and also the subsequent ﬂame propagation within the combustible ﬂowing streams, increases virtually linearly with the proportion of carbon dioxide in the methane– carbon dioxide mixture. Hence the unsteadiness of ﬂame initiation and its propagation contributes further to the difﬁculties encountered in the combustion of methane in the presence of carbon dioxide. Mixtures around the stoichiometric

Apparent flame speed relative to V max for methane

6.4 Some Combustion Characteristics

1.0

Carbon dioxide in methane Re = 2000 0% 10%

0.8

30%

0.6

50% 70%

0.4 0.4

0.6

0.8 1.0 1.2 Equivalence ratio (f)

1.4

1.6

Figure 6.15 Variation of the flame propagation rates within flowing mixtures of methane–carbon dioxide–air for a flow Reynolds number of 2000 and different volumetric concentrations of carbon dioxide in the methane, at ambient conditions [25].

value remain throughout as the most tolerant to the presence of carbon dioxide or nitrogen with methane. The employment of catalysts is a very common approach for enhancing the reaction rates in combustion processes. However, their use with methane as the fuel in comparison tends to remain relatively limited. In principle, combustors when suitably ﬁtted with appropriate catalysts can permit operation with leaner overall fuel mixtures and/or lower mean temperatures. They would accept fuel mixtures that contain substantially more diluents with the methane while maintaining low levels of emissions [12, 14]. This is an area that is worthy of further research effort, particularly if non-noble catalysts can be made to meet the demanding requirements such as for reliability and durability. Some very limited combustion data on low heating value fuels under conditions of high temperature and pressure may be derived from engine experiments. For example, the combustion period, which is the measured time to complete combustion in an engine, can be considered as the inverse of the mean ﬂame speed under the same conditions. Figure 6.16 shows how the inverse of the ﬂame speed increases as the concentration of carbon dioxide and that of nitrogen in methane are increased. Carbon dioxide would bring an earlier ﬂame propagation failure than similar concentrations of nitrogen under the same operating conditions [26]. Also, the slowing of the ﬂame propagation with increased addition of the diluent to methane is reﬂected by a continued and rapid increase in exhaust temperature (Figure 6.17). This can be of concern in spark ignition and gas turbine engine applications where excessively high exit temperatures are to be avoided. To ensure that satisfactory performance of a combustion system and its burners while accepting changes in the composition of the fuel used in general and increased concentration of diluents producing low value heating fuels in particular represents challenges that often cannot be overcome without resorting to empirical, essentially trial-and-error approaches. Numerous factors need to be considered and their impact addressed satisfactorily in the design and operational characteristics of the device.

j157

j 6 The Combustion of Low Heating Value Gaseous Fuel Mixtures Combustion duration (°CA BTDC)

158

100

CO2

90 80 70

N2

60 H2 50

CR = 8.5, T in = 22°C

40

ST = 15 °CA BTDC

30 20

0

5

10

15

20

Diluents ratio (%) Figure 6.16 Variation of the combustion duration in a spark ignition engine when operating on stoichiometric mixtures of methane with different volumetric concentrations of carbon dioxide and nitrogen as diluents; the corresponding presence of some hydrogen is also shown [26].

Some of the main key parameters that need to be considered, whether premixed or diffusion burning is involved, are the following [27–30]: 1) 2)

3) 4)

The resulting ﬂame characteristics such as ﬂame length, penetration, ﬂash back, lift-off, blow-out, impingement, pulsation, and noise. Thermal characteristics that include mean and peak combustion temperatures, radiation and other modes of heat transfer, heat pick-up by the burner body, and the temperature of the working parts. Fuel composition, supply pressure requirements, pressure drop, compression work needed and whether fuel pilot injection is to be used. Emissions (UHC, NOx, CO, and particulates), build-up and detachment of deposits.

Exhaust temperature (K)

600

CR = 8.5 ST = 15°CA BTDC N = 900 rev min-1

550

Operation limits

Adding CO2 Adding N2

500

450

0

20

40

60

80

100

CO2/(CO2 + CH4), N2 / (N2 + CH4) (%) Figure 6.17 Variation of the exhaust gas temperature in a spark ignition engine operating on stoichiometric mixtures of methane with the diluents carbon dioxide and nitrogen [26].

6.5 Some Operational Considerations

5) 6) 7) 8) 9)

Application of swirlers, ﬂame stabilizers and exhaust gas recirculation. Size, load capacity, combustion, and energy production efﬁciency. Corrosion and compatibility of materials. Turn-down ratio, ﬂexibility, start-ups and shut-downs, and their frequency. Maintenance needs, ease and cost of manufacture, replacement, and operation.

6.5 Some Operational Considerations

Much work has been reported in the technical literature about experience with the combustion of low heating value fuel mixtures in a wide range of devices and conditions. These reports tend to be limited in scope and invariably relate to a speciﬁc or a narrow range of fuel gases focusing mostly on a speciﬁc system design or operational features. Accordingly, the following is a listing of some general operational factors that relate primarily to the combustion of methane in association with carbon dioxide and/or nitrogen [31–48]: .

.

.

.

In general, dilution of methane with CO2, N2, or H2O below the 15% range may be considered approximately as though the diluents are interchangeable with excess diluting air. The heating value of the fuel may be raised through supplementation with a high-grade fuel such as natural gas, or by burning the gas in two stages where a high heating value gas is used to ensure adequate ﬂame stabilization. Operation on methane–diluent mixtures will reduce the overall peak combustion temperature ensuring lower emissions of NOx. However, this is expected to lead to some possible increase in greenhouse gas emissions in the form of carbon dioxide, possibly with higher unconverted methane and carbon monoxide exhaust concentrations. The extent of these increases in emissions will depend on the composition of the fuel used and the speciﬁc operating and design conditions of the equipment. The emission of particulates under such low combustion temperature conditions will remain essentially insigniﬁcant. Also, through the increased presence of carbon dioxide and cooler discharge gases, the noise level is lowered. Any change in the quality of the fuel fed to a burner requires that the corresponding Wobbe index should not vary from the design value with normal fuels by more than 5–7 %. For operation on the poor quality fuel with the same total heat output would require the volumetric ﬂow of the fuel gas to be sufﬁciently larger than when operating on normal quality fuel gas. Appropriate modiﬁcations of piping, valving, and controls need to be made. The ﬂammability limits cannot be employed for evaluating whether the fuel mixture can be burned in a given device or not. The limit values can only serve as a very rough approximation of the conditions needed for burning the mixture. A better estimate needs to consider the stability of the ﬂame, which would demand richer mixtures than those represented by the corresponding limit values. Laboratory-scale experiments alone do not necessarily produce results that are

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.

.

.

.

.

.

applicable to industrial furnaces, and full-scale testing is almost always out of the question. The heating value on a volume basis is a key indicator since pipeline capacity and associated equipment depend on the volume of gas that needs to be handled for a certain thermal loading. This is particularly important for equipment operating at high pressure. Flame temperature is also important because of its effect not only on the combustion rate but also on the amount of heat that can be recovered in a given device. Since combustion is more difﬁcult and wall combustion chamber temperatures are lower, complete combustion requires longer retention times. Operational difﬁculties are usually associated with start-up and transient and light load changes requiring a high heating value fuel to be used. Operation on the low heating value fuel is then reserved primarily for relatively high steady load operation. Additional measures may include preheating the gas ahead of the burner to a higher temperature through mainly heat circulation from the exhaust gases using high-efﬁciency heat exchange. However, care needs to restrict the upper limit of temperature because of constraints on the materials and equipment used and/or increase in pollutants formed at high temperature. Improved ﬂame stability may be achieved by generating a zone in the combustor in which the ﬂow velocity is equal to the local burning velocity or in a stirred zone with intense back-mixing of products into the fresh mixture or by preheating of reactants. Heat recirculation between the combustion gases and the fresh mixture can be achieved simply through radiation heat transfer to walls of the combustor with convective heat exchange between the entering mixture and the wall. To increase the radiative heat recirculation, refractory surfaces are often employed in the combustion device. High-temperature heat exchangers tend not to be used owing to their cost and increased pumping pressure losses and corrosion problems. The continuous ﬂow nature of the operation of gas turbines reduces signiﬁcantly the constraints normally placed on fuel properties for proper combustion and provides a considerable margin for ensuring clean and satisfactory combustion. Since heavy-duty industrial gas turbines are essentially fuel ﬂexible, they are prime candidates for effectively meeting the challenges of operating on diluted methane. Of course, the injection of low heating value gas into the gas turbine combustors requires the compression of very high gas ﬂow rates and dedicated combustors. Air may also be bled to prevent surging. For these reasons, the gas is often augmented by mixing with natural gas. The low heating value fuel gas produced in blast furnaces or through the gasiﬁcation of coal has been reported to be suitable for combustion in gas turbines when the combustion chamber, control equipment and gas supply pipes were suitably modiﬁed. The pressure of the fuel needs to be raised to that of the combustor pressure. In most combustors for low heating value fuel mixtures, the ﬂame is stabilized by a high-grade fuel supply either in the main stream or to pilot devices, bringing about substantial improvements of the tolerance to increased concentrations of

References

.

.

diluents in the fuel burned. In premixed burners, improved ﬂame stabilization is achieved through installation of a bluff body and swirl ﬂame stabilization. Judicious recirculation of some exhaust gases, particularly from optimum regions of the combustion zone, can inﬂuence the course of the combustion process effectively. The excess enthalpy approach to combustion, when some of the thermal energy released is fed back to the fresh reactants so that the temperature obtained is much higher than its counterpart when obtained with normal temperatures, may be employed to permit operation with further lowering of the oxygen concentration or excess diluents without increasing the emission of NOx [18, 37]. There is also the possibility of employing ﬂuidized bed combustion to recover the thermal energy while maintaining very low emissions. Throughout, the mixing time relative to the chemical reaction time and whether adiabatic or heat losses are involved are important. Also, the length of the ﬂame inﬂuences the heat transfer since longer ﬂames may be more luminous and have lower temperatures than similar ﬂames at the same operating conditions.

6.6 Conclusion

The extent of diluent concentration in the fuel gas that can be tolerated by a combustion system will depend on the system design, its ﬁeld of application, operating conditions, and the fuel gas composition. Generally, combustion systems are more tolerant of the presence of nitrogen with methane compared with that of carbon dioxide. The presence of some hydrogen or higher hydrocarbons with the methane can increase the tolerance to the presence of diluents in the fuel. With the presence of relatively small concentrations of diluents with the methane, usually few or no changes to equipment design and operation are needed. However, for high diluent concentrations, various changes to the burner and associated equipment are necessary. These may include the need for dedicated burners and combustors and the employment of an auxiliary supply of good-quality fuel either for starting and lowload applications and/or for supplementing the low heating value fuel through direct mixing or piloting ﬂame actions.

References 1 Bartok, W. and Saroﬁm, A.F. (1991) Fossil

4 Weinberg, F. (1979) The First

Fuel Combustion, John Wiley and Sons, Inc., New York. 2 Borman, G.L. (1998) Combustion Engineering, McGraw-Hill, New York. 3 Odgers, D. and Kretchmer, D. (1986) Gas Turbine Fuels and Their Inﬂuence on Combustion, Abacus Press, New York.

Half-Million Years of Combustion Research and Todays Burning Problems. Energy and Combustion Science, Pergamon Press, Oxford, pp. 17–31. 5 Arbon, I.M. (2002) Worldwide use of biomass in power generation and

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combined heat and power schemes. Proc. Inst. Mech. Eng. A J. Power, 216, 41–57. Brunner, C.R. (1991) Handbook of Incineration Systems, McGraw-Hill, New York. Su, S., Beath, A., Guo, H., and Mallet, C. (2005) An assessment of mine methane mitigation and utilization technologies. Prog. Energy Combust., 31, 123–170. (1986) North American Combustion Handbook, 3rd edn, vol. 1, North American Mfg. Co., Cleveland, USA. Lewis, B. and von Elbe, G. (1987) Combustion, Flames and Explosions of Gases, 3rd edn, Academic Press, New York. Karim, G.A. and Wierzba, I. (1992) Methane–carbon monoxide mixtures as a fuel. SAE Paper No. 921557, published in Natural Gas: Fuels and Fueling, SP-927, Society of Automotive Engineers, Warrendale, PA. Karim, G.A. and Singh, R. (1968) Calculating the temperature rise following the combustion of natural gas. Can. Gas Process. J., 26–28. Glassman, I. (1977) Combustion, Academic Press, New York. Zhou, G. (1003) Analytical Studies of Methane Combustion and the Production of Hydrogen and/or Synthesis Gas by the Uncatalyzed Partial Oxidation of Methane. PhD thesis, University of Calgary. Grifﬁths, J.F. and Barnard, J.A. (1995) Flame and Combustion, Blackie Academic & Professional, Glasgow. Weinberg, F.J. (1986) Advanced Combustion Methods, Academic Press, New York. Karim, G.A., Hanaﬁ, A.S., and Zhou, G. (1992) A kinetic investigation of the oxidation of low heating value fuel mixtures of methane and diluents. In Emerging Energy Technology, ASME PD-41 (ed. S.R. Gollahalli), p. 103. Hanna, M.A. (1983) The Combustion of Diffusion Jet Flames Involving Gaseous Fuels in Atmospheres Containing Some Auxiliary Gaseous Fuels. PhD thesis, University of Calgary. Babkin, V.S., Wierzba, I., and Karim, G.A. (2002) The phenomenon of energy concentration in combustion waves and its application. Chem. Eng. J., 56, 279–285.

19 Khalil, E.B. and Karim, G.A. (2002) A

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kinetic investigation of the role of changes in the composition of natural gas in engine applications. J. Eng. Gas Turb. Power, 124, 404–411. Coward, H.F. and Jones, G.W. (1952) Limits of Flammability of Gases and Vapors. Bulletin 503, National Bureau of Mines, Washington, DC. Zabetakis, M.G. (1965) Flammability Characteristics of Combustible Gases and Vapors. Bulletin 627, National Bureau of Mines, Washington, DC. Karim, G.A., Boon, S., and Wierzba, I. (1983) The lean ﬂammability limit of some gaseous mixtures involving methane. In Proceedings of International Gas Research Conference, pp. 980–990. Wierzba, I., Karim, G.A., and Shrestha, O.M. (1996) Approach for predicting the ﬂammability limits of fuels/diluent mixtures in air. J. Inst. Energy, 69, 122–130. Pritchard, R., Guy, J.J., and Conner, N.E. (1977) Handbook of Industrial Gas Utilization, Van Nostrand Reinhold Company, New York. Karim, G.A., Wierzba, I., and Soriano, B. (1986) The limits of ﬂame propagation within homogeneous streams of fuel and air. Trans. ASME/J. Energy Sources Technol., 108, 183. Li, H., Karim, G., and Sohrabi, A. (2009) The Lean Mixture Operational Limits of a Sparke Ignition Engine When Operated on Fuel Mixtures. Trans. ASME, J. Eng. Gas Turbine Power, 131, 12801–12808. Baukal, C.E. (2004) Industrial Burners Handbook, CRC Press, Boca Raton, FL. Cox, R.W., Hoggarth, M.L., and Reay, D. (1967) Burning natural gas in industrial burners. Journal of Institute of Fuels, 40, 498–512. Faulkner, E.A. (1986) Guide to Efﬁcient Burner Operation – Gas, Oil and Dual Fuel, 2nd edn, Fairmont Press, Lilburn, GA. Reed, R.D. (1981) Furnace Operation, 3rd edn, Gulf Publishing, Houston, TX. Akhmedov, R.B., Talibdzhanov, Z.S., and Musaev, I.K. (1974) An investigation of the process of combustion and heat transfer when burning natural gas mixtures with

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39

coke oven and blast furnace gases. Teploenergetica, 21, 76–80. Becker, T. and Perkavec, M. (1996) Low Btu gas applications in gas turbines. In Proceedings of CIMAC Congress, pp. 233–244. Battista, R.A., Pandalai, R.P., and Hill, M.B. (1982) Low Heating Value Fuel Burning Capabalities of General Electric Gas Turbine. ASME 82-GT-255. Bollettini, U., Breussin, F.N., and Weber, R. (2000) A study on scaling of natural gas burners. IFRF Electron. Combust. J., 20006, 1–24. Gallahalli, S.R. (1977) Effects of diluents on the ﬂame structure and radiation of propane jet ﬂames in concentric streams. Combust. Sci. Technol., 15, 147–160. Gallahalli, S.R. and Zadeh, A. (1985) Flame structure of attached and lifted ﬂames of low caloriﬁc values. Energy Sources, ASME, 8, 43–66. Gupta, A.K. (2004) Thermal characteristics of gaseous fuel ﬂames using high temperature air. J. Eng. Gas Turb. Power, 126, 9–19. Hallet, A.G., Hay, A., and Sheridan, R. (1987) The application of self-recuperative and regenerative burners in the steel industry. J. Inst. Energy, 60,34–41. Karim, G.A. and Lam, H.T. (1986) Ignition and ﬂame propagation within stratiﬁed methane–air mixtures formed by convective diffusion. In 21st Combustion Symposium (International), pp. 1909–1915.

40 Mellish, C.E. and Linnett, J.W. (1952) The

41

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inﬂuence of inert gases on some ﬂame phenomena. In Fourth Symposium on Combustion (International), pp. 407–420. Molliere, M. (2005) Expanding fuel ﬂexibility of gas turbines. Proc. Inst. Mech. Eng. A J. Power, 219, 109–119. Morgan, G. and Kane, W. (1952) Some effects of inert diluents on ﬂame speeds and temperature. In Fourth Symposium on Combustion (International), pp. 313–320. Purvis, C.R. and Craig, J.D. (1998) A small scale biomass fueled gas turbine power plant. Proceedings of Bioenergy 98 – The Eighth Biennial National Bioenergy Conference, Madison, WI. Winter, E.F. and Rotte, J.W. (1983) The improved utilization of gaseous fuels: overview of technological development. In Proceedings of the International Gas Research Conference, London, UK, IGRC/FO2-83, pp. 1–15. Zaba, T. (1977) Low-grade fuel used in gas turbines. Oil Gas J., 91, 114–123. Brzustowski, T. (1976) Flaring in the energy industry. Prog. Energy Combust., 2, 129–124. Chomiak, J., Longwell, P., and Saroﬁm, A. (1989) Combustion of low caloriﬁc value gases; problems and prospects. Prog. Energy Combust., 15, 109–129. Strahle, W.C. (1993) An Introduction to Combustion, Gordon and Breach, New York.

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7 Hydrogen Combustion and Emissions in a Sustainable Energy Future Suresh K. Aggarwal and Alejandro M. Briones 7.1 Introduction

Meeting global energy demand in a sustainable manner represents one of the major challenges of the twenty-ﬁrst century. The word sustainable implies providing energy security and addressing climate change concerns caused by greenhouse gas (GHG) emissions, that is, providing energy in a carbon-neutral manner. The global energy demand continues to rise, and is expected to increase by 50% until 2030, accordingly to the International Energy Agency (IEA), with fossil fuels (oil, coal, gas) representing about 80% of the worlds energy mix [1]. As Figure 7.1 shows, oil and coal represent the two largest components of the energy supply, providing 34% and 25% of the energy, respectively, but also the two dominant sources of GHG emissions. The picture becomes more discouraging in the United States, where fossil fuels currently provide 85% of the energy, with oil and coal accounting for 39% and 22%. By 2020, the use of fossil fuels, which totaled approximately 3.3 TW (terawatt) in 2000, is projected to increase by 32%, maintaining roughly the same proportions of oil, natural gas, and coal. In 2000, the United States imported 52% (net) of its oil supply, which accounted for about one-quarter of its trade deﬁcit. By 2020, the oil import percentage is expected to increase to 65%. Clearly, as long as oil continues to be the most signiﬁcant component of United States energy use, it will be a large contributor to trade deﬁcits, to the cumulative effects of CO2 emissions, and perhaps to geopolitical instability. Ball and Wietschel [2] in a recent publication provide a thorough discussion of the global energy supply and demand, and the various options available. While there is considerable debate regarding the future energy supply, most estimates indicate that conventional oil production will peak some time around 2010–2020. Therefore, this scenario of threatening energy security and serious climate change concerns provides a compelling argument for developing alternative energy sources, with the principal options being biofuels (biodiesel and bioethanol), biomass, synthetic liquid fuels [Fischer–Tropsch (FT) fuels] from coal or gas, unconventional oil from oil

Handbook of Combustion Vol.3: Gaseous and Liquid Fuels Edited by Maximilian Lackner, Franz Winter, and Avinash K. Agarwal Copyright 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32449-1

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Figure 7.1 The World Energy Outlook – 2008: Source International Energy Association (IEA). Mtoe represents million tonnes of oil equivalent, with 1 toe ¼ 11.63 MW h.

sands or oil shale, electricity as a fuel for battery electric vehicles (BEV s) or plug-in hybrid electric vehicles (PHEVs), and hydrogen. In order to get a broad perspective on the role of hydrogen in energy economy, it is relevant ﬁrst to provide an overview of potential alternative fuel-related energy sources. Other alternative sources such as wind, solar (photovoltaic), and nuclear are not discussed in this chapter. Synthetic fuels generally refer to liquid fuels (FT fuels) that are produced from coal, natural gas, or biomass through the FT process. There are numerous large-scale commercial projects around the world focusing on producing FT fuels using coal-to-liquid (CTL), gas-to-liquid (GTL), or biomass-toliquid (BTL) conversion processes. According to the US Department of Energy projection, the domestic consumption of FT fuels made from coal and natural gas will rise to 3.7 million barrels per day in 2030 based on a price of $57 per barrel of high-sulfur crude [3]. The major advantage of these fuels is that they can be utilized within existing infrastructure, and their use can be designed for optimal combustion in an engine and thus signiﬁcantly reduce local emissions (due to low sulfur content and low particle emissions). However, their production from fossil energy sources is much more CO2 intensive than conventional reﬁning, especially in the case of CTL. Thus the production of syngas (a mixture of H2 and CO) and H2 rather than FT fuels. Synthetic fuels also include oil and gas extracted from oil shale and oil sands [4], which represent a huge oil resource. According to a 2005 estimate [5], the total world resources of oil shale is sufﬁcient to yield 2.8–3.3 trillion barrels of shale oil, which exceeds the worlds proven conventional oil reserves, estimated at 1.317 trillion barrels. There is signiﬁcant interest in these fuels due to the rising energy demand and concerns about energy security. They have a high volumetric energy density, are easy to handle, and can use the existing infrastructure. However, recovering oil from oil shale involves several environmental issues, such as land

7.1 Introduction

use, water use, waste disposal, and GHG emissions. There are also economic considerations, although continuous advances in extraction technology keep lowering the cost of oil recovery. Biofuels represent a renewable energy source and can be produced from a variety of feedstocks. While these include a variety of fuels depending upon the feedstock and manufacturing process, the two most representative biofuels are ethanol and biodiesel. For instance, ethanol or bioethanol at present is mostly produced from food crops, such as corn, starch, and sugarcane, but can also be produced from nonfood-crop sources including miscanthus, switchgrass, cellulosic waste, and biomass [6]. As a transportation fuel, it is mostly used in spark-ignition engines as a blend with gasoline, although blending it with diesel fuel for diesel engines is being actively pursued [7, 8]. Flex-fuel vehicles are designed to run on ethanol–gasoline blends ranging from E-0 (100% gasoline) to E-85 (ethanol:gasoline 85:15%, by volume), with only a few engine and fuel system modiﬁcations. Ethanol can also be used as a source of hydrogen in fuel cells and other applications. Similarly, biodiesels can be produced from a variety of feedstocks including vegetable oils (soybean, rapeseed, palm oil, etc.), animal fat (tallow, lard, etc.), and non-food crops, such as jetropha and karanja [9, 10]. There is also growing interest in producing biodiesels from algae [11]. Biodiesels can be used in pure form (B100), or blended with petroleum diesel in any concentration, without any signiﬁcant engine and fuel system modiﬁcations. Biofuels being a renewable energy source offer advantages with respect to energy security. Moreover, they can be used within the existing infrastructure, especially as blends optimized according to the local conditions, and generally reduce local emissions, especially particulates [12, 13]. To a certain extent, they are also considered carbon neutral in that the combustion of biofuels may not give any net contribution to the GHG emissions, since an equivalent amount of CO2 is absorbed by photosynthesis during the growth of new mass. There are, however, concerns over their sustained long-term use, which include (i) biodiversity as large amount of land may be required, and (ii) the food versus fuel debate, and (iii) being carbon neutral on the basis of life-cycle analysis [14]. Nevertheless, biofuels are expected to play an important role in the global energy supply, and could become a signiﬁcant component of the sustainable energy paradigm, especially if non-food crops and marginal lands can be used to produce them in a signiﬁcant manner. Biomass represents a viable renewable energy source, as it can be obtained from many different plants and wastes. It can be used to produce biofuels, hydrogen, electricity, and liquid fuels through the FT process. It is generally considered carbon-neutral on a life-cycle basis. For instance, the current commercial biomassgenerating industry in the United States produces about 0.5% (1700 MW) of the domestic electric supply, which avoids approximately 11 million tons of CO2 emissions from fossil fuel combustion, and about 2 million tons of CH4 emissions from the biomass residue, which would otherwise by disposed of as burial. It holds great promise as a source of bioenergy, especially for the production of hydrogen, since it provides signiﬁcant ﬂexibility in terms of feedstock, local availability, and ﬁnal product.

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7.1.1 Background: Production, Storage and Utilization

Hydrogen is the simplest and most abundant of the chemical elements in the universe, constituting roughly 75% of its elemental mass, mostly in the atomic and plasma state. On Earth, under standard conditions, it exists as the diatomic gas (H2), but its concentration is negligible (1 ppm by volume), as it escapes from Earths gravity due to its low weight. However, in chemically combined form, it is the third most abundant element on Earth. Consequently, hydrogen is considered an energy carrier, similar to electricity, since it is produced from other compounds, mostly from hydrocarbons and water. On a commercial scale, most of it is currently produced by steam reforming of methane (SRM) in natural gas [15], although other fuels such as methanol, ethanol, butane, and higher hydrocarbons are being studied and used for speciﬁc applications [16, 17]. Most of the hydrogen produced using SRM is used in industrial applications, such as fertilizers, petroleum, and in the chemical industries. It is also increasingly being produced using other sources, which include coal and biomass gasiﬁcation [18–20], splitting of water using electric, solar, or nuclear energy [21–23], and direct dissociation of methane and other hydrocarbons [24], and algae bioreactors [25]. As part of the energy future, the various hydrogen sources can be grouped into three types, namely fossil fuels (coal, natural gas, petroleum, oil shale, etc.), renewable sources (biofuels, water, photovoltaic, solar, algae, etc.), and nuclear (e.g., using thermal energy from nuclear reactions for water splitting). Most of the hydrogen is currently produced from fossil fuels. For hydrogen to be part of a sustainable energy future, renewable and nuclear sources need to play a more signiﬁcant role in hydrogen production, and cost-effective carbon capture and storage technologies need to be developed. In addition, a new class of robust and cost-effective catalysts is required to make the H2fuel cell (FC) option economically competitive. In the context of this chapter, major research efforts are also needed to improve efﬁciencies and reduce costs and emissions of hydrogen powered combustion systems. For using H2 in the transportation sector, options may include H2internal combustion engines (ICEs), H2 FCs, hybrid systems (H2 ICE–H2 FC and H2 ICE–battery combinations), and H2 blended with other fuels. Similarly, for stationary power generation, options include H2 turbines and H2 FCs, preferably in an IGCC (integrated gasiﬁcation combined cycle) conﬁguration, and H2 blended with other fuels (i.e., hydrocarbons, biofuels, and CO). The considerations of cost-effectiveness, high efﬁciency, and low emissions will likely require a blended fuel strategy in the near- and mid-term future. Hydrogen versus electricity is another open issue; beneﬁts of generating electricity through the H2 route (i.e., H2 FCs) versus directly using biosources are not clear at present. While the realization of a hydrogen-based economy will continue to be debated, hydrogen as an energy carrier will play an increasingly signiﬁcant role in meeting the worlds energy demands and addressing environmental concerns. It clearly represents an important component of the drive for decarbonization of fossil energy and the development of renewable sources. In this context, it meets the three important

7.2 Theory and Applications in Research

criteria: a promising low-carbon alternative reducing emissions of GHG, providing energy security, and reducing local pollutants, that is, NOx and particulates. This chapter discusses fundamental aspects pertaining to the utilization of hydrogen through processes that convert chemical energy into thermal energy, which can then be harnessed as mechanical energy, such as in transportation systems [26], or as thermal and electrical energy, such as through an IGCC system [18]. Fundamental theory and applications in research on hydrogen combustion and emissions are discussed in the next section, followed by its applications in industry in the subsequent section. The outlook including the future technological and scientiﬁc advances needed are then discussed, followed by a conclusions section.

7.2 Theory and Applications in Research

Hydrogen has some distinct thermo-transport properties that make its combustion and emission characteristics notably different from those of hydrocarbons fuels. For instance, due to high diffusivity and low ignition energy (cf., Table 7.1), it has the widest ﬂammability limits and the highest laminar ﬂame speed of any hydrocarbon fuel. This section provides an overview of such characteristics. First, a theoretical framework is provided by describing the governing equations along with the Table 7.1 Properties of H2, gasoline, and Jet A fuel at 298.15 K and 1 atm.

H2 Physical properties Density (kg m3) Melting point (K) Boiling point (K) Heat of fusion (kJ kmol1) Heat of vaporization (kJ kmol1) Lower heating value (MJ kg1) Physicochemical properties of H2–air mixture Lean ﬂammability limit (wLFL) Rich ﬂammability limit (wRFL) Minimum ignition energy (mJ) Shortest quenching distance (mm) Unstretched laminar ﬂame speed SL (cm s1) Adiabatic ﬂame temperature at w ¼ 1.0 (K) Thermodynamic and transport properties Speciﬁc heat capacity at constant pressure (kJ kmol1 K1) Thermal conductivity (W m1 K1) Diffusivity (DH2 air ) (m2 s1)

Gasoline

Jet A

0.0899 14.01 20.28 117 904 119.96

730 (273 K) — 300–500 — 308 800 44.79

806 (294.26 K) 263 450–547

43.4

0.10 7.14 0.019 0.64 300 (w ¼ 1.8)

0.7 4.0 0.24 2.0 37–43 (w ¼ 1.0)

— — — — —

2483

2580

2670

28.836

—

—

0.1805 (300 K) 0.611 (273 K)

— —

— —

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transport and chemical kinetic models needed to solve these equations. Then, fundamental combustion phenomena, such as explosion limits, ﬂammability limits, ignition, and laminar ﬂames, are brieﬂy described [27]. The theoretical and numerical analysis of combustion characteristics is based on the governing equations for the conservation of mass, momentum, and total energy for a reacting gaseous mixture containing N species. These equations in a general form are as follows: Continuity: qr þ r ðrVÞ ¼ 0 qt

ð7:1Þ

Momentum: N X q ðrVÞ þ r ðrVVÞ ¼ rp þ r t þ r Yi f i qt i¼1

ð7:2Þ

Energy: 0 1 N X qðrHÞ qp Ji A @ þ r ðrVHÞ ¼ þ r ðt VÞ þ r Yi f i V þ r r q qt qt rYi i¼1

N n X

o hf0;i vi þ q_ rad

i¼1

ð7:3Þ

Species continuity: qðrYi Þ þ rðrVYi Þ ¼ r Ji þ vi qt

ð7:4Þ

For certain applications, radiative heat transfer may be important. Then a sink term is included in the energy equation. This term can be modeled using various approaches [28]. For laminar ﬂames, an optically thin gas assumption [29] is often used to represent the sink term in the form q_ rad ¼ 4sKp T 4 T0 4 ð7:5Þ where T is the local ﬂame temperature, T0 the ambient temperature, s the Stefan– Boltzmann constant, and Kp accounts for the absorption and emission from the participating gaseous species. For turbulent ﬂames, more rigorous approaches have been used, depending upon the application. In order to make the above set complete, constitutive relations are needed for momentum (t), heat (q), and mass (Ji) diffusion ﬂuxes, along with the equations of state for density and temperature. Assuming Newtonian ﬂuid and ideal gas mixture, these are as follows: Stress tensor: 2 t ¼ mðr VÞI þ m rV þ rVT 3

ð7:6Þ

7.2 Theory and Applications in Research

Heat ﬂux: q ¼ k rT þ

N N X N X X Jj Xj DTi Ji Hhf ;i Ji þ Ru T MWi Dij rYi rYj i¼1 i¼1 j¼1 ð7:7Þ

Mass diffusion: Ji ¼ r Di rYi DTi

rT T

ð7:8Þ

Mass fraction of species i ¼ N: YN ¼ 1

N 1 X

ð7:9Þ

Yi

i¼1

Equation of state: r¼

P MW Ru T

ð7:10Þ

Total enthalpy: H¼

N X i¼1

ðT

Yi hf ;i þ

Cp dT

ð7:11Þ

TRef

The heat ﬂux equation contains three terms representing the Fourier heat conduction, species interdiffusion, and the Dufour effect. The mass diffusion equation has two terms, representing the mass diffusion (represented by Ficks law) and the Soret effect (species mass ﬂux due to a temperature gradient), which is the reciprocal of the Dufour effect (energy ﬂux due to a species gradient). In addition, mass diffusion can be caused by pressure and body force gradients, but these effects are negligible in most combustion applications [30]. The Dufour term is also generally negligible. However, the Soret effect may be important in combustion applications. The solution of the governing equations requires description of the thermotransport properties, which include dynamic viscosity (m), thermal conductivity (k), diffusion coefﬁcients (Di, DiJ, and DTi ), speciﬁc heat (Cpi), and enthalpy of formation (hf ;i ). These properties are temperature dependent and also become pressure dependent at near and above the critical pressure. Various levels of treatments are available, and the reader is referred to [31–34] for details. Finally, an appropriate reaction mechanism is needed to provide the individual reaction rates of reactions in order to calculate the net reaction rate of species i (vi). 7.2.1 Chemical Kinetic Models

Hydrogen oxidation chemistry represents the most fundamental and important building block in the hierarchy of hydrocarbon chemistry. Consequently, its

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Table 7.2 Commonly used chemical kinetic schemes to model H2 oxidation.

Mechanism

Reactions

Li et al. [38]

19

Mueller et al. [37]

19

Kim et al. [36]

19

Yetter et al. [35]

19

Sivaramakrishnan et al. [39]

19

Davis et al. [40]

19

Ó Conaire et al. [41]

19

Wang and Rogg [43] GRI-Mech. 3.0 [42]

17 26

Validation

298 T 3000 K 0.25 w 5.0 0.3 P 87 atm 850 T 1040 K 0.3 P 15.7 atm 850 T 1010 K 1.0 P 15.7 atm 823 T 2870 K 0.0005 w 6.0 0.3 P 2.2 atm 1000 T 1500 K 0.5 w 1.0 20.7 P 493 atm 298 T 2625 K 0.4 w 4.5 1.0 P 20 atm 298 T 2700 K 0.2 w 6.0 0.05 P 87 atm — —

Full oxidation mechanism H2–O2

H2–O2–CO H2–O2–CO H2–O2–CO

H2–O2–CO

H2–O2–CO

H2–O2

H2–O2–CO CH4–O2

chemistry has been extensively investigated, and several detailed mechanisms have been developed and validated using different combustion conﬁgurations. Table 7.2 lists several of these mechanisms, including those of Dryer and co-workers [35–38], Wang and co-workers [39, 40], Westbrook and co-workers [41], and H2 sub-mechanism in GRI-Mech. 3.0 [42]. Most of these mechanisms involve eight species, namely H2, O2, H, O, OH, HO2, H2O2, and H2O. The mechanisms have been validated or optimized using experimental data from ﬂow reactors, shock tubes, and laminar premixed ﬂames for a range of conditions deﬁned in terms of temperature, pressure, and equivalence ratio. Further details are provided in [69]. Some optimization studies have considered syngas (H2–CO) mixtures rather than H2. These are also included in Table 7.2. As discussed [69, 72], the Li et al. mechanism [38] has been found to provide the best match with measurements over a wide range of equivalence ratio and pressure, using various targets, including shock tube ignition delay and laminar ﬂame speed data. 7.2.2 Explosion, Ignition, and Reaction Limits

A fundamental characteristic of H2–air mixtures is the existence of multiple ignition and extinction states, which have important consequences for practical systems [44].

7.2 Theory and Applications in Research

10,000

Th

ird

lim

it

Pressure (mmHg)

1000 Condition no explosion 100

10 Explosion First lim it

0

400

440 480 520 Temperature (°C)

560

Figure 7.2 Explosion limits of a stoichiometric H2–O2 mixture [45].

These states were ﬁrst identiﬁed in terms of the explosion limits [45], exhibiting multiple combustion and non-combustion regimes when plotted in terms of pressure and temperature for a given equivalence ratio (w). Figure 7.2 shows the explosion limits for a stoichiometric H2–O2 mixture contained in a spherical glass vessel of 7.4 cm inner diameter. In the explosion regime, the rate of chain-branching reactions exceeds that of chain-terminating reactions, while the converse is true in the no-explosion regime. Thus the explosion limit is characterized by the two rates being equal, and indicates a boundary between explosion and no-explosion regimes. As shown in Figure 7.2, there are three explosion limits, exhibiting the well-known Z-shaped curve in terms of pressure and temperature. The explosion limits have been used for the validation of H2 oxidation mechanisms, and are also relevant for studying the ignition and combustion behavior of non-premixed, premixed, and partially premixed systems. For instance, the ignition of non-premixed and premixed systems exhibits multiple states (ignition limits), which qualitatively mimic the explosion limit behavior. Similarly, the mass burning rates of H2–O2, H2–air, and other mixtures exhibit non-monotonic variations with pressure, which been explained in terms of the explosion or reaction limits. The explosion limits can be explained in terms of the key elementary steps in hydrogen oxidation, which is generally initiated by the following two steps [30, 45]: ð7:12Þ H2 þ M ! 2H þ M þ 443:7 kJ mol1

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H2 þ O2 ! 2OH þ 213 kJ mol1

ð7:13Þ

Reaction 7.12 is more endothermic than Reaction 7.13, but is more likely to occur at high temperatures, whereas Reaction 7.13 is likely to occur at low temperatures. The initiation steps are followed by the chain-branching steps: H þ O2 ! OH þ O 62:6 kJ mol1 ð7:14Þ O þ H2 ! OH þ H þ 8:4 kJ mol1

ð7:15Þ

OH þ H2 ! H þ H2 O þ 70:0 kJ mol1

ð7:16Þ

Reaction 7.14 is the rate-limiting chain-branching reaction, whereas Reactions 7.15 and 7.16 are the chain-branching and chain-propagating reactions, respectively. The ﬁrst explosion limit is characterized by competition between these chain-branching reactions (especially Reaction 7.14 and chain termination and radical destruction that occur due to Hand OHatoms colliding with the walls of the vessel. Since wall collisions become more predominant at lower pressures compared with molecular collisions, the ﬁrst limit is shifted to higher pressures as the temperature is decreased. At higher pressures, the second explosion limit is approached, and again characterized by competition between chain-branching (Reaction 7.14) and chainterminating (Reaction 7.17) reactions. The main chain-terminating reaction is the three-body reaction ð7:17Þ H þ O2 þ M ! HO2 þ M 200:9 kJ mol1 The hydroperoxyl radical (HO2) is considered to be a metastable species (i.e., it is not stable but is long-lived). Although the relative rates of Reactions 7.14 and 7.17 are not directly dependent on w, they exhibit different sensitivities to temperature and pressure. Since Reaction 7.14 has a stronger dependence on temperature than Reaction 7.14, it is more sensitive to variations in w. In addition, Reaction 7.14 scales linearly with pressure, whereas Reaction 7.17 has a quadratic variation with pressure [45]. Therefore, with increasing pressure the second explosion limit is shifted towards higher temperatures. Moreover, the HO2 diffuses towards the vessel wall and is removed through 2HO2 ! H2 þ 2O2

ð7:18Þ

2HO2 ! H2 O2 þ O2

ð7:19Þ

A further increase in pressure increases the HO2 concentration signiﬁcantly, and the following chain propagating reactions become signiﬁcant: HO2 þ H2 ! H2 O2 þ H

ð7:20Þ

H2 O2 þ M ! 2OH þ M

ð7:21Þ

7.2 Theory and Applications in Research

The net effect of Reactions 7.20 and 7.21 is to convert the relatively inactive HO2 radical into three more active radicals (H and 2OH). Therefore, at some higher pressure there is a rapid increase in radical concentration, leading to the third limit, which is characterized by competition between Reactions 7.17 and 7.18–7.21. Because Reactions 7.17 and 7.18–7.21 are pressure and temperature sensitive, respectively, this limit is consequently shifted to higher pressures with decrease in temperature. Since HO2 under these conditions is highly unstable, the chain reactions cannot be terminated by wall effects, but through the following radical recombination steps [46]: ð7:22Þ H þ OH þ M ! H2 O þ M 498:1 kJ mol1 2H þ M ! H2 þ M 2O þ M ! O2 þ M

435:3 kJ mol1

502:3 kJ mol1

ð7:23Þ ð7:24Þ

7.2.3 Ignition Characteristics: Ignition Delays and Ignition Limits

Ignition of H2–air mixtures is of critical importance in many practical systems, including gas turbines and diesel and homogeneous charge compression ignition (HCCI) engines. Because of its high ﬂammability, it is also particularly important regarding safety issues [47, 48]. Laminar ignition of H2–O2–diluent mixtures has been investigated using a variety of systems, including shock tubes and counterﬂow conﬁgurations. The shock tube experiments involve ignition in a homogeneous mixture so as to focus on chemical kinetic effects without the complication of transport effects. The shock tube data are presented in terms of the ignition delay time as a function of temperature for a given set of conditions [49], and often used for the development and validation of chemical kinetic models. Law and co-workers [50, 51] reported extensive studies on the ignition of H2–air mixtures using a counterﬂow conﬁguration, involving two opposing jets, one containing H2 (non-premixed) or H2–air mixture (premixed) and impinging against the other heated air (non-premixed) or N2 (premixed) jet. For non-premixed mixtures, the ignition corresponds to the lower turning of the well-known S-shaped response of temperature with the Damk€ohler number [52]. The ignition behavior is reported in terms of the minimum ignition temperature as a function of strain rate, pressure, and dilution. Law and co-workers [50, 51] examined the transport and chemical kinetics effects and identiﬁed ignition limits, which have many common features with the explosion limits. Figure 7.3 (from Ref. [51]) presents ignition limits for premixed H2–air mixtures along with the homogeneous explosion limits discussed earlier. The ignition limits qualitatively mimic the explosion limits in many respects. For instance, similarly to explosion limits, the ignition limits represent boundaries between no ignition and ignition regimes, and exhibit a Z-shaped curve in terms of the system pressure and temperature (ignition

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Figure 7.3 (Homogeneous) explosion and counterflow (premixed) ignition limits; conditions for the explosion limits are the same as for Figure 7.2 The counterflow ignition limits were obtained at w ¼ 0.236 and as ¼ 216 s1.

temperature). However, the ignition limits are shifted towards higher temperatures, and exhibit ﬁve branches corresponding to ﬁve limits. Moreover, these limits are inﬂuenced by the density-weighted strain rate (as) and w (premixed systems), as well as transport processes [63, 64]. The ﬁrst, second and third ignition limits are qualitatively analogous to the corresponding explosion limits, except that there are signiﬁcant transport effects causing (i) diffusive loss of H radicals from the ignition kernel, which increases with decreasing pressure, which affects the ﬁrst limit, and (ii) preferential diffusion of H2 over O2, which converts HO2 to more reactive H radical through Reaction 7.20. These two effects steepen the ﬁrst and second limits, respectively. From the third ignition limit, with further increase in pressure, the ignition curve turns again to form a segment corresponding to the fourth ignition limit. Here H radicals formed through Reaction 7.20 react with HO2 through HO2 þ H ! H2 þ O2

ð7:25Þ

HO2 þ H ! 2OH

ð7:26Þ

Reaction 7.25 is chain terminating and competes with Reaction 7.20 that produces H. On the other hand, Reaction 7.26 is overall chain propagating and competes with Reaction 7.25. Hence the fourth limit is determined by the competition between Reaction 7.25, which has a negative effect on ignition, and Reactions 7.14, 7.20, and 7.26, which have positive effect on ignition. Further details were discussed by

7.2 Theory and Applications in Research

Zheng and Law [64]. As pressure is further increased, it increases the HO2 concentration substantially via Reaction 7.17. Thereby, the HO2 recombination reaction becomes signiﬁcant as it increases quadratically with HO2 concentration, that is, 2HO2 ! H2 O2 þ O2

ð7:27Þ

This reaction reduces the rates of Reactions 7.25 and 7.26. The hydrogen peroxide (H2O2) radical further decomposes into 2OH via Reaction 7.21. Although Reaction 7.25 is still signiﬁcant, Reactions 7.26, 7.25, and 7.21 become more dominant, and these reactions yield the ﬁnal reversal, representing the ﬁfth limit over which ignition becomes easier again with increasing pressure. Further, Law [44] identiﬁed the role of radical runaway and demonstrated that ignition can also be described as radical rather than thermal runaway. This was based on the observation that because of the small radical pool required for system runaway, the chemical heat release at the ﬁrst and second limits is small enough that there is no thermal feedback. This is illustrated in Figure 7.4, which presents a comparison between the system S-curve responses computed using the full governing equations and those using decoupled equations by suppressing either the chemical heat release term or species reaction rate terms. By suppressing the chemical heat release term in the energy equation, the numerical simulations still reproduced the lower branch and the ignition turning point segment of the complete solution for the ﬁrst and second

Figure 7.4 Maximum H-atom mole fraction as a function of temperature computed using full governing equations, and with chemical reactivity neglected, heat release neglected, and both heat release and chemical reactivity neglected [44].

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limits. The third limit, however, could not be reproduced in the absence of chemical heat release. Therefore, in contrast to the ﬁrst and second ignition limits, the third ignition limit needs both feedbacks. In addition, by suppressing the chemical reaction rates of major reactant species (H2, O2, and N2), the lower branch and ignition turning point were still reproduced. These results have important implications for modeling ignition since the response of the minor species can be calculated by using proﬁles of this decoupled environment obtained from nonreactive conservation equations. 7.2.4 Flammability Limits

The ﬂammability limits represent a fundamental property of a given fuel–air mixture, and deﬁned in terms of the leanest and the richest fuel–air mixtures that support a ﬂame. They are called the lean and rich ﬂammability limits, expressed in terms of lean (wL) and rich (wR) equivalence ratios. Zabetakis [53] determined the ﬂammability limits by considering ﬂame propagation in a 5.1 cm diameter and 150 cm long vertical tube. A mixture is said to be ﬂammable if a ﬂame can propagate through the entire tube. The experiments were conducted for both upward and downward ﬂames. The ﬂammability limits were found to be wider for upward-propagating ﬂames, due to the buoyancy-induced ﬂow, which preheats the reactants and also reduces the ﬂame stretch. Thus, the ﬂammability limits were 0.1 < w < 7.14 (4–75% by volume) for upward propagating ﬂame, and 0.24 < w < 7.14 (9–75% by volume) for downward propagation. As indicated in Table 7.1, hydrogen has much wider ﬂammability limits than any hydrocarbon or biofuel, which is due to its low ignition energy and high diffusivity. The wide ﬂammability limits have signiﬁcant consequences for fundamental combustion phenomena, such as ignition, extinction, ﬂame propagation, and stability, and for practical devices. For instance, the low lean ﬂammability limit is critical to the design of combustion devices with ultra-low emissions and high power density. The ﬂammability limits are inﬂuenced by the ﬂame conﬁguration and the system pressure and temperature [44, 45, 54]. An increase in temperature makes the limits wider, whereas an increase in pressure slightly lowers the lean ﬂammability limit of hydrogen and of common hydrocarbons fuels. However, the rich ﬂammability limit is lowered for hydrogen, but increased for the hydrocarbon fuels, as the pressure is increased. The lean ﬂammability limit of H2–air mixtures is also modiﬁed by preferential diffusion, especially for upward ﬂame propagation. Due to ﬂow contributed by gravity force, the streamlines diverge well ahead of the combustion wave in upstream ﬂame propagation. Therefore, the transport of gas from the region of the tube axis to the wall takes place to some extent ahead of the ﬂame thermal gradient, reducing the velocity gradient near the ﬂame surface. This results in less ﬂame stretch and, hence, survival of the ﬂame at lower w. Moreover, Coward and Brinsley [55] observed that a portion of hydrogen remains unburned for lean mixtures. Based on Marksteins theory, preferential diffusion promotes the

7.2 Theory and Applications in Research

formation of cellular cells characterized by regions of burned and unburned fuel extending the lean ﬂammability limit. For instance, Qiao et al. [56] showed by decreasing the Markstein number through dilution that hydrogen–oxygen ﬂames become more susceptible to preferential diffusion instability, which in turn increases the ﬂame speed (SL) and counteracts the effect of diluents to reduce SL. Clusius et al. [57] determined the ﬂammability limits for upward propagation in mixtures of hydrogen and oxygen and of deuterium and oxygen, and found that hydrogen was more ﬂammable than deuterium, and the enhanced ﬂammability was attributed to the preferential diffusion effect. 7.2.5 Laminar Flames: Premixed, Non-Premixed and Partially Premixed

Laminar hydrogen ﬂames have been extensively studied using a variety of conﬁgurations. Table 7.3 provides a summary of studies dealing with various hydrogen ﬂames. There are three canonical laminar ﬂame structures: premixed, nonpremixed, and partially premixed ﬂames. In the following, we discuss the characteristics of each. 7.2.6 Laminar Premixed Flames

Premixed ﬂames have been studied using a number of conﬁgurations, including Bunsen burner, outwardly and inwardly propagating spherical ﬂames, counterﬂow ﬂames, and stretch-free propagating ﬂames in a tube. These studies have focused on propagation characteristics including ﬂame speed, ﬂame structure, ﬂame/stretch interactions, and stability. Figure 7.5 presents the typical structure of a freely propagating laminar premixed H2–air ﬂame established at w ¼ 4.0. Across the reaction zone, the mole fractions of the reactants (H2 and O2) decrease and the temperature increases rapidly, reaching a maximum, which is close to the adiabatic ﬂame temperature (Tad). The intermediate species (i.e., H, O, OH, HO2, and H2O2) proﬁles peak within the reaction zone. Moreover, H and OH radical diffuse towards both reactants and products. For this fuel-rich condition, whereas O2 is completely consumed in the reaction zone, a signiﬁcant amount of H2 escapes the ﬂame, which can be attributed to its high diffusivity and oxidation chemistry. For the corresponding fuel-rich hydrocarbon ﬂames, most of the fuel is consumed. For instance, a methane–air ﬂame consists of three overlapping layers: (1) chemically inert preheat zone, (2) fuel consumption layer, and (3) oxidation layer. In the fuel consumption layer, CH4 is mostly consumed to form H2 and CO, and these species are then transported to the oxidation layer, and converted to products (H2O and CO2) [44, 58, 74]. Another notable difference is that H2O is the only major product of combustion for H2 ﬂames compared with H2O, CO2, CO, and UHC for hydrocarbon ﬂames. This provides a major advantage for hydrogen in terms of emissions of greenhouse gases.

j179

OPS OPS CF

CF

OPS CF CF

CF Jet FP, PSR Jet Jet CF CF Jet

Flame speed Flame speed Ignition

Ignition

Extinction Extinction Emissions

Emissions Emissions Chemical kinetics Edge ﬂame Edge ﬂame Partial premixing Partial premixing Partial premixing

PF NPF NPF PPF NPF NPF PF PPF PPF PPF PPF PF, PPF

PF

PF PF NPF

Flameb)

Num. Num. Num. Num. Num. Num. Num. Num.

Exp.–Num. Exp.–Num. Exp.–Num.

Exp.–Num.

Exp.–Num. Exp. Exp.– Num.

Methodc)

Effect of pressure on extinction and NO formation NO formation under radiation and chemistry–turbulence interactions Mechanism reduction Flame structure Effect of strain on ﬂame propagation Flame structure Effect of pressure on ﬂame structure Flame structure

Flame–stretch interactions Laminar ﬂame speed and Markstein length Inhomogeneous ignition temperatures at various weighted strain rates and pressures Inhomogeneous ignition temperatures at various weighted strain rates and pressures Effect of inert diluents on Markstein numbers and ﬂame speeds Extinction strain rates Effect of inert diluents on NOx

Investigation

a) Conﬁgurations: CF ¼ counterﬂow; FP ¼ freely propagating; OPS ¼ outwardly propagating spherical. b) Flames: PF ¼ premixed; NPF ¼ non-premixed; PPF ¼ partially premixed. c) Exp. ¼ experimental; Num. ¼ numerical.

Configurationa)

Studies on laminar H2 flames.

Topic

Table 7.3

[67] [68] [69] [70] [71] [72] [34] [73]

[56] [65] [66]

[64]

[58–60] [61] [62, 63]

Ref.

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7.2 Theory and Applications in Research

Figure 7.5 Temperature and species profiles for an unstretched freely-propagating laminar premixed H2–air flame established at w ¼ 4.0 [58].

7.2.6.1 Laminar Flame Speed Laminar ﬂame speed represents another fundamental property of a given fuel–air mixture. It is of critical importance with regard to ﬂame spread, stabilization, and ﬂashback in practical systems. For instance, in IGCC premixed burners, the problem of ﬂashback and combustion instability represents a major challenge to the designer, especially due to wide variations in fuel composition. Similarly, it is an important property for designing and optimizing hydrogen-powered internal combustion engines, where backﬁre and inadequate mixing time due to rapid ﬂame propagation are important issues. It is also an important parameter for characterizing premixed turbulent ﬂames, and commonly used for assessing the accuracy of reaction mechanisms. Faeth and co-workers [75–77] and Law and co-workers [78, 79] performed extensive studies on laminar H2–O2–diluent premixed ﬂames at different pressures. Using spherical outwardly/inwardly propagating ﬂames, and counterﬂow stationary ﬂames, they obtained stretched and unstretched ﬂame speeds for a range of equivalence ratios and pressures. They also examined the effects of fuel/air composition and pressure on ﬂame–stretch interactions and diffusive–thermal instability, characterized in terms of the Markstein number. Consequently, the effects of equivalence ratio, pressure, and various diluents on the laminar ﬂame speed and diffusive–thermal instability of H2 premixed ﬂames are fairly well characterized. The effects of w and p on the laminar ﬂame speed are depicted in Figures 7.6 and 7.7. Figure 7.6 presents a comparison of predicted and measured ﬂame speeds versus w at different pressures, and Figure 7.7 presents the computed and measured ﬂame speeds versus pressure at two different w values. The comparison includes the measurements of Aung et al. [58, 59], Kwon and Faeth [60] and Tse et al. [79], while the predictions are based on the Mueller et al. [80] and GRI-Mech 3.0 [42] mechanisms. As indicated in Figures 7.6 and 7.7, the Mueller et al. [37] mechanism is able

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350

(a)

1 atm

300

Flame speed (cm s-1)

x x x

x

x

250

x

x

x

x

200

x

x 150 Aung et al.

x

Kwon and Faeth

100

x

Tse et al.

x

Mueller

50

GRI-Mech 3.0 0 1

2

3

4

f 350

(b)

5 atm

300

Flame speed (cm s-1)

x

x

x

250

x x

x 200

x 150

15 atm

x 100

x

x

x Tse et al. Mueller GRI-Mech 3.0

x x

x

x

50 x

x 0

0.5

1

1.5

2

2.5

3

3.5

4

f Figure 7.6 Measured and predicted unstretched laminar flame speeds as a function of w for (a) H2–air flames at 1 atm and (b) H2–O2–He flames at 5 atm, where O2/ (O2 þ He) ¼ 0.125, and at 15 atm, where O2/

(O2 þ He) ¼ 0.080. Measurements are from Aung et al. [58, 59], Kwon and Faeth [60] and Tse et al. [79]. Predictions are based on Mueller et al. [37] and GRI-Mech 3.0 mechanisms.

7.2 Theory and Applications in Research

300 f = 3.0

(a)

Flame speed (cm s-1)

250

200

Aung et al.

150

Mueller GRI-Mech 3.0 100

0

1

2 3 Pressure (atm)

4

150 (b)

f = 2.0

Flame speed (cm s-1)

125

100

x x

75

x 50

x 25

0

10

12

Tse et al. Mueller GRI-Mech 3.0 14 16 Pressure (atm)

Figure 7.7 Measured and predicted unstretched laminar flame speeds as a function of pressure for (a) H2–air flames at w ¼ 3, and (b) H2–O2–He at w ¼ 2, where O2/

18

20

(O2 þ He) ¼ 0.080. Measurements are from Aung et al. [58, 59] and Tse et al. [79]. Predictions are based on Mueller et al. [37] and GRI-Mech 3.0 mechanisms.

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to reproduce qualitatively the trend of the experimental data better than GRI-Mech 3.0, which overestimates ﬂame speeds at low pressure and underestimates at higher pressures, and becomes increasingly less accurate at higher pressures. More extensive validation of reaction mechanisms using unstretched ﬂame speed data has been reported by Str€ohle and Myhrvold [69], and additional studies on premixed ﬂames are listed in Table 7.2. Figure 7.7 further indicates that the ﬂame speed generally decreases with increase in pressure for any given w. The ﬂame speed is proportional to the square root of the p product of diffusivity and chemical reaction [i.e., SL (D vi)], with D being inversely proportional to pressure. The variation of ﬂame speed with pressure can be expressed as SL p(n 2)/2, where n is the overall reaction order, which serves as an indication of the pressure effect on the ﬂame speed and mass burning rate. It is related to the reaction limits discussed earlier through the equation n(Tad) ¼ 2 q ln _ ln(p), where m _ is the mass burning rate and Tad the adiabatic ﬂame temperature. (m/q For H2–air ﬂames, n ﬁrst decreases with pressure until it reaches a minimum and then it increases. The initial decrease is due to the competition between Reactions 7.14 and 7.17, while the subsequent increase is due to the fact that the HO2 generates new radicals through Reactions 7.20 and 7.21. Moreover, it is found that n > 2 for w ¼ 1–3 and p < 1.0 atm, whereas n < 2 for all w and p > 1.0 atm [44]. Because n > 2 for w 3 and p < 1.0 atm, the ﬂame speed in Figure 7.7a does not decrease monotonically with pressure. On the other hand, n < 2 for w ¼ 2.0 and p > 1.0 atm, and the ﬂame speed in Figure 7.7b decreases monotonically with pressure. In addition, as pressure is increased, the w value corresponding to the peak ﬂame speed decreases; for example, w 1.5 and 1.4 for p ¼ 5 and 15 atm, respectively (cf. Figure 7.6). This sufﬁciently off-stoichiometric rich SL peak is a consequence of the dependence of SL on the combined effect of adiabatic ﬂame temperature (Tad) p and Le. Note that although the thermal theory of Mallard and Le Chatelier, which p only considers the energy equation, shows that SL is proportional to (avi), the comprehensive theory of Zeldovich, Frank-Kamenetsky and Semenov that includes both the energy and species conservation equations with Le „ 1 shows the depenp dence of SL on (Levi) [30]. The adiabatic ﬂame temperature (Tad), which depends on the equivalence ratio (w), exerts a dominant inﬂuence on SL through the p Arrhenius kinetics (i.e., vi). For atmospheric hydrogen–air ﬂames, Tad peaks at w ¼ 1.07. For hydrocarbon ﬂames, the equivalence ratio (w) at which Tad peak deﬁnes p closely the w value at which SL peaks because Le does not vary substantially from Le ¼ 1.0 with w. For hydrogen–air ﬂames, Le ¼ 0.33 and 2.3 at fuel-lean and fuel-rich limits, respectively. Hence the effect of Le is to reduce SL in the lean side and increase it on the rich side. This is the reason why SL peaks at off-stoichiometric conditions for atmospheric hydrogen–air ﬂames. Moreover, SL peaks at nearly the same offstoichiometric condition as p is increased. This is an important observation since most practical combustors operate at high pressures, and ﬂashback and ﬂame stabilization are important considerations in these systems, such as H2-fueled spark ignition engines at high loads.

7.2 Theory and Applications in Research

7.2.6.2 Flame–Stretch Interactions Flame–stretch interactions due to preferential diffusion of heat and mass can strongly affect the ﬂame speed and stability characteristics. For outwardly propagating spherical premixed ﬂames, there are three types of ﬂow instabilities, viz., preferential diffusion, hydrodynamic, and buoyant instability [81]. Preferential diffusion instability is manifested by small cellular formation, which occurs when the mixture exhibits a Markstein number (Ma) that becomes negative. Fortunately, these cellular structures are not evident at small radii, allowing stretched ﬂame speed (SL) measurements. Hydrodynamic instability is related to the Reynolds number (Re) and identiﬁed by more regular cellular structures on the ﬂame surface, and is typically observed at relatively large ﬂame diameters (e.g., >60 mm) [81]. This stability is promoted at high pressures because of an increase in Re with pressure. Finally, buoyant instability is manifested by distortion of the spherical shape and also upward motion of the centroid. In the study of ﬂame–stretch interactions, the measurements of SL need to be obtained in the absence of the above ﬂame instabilities. The effect of stretch on ﬂame speed is expressed as SL /SL ¼ 1 þ MaKa [82], where Ka ¼ kdD/SL is the Karlovitz number, which represents the dimensionless stretch rate. This expression holds true for a ﬂame thickness to ﬂame radius ratio (dD/rf ) of <0.02, so that the effects of ﬂame thickness (dD), curvature, and unsteadiness on SL can be neglected. Figure 7.8 illustrates the relationship between SL /SL and Ka for various equivalence ratios. Note the slope of each plot, which is Ma, is independent of Ka. The maximum Ka for the ﬂames presented in this ﬁgure does not exceed 0.5. If quenching effects were signiﬁcant, that is, Ka 1.0, the response of SL would be more complex [83]. Both measurements and predictions seem to support the linear variation of SL/SL with Ka to yield a constant Ma for a given w. As w is increased from 0.6 to 4.5, the ﬂame transitions from unstable (Ma < 0) to stable (Ma > 0) preferential diffusion condition. For a positive stretch ﬂame, its convex nature towards the fresh mixture defocuses heat, while focusing the deﬁcient reactant. Thus, for Le > 1.0 the defocusing effect dominates, leading to a negative correlation between ﬂame speed (SL) and stretch (k) (i.e., Ma > 0), whereas for Le < 1.0 the focusing effect dominates leading to positive correlation between SL and k?(i.e., Ma < 0). This is consistent with the variation of Le with w? from Le < 1.0 at fuel lean conditions to Le > 1.0 at fuel rich conditions, as discussed in the previous section. The properties of preferential diffusion–stretch interactions are seen more concisely from measured and predicted Markstein numbers (Ma) presented in Figure 7.9. In contrast to hydrocarbon premixed ﬂames [84], pressure does not have a large effect on Ma. In fact, the effect of H2 addition to hydrocarbon fuels is to reduce the sensitivity of SL to Ka, that is, to reduce Ma, indicating a tendency towards preferential diffusion instabilities) [32, 85, 86]. Despite the small change in Ma with pressure, the ﬂames at fuel-lean conditions tend to become preferential diffusion unstable with increasing pressure. Flame–stretch interactions have also been investigated in counterﬂow twin hydrogen premixed ﬂames (Ka > 0) [44, 78] and inwardly propagating spherical premixed ﬂames (Ka < 0) [87, 88]. Recently, the relationship between ﬂame speed

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Figure 7.8 Measured and predicted laminar burning velocities as a function of Karlovitz number and equivalence ratio for outwardly spherical hydrogen–air premixed flames atmospheric pressure [60].

and stretch was extended to propagating partially premixed ﬂames (i.e., triple ﬂame) (Ka > 0) [32, 86], and the ﬂame speed response to stretch was found to be consistent with the analyses presented in this section. The study of ﬂame–stretch interactions is relevant for many practical applications. For instance, in spark ignition engines the propagating ﬂame that develops following ignition is subjected to stretch effects, which in combination with preferential diffusion could exhibit cellular structures. These ﬂame patterns, which represent alternating regions of intensiﬁed and weakened burning, cause local extinction. For example, Law and Sung [88] reported a rich propane–air Bunsen burner ﬂame with surfaces of cellular structures separated by extinguished regions of ridges. This phenomenon has also been observed for lean hydrogen–air Bunsen burner ﬂames. Moreover, stretch effects and ﬂame propagation are essential elements of the ignition process [89]. As discussed in Section 2.4, coupled stretch–preferential diffusion effects also modify the ﬂammability limits. Moreover, ﬂame–stretch interactions are

7.2 Theory and Applications in Research

Figure 7.9 Measured and predicted Markstein numbers (Ma) as a function of pressure for outwardly propagating spherical H2–O2–inert gas premixed flames [60].

also used for obtaining the unstretched laminar ﬂame speed (SL ) of mixtures needed for the validation of chemical mechanisms and to develop phenomenological theories of combustion processes. 7.2.6.3 Reaction Limits Reaction limits are analogous to explosion and ignition limits, except that this term is used in the context of freely propagating premixed ﬂames. These limits also represent combustion regimes dominated by chain-branching reactions and chain-terminating reactions, as illustrated in Figure 7.10, in terms of the mass burning rate plotted versus pressure at different w. In the ﬁrst limit (I), the mass burning rate increases with pressure until a local maximum (mmax) is reached. Then it decreases with

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0.2

Mass burning rate (g cm-2 s-1)

f = 5.00 0.15

Mueller 0.1

f = 5.00

GRI-Mech 3.0

f = 6.00 0.05 I

II f = 6.00 III f = 7.00

0

0

5

10 15 Pressure (atm)

20

Figure 7.10 Mass burning rate as a function of pressure for w ¼ 5.0, 6.0, and 7.0 [34]. Calculations were performed with Mueller et al. [37] and GRI-Mech. 3.0 mechanisms.

pressure, as chain-terminating Reaction 7.17 dominates compared with chainbranching Reaction 7.14, until a local minimum (mmin) is reached, indicating the end of the second reaction limit (II). The mass burning rate then increases with pressure, indicating the third reaction limit (III). Moreover, the locations of both the local maximum (mmax) and minimum mass burning rates (mmin) shift towards higher pressures as w decreases. This is due to an increase in adiabatic ﬂame temperature, which enhances the temperature-sensitive, chain-branching reactions (Reactions 7.14–7.16), compared with chain-terminating reaction (Reaction 7.17). Therefore, higher pressures are required for Reaction 7.17 to become dominant [34, 90]. Hence the mechanism characterizing these reaction limits is similar to that associated with the explosion and ignition limits. It is also important to note that although the two mechanisms are able to predict the reaction limits, there are signiﬁcant differences in terms of mass burning rate and locations of the turning points. Therefore, a reliable reaction mechanism should correctly reproduce the dependence of mass burning rate on pressure. Hence future studies should focus on providing an experimental database for the reaction limits for the validation of chemical kinetic models. In addition, more realistic conﬁgurations should be considered for analyzing the ignition and reaction limits. 7.2.6.4 Non-Premixed Flames Laminar non-premixed H2–air and H2–O2 ﬂames have been extensively studied using counterﬂow and co-ﬂow conﬁgurations. Figure 7.11 presents the structure of a counterﬂow H2–air non-premixed ﬂame in terms of temperature and major species

7.2 Theory and Applications in Research

1

2500 a s = 100 s-1

H2

N2

Mole fraction

0.8

2000

0.6

1500

0.4

1000 H2O O2

0.2

0

0

0.2

0.4

0.6 0.8 Distance(cm)

1

Temperature(K)

Temp.

500

1.2

0

Figure 7.11 Measured [91] and predicted temperature and species mole fraction profiles for an H2–air non-premixed flame at as ¼ 100 s1 and 1 atm. Predictions are based on the Mueller et al. mechanism [37].

proﬁles. The temperature increases from the fuel side until it reaches maximum on the air side. Due to its high diffusivity, hydrogen is able to penetrate the stagnation plane, and the ﬂame is established on the air side. In contrast to premixed ﬂames, H2 and O2 are completely consumed in the reaction zone, producing H2O. Candel and co-workers [92] investigated H2–O2 non-premixed ﬂames over a wide range of conditions ranging from subcritical to supercritical. They obtained the following correlations for the ﬂame thickness (dD) and heat release rate (q) with p p pressure: dD 1/ (pas) and q (pas). The strain rate at the extinction limit exhibits a quasi-linear dependence on p. Pellet et al. [65] reported extensive data on the extinction strain rates of counterﬂow nitrogen-diluted hydrogen non-premixed ﬂames for various conditions, such as nozzle diameter, distance between the nozzle, and nitrogen dilution from 0 to 86% (by volume). Furthermore, as illustrated in Figure 7.12, Sohn et al. [67] showed that at low pressures the extinction strain rate (aE) increases with pressure, whereas it decreases at moderate pressures and increases again at high pressures. The ﬁrst increase in aE is because the ﬂame temperature (Tad) initially increases with p. This in turn enhances the chainbranching reactions (Reactions 7.14–7.16) in comparison with the chain-terminating reaction (Reaction 7.17). Moreover, aE pn1 [93, 94] and therefore the results in Figure 7.12 are consistent with theory since n > 2 for p < 1.0 atm and n < 2 for p > 1.0 atm (cf. Section 7.2.6.1). Further increase in pressure enhances Reaction 7.17 and the ﬂame becomes weaker, leading to reduced aE. At even high pressures additional chain-propagating reactions (Reactions 7.20 and 7.21) become important and the ﬂame is strengthened again, and, therefore, increasing aE. The effect of N2

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Figure 7.12 Extinction strain rates (aE) as a function of pressure for 12%H2-88%N2 and 14%H286%N2 (by volume) premixed flames [67].

dilution is to enhance Reaction 7.17 with respect to Reactions 7.14–7.16 and therefore N2 reduces aE and shifts the maximum and minimum aE peaks to lower p. From the environmental standpoint, hydrogen is nearly a clean fuel, whereas fossil fuels such as gasoline, diesel, and coal all produce airborne pollutants, such as soot, CO2, CO, UHC, and NOx. All of these pollutants contribute to smog and also play a major part in acid rain and global warming. However, owing to its high adiabatic ﬂame temperature, hydrogen combustion produces signiﬁcant NOx. Therefore, by increasing the strain rate (as) the ﬂame temperature is lowered and NOx emissions are reduced. Pressure is also a controlling parameter for NOx formation. There are two mechanisms associated with NOx formation in hydrogen ﬂames: Zeldovich (Reactions 7.28–7.30) and NO formation through intermediate NO2 (Reactions 7.31 and 7.32): N2 þ O ! NO þ N

ð7:28Þ

O2 þ N ! NO þ O

ð7:29Þ

N þ OH ! NO þ H

ð7:30Þ

NO þ HO2 ! NO2 þ OH

ð7:31Þ

NO2 þ H ! NO þ OH

ð7:32Þ

The Zeldovich or thermal mechanism is favored at high temperatures because it requires the breaking of the tight N2 bond. Consequently, the Zeldovich mechanism is dominant at low pressures, whereas NO formation via intermediate NO2 becomes important at moderate pressures due to Reaction 7.17. At moderate pressures, the ﬂame temperature decreases for a given as due to enhanced recombination Reaction 7.17 and, consequently, the maximum NO formation decreases. At higher pressures, the net effect of Reactions 7.31 and 7.32 is to H þ HO2 ! 2OH, through which

7.2 Theory and Applications in Research

Figure 7.13 Maximum mole fraction of NO as a function of pressure for as ¼ 0.8 s1 [67].

radicals H, OH, and O are produced [67]. This enhances the formation of thermal NO. The resulting variation of the maximum NO mole fraction with p is shown in Figure 7.13. NO is found to be dominant over NO2 at least for the conditions presented in this ﬁgure. H2–air non-premixed jet ﬂames have also been investigated computationally and experimentally. Toro et al. [95] found that radiative losses are negligible in laminar H2–air axisymmetric jet ﬂames and that variations in jet velocity lead to only modest changes in the axial proﬁles of major species and temperature. Moreover, Lee and Chung [96] showed that in comparison with hydrocarbon ﬂames, such as propane and n-butane ﬂames, hydrogen ﬂames remains attached to the burner even up to jet velocities comparable to the speed of sound, which is 1320 m s1 for hydrogen. The interactions of non-premixed H2–air ﬂames with vortices have also been studied [97, 98]. 7.2.6.5 Partially Premixed Flames (PPFs) Partially premixed ﬂames (PPFs) contain multiple (e.g., two or more) reaction zones, and their structure is determined by interactions between the reaction zones. PPFs occur widely in practical combustion systems either by design or under conditions arising due to various phenomena, such as poor mixing, spray vaporization [99], ﬂame lift-off [100], and local extinction followed by reignition in turbulent ﬂames [101]. For instance, the combustion processes in diesel [100] and sparkignition [102] engines are dominated by two-stage or partially premixed combustion. Figure 7.14 presents the computed structure of a typical H2–air PPF containing two reaction zones, established in a counterﬂow conﬁgurations at w ¼ 3.0, p ¼ 1 atm, and a global strain rate as ¼ 600 s1. The range of double-ﬂame regime for atmospheric H2–air PPFs is approximately 3.0 < w < 6.0 and 500 < as < 2400. Thus, H2–air PPFs are established at relatively high strain rates and a wide range of premixedness compared with hydrocarbon PPFs. This is due to high laminar ﬂame speeds and wide ﬂammability limits of H2–air mixtures compared with those of hydrocarbon–air

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1200

(a)

Velocity Temp.

Mole fraction

1000

2000

0.8

800 1600

0.6

Stagnation line

600 1200

H2

0.4

O2

H2O

0.2

400 200

800

Temperature (K)

1

0 H 0

0

0.2

0.016

0.4

400 0.6 0.8 Distance (cm)

1

-200

1.2

(b)

Mole fraction

0.012 O 0.008

0.004 OH 0

-1 Heat release (erg cm-3 s )

1.2E+11

0.2

0.4

0.6 0.8 Distance (cm)

1

1.2

0.08

(c) Heat release H2O ROP

0.06

8E+10 0.04 4E+10 0.02

0 0

0.2

0.4

0.6 0.8 Distance (cm)

Figure 7.14 Profiles of temperature, axial velocity, species mole fractions, heat release rate, and H2O rate of production (ROP) plotted versus distance from the fuel nozzle for an

1

1.2

H2O ROP (mole/cm-3 s-1)

0

0

H2–air PPF at w ¼ 3.0 and as ¼ 600 s1. Species H2, O2, H2O, and H are plotted in (a) and OH and O in (b). The stagnation plane is indicated by the dashed line.

7.2 Theory and Applications in Research

mixtures. For the ﬂame depicted in Figure 7.14, the fuel and air stream velocities were 266.9 and 190.5 cm s1, respectively. The fuel stream velocity was chosen such that it was above the laminar ﬂame speed (220 cm s1) at w ¼ 3.0. The ﬂame in Figure 7.14 contains a rich premixed zone (RPZ) on the fuel side and a non-premixed zone (NPZ) on the oxidizer side. The RPZ is located where the local velocity matches the stretched laminar ﬂame speed at a given w. Note that the axial velocity (cf., Figure 7.14a) decreases to a local minimum, which represents the stretched laminar ﬂame speed just ahead of the RPZ, and then increases in the RPZ due to thermal expansion. The RPZ has a thickness of about 0.11 cm and a peak temperature of about 1500 K, which is lower than that of the corresponding unstretched premixed ﬂame at w ¼ 3.0. This is attributed to the fact that the ﬂame is positively stretched and that Le 2.0 in the premixed zone [78]. The adiabatic temperature of a premixed ﬂame with Le above (below) unity is reduced (increased) when a ﬂame is positively stretched [103]. The location of the NPZ is deﬁned by the maximum temperature, which is just downstream of the stagnation plane. Based on the heat release rate proﬁle shown in Figure 7.14c, the NPZ thickness is about 0.16 cm. The premixed zone is characterized by the partial consumption of H2, production of H radicals and H2O, and complete consumption of O2. Previous investigations of hydrocarbon PPFs [104–106] have shown that the fuel is completely consumed in the RPZ to provide intermediate fuel species such as CO and H2 (and C2H2 in some cases), which are then transported to and consumed in the NPZ. In contrast, for H2–air PPFs, the fuel is only partially consumed in the RPZ, with the remainder being transported to and consumed in the NPZ. Another difference between the H2 and hydrocarbon PPFs pertains to the chemical activity in the region between the two reaction zones. For hydrocarbon–air PPFs, this region has negligible chemical activity. However, for H2–air PPFs, there is chemical activity in this region, characterized by the production of H2 through the recombination of H radicals, indicated by the increasing H2 and decreasing H mole fractions (cf., Figure 7.14a). The NPZ is characterized by the consumption of the remaining hydrogen, and the peak values of the temperature and H2O mole fraction. The peak temperature is about 2150 K, which is lower than the adiabatic ﬂame temperature of 2350 K. This is due to the signiﬁcantly broadened reaction zone compared with that of a nonpremixed ﬂame, and heat transfer to the RPZ. In both the reaction zones, the local temperature peak coincides with the corresponding peaks in H2O and H mole fractions, and where the consumption of O2 occurs. This indicates that the heat release is associated with H2O formation, as illustrated in Figure 7.14c. In addition, a rate of production analysis [72] indicated that Reactions 7.17 and 7.16 are the dominant exothermic reactions, and Reaction 7.16 is the dominant reaction that produces H2O. The maximum heat release occurs in the RPZ. In both reaction zones [34, 72], consumption of reactants occurs primarily through Reactions 7.14–7.17. Rortveit et al. [66] investigated the effect of dilution on NOx emissions, and observed higher NO emissions from non-premixed ﬂames compared with those from partially premixed ﬂames for the same amount of N2 dilution. Briones et al. [34] studied the effect of pressure on the structure of hydrogen–air partially premixed

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ﬂames, and observed that as the pressure increased, it reduced the physical separation between the two reaction zones. This was attributed to the effects of pressure on (i) ﬂame speed associated with the rich premixed zone, which moved this zone downstream, and (ii) mass diffusivity, which moved the non-premixed zone upstream (towards the fuel nozzle). At higher pressures, however, these effects are signiﬁcantly reduced, and the ﬂame maintains its twin-like ﬂame structure even at very high pressures. Although PPFs are relevant to numerous combustion systems in propulsion and power generation, they have not been as extensively studied in other conﬁgurations. Katta and Roquemore [73] investigated the effect of equivalence ratio (w) and preferential diffusion on the structure of axisymmetric H2–air ﬂames. Under fuel-lean and stoichiometric conditions, the ﬂame exhibited a single ﬂame structure, whereas under fuel-rich conditions it exhibited a PPF structure, containing an inner RPZ and an outer NPZ. The NO is primarily formed in the hot regions for w < 1 and in the NPZ for w > 1. In addition, the tip opening was observed, which was due to local extinction caused by the preferential diffusion effect. For this convex ﬂame towards the unburned mixture the focusing effect dominates for w > 1 (Le > 1) and burning is intensiﬁed because the incoming ﬂow is preheated. In contrast, the defocusing effect dominates for w < 1 (Le < 1) causing extinction to occur at the ﬂame tip which is subjected to the highest stretch. Future studies should focus on high-pressure PPFs, including triple ﬂames, under more realistic conditions. Such ﬂames are important in the characterization of lifted ﬂames in jets, ﬂame propagation in mixing layers, and autoignition fronts. 7.2.7 Turbulent Ignition and Combustion

Although laminar ﬂame studies provide valuable fundamental and practical information, it is important to investigate turbulent ignition and combustion characteristics because of their direct relevance to most combustion systems. Due to space limitation, only a brief overview of this topic is provided here. Mastorakos [107] recently presented an extensive review of studies on turbulent ignition and subsequent ﬂame establishment. Law and co-workers [108, 109] extended their laminar counterﬂow experiments to examine ignition in turbulent ﬂows, and observed that the ignition temperature has a non-monotonic dependence on turbulence intensity. It decreases compared with the laminar value for low-velocity ﬂuctuations, due to enhanced mixing by turbulent eddies that are similar in size to the ignition kernel, but eventually increases for high-velocity ﬂuctuations. Investigations on unsteady laminar ﬂows examined ignition in unsteady strained mixing layers [110, 111] and in mixing layers containing vortical structures [112, 113], and provided insights into turbulent ignition behavior. For example, their results indicated that the ignition location is characterized by a low scalar dissipation rate. Similar results were reported in several experimental and computational studies on turbulent ignition [114–117], which considered autoignition of hydrogen jets in co-ﬂowing heated air. It was observed that ignition occurs at

7.2 Theory and Applications in Research

the most reactive mixture fraction jMR, and at sites along jMR with low scalar dissipation rate. Further, the mixture fraction jMR was generally found to be independent of the initial mixing layer thickness, and turbulent time and length scales. However, the scalar dissipation rates and their distribution, which affect the ignition delay time, were strongly inﬂuenced by the initial conditions, differential diffusion, and turbulence characteristics. Further details are provided in [107]. Several experimental and numerical investigations have also examined the growth of ﬂamelets or ignition kernels, following the appearance of ignition spots, and subsequent development and establishment of the ﬂame. A common conﬁguration in these studies has been a diluted or undiluted hydrogen jet in a co-ﬂow of heated air [107, 117]. Hydrogen jets in vitiated air have also been studied. A common observation in these studies is that the ﬂame development is initiated by the appearance of ignition kernels, and depending on the ﬂow conditions, the ﬂame may be established as a lifted ﬂame with an edge or triple ﬂame structure, or propagate upstream and become established as a burner-attached ﬂame, or may lead to ﬂashback. The computational studies employed various modeling approaches including transported scalar PDF [118], conditional moment closure [117], ﬂame surface density models [119], LES, and DNS [107]. Both reduced and detailed chemistry models have been used. Turbulent ignition and combustion will continue to be an area of active research, as many issues pertaining to ignition, ﬂame dynamics, and emissions are not adequately resolved at present. As discussed by Mastorakos [107], more work is needed to obtain a uniﬁed picture of various effects, such as differential diffusion and turbulent characteristics, on ignition processes. Whereas the autoignition characteristics in turbulent ﬂows have been extensively examined, future studies should focus on forced ignition in hydrogen jets, such as induced by a spark or laser. Future work should also focus on ﬂame dynamics, including their propagation and stabilization, and emissions under conditions relevant to practical combustors. 7.2.8 Detonation in H2–Air Mixtures

A deﬂagration wave propagating in a combustible mixture, which is contained in a tube, can accelerate and undergo transition to a detonation wave that travels at speeds 5–10 times the speed of sound in the original mixture. In the laboratory, a detonation can be produced in several ways, for instance, igniting a premixed combustible mixture at the closed end of a sufﬁciently long open tube, or by passing a sufﬁciently strong shock through a combustible mixture in a shock tube. A steady one-dimensional detonation can be described as a shock wave followed by a thin reaction zone. The shock raises the mixture temperature through compression heating, while heat release in the reaction zone sustains the shock propagation. However, the processes associated with the deﬂagration-to-detonation transition (DDT) and detonation structure are generally more complex and involve multi-dimensional, multi-scale phenomena [120].

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Studies on detonation are important from fundamental and practical considerations including propulsion (e.g., pulse detonation engines) and safety (i.e., explosions). The safety issue is particularly important in the context of hydrogen storage and handling [47]. Since hydrogen readily forms a combustible or explosive mixture, it is important to characterize conditions for ignition and detonation initiation and propagation in such mixtures. Detonations were ﬁrst discovered by Berthelot and Vielle [121] and Mallard and Le Chatelier [122] in 1881. Since then, the detonation phenomenon has been extensively studied, and a large body of literature on the experimental, theoretical, and computational analysis of detonations has been reported. Theoretical treatments including reviews of studies up to the 1980s can be found in Williams [123]. Subsequent work dealing with various aspects, including DDT, detonation structure, propagation, stability, dynamic characteristics, and pulse detonation engines, has been discussed in reviews by Lee [124], Roy et al. [125], Ciccarelli and Dorofeev [126], and Shepherd [120]. A comprehensive review on detonation is clearly beyond the scope of this chapter, hence only a brief overview is provided, focusing on detonation studies involving H2–air mixtures. Theoretical treatments include various steady one-dimensional analyses, such as the well-known Chapman–Jouget model that provides the detonation velocity and thermodynamic properties, and the ZND detonation model of Zeldovich, vonNeumann and Doring. However, as discussed in [120], the one-dimensional detonation structure is highly unstable and prone to result in transient, three-dimensional detonation with nonuniform reaction zone and cellular structures. Numerous experimental studies have been reported, using optical and laser-based methods and providing Schlieren images and also detailed information about these structures. These studies are discussed in [120]. Similarly, extensive numerical simulations have been performed for different conﬁgurations and fuels, including hydrogen [127–129]. A majority of these simulations are based on the solution of Euler (inviscid) equations in one or two spatial dimensions, using global (one-step) and reduced mechanisms for H2 oxidation. Some investigations have employed speciﬁc reduced mechanisms in order to examine the explosion limits and crossover effects, as discussed earlier, for hydrogen detonations. A relatively small number of investigations have also employed detailed mechanisms for H2–O2–Ar mixtures and performed simulations in one, two, and three dimensions. Oran and co-workers [128–130] reported comprehensive two- and three-dimensional simulations for such mixtures using Navier–Stokes equations. Many of these studies have been discussed in the review by Shepherd [120]. Oran and co-workers [128, 129] reported extensive investigations of detonation initiation, structure, and propagation in H2–air mixtures in channels without and with obstacles. Since obstructed channels provide a convenient environment for studying DDT and detonation propagation in a controlled manner, a number of computational and experimental studies [126] have employed this conﬁguration. As discussed by Oran and co-workers [129], this conﬁguration has become a model for the evaluation of fuel safety using laboratory experiments. Figure 7.15, taken from the cited study, illustrates the ﬂame development and DDT in a typical obstructed

7.2 Theory and Applications in Research

Figure 7.15 Flame acceleration and DDT in H2–air mixture in a channel with obstacles. HS, hot spots; F1, new flame; D1, D2, D3, D4, detonations. Times in milliseconds are shown in frame corners. Taken from [129].

channel conﬁguration. The stoichiometric H2–air mixture is ignited at the upper left corner (ﬁrst frame on the left), resulting in the expansion of the initial burned region that quickly spreads past the ﬁrst obstacle, and leads to the development of a ﬂame. As this ﬂame propagates past obstacles, it accelerates due to the enhanced burning rate caused by the increased ﬂame surface area. This strengthens the shocks that were initially produced by the ignition pulse, leading to DDT. The initial detonation is indicated by D1 in the frame at 0.815 ms. Further details of the mechanisms for ﬂame acceleration and the detonation characteristics are discussed in the cited study. In summary, capabilities now exist to perform three-dimensional simulations in practical conﬁgurations using detailed chemistry. Future computational and experimental studies should focus on such conﬁgurations. Shock tube data are also needed for the validations of chemistry models under thermodynamic conditions relevant to detonations in H2–air mixtures.

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7.3 Applications in Industry

This section discusses hydrogen combustion with respect to its applications in transportation and power generation. Perhaps the earliest application of hydrogen combustion was in the form of town gas, consisting of 50% H2 with the rest being CH4, CO2, and CO, which provided lighting and heating for Europe and America from the 1800s to the mid-1900s. Subsequently, H2 was extensively used for airships until the famous 1937 Hindenburg disaster at Lakehurst Naval Air Station, New Jersey, after its transatlantic ﬂight from Germany. Although there have been many theories regarding the start (ignition) and spread of this ﬁre (combustion), H2 was not found to be the cause. Moreover, two-thirds of the people on board survived, and most of the casualties were not due to ﬁre or smoke. Hydrogen-powered rockets have been in existence for more than half a century, as the National Aeronautics and Space Administration (NASA) in the United States has been using H2 for its liquid rocket engines, including Space Shuttle engines, and for fuel cells to generate electricity. While the history of H2 ICEs is relatively sparse, the basic idea has been in existence for over 200 years. In 1806, Francois Isaac De Rivaz, an inventor in Switzerland, built the ﬁrst ICE powered by a mixture of H2 and O2, although it was not a commercial success. In 1863, Etienne Lenoir built an automobile, named the Hippomobile, powered by a one-cylinder H2 ICE, which covered 9 miles in about 3 hours. He sold about 350–400 Hippomobiles. During the last several decades, there have been considerable research and development activities pertaining to H2 rocket engines, H2 ICEs, and H2 turbines. Although the research on H2 rocket engines is considerably more mature [131], signiﬁcant progress has also been reported on H2 ICEs. Gas turbine combustors using H2 are also being investigated [132–134], although relatively few fundamental studies have been reported. We provide here a brief overview of H2 combustion pertaining to these applications. 7.3.1 H2 Rocket Engines

Hydrogen combustion in rocket engines has been an active area of research for several decades [131]. In many engines, such as the Space Shuttle Main Engine and the Ariane 5 Vulcain engine, liquid oxygen (LOx) and vaporized hydrogen are injected coaxially through a large number of injectors. The multiphase reacting ﬂow processes associated with these cryogenic propellants are complex. However, recent experimental and computational studies have provided a good qualitative understanding of the phenomenon. For chamber pressures below the critical pressure of a given propellant, the liquid jet undergoes classical processes of atomization [135], droplet gasiﬁcation [136], mixing of reactants, and establishment of lifted spray ﬂames [137]. For chamber pressures above the critical pressure, which is often the case, liquid jets undergo a transcritical change of state [138] as the interfacial ﬂuid temperature increases above the saturation or critical temperature of the local mixture and supercritical phenomena associated with the atomization and combustion of cryogenic propellants become important. The atomization then involves

7.3 Applications in Industry

Figure 7.16 Image of the flame formed between LOx, injected from the central jet, and gaseous hydrogen (GH2 from the outer jet) in a typical cryogenic fuel injector [146]. A slice of the

average emission intensity is shown in color. The average oxygen jet position is shown in grayscale. The near-injector region is expanded on the left.

diffusion-dominated transport in the presence of large property gradients, and the establishment of a diffusion ﬂame [139], which is stabilized in the wake of the oxygen injector lip. Candel and co-workers [140–142] have reported extensive experimental and computational studies of the transcritical and supercritical phenomena. Yang and coworkers [137, 143] and Oefelein [144, 145] have developed theoretical–computational models for simulating the transcritical transport and combustion processes. These models include detailed representation of high-pressure effects, thermodynamic non-idealities, transport anomalies during the transcritical change of state, and both reduced and detailed chemistry models. Computations have been reported for laminar and turbulent ﬂames, using DNS and LES methodologies [144, 145] for the latter. Figure 7.16, taken from [146], presents an experimental image of a typical LOx–H2 combustion. The ﬂame is stabilized as an edge ﬂame (due to partial premixing of reactants) in an initially laminar region in the wake of the oxygen injector lip. Further downstream, it develops as a turbulent diffusion ﬂame close to the oxygen stream. This conﬁguration involving coaxially injected LOx–H2 under transcritical conditions has been investigated in several experimental [141, 142, 147] and computational studies [144]. Figure 7.17, taken from [144], shows the computed structure of a LOx–H2 ﬂame in terms of instantaneous (a) vorticity, (b) density, and (c) temperature ﬁelds in a similar conﬁguration. A turbulent diffusion ﬂame is established in the vicinity of the oxygen stream, indicated by OH contours in Figure 7.17a. A notable feature of such ﬂames is that signiﬁcant real gas and liquid behavior coexist locally in colder regions of the ﬂow characterized by steep gradients in properties (on the LOx side), with ideal gas and transport characteristics existing within the reaction and product zones. 7.3.2 H2 Gas Turbine Combustors

The use of H2 gas turbines for propulsion and/or stationary power generation is relatively less well developed compared with that for rocket and internal combustion engines. There were activities in the 1970s dealing with H2-powered aircraft engines [148, 149]. Due to concerns about global warming and the rapid depletion of conventional fuels, there is now renewed interest worldwide, as several countries

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Figure 7.17 Computed structure of a LOx–H2 flame in terms of instantaneous (a) vorticity, (b) density, and (c) temperature fields in a configuration similar to that shown in Figure 7.16. A turbulent diffusion flame is established in the vicinity of the oxygen stream, indicated by OH contours in (a).

and aircraft/engine manufacturers have active research and development programs on H2-powered engines and fuel cells. These include (i) H2-powered scramjet research vehicles, for example, X-43A-as part of NASAs Hyper-X program to develop air-breathing propulsion systems for hypersonic ﬂights, (ii) the European Unions H2-powered jet propulsion project, (iii) Boeings H2-based fuel cells to power small jet airplanes, and (iv) the US Department of Energy-supported Siemens–Westinghouse Advanced Turbine Systems Program to develop a fuel-ﬂexible (hydrogen or syngas) advanced gas turbine for Integrated Gasiﬁcation Combined Cycle (IGCC) [150]. There are, however, few details available on these programs.

7.3 Applications in Industry

Figure 7.18 Images for BMW-7 H2 engine operating at 2000 rpm and full load. (a) A homogeneous charge combustion; (b) stratified combustion. The difference is that the injection timing is very early in (a) whereas it is later in (b).

The spark timing is identical for each. The images portray OH light intensity, that is, higher light intensity means higher temperature, implying higher NOx.

There are numerous fundamental research activities on hydrogen combustion and emissions, as outlined in the preceding sections. However, relatively few studies have been reported that are speciﬁc to H2-powered gas turbine systems. Hence there are opportunities for signiﬁcant research focusing on ignition, ﬂame dynamics, stability, ﬂashback, and emissions under different ﬂow and pressure conditions relevant to H2-powered jet engines. There are also important practical storage and safety issues for using hydrogen in jet engines. For example, H2 has about 2.7 more chemical energy per unit mass than JP-8 fuel, but it contains about one-quarter of the chemical energy (when stored under high pressure) of JP-8, requiring a trade-off between larger volume and lower fuel weight. Even using liquid hydrogen will require twice the volume compared with that for JP-8. Then, the cost and safety issues associated with the manufacture and storage of cryogenic H2 will become important. Moreover, whereas jet fuel is stored in the wings, H2 has to be stored in the fuselage rather than in the wings due to high volatility and ﬂammability [132]. Other viable options for H2 gas turbines include relatively smaller systems for auxiliary power units, hybrid systems using both a fuel cell and a gas turbine [151], and using blends of H2 and other fuels. With regard to blends, the technology for using syngas in IGCC systems is highly developed, and numerous such systems are currently operational. 7.3.3 H2 Internal Combustion Engines

There has been signiﬁcant research pertaining to H2 ICEs and their viability using spark ignition (SI), direct injection (DI), and mixed modes is now well established. These engines are currently being used, although not on a commercial scale, to power

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buses, cars, trains, and other vehicles. Research prior to 1975 has been reviewed by Escher and Euckland [152], and Das [153] and White et al. [154] have provided reviews of more recent work. Due to its distinctive thermo-transport properties compared with hydrocarbons fuels, hydrogen offers certain advantages and disadvantages, and also opportunities for signiﬁcant further research. Some key properties under standard conditions are listed in Table 7.1. Properties that inﬂuence engine performance include low density, wide ﬂammability limits, low ignition energy, high autoignition temperature, and high burning velocity. The last two properties provide a high research octane number (RON) (>120 compared with 90–99 for gasoline), which means less tendency for knocking and ability to operate at higher compression ratios and thus higher efﬁciency compared with gasoline engines. The high RON also makes it more suitable for SI engines, although there have been studies on H2powered compression ignition (CI) and HCCI engines [155, 156]. The wide ﬂammability limits (0.1 < w < 7.1) provide excellent control and ﬂexibility in engine operation, and developing strategies for low NOx, high efﬁciency, and high power density. The low lean ﬂammability limit (wL ¼ 0.1) and low ignition energy enable H2 SI engines to operate under highly dilute conditions (w 0.5), providing ultra-low NOx emissions, and possibly unthrottled operation at low loads. Therefore, NOx is the major or perhaps only undesirable emission from H2 ICEs, with small amounts of CO2, CO, and HC (hydrocarbons) emitted due to the burning of oil [154]. However, since NOx is only formed through the thermal or Zeldovich mechanism [157, 158], its emission increases sharply at high load, when the engine is operating near w ¼ 1. The ability to operate at high load with port fuel injection (PFI) is further limited due to the low minimum ignition energy near stoichiometric conditions (cf., Table 7.1), which signiﬁcantly increases the occurrence of preignition [159] and backﬁre [160] events. This generally leads to a tradeoff between power output and NOx emissions,and effectively limits the engine operation to lean conditions (w 0.5), assuming that low NOx is the primary consideration. As a consequence, the power density is reduced considerably, unless strategies such as aftertreatment, turbocharging, intake-air pressure boosting [154], and exhaust gas recirculation [161], or advanced designs such as high-pressure direct injection [160] or multi-mode operation [162], are employed. Another disadvantage with PFI is low volumetric efﬁciency due to the low density of H2. Many of these problems with PFI can be mitigated by using on-board liquid H2 [26, 163], which provides additional beneﬁts due to the intakecharge cooling caused by cold H2. These include improved volumetric efﬁciency and power density, reduced probability of preignition, and low NOx emission, since the ﬂame temperature is reduced. Wallner et al. [26] performed extensive tests with the BMW-7 H2 engine with cryogenic on-board storage, and reported near-zero NOx emission and good fuel economy and power density. Figure 7.4, taken from [164], shows qualitatively the effect of injection timing on NOx for this engine operating at 2000 rpm and full load (S. Ciatti, personal communication, 2008). Using stratiﬁed combustion by retarding the SOI, the NOx is signiﬁcantly reduced. There are, however, important technical and cost issues associated with the use of on-board cryogenic H2, which will require signiﬁcant future effort [163]. With gaseous H2, the preferred option is to use direct injection, which can provide high power density and volumetric efﬁciency, and also better control of preignition

7.4 Outlook

events by controlling the start of injection. Moreover, with DI, the low lean ﬂammability limit offers many innovative strategies for developing engines with high efﬁciency and power density, without sacriﬁcing low NOx. For instance, the injection timing can be controlled so as to have an early injection that provides a lean homogeneous mixture (w 0.5) at low load, and a late injection that provides a stratiﬁed mixture with global w 1 at high load. Another option is a multi-injection strategy, that is, at low load to use one early injection to provide a lean homogeneous mixture, whereas at high load to use two injections, with the ﬁrst injecting early to provide sufﬁcient mixing time to have a lean homogeneous mixture, and the late second injection to have a stratiﬁed mixture (with global w 1) just prior to ignition [164, 165]. The multi-injection appears to be a better option, as it offers more ﬂexibility for different operating conditions. However, more comprehensive studies are needed to develop the optimum strategies. Clearly, H2 ICEs represent an active area of research, and several multi-mode and hybrid approaches are being explored to make such engines competitive with gasoline- and diesel-powered engines. For additional details on various options, the reader is referred to [154] and many studies cited therein. It is also important to note in this context that although there has been considerable research pertaining to H2 ICEs, it is still in its infancy compared with that on SI and diesel engines during the last several decades. This makes the outlook for H2 ICEs optimistic. While the long-term future of hydrogen may depend on the development of costeffective fuel cell technologies using H2 from renewable sources, the H2 ICEs are expected to be a more feasible option in the foreseeable future. Whereas the H2 ICEs are already here with a high potential for major improvements, technological breakthroughs for H2 FCs remain in the realm in possibility. Perhaps these breakthroughs will determine the long-term future of H2 ICEs. Considerations of cost effectiveness, development efforts required for H2-powered systems, and emissions may lead to a blended fuel (instead of pure H2) strategy, using a mixture of H2 and fossil fuel (natural gas, syngas gas, and others depending on the application), in the near- and mid-term future.

7.4 Outlook

As the global energy demand grows and GHG emissions come into focus, hydrogen is expected to play an important role towards building a sustainable energy future. One can imagine a scenario in which cars are powered by hydrogen and homes are heated by electricity generated from an IGCC plant (or equivalent) also powered by hydrogen. However, signiﬁcant technological and scientiﬁc advances will be required with respect to H2 production, storage, and utilization in order to make this a reality. With respect to production, the low-carbon economy will require that an increasingly greater amount of H2 comes from renewable energy sources, such as biomass, algae, and biofuels that are produced from non-food feedstock. In addition future efforts should focus on developing technologies for producing H2 from water using solar and nuclear energy.

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As discussed in the preceding sections, signiﬁcant advances have been made in both fundamental and applied research concerning hydrogen combustion and emissions. Consequently, extensive database and modeling capabilities have been developed, along with a good understanding of issues associated with the use of H2 for power generation and transportation. However, further research is needed to improve efﬁciencies of and reduce emissions from hydrogen-powered combustion systems in a cost-effective manner. Fundamental research in this regard should focus on ignition, extinction, ﬂame dynamics, including liftoff, stabilization, and blowout, and also emissions under conditions relevant to such systems. Concurrently, the applied research should develop strategies for improving engine efﬁciencies and reducing NOx emissions, and the reliability of hydrogen storage and delivery systems. Modeling capabilities should be further improved for the simulation of high-pressure laminar and turbulent ﬂames using accurate chemistry and transport models. For hydrogen-powered systems for transportation, options may include H2 ICEs, H2 FCs, hybrid systems, and H2 blended with other fuels. Similarly, for hydrogenbased power generation, options may include H2 turbines, H2 FCs, and blended fuels, that is, blending H2 with fossil and/or biofuels. Research and development efforts are needed to identify the optimum system (or systems) for different applications with respect to cost, efﬁciency, and emissions. In addition, cost-effective carbon capture and storage technologies are needed, along with a new class of robust catalysts in order to make the H2 FC option economically competitive. There are also important technical and economic issues associated with the use of on-board (cryogenic) H2, which need to be addressed As a ﬁnal note, it should be mentioned that an optimum solution, based on the considerations of cost, efﬁciency, and emissions, may lead to a blended fuel strategy. Such a strategy can exploit the advantages of both hydrogen and hydrocarbon fuels, while mitigating the disadvantages associated with each fuel, and allow the use of existing infrastructure. For instance, the technology for using syngas in IGCC systems is highly developed, and numerous such systems are currently operational. Moreover, many production processes, such as biomass gasiﬁcation and fuel reforming, produce syngas in an intermediate step. The blended fuel strategy also facilitates the use of locally available fuel sources.

7.5 Conclusion

This chapter has provided an overview of issues pertaining to hydrogen production, storage, and utilization in the context of a sustainable energy future. Fundamental studies dealing with hydrogen ignition, extinction, combustion, and emissions in laminar and turbulent ﬂows have been reviewed. Premixed, non-premixed, and partially premixed ﬂames in counterﬂow and co-ﬂow conﬁgurations have been considered, and various fundamental properties, including ﬂammability limits, laminar ﬂame speed, ﬂame structure, NOx emission, ﬂame–stretch interactions, and ﬂame stability, have been discussed. High-pressure phenomena such as explo-

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of premixed hydrogen/air by heated counterﬂow under reduced and elevated pressures. Combustion and Flame, 136, 168. Pellet, G.L., Isaac, K.M., Humphreys, W.M. Jr., Gartrell, L.R., Roberts, W.L., Dancey, C.L., and Northam, G.B. (1998) Velocity and thermal structure, and strain-induced extinction of 14 and 100% hydrogen–air counterﬂow diffusion ﬂames. Combustion and Flame, 112, 575–592. Rortveit, G.J., Hustad, J.E., Li, S.-C., and Williams, F.A. (2002) Effects of diluents on NOx formation in hydrogen counterﬂow ﬂames. Combustion and Flame, 130, 48–61. Sohn, C.H. and Chung, S.H. (2000) Effect of pressure on the extinction, acoustic pressure response, and NO formation in diluted hydrogen–air diffusion ﬂames. Combustion and Flame, 121, 288–300. Barlow, R.S., Smith, N.S.A., Chen, J.-Y., and Bilger, R.W. (1999) Nitric oxide formation in dilute hydrogen jet ﬂames: isolation of the effects of radiation and turbulence-chemistry submodels. Combustion and Flame, 117, 4–31. Str€ ohle, J. and Myhrvold, T. (2006) Reduction of a detailed reaction mechanism for hydrogen combustion under gas turbine conditions. Combustion and Flame, 144, 545–557. Im, H.G. and Chen, J.H. (1999) Structure and propagation of triple ﬂames in partially premixed hydrogen–air mixtures. Combustion and Flame, 119, 436–454. Im, H.G. and Chen, J.H. (2001) Effects of ﬂow strain on triple ﬂame propagation. Combustion and Flame, 126, 1384–1392. Briones, A.M. and Aggarwal, S.K. (2005) A numerical study of H2–air partially premixed ﬂames. International Journal of Hydrogen Energy, 30, 327. Katta, V.R. and Roquemore, W.M. (1995) Numerical studies on the structure of two-dimensional H2/air premixed jet ﬂames. Combustion and Flame, 102, 21–40.

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8 Combustion in Gas-fueled Compression Ignition Engines of the Dual Fuel Type Ghazi A. Karim 8.1 Introduction

The conventional internal combustion engine or clones of its mode of working will be with us for some time to come. However, the demand for higher output efﬁciencies, greater mean effective pressures, increased reliability, and ever-reduced emissions will continue to rise in intensity at the same time. Also, there is a perceived increased need, whether for environmental, economic, or resource reasons, to operate on a multitude of natural and processed gaseous fuels and their mixtures. Of course, much research and development effort is being expended worldwide to provide solutions and make incremental progress toward achieving these goals. It is evident that the main effort needed is via better control of the relatively complex physical and complex chemical processes of combustion. Much of the remedial technologies that have been successfully developed in recent years, which include catalytic conversion of exhaust gases and exhaust gas recirculation, were the product of empiricism of modifying the chemical reactions of ﬂame propagation and of processing the exhaust gases chemically before discharging them into the atmosphere. Suitable controls of the physical aspects of the combustion process in engines, such as through improvements to the ﬂow patterns, mixing, and heat transfer have also contributed to the success of the internal combustion engine in recent years. However, the projected rate of progress in these areas remains largely insufﬁcient to fulﬁll the demanding objectives. There is a need to investigate and develop various approaches that can provide a new dimension for manipulating the combustion process, especially its chemical aspects, so as to secure substantial progress towards improved efﬁciency, better control of exhaust emissions, and greater ﬂexibility in choosing the fuel.

8.2 The Gas-Fueled Dual Fuel Engine

The dual fuel engine is basically a conventional compression ignition engine of the diesel type where the injected liquid fuel provides the source for ignition of the Handbook of Combustion Vol.3: Gaseous and Liquid Fuels Edited by Maximilian Lackner, Franz Winter, and Avinash K. Agarwal Copyright Ó 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32449-1

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cylinder charge of a gaseous fuel–air mixture. In the duel fuel engine, much of the energy release comes from the combustion of the gaseous fuel while a small amount of diesel liquid fuel provides ignition through timed cylinder injection. Such operation, with optimum conversion methods, has the potential to provide operational characteristics that are comparable or superior to those of the conventional diesel or spark ignition engines. This may be achieved while displaying enhanced emission characteristics, quieter and smoother operation, improved low ambient temperature operation, and reduced thermal loading [1]. This superior performance may be achieved only if sufﬁciently effective measures can be ensured both for the avoidance of knock, at high loads, and incomplete gaseous fuel utilization at relatively light loads. Usually, the main aim while maintaining acceptable diesel operation is to maximize the replacement of the diesel fuel by gaseous fuel, maintaining acceptable levels of emissions and engine performance with a wide range of gaseous fuels. Dual fuel engine operation can be viewed in two broad categories depending on the relative amount of gaseous fuel with respect to that of the diesel liquid fuel. The ﬁrst, more common, category is to inject a relatively small quantity of diesel liquid fuel primarily to provide ignition of the lean mixtures of the gaseous fuel and air. The bulk of the energy release then comes from the combustion of the gaseous fuel components. The second category is associated with the supplementation of the liquid fuel supplies of a fully operational diesel, well beyond the light load operational range, with the addition of the gaseous fuel to engine air. Ideally, there is a need for optimum variation of the liquid fuel quality in relation to the type of gaseous fuel and its supply rate so as to provide for any speciﬁc engine the best performance over the whole load range [2]. Such an idealized approach, mainly due to its complexity, remains not widely implemented in the majority of dual fuel engine installations [3]. The high compression ratio diesel engine is eminently suitable for dual fuel operation with methane, the main component of a wide range of natural and biogases. However, largely by virtue of the provision of an adequate and consistently timed pilot liquid fuel injection and ignition in the high compression ratio with excess air engine, almost any gaseous fuel or vapor in principle can be utilized with varying degrees of success. Gaseous fuels such as methane, ethane, propane, butane, hydrogen, and ethylene have been employed [4]. Moreover, various gaseous fuel mixtures, including those containing diluent gases such as natural gases, biogases, landﬁll gases, and liqueﬁed petroleum gases, have been successfully used. Also, information exists in the open literature relating to the performance of dual fuel engines when a wide range of liquid fuels were mixed with the incoming engine air [5]. Dual fuel engines have found applications in numerous ﬁelds. They have been employed in mobile sources such as ﬂeet vehicles, heavy-duty trucks, buses, railway locomotives and marine vessels and in construction and agricultural ﬁeld applications. Stationary applications have included engine electric power generators, pumps, and co-generation sets. Often, the conversion of common diesel engines to operate on a wide range of gaseous fuels tends to undermine some of the superior characteristics of the compression ignition mode. Some factors contributing to this trend are the following:

8.3 Dual Fuel Combustion

1) 2)

3)

4)

5)

6)

7)

Diesel engines tend to vary widely in type, size, and speed ranges, making their conversion too demanding when insisting on maintaining optimal performance. The operation of dual fuel engines, especially when using certain gaseous fuel mixtures, can be severely limited by the onset of knock. Its avoidance while maintaining the efﬁcient high compression ratios of diesel engines often remains too difﬁcult to achieve satisfactorily with some gaseous fuels and engines. There is a consistent need to develop operational methods to reduce the emission of oxides of nitrogen by a judicious choice of pilot fuel injection and gaseous fuel combustion characteristics. Dual fuel engines at light load employ very lean gaseous fuel–air mixtures, which can lead to signiﬁcant emissions of unconverted gaseous fuel, carbon monoxide, and other products. Catalytic treatment of exhaust gas tends to be less effective with some of the gaseous fuel mixtures. Care is needed in the minimization of the liquid diesel fuel component relative to the gaseous fuel, since it may increase exhaust emissions and enhance the incidence of knock and cyclic variations. However, particulate emissions tend to be much less than for comparable diesel engine operations. The interaction between the liquid pilot spray and the gaseous fuel is not only thermal but also has a kinetic component. These can bring about an extension of the ignition delay and increased emissions. The direct injection of the gaseous fuel into the cylinder as in some engine systems represents an added complexity, since a high-pressure gas supply is needed and the relative phasing of the mixing processes of the injected fuel with the pilot requires careful control and understanding [6].

Continuing research and development are being directed at bringing about improvements to our understanding of the processes of combustion and permitting the development of new approaches that may be superior to those available at present. This effort is being increasingly supplied by advances in the modeling of dual fuel combustion while using 3D, CFD approaches with increasingly more realistic detailed chemical kinetic simulation [7]. 8.3 Dual Fuel Combustion

The combustion process in a typical engine during dual fuel operation depends both on the spray and ignition characteristics of the diesel pilot and on the type of gaseous fuel used and its overall concentration in the cylinder charge. The combustion energy release characteristics in a typical dual fuel engine reﬂect throughout the relatively complex physical and chemical interactions that take place between the combustion processes of the two fuel systems. During the compression process, the usually premixed gaseous fuel–air charge becomes subjected increasingly with time to higher temperatures and pressures as the top dead center (TDC) position is approached. The preignition reaction activity within the cylinder charge may progress signiﬁcantly, especially with certain reactive fuels and operating conditions.

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216

Maximum energy release rate (W)

It can be shown that with higher hydrocarbon fuels operation and even sometimes with methane operation, some reactions can proceed during the compression process, even in the absence of pilot injection [8, 9], particularly in regions where high surface temperatures may be present. As was noted through computational modeling employing detailed chemical kinetics for the oxidation reactions of the gaseous fuel in air, the mean temperature of the charge can increase perceptibly as the TDC position is approached due to increasing chemical activity [10]. This is especially prominent with mixtures that are not very lean. This increase in temperature may compensate to an extent greater than the lowering of the charge temperature at constant compression ratio due to changes in the thermodynamic properties of the cylinder content, as a result of increased polyatomic gas concentration and also heat transfer effects. These reactions, even in the absence of the pilot such as in homogeneous charge compression ignition (HCCI) operation, may produce some partial oxidation products such as radicals, aldehydes and carbon monoxide. The concentrations of these can build up signiﬁcantly during the latter part of the compression stroke to inﬂuence the ignition and combustion processes of the high hydrocarbon diesel fuel pilot. These processes will contribute directly to the combustion of some of the gaseous charge entrained into the pilot and in its immediate vicinity and indirectly to that in the rest of the charge. After ignition of the liquid fuel pilot, the subsequent energy release would reﬂect that. However, turbulent ﬂame propagation from the pilot ignition regions will not proceed throughout the charge until the concentration of the gaseous fuel is beyond a limiting concentration that would vary with the fuel employed and operating conditions [11, 12]. Figure 8.1 shows the variation in the maximum energy release rates with changes in equivalence ratio during compression in a dual fuel engine for methane, propane, and hydrogen. The corresponding calculated maximum cylinder mixture temperature during compression is shown in Figure 8.2. The combustion energy release rate of a dual fuel engine for convenience can be considered as made up of essentially three overlapping components [4]. The ﬁrst (I), as shown in Figure 8.3, is due to the combustion of the pilot. The second (II) is due to 100

Comp. ratio=19.2 Intake temp.=310 K

80

Methane

60 40

Propane

20 Hydrogen

0 0.0

0.2

0.4 0.6 Equivalence ratio

0.8

1.0

Figure 8.1 Calculated maximum energy release rate changes during compression in a dual fuel engine with equivalence ratio for methane, hydrogen, and propane [10].

8.3 Dual Fuel Combustion

Figure 8.2 Calculated maximum cylinder mixture temperature changes during compression with equivalence ratio for hydrogen, methane, and propane [10].

Heavy load II

I

III

Energy release rate

Energy release rate

the combustion of the gaseous fuel component that is in the immediate vicinity of the ignition and combustion centers of the pilot. The third (III) is due to any preignition reaction activity and subsequent turbulent ﬂame propagation (and sometimes autoignition) within the overall lean mixture. With very lean gaseous fuel–air mixtures the bulk of the energy release is expected to come from the ignition and subsequent rapid combustion of the small pilot zone (I). It comes also from the combustion of part of the gaseous fuel–air mixtures entrained into the burning pilot jet spray and from the immediate surroundings of such a zone where higher temperatures and relatively richer mixture regions are present. Under these conditions, only relatively little contribution to the energy release may come from the bulk of the gaseous fuel–air charge further away from the inﬂuence of the pilot zone. Within the very lean mixtures, no consistent ﬂame propagation will take place from the ignition centers and pilot inﬂuenced burning regions. An increase in the size of pilot zone, whether through increasing the mass of fuel injected or its distribution in very lean mixtures, will tend to increase more than proportionally the total energy

Light load II I III

Crank angle

Crank angle

Figure 8.3 Schematic representation of the possible components of energy release rate development for a dual fuel engine at heavy and light loads [4].

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218

80 0.668 0.631 0.435 0.552

60 40 20 0 -20 -10

0

10 20 30 40 Crank angle (º)(ATDC)

50

Figure 8.4 Heat release rate development with crank angle for a dual fuel engine operating on methane for various total equivalence ratio values [15].

released and its associated rates. Greater amounts of gas–air mixtures then will be oxidized due to the larger amount of mixtures entrained within the pilot combustion zone and as a result of the widening burning regions in the vicinity [13]. Greater energy release and rates will be evident also due to some partial ﬂame propagation and increased preignition reaction activity of the rest of the charge [14]. Increasing the concentration of the gaseous fuel further will eventually permit, after pilot ignition, ﬂame propagation throughout the rest of the charge resulting, as shown in Figure 8.4, in a sudden increased contribution to the total energy release [15]. A continued increase in the concentration of the gaseous fuel in air will result in a greater overlap between the second and third energy release regions and will lead to their amalgamation, further increasing much of the energy release immediately following the commencement of the autoignition of the pilot. Such intense rapid energy releases may then be associated with the onset of knock [16].

8.4 The Ignition Delay

The introduction of the gaseous fuel with the intake air produces variations in the physical and transport properties of the mixture, such as the speciﬁc heat ratio and to a lesser extent the heat transfer parameters. Also, changes in the intake partial pressure of oxygen due to air displacement by the gaseous fuel, changes in the preignition reaction activity and its associated energy release and the effects of residual gas can bring substantial changes to the preignition processes of the pilot and hence the length of the delay period. Accordingly, in the homogeneous gas-fueled dual fuel engine the ignition delay, as shown typically in Figure 8.5, displays trends signiﬁcantly different from those observed in the corresponding diesel engine operation [17]. The delay tends to increase with the increased gaseous fuel admission up to a detectable maximum value and then drops to a minimum well before reaching the total stoichiometric ratio (i.e., that equivalence ratio based on the combined gaseous and liquid fuels

8.4 The Ignition Delay Crank angle at ignition, (º)

12

Diesel only Diesel+hydrogen

9

Diesel+methane Diesel+ethylene Diesel+propane

6 3 0

Pilot=0.4kg h-1

-3 Intake temp.=20ºC Inj. angle=20º BTDC

-6 0.0

0.2 0.4 0.6 Total equivalence ratio

0.8

Figure 8.5 Variation of the point of ignition with total equivalence ratio of a dual fuel engine operating on hydrogen, methane, ethylene, and propane using a fixed pilot quantity [17].

and the available air). As seen in Figure 8.5, the admission of propane, which is regarded normally as a relatively fast reactive fuel in air in comparison with methane, in fact tends to produce delay values greater than those observed with methane [18]. The peak temperature at TDC varies markedly with the type of gaseous fuel used and its concentration in the cylinder charge as show in Figure 8.2. For a constant compression ratio, unthrottled engine operating at a ﬁxed intake temperature, in the absence of pilot fuel injection, the addition of hydrogen to the engine intake air reduces the maximum temperature level of the charge only relatively little with the increase in equivalence ratio. The corresponding values of temperature with the addition of methane or propane decrease more markedly. With propane addition for the case shown, a drop in temperature of around 100 K can be observed for the stoichiometric mixture. The heat loss during compression, which is also strongly dependent on the gaseous fuel used and its concentration, inﬂuenced the temperature also. The corresponding calculated maximum heat transfer rates decreased only slightly with the increased admission of propane or methane, but the rates increased very signiﬁcantly with the increased admission of hydrogen [10]. The contribution of the preignition energy release to the development of temperature is also strongly dependent on the gaseous fuel used. As shown in Figure 8.1, for calculated values using detailed kinetics the increased admission of hydrogen continues to increase the energy release rate, which enhances the charge temperature. However, with methane or propane admission the peak values of the energy release rate during compression display maximum values that are signiﬁcantly on the lean side of stoichiometric. For any equivalence ratio, the rates associated with methane admission are higher than those with propane or hydrogen. This is somewhat unexpected in view of the relatively slower reaction rates of methane. It is a reﬂection of the changes brought about to the mean charge temperature with fuel gas admission. The peak value of the release rate for propane operation is obtained with a leaner mixture than that with methane. For the typical case shown, the two equivalence ratios are around 0.30 and 0.60 for propane and methane operation, respectively. Hence the increased admission of either methane or propane may not

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necessarily enhance the preignition reactivity of the gaseous fuel–air mixture under constant compression ratio engine conditions. The admission of diluent gases such as carbon dioxide or nitrogen into a diesel engine charge can also produce changes in the compression temperature. The mean value of the charge temperature during compression decreases virtually linearly with increasing concentration of carbon dioxide, whereas the admission of nitrogen, as would be expected, hardly affects the temperature. These trends result primarily from the marked changes in the effective speciﬁc heat ratio of the mixture such as with the increased presence of carbon dioxide. Also, it would be expected that the increase in the length of the delay with the increased admission of carbon dioxides can be considered to be largely a consequence of this lowering in charge temperature. Consequently, in dual fuel engines, the small quantity of the pilot liquid fuel is injected into a mixture of gaseous fuel and air at a mean temperature and pressure that may be different from the corresponding values for plain diesel operation. Hence it would be expected that the processes of atomization, vaporization, and distribution of the small quantity of pilot fuel in addition to the preignition reaction processes would be affected by any changes in the ﬂow, thermal, and transport characteristics of the charge. Accordingly, there is a need to optimize appropriately the injection characteristics for the dual fuel operation instead of maintaining operation in accordance with pure diesel operation. Such a measure often is not followed potentially for transport applications because of the need to retain satisfactory full diesel operation whenever needed. The preignition reaction processes are widely different for the higher hydrocarbon pilot fuel vapor and for the gaseous fuel component such as methane or hydrogen. For example, n-heptane, a fuel which may represent adequately the behavior of the light fractions of diesel fuel, begins to undergo very complex multistage ignition reactions at relatively much lower temperature than for the gaseous fuel such as with methane which proceeds to ignition via a single stage. The presence of any liquid pilot fuel vapor in various regions of the gaseous fuel–air charge in a dual fuel engine may bring about signiﬁcant changes to the overall rates of preignition reactions and the associated energy release rates [19, 20]. Figure 8.6 shows the very rapid reduction in the ignition delay and subsequent combustion time with increasing mole fraction of heptane which can be considered to be a representative of diesel fuel, in its mixture with methane, and to those that exist at the time of pilot fuel injection. This behavior has been calculated using fully detailed kinetics for constant volume conditions at initial temperatures and pressures that are comparable to those found near TDC in dual fuel engines. For the lower temperature case of 650 K, the reduction in these times is by a few orders of magnitude. Also, Figure 8.6 shows that the presence of relatively minute quantities of heptane vapor with the methane speeds up the rates of ignition processes very substantially. The low concentration of the heptane begins to react with the oxygen well ahead of the methane, producing both some exothermic energy release and key transient products that serve to provide some of the radicals needed for the methane oxidation. Accordingly, the chemical interaction that goes on within the two fuel systems in various regions of the charge in a duel fuel engine must be sufﬁcient to ensure the continued progress of the combustion process. Such a

8.5 Combustion Under Light Load Conditions

12

4 2 0 -0.5

β=0.0

β=0.006 β=0.001

6

β=0.06

β=0.1

β=0.6

β=0.3

8

β=1.0

Pressure (MPa)

10

T0=800 K, P0=2.8 MPa

0.0

0.5

1.0 1.5 2.0 Log time (ms)

2.5

3.0

Figure 8.6 Variation of the pressure development with the logarithm of time due to autoignition reactions for n-heptane–methane mixtures in air; b is the fraction of n-heptane in the fuel mixture [25].

physical and chemical scenario is sufﬁciently complex to need further advances in research for proper control of combustion and emissions. A feature of the combustion process in dual fuel engines operating on lean mixtures is that part of the gaseous fuel and some of the species produced in the combustion process can survive to the exhaust stage. These species through the residual gases can play important chemical and thermal roles in the ignition and combustion processes of subsequent cycles [21], and may contribute to the extent of cyclic variations. The thermal effect of the residual gases is reﬂected in changing the mixture temperature at the commencement of compression and inﬂuencing the temperature of the cylinder surfaces. The kinetic effect of these residuals may result in increasing the activity of preignition reactions during the latter stages of compression. The agreement between calculated and experimental values [22] for the minimum compression ratio needed for autoignition of methane–air mixtures in the absence of pilot injection could be achieved only when full account was taken of the thermal and kinetic effects of the residuals [21]. It can be seen that the length of the ignition delay in a dual fuel engine is affected by the presence of a gaseous fuel with the air during the ignition of the pilot liquid fuel. When the effects of changes in the mean temperature of the charge and its partial pressure of oxygen due to the increased admission of the gaseous fuel are corrected for, the delay period of the pilot follows a similar trend for the different gaseous fuels [10].

8.5 Combustion Under Light Load Conditions

The presence of a gaseous fuel with the engine air, as shown earlier, can have a signiﬁcant effect on the cylinder charge during compression affecting markedly the processes of pre-ignition and subsequent combustions of the pilot and the surrounding mixture. The combustion zone enveloping a jet diffusion ﬂame can extend

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222

6 5 4 3 2 0.2

0.3

0.4 0.5 0.6 Pilot quantity (kg h-1)

0.7

Figure 8.7 Changes in the apparent lean mixture operational limit with the pilot quantity for a dual fuel engine operating on methane [12].

signiﬁcantly both radially and axially as the concentration of the gaseous fuel in the surrounding air is increased until a concentration is reached that would permit ﬂame propagation to sweep through the entirety of the jet surroundings. The limiting concentration can be viewed as a simple function of the effective ﬂammability limit of the gaseous fuel in air under the prevailing conditions. Moreover, it can be shown that the ignition of the droplets of the injected pilot will be affected similarly by the presence of the gaseous fuel and its concentrations. Both the reaction time and the associated energy release will be modiﬁed increasingly by the raising of the concentration of the gaseous fuel in the droplet surroundings [19]. Figure 8.7 shows typically the observed variation in the effective lean mixture limit of a dual fuel engine operating on methane with changes in the pilot quantity [12]. Accordingly, an important general feature of the dual fuel engine is its relatively poor performance under light load conditions when very lean mixtures of gaseous fuel in air are employed. The extent of this relative deterioration in performance depends largely on the pilot quantity employed, the gaseous fuel used, the engine employed and the prevailing operating conditions. Under these conditions, as shown earlier, not only does the ignition delay increase with the introduction of the gaseous fuel but also a signiﬁcant proportion of the fuel will not burn completely, despite the presence of much excess air and a consistent pilot ignition. The ﬂames originating from pilot ignition regions cannot propagate in time far enough into the lean mixtures leaving some of the gaseous fuel unreacted to the exhaust stage. Normally, associated with this low gaseous fuel utilization at lean mixtures is a signiﬁcant increase in the carbon monoxide concentration well beyond the relatively low values that are normally observed with the corresponding diesel operation. Figure 8.8 shows the variation in the concentration of carbon monoxide in the exhaust gas with the increased admission of methane. Figure 8.9 shows the corresponding concentration of the unconverted methane [23]. By using higher gaseous fuel concentrations, the effective ﬂammability limit will be reached when the fuel conversion becomes more complete and engine performance is substantially improved. Thus, when converting diesel engines to dual fuel operation, the tendency has been to retain diesel operating

Log10 of percentage of CO in dry exhaust

8.5 Combustion Under Light Load Conditions

-0.4 -0.6 -0.8 -1.0 -1.2

Methane addition Pilot=0.20kg h-1

-1.4

1000 rev min-1

-1.6 -1.8 -2.0 0.0

0.1

0.2 0.3 0.4 Gas equivalence ratio

0.5

Figure 8.8 Variation of the logarithm of the dry volumetric concentration of carbon monoxide in the exhaust with the gas equivalence ratio of a dual fuel engine operating on methane [23].

1)

Using relatively large pilot quantities with optimum injection characteristics such as by employing the lowest injection nozzle opening pressure and advancing the injection timing somewhat without undermining diesel operation. Increasing the cetane number of the liquid pilot will also enhance signiﬁcantly light load operation and/or permit operation with gaseous fuel mixtures that may contain high concentrations of the diluents nitrogen or carbon dioxide [24].

Methane in dry exhaust (% vol.)

for idling and low load operations. Otherwise, the speciﬁc energy consumption when based on the pilot diesel fuel and the gaseous fuel supplied and the associated power output and emissions will be markedly inferior to those of straight diesel operation. This is particularly evident for the relatively slow burning methane and at low intake temperature operation such as when using the insufﬁciently heated boil-off from liqueﬁed natural gas. Accordingly, normal dual fuel operation at light load when using relatively small pilot quantities may be improved through the following procedures [18]:

2.0 1.6

Methane addition

1.2 0.8 1000 rev min-1

0.4 0.0 0.0

Pilot=0.20kg h-1

0.1

0.2

0.3

0.4

0.5

0.6

Gas equivalence ratio Figure 8.9 Variation of the volumetric concentration of the unconsumed methane with gas equivalence ratio for a dual fuel engine operating on methane using a fixed pilot quantity [23].

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2)

3)

4)

5)

Partial restriction of the air component of the charge so as to produce an effectively richer mixture for the same mass of fuel gas. However, this throttling should be employed with care so as not to undermine pilot ignition. In turbocharged dual fuel engine applications at light load, excess air may be made to bypass the engine altogether. Also in multi-cylinder applications, skip ﬁring, where some cylinders may be operating wholly on the diesel mode whereas others are operating as dual fuel but with less lean mixtures, may provide improved overall performance. Slight preheating of the lean intake gas–air mixture, such as through heat exchange with the exhaust gas or increasing the water jacket temperature, provides a higher mixture temperature at the end of compression and reduces the fraction of the gaseous fuel surviving the combustion process. This is consistent with the tendency of the ﬂammability limits of all common gaseous fuels to widen approximately linearly with the average mixture temperature. In turbo-charged engine applications, the feeding back of some warm excess air into the compressor intake at light load may also help. The selective addition at light load of small amounts of a suitable auxiliary fuel to the main gaseous fuel supply, such as hydrogen, higher hydrocarbons, or gasoline vapor, may improve dual fuel engine operations. Such an approach is to be avoided beyond the light load range since apart from affecting the speciﬁc energy consumption adversely, it can bring about an earlier onset of knock [25]. Some stratiﬁcation of the gaseous fuel component in relation to the air, if it can be achieved effectively in practice, such as through direct in-cylinder gas injection, may improve light load operation through arranging for a slightly richer mixture in the vicinity of the pilot fuel. Also, resorting to some uncooled exhaust gas recirculation may also help. Optimum stratiﬁcation also may reduce exhaust emissions signiﬁcantly.

Most probably, resorting to a judicious combination of these measures can be effective in improving the light load performance of the dual fuel engine while retaining the capacity to operate efﬁciently as a diesel, whenever needed. 8.6 Exhaust Emissions

The main constituents of the exhaust gas of dual fuel engines are unburned hydrocarbons in the form of unconverted gaseous fuel, carbon monoxide, carbon dioxide, oxides of nitrogen and particulate emissions. As was indicated previously, turbulent ﬂame propagation from the ignition regions of the pilot may not proceed in time throughout the charge until the concentration of the gaseous fuel reaches beyond a limiting value that varies with the operating conditions. When operating with a sufﬁciently lean charge, a signiﬁcant amount of the gaseous fuel and products of the pre-ignition and partial combustion process survives to the exhaust stage. As shown, typically in Figure 8.9, the concentration of the unconverted methane in the exhaust gas increases initially almost proportionally with the extent of gas admissions. As the concentration of the gaseous charge approaches the limiting value, the

8.6 Exhaust Emissions

concentration of methane drops rapidly so that eventually it becomes well below the corresponding values usually encountered with the gas-fueled spark ignition counterpart [23]. Any unconsumed methane tends to be much less likely than other hydrocarbons to produce photochemical smog. However, methane emissions which do not respond too well to catalytic oxidation are known to be much more potent as a greenhouse gas than carbon dioxide and will increasingly require control. The relative size of the pilot employed and its injection characteristics have a controlling inﬂuence on the emission of methane at any equivalence ratio. Relatively large pilots need to be used at very light load but they may be reduced in size very substantially at high load. The recent increase in the practice of using direct injection of the gaseous fuels in multi-cylinder installations and for two-stroke engines, although it increases complexity, can permit a further substantial reduction in the exhaust emissions of methane. As part of the sequencing of the oxidation reactions of methane in air, carbon monoxide is produced in the early stages of the oxidation under favorable conditions. Much of this carbon monoxide oxidizes later to carbon dioxide. For very lean mixtures, some of this carbon monoxide may not be oxidized sufﬁciently due to the failure of the combustion process to encompass the whole charge and the relative slowness of the reaction processes. Hence, at very light load, when very lean mixtures and especially when small pilot quantities are involved, some carbon monoxide may survive to the ﬁnal exhaust phase, as shown typically in Figure 8.8. The percentage of the total carbon present in both the pilot fuel and the methane that eventually appears as carbon monoxide in the exhaust gas remains relatively small compared with the concentrations normally found in diesel operations [23]. The carbon monoxide in the exhaust gases of dual fuel engines, which originates from the gaseous component in regions mainly within and adjoining the burning pilot, will depend on the size of these regions. For the same equivalence ratio, the use of a larger pilot tends to produce higher carbon monoxide concentrations. The increase in intake charge temperature enhances the oxidation of carbon monoxide within the time available. Changes in the injection characteristics of the pilot for the same pilot mass and equivalence ratio can modify the extent of methane conversion and the exhaust concentrations of carbon monoxide. The contribution of the pre-ignition reactions of the bulk of the charge to carbon monoxide exhaust emissions tends to be variable but remains generally small [25]. The production of oxides of nitrogen depends primarily on the peak value of the combustion temperature, the effective volume of the combustion zone, the availability of oxygen and on whether sufﬁcient time for the oxygen–nitrogen reactions is available to proceed to signiﬁcant levels of completion. In dual fuel engines, much of the NOx production is associated with the pilot zone where very high local temperatures are achieved and longer reaction times are possible. Some further NOx production, but to a much lesser extent, will be also from heated mixture regions from the vicinity of the pilot combustion zone. Increasing the pilot will have an important contribution to the increased production of NOx as the size of the combustion zone is similarly increased. With very lean mixture operation, relatively very little NOx production is expected from regions in the rest of the gaseous mixture

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charge. The production of oxides of nitrogen is inﬂuenced markedly by both the quantity of the pilot employed and the overall equivalence ratio. Resorting to operating dual fuel engines on relatively large pilots at light load with very lean gaseous fuel–air mixtures, but on increasingly smaller pilots as the load is increased, will maintain the oxides of nitrogen at levels below those encountered in the corresponding diesel operations. With small pilots and the overall lean mixture operation of dual fuel engines, the level of oxides of nitrogen produced will also be lower than in comparable spark ignition operation, provided that the onset of knock is avoided. Since the bulk of the energy release in dual fuel operation comes from the combustion of the gaseous fuel, a very important feature is that the extent of smoke and particulate emissions will be very much lower than those encountered with the corresponding diesel operation. This is achievable over the whole power output range, even when very low intake mixture temperatures are involved, such as with the boil of gas in liqueﬁed natural gas (LNG) operation. This feature makes the conversion of diesel engines to dual fuel engine operation distinctly attractive over the whole load range.

8.7 Knock in Dual Fuel Engines

The energy release in dual fuel engines represents the combined contribution of the pilot fuel regions together with the propagation of the rapid turbulent ﬂame fronts originating from these regions. This mode of combustion is primarily responsible for the ability of dual fuel engines to burn mixtures that are much leaner than those normally possible in gas-fueled spark ignition engines. A prime requirement of any alternative gaseous fuel for satisfactory operation in dual engines is that its mixture with air should not autoignite spontaneously prior to, during or following the rapid pilot energy release. Failure to do so can lead to the onset of knock, which manifests itself in excessively rapid rates of pressure rise and overheating of the walls leading to a signiﬁcant decrease in efﬁciency with increased cyclic variations [26]. Persistent knock is highly objectionable and needs to be avoided, otherwise it may lead to engine failure. Knock in dual fuel operation is of an autoignition nature involving mostly the gaseous fuel–air mixture. Depending on the size of the pilot and its mode of injection, knock can be perceived to involve autoignition of that portion of the charge in the neighborhood of these ignition centers leading to very high rates of pressure rise and a consequent very rapid burning of the remaining parts of the charge. With much smaller pilots, the energy release during the initial stages of ignition and the resulting turbulent ﬂame propagation can lead under certain conditions to autoignition of the charge well away from these ignition centers in the end gas regions ahead of these ﬂames, in a manner that resembles the occurrence of knock in spark ignition engines [27, 28]. The knock-limited power output for any fuel and pilot setting has been shown to deteriorate logarithmically with the inverse of the intake absolute temperature [29] (Figure 8.10). Accordingly, the onset of knock may be avoided or curtailed through a

8.7 Knock in Dual Fuel Engines

Log10 (BMEP)K.L.

2.08 2.00 1.92

ree

kf noc

K

100% methane

100% Propane 90% Methane 10% Propane 75% Methane 25% Propane

1.84 1.76 1.68 1.60 2.2

t

limi

50% Methane 50% Propane

2.4

2.6

2.8

3.0

1/T (K-1 × 103) Figure 8.10 Variation of the logarithm of the knock-limited brake power output with the inverse of intake temperature for a range of methane–propane mixtures using a fixed pilot quantity [29].

number of design and operational measures such as lowering the induction and water jacket temperatures and delaying the commencement of pilot injection. Other effective measures, such as lowering the compression ratio, are usually not resorted to since they would undermine the operation of the engine as a diesel. However, one of the prime means of avoiding the onset of knock in a natural gas-fueled dual fuel engine is through careful control of the composition of the gaseous fuel mixtures. This can be accomplished through measures such as reducing to a minimum the concentration of any higher hydrocarbon components with the methane and/or increasing the concentration of any diluents such as carbon dioxide or nitrogen that may be present with the fuel mixture, and avoiding having droplets of fuel condensates with the original gas. Of course, in principle an effective procedure for reducing the incidence of knock is through optimum distribution of the gaseous fuel-to-air ratio by a proper stratiﬁcation of the gaseous fuel component within the cylinder such as via timed gaseous fuel injection during the latter parts of the compression stroke or to a lesser degree of effectiveness via its timed injection just outside the cylinder. This stratiﬁcation aims at having less reactive leaner gaseous fuel–air mixtures in regions away from the ignition centers of the pilot where richer mixtures are located. This would require careful control of the gaseous fuel distribution such that it will not hinder pilot fuel ignition or encourage the autoignition of regions adjacent to the pilot while reducing the tendency of the charge away from these centers to autoignite despite the high rates of increase in cylinder pressure and temperature. Normally, the knocking operational regions with methane are beyond most common operational conditions unless highly supercharged large-bore engines and unoptimized pilot quantity and injection characteristics are employed. However, for some other gaseous fuel mixtures containing the more reactive higher hydrocarbons, the occurrence of knock in unsuitably modiﬁed diesel engines is often encountered. Enhancing the cetane number of the pilot fuel can have a beneﬁcial effect by delaying the onset of knock slightly.

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8.8 Some Predictive Modeling

To be able to carry out effective design changes, optimize engine performance, and manage to operate different types of engines on a wide range of gaseous fuels and liquid fuel pilots requires continuing improvements in our understanding of the relatively complex combustion processes that occur in dual fuel engines. In this effort, relying almost exclusively on experimental testing approaches has serious limitations. These critically need supplementing throughout by the continued development and upgrading of effective predictive modeling approaches. These may be focused speciﬁcally on certain operational features of the engine such as the onset of knock or attempt to provide comprehensive analytical simulations of the whole combustion process and its operational consequences, including the nature and extent of emissions. In comparison with the extensive efforts expended over the years on the modeling of the combustion processes in spark ignition, compression ignition, and HCCI engines, much less effort has been devoted to modeling the combustion process in dual fuel engines. This is mainly a reﬂection of the relative complexity of these processes and the much greater number of associated controlling variables. Single-zone thermodynamic models, when based on zero-dimensional simulation of the combustion process in engines, generally assume that uniform temperature and composition are retained throughout the whole charge. Such a class of modeling approaches has been widely used mainly because of their relative simplicity. A notable example of these is deriving the effective rates of heat release and associated overall features of engine performance obtained from experimentally based cylinder pressure development records, such as those shown in Figure 8.4 [15]. Through the additional consideration of the overall chemical reaction activity of the cylinder charge, an approximate model was derived for determining the conditions for the onset of knock and the associated knock-limited power output with different fuels [30]. Figure 8.10 shows the predicted knock-limited power output of a dual fuel engine to be dependent logarithmically on the inverse of the absolute intake mixture temperature. An extension of such an approach was to assume that the ignition of the small quantity of pilot fuel takes place sufﬁciently rapidly to be considered to take place under constant volume conditions while the reaction kinetics of the gaseous fuel-air charge can be modeled in detail. Approaches such as these could provide improved indications of the effects of changes in engine operating conditions and some of its design variables on some of the key performance parameters, such as predicting the incidence of knock, power output, efﬁciency, and NOx emissions [30–32]. Later modeling approaches developed quasi-dimensional combustion modeling described as thermodynamic multi-zone approaches. These were based on considering the whole cylinder charge to be made up of different zones that continue to change in size and properties. These were based mainly on an assumed understanding of the combustion processes obtained through appropriate theory, empiricism, and experimentation while incorporating sufﬁciently detailed chemical

8.9 Some Design and Operational Considerations

kinetics. Such approaches could be used to predict the conditions for the onset of knock, exhaust gas recirculation (EGR) effects, and emissions for duel fuel engines. More recently, further progress has been made in the computer simulation of dual fuel engine operation [7, 9, 31, 32], using three-dimensional comutational ﬂuid dynamics (3D-CFD) turbulent combustion modeling while accounting in detail for the kinetics of combustion of the pilot and gaseous fuels. Such approaches are the subject of intense continuing research that promises to provide comprehensively detailed information about the combustion process, albeit tending to be laboriously and extensively complex.

8.9 Some Design and Operational Considerations

There is a wide variety of design arrangements and retroﬁt equipment that have been specially developed over the years to render normal diesel engines capable of dual fuel operation so as to consume a variety of gaseous fuels. Usually these approaches depend largely on whether the main objective is to retain intact diesel operation up to the full load limit or focus the conversion primarily on the efﬁcient consumption of gaseous fuels while ensuring signiﬁcant economy in the total consumption of the liquid diesel fuel [33–36]. This will mean also whether the main diesel fuel injection equipment and associated controls and hardware remain unaltered so as to deliver when needed relatively large-sized liquid diesel fuel pilots. These will often contribute much more than 10% of the total energy input [33, 37]. Should the engine be made to operate in the dual fuel mode while requiring a much smaller size pilot and retaining without modiﬁcation the normal diesel injection and associated equipment, then poor engine performance will ensue, resulting in increased cyclic variations and emissions, overheating of injectors, and poor combustion [38]. Dual fuel operation needs to be optimized, but often this is not done, especially because of the complexities involved and the need for the engine to perform optimally on two completely different fuel systems. Nevertheless, the changeover from one mode to another is usually done automatically without interruption to the engine output and speed even when operating at full load and maximum speed. Usually, for maximum efﬁciency in stationary engine applications, the heat of the exhaust gas is used in waste heat recovery co-generation systems. In dual fuel engines, the ratio of the amount of fuel gas admitted to that of diesel fuel injected is usually inﬁnitely variable. However, engines may be made to operate on a constant pilot fraction or mass to minimize the relative quantity of the diesel fuel employed. As more power is required, the quantity of the pilot is reduced. Alternatively, the engine may be started, idled, and operated at light load on diesel alone. When the gas is added to increase the power output, further measures may be enacted to ensure the complete conversion of the gaseous fuel with acceptable associated engine performance. As indicated earlier, a common practice to counter poor light load dual fuel engine characteristics is to use a relatively large-sized pilot. Such an approach is particularly common in land transport applications.

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Furthermore, the injection timing of the pilot is often kept ﬁxed. Usually, it is only with the ﬁtting of the right control equipment and a separate pilot injector that the injection timing may be varied. Such measures require optimization in terms of the operating conditions and any changes in the composition of the gaseous fuel employed. When a suitably sized smaller dedicated fuel injector and associated equipment are ﬁtted permanently into the cylinder head, a separate and independently varying injected pilot fuel can be ensured. With larger size cylinders, such as those employed in stationary engine applications, extremely small-sized pilots are often provided [39]. These are sometimes described loosely as micro-pilots, requiring that the cylinder head and sometimes the combustion chamber and piston be modiﬁed signiﬁcantly and permanently so that a suitably sized and designed prechamber can be ﬁtted, as shown schematically in Figure 8.11 [40]. Such an arrangement ensures that an auxiliary pilot fuel nozzle can be ﬁtted so as to inject a closely controlled amount of fuel into the chamber. On ignition, the resulting burning rich fuel–air mixture emerges into the main chamber to ignite and burn the bulk of the gas fuel–air charge. With such approaches, the pilot size can be varied very widely and reduced signiﬁcantly so that it provides satisfactorily less than a few percent of the total energy release. Such arrangements are rarely employed in smaller sized engines or for mobile applications, mainly due to the associated size and manufacturing limitations. Moreover, occasionally the type and quality of the pilot used in such installations may be different from those of the regular quality main diesel fuel. This is since there can be some distinct advantages associated with using exclusively for the small pilot a higher cetane number fuel relative to that of the main diesel fuel [24]. However, such approaches tend to remain relatively uncommon due to the associated increase in cost, the complexity of controls, and fuel storage on-board. Unlike prechambers of regular diesel engines, those in dual fuel engines tend not to

Figure 8.11 Schematic of the combustion chamber of a MAN-B&W dual fuel engine showing a prechamber and the arrangement of the main and pilot fuel nozzles [40].

8.9 Some Design and Operational Considerations

experience signiﬁcant losses in fuel economy. This is because only a small pilot quantity is injected into the prechamber, which can be very small. The resulting pressure and heat losses due to the prechamber application then tend to be negligible and more than offset by the increased combustion efﬁciency in the main chamber [1]. The manner in which the fuel gas is admitted into the engine cylinder can vary very widely, depending not only on the type of gaseous fuel used but also on whether the engine remains dedicated primarily to diesel operation with only occasional switching over to unoptimized dual fuel operation, or it is aimed to operate the engine exclusively as a dual duel engine with occasional not necessarily fully optimized performance as a diesel engine. When the fuel gas is to be fumigated into the intake manifold to mix with the incoming air, then a wide range of carburetion and injection devices with associated control and safety equipment may be ﬁtted [41]. Figure 8.12 shows an example of one of the many arrangements for introducing the fuel gas into the incoming air just outside the inlet to the cylinder. The relatively simple carburetion and continuous port gas injection in comparison with timed injection are less suited for efﬁcient low emission operation, especially with two-stroke engine applications. Alternatively, for improved combustion control and performance, the fuel gas is introduced individually into each cylinder. This is done through timed injection just outside the intake valve or directly into the cylinder and even sometimes made into the early part of the compression stroke. Such approaches require specialized fuel gas injection equipment, which contributes to increased capital cost and equipment control complexity. However, they tend to result in a superior dual fuel engine performance in comparison with that of the carbureted fumigation approaches. Throughout, with these varieties of fuel gas introduction methods, great

Figure 8.12 Schematic of a dual fuel engine cylinder head showing the fumigation of the fuel gas employing an independent gas valve.

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care needs to be taken to ensure satisfactory mixing of the air with the fuel well ahead of the instant of pilot fuel injection unless deliberate arrangements are made to produce suitably stratiﬁed mixtures. Such an approach often cannot be achieved effectively and so far it has rarely been employed. There have also been attempts to inject the fuel gas later than the injection and ignition points of the pilot fuel around the end of compression [42, 43]. Not only would such approaches require compression of the fuel gas to sufﬁciently very high pressures so that it can be injected rapidly into the main chamber, but it would also be necessary to ensure proper rapid mixing between the burning pilot fuel and the incoming injected fuel gas. The fuel gas mixer is combined with a gas control valve so that the fuel-to-air ratio may be suitably controlled. A venturi may be employed to aid in the introduction and mixing of the gas into the air stream. Additionally, a throttle valve may be placed before the intake manifold to improve engine operation at low power levels. The expansion of the fuel gas down to the intake manifold pressure can produce substantial cooling that can interrupt the steady supply of the gas, requiring heating of the gas regulator with the engine coolant to prevent it from freezing. For turbocharged engines, often the fuel gas is introduced beyond the exit of the compressor when sufﬁciently high-pressure fuel gas supply is provided. However, for some applications, fuel gas carburetion may be effected either before or after the turbocharger. In such turbocharged applications, the exhaust turbine is ﬁtted with a bypassing arrangement so that part of the exhaust gas can be diverted around the turbine to avoid instability of its operation and excessive heating. Throughout, more than just adequate measures are taken to ensure the safety of operation of dual fuel engines irrespective of the type of fuel and load being employed. Transient operating engine conditions are usually more likely to represent a greater hazard, especially when highly turbocharged engines operating with exhaust gas recirculation are employed. It is evident that due to the potential complexity of the design of and support equipment for dual fuel engine operation, the challenges associated with mobile transport applications involving relatively small-sized equipment, and frequently transient multi-load operation, tend to be more serious than those for stationary constant-speed applications that are commonly associated with electric power production. However, the fact that dual fuel engines are called upon to operate on a wide range of gaseous fuels of different compositions and quality remains a complicating factor that needs to be dealt with satisfactorily. As indicated earlier, there is a need to adopt various approaches to improve dual fuel engine performance and emissions at light load. Accordingly, the engine may be started as a diesel, then transferred gradually to dual fuel operation and often just before shutdown it is reverted back to diesel operation. Also, whenever possible, a shorter valve overlap than with conventional diesel engines is employed with dual fuel engine applications. Additives are rarely used with dual fuel engines except those that may be used to enhance the ignition quality of the pilot diesel fuel. Some past work [44], however, showed that there are distinct advantages with the introduction of some tetraethyllead (TEL) into the gaseous fuel, which reduces the tendency to knock, especially for

References

highly charged dual fuel engines. However, to employ such an approach is out of the question these days because of the banning virtually everywhere of any engine applications that may involve the use of TEL additives. Moreover, it remains a challenge to develop an effective catalytic converter for dual fuel engine applications because of the relative difﬁculties in making lean mixtures of methane in air react catalytically sufﬁciently rapidly and the low exhaust temperatures at light load [45]. However, suitable converters have been developed and applied with some success for highly loaded engine conditions.

8.10 Conclusion

The dual fuel engine not only consumes a wide range of gaseous fuel resources effectively, but also has the potential to avoid many of the current and future problems facing the diesel engine, including the need for very signiﬁcant reductions in exhaust emissions. There is a continuing need to improve our understanding of the combustion processes involved so as to carry out the conversion of a wide range of diesel engines to operate efﬁciently, cleanly and economically on a wide range of gaseous fuels. Obviously, for any engine and fuel systems, a multitude of variables needs to be controlled simultaneously and optimally to enhance light load performance, reduce ignition delays, avoid knock, and reduce exhaust emissions over the whole load range. There is always a need to evaluate the consequences of a number of control approaches, adopt the positive features that may be identiﬁed while keeping any increase in engine complexity and cost at low levels. This is being achieved through employing a combination of effective computational procedures together with experimental testing.

Acknowledgments

The author has drawn extensively on the research work of his various past and current associates, and gratefully acknowledges such contributions. The support of the Natural Sciences and Engineering Council of Canada (NSERC) is gratefully acknowledged.

References 1 Turner, S.H. and Weaver, C.S. (1994) Dual

3 Danyluk, P.R. (1993) Development of

Fuel Natural Gas/Diesel Engines. Gas Research Institute, No. GRI-94/0094. 2 Karim, G.A. (1987) The dual fuel engine, in Automotive Engine Alternatives (ed. R.L. Evans), Plenum Press, New York, pp. 83–104.

a high output duel fuel engine. ASME Paper No. 93-ICE-20. Proceedings of Energy Sources Technology Conference. 4 Karim, G.A. (2003) Combustion in gas fuelled compression ignition engines of

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the dual fuel type. J. Eng. Gas Turbines Power, 125, 827–836. Karim, G.A. and Amoozegar, N. (1983) Determination of the Performance of a Dual Fuel Diesel Engine with the Addition of Various Liquid Fuels to the Intake Charge. SAE Paper No. 830265. Dumitrescu, S., Hill, P.G., Li, G.G., and Ouellette, P. (2000) Effects of Injection Charges on Efﬁciency and Emissions of a Diesel Engine Fuelled by Direct Injection of Natural Gas. SAE Paper No. 2000-01-1805. Liu, K. and Karim, G.A. (2009) Three dimensional computational ﬂuid simulation of diesel and dual fuel engine combustion. J. Eng. Gas Turbines Power, 131, 12804. Wong, Y. and Karim, G.A. (2000) A Kinetic Examination of the Effects of Recycled Exhaust Gases on the Autoignition of Homogeneous n-Heptane–Air Mixtures in Engines. SAE Paper No. F1236. Liu, K. and Karim, G.A. (2008) A 3D Simulation with Detailed Chemical Kinetics of Combustion and Quenching in an HCCI Engine. SAE Paper No. 08SFL-0027. Liu, Z. and Karim, G.A. (1998) An examination of the ignition delay period in gas fuelled diesel engines. J. Eng. Gas Turbines Power, 120, 225–231. Badr, O., Karim, G.A., and Liu, B. (1998) An examination of the ﬂame spread limits in a dual fuel engine. Appl. Thermal Eng., 19, 1071–1080. Bade Shrestha, S.O. and Karim, G.A. (2006) The operational mixture limits in engines fuelled with alternative gaseous fuels. J. Energ. Resour. ASME, 128, 223–228. Karim, G.A., Kibriya, M.G., Lapucha, R., and Wierzba, I. (1988) Examination of the Combustion of a Fuel Jet in Homogenously Pre-mixed Lean Fuel–Air Stream. SAE Paper No. 881662. Wierzba, P., Karim, G.A., and Wierzba, I. (1992) An analytical examination of the combustion of a turbulent fuel in an environment containing premixed fuel or a diluent and air. ASME J. Energy Resour. Technol., 117, 234–239.

15 Karim, G.A. and Khan, M.O. (1968)

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Examination of effective rates of combustion heat release in a dual fuel engine. J. Mech. Eng. Sci., 10, 13–23. Karim, G.A., Klat, S.R., and Moore, N.P.W. (1967) Knock in dual fuel engines. Proc. Inst. Mech. Eng., 181 (Pt. 1), 453–466. Karim, G.A. and Burn, K.S. (1980) The Combustion of Gaseous Fuels in a Dual Fuel Engine of the Compression Ignition Type with Particular Reference to Cold Intake Temperature Conditions. SAE Paper No. 800263. Karim, G.A. (1991) An Examination of Some Measures for Improving the Performance of Gas Fuelled Diesel Engines at Light Load. SAE Paper No. 912366. Samuel, P. and Karim, G.A. (1994) An Analysis of Fuel Droplets Ignition and Combustion Within Homogeneous Mixtures of Fuel and Air. SAE Paper No. 940901. Khalil, E., Samuel, P., and Karim, G.A. (1961) An Analytical Examination of the Chemical Kinetics of the Combustion of n-Heptane–Methane Air Mixtures. SAE Paper No. 961932. Liu, Z. and Karim, G.A. (1996) An Examination of the Role of Residual Gases in the Combustion Processes of Motored Engines Fuelled with Gaseous Fuels. SAE Paper No. 961081. Downs, D., Walsh, A.D., and Wheeler, R.W. (1951) A study of the reactions that lead to knock in the spark ignition engine. Philos. Trans. R. Soc. London, 243, 463–524. Karim, G.A., Liu, Z., and Jones, W. (1993) Exhaust Emissions from Dual Fuel Engines at Light Load. SAE Paper No. 932822. Gunea, C., Razavi, M.R., and Karim, G.A. (1998) The Effects of Pilot Fuel Quality on Dual Fuel Engine Ignition Delay. SAE Paper No. 982453. Khalil, E.B. and Karim, G.A. (2001) A kinetic investigation of the role of changes in the composition of natural gas in engine applications. J. Eng. Gas Turbines Power, 124, 404–411.

References 26 Liu, Z. and Karim, G.A. (1997) Simulation

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of the combustion processes in gas fuelled diesel engines. Proc. Inst. Mech. Eng. A J. Power, 211, 159–171. Karim, G.A. and Lui, Z. (1992) A Prediction Model of Knock in Dual Fuel Engines. SAE Paper No. 921550. Karim, G.A. and Zhoada, Y. (1990) Modeling of the combustion process in a dual fuel direct injection engine. ASME J. Energy Resour. Technol., 112, 34–42. Karim, G.A. (1964) An analytical approach to the uncontrolled combustion phenomena in dual fuel engines. J. Inst. Fuel, 37, 530–536. Liu, Z. and Karim, G.A. (1995) The Ignition Delay Period in Dual Fuel Engines. SAE Paper No. 950466. Kusaka, J., Tsazuki, K., and Daisho, Y. (2002) A Numerical Study on Combustion and Exhaust Gas Emissions Characteristics of a Dual-fuel Natural Gas Engine Using a Multi-dimensional Model Combined with Detailed Kinetics. SAE Paper No. 2002-01-1750. Zhang, Y., Kong, S.C., and Reitz, R.D. (2003) Modeling and Simulation of a Dual Fuel (Diesel/Natural Gas) Engine with Multi-dimensional CFD. SAE Paper No. 2003-01-0755. Rain, R.R. and McFeatures, J.S. (1989) New Zealand Experience with Natural Gas Fuelling of Heavy Transport Engines. SAE Paper No. 892136. Park, T. and and Atkinson, R. (1999) Operation of a Compression Ignition Engine in a HEUI Injection System on Natural Gas with Diesel Pilot Injection. SAE Paper No. 1999-01-3522. Muti, P. (1996) Pilot Ignited Natural Gas Combustion Diesel Engine. PhD Thesis, Mechanical Engineering University of British Colombia.

36 Pooria, M.P. and Ramesh, A. (1999)

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Experimental Investigation of the Factors Affecting the Performance of LPG Dual Fuel Engine. SAE Paper No. 1999-01-1123. Sinclair, M.S. and Haddon, J.J. (1991) Operation of a Class 8 Truck on Natural Gas/Diesel. SAE Paper No. 911666. Diasho, Y. and Takahaski, Y.I. (1995) Controlling Combustion and Exhaust Emissions in a Direct Injection Diesel Engine Dual Fuelled with Natural Gas. SAE Paper No. 952436. Gebert, K., Beck, N.J., Barkhimer, R.L., and Wong, H. (1997) Strategies to Improve Combustion and Emission Characteristics of Dual Fuel Pilot Ignited Natural Gas Engines. SAE Paper No. 971712. Challen, B. and Baranescu, R. (eds) (1998) Diesel Engine Reference Book, 2nd edn, SAE International. Varde, K.S. (1983) Propane Fumigation in a Diesel Injection Type Diesel Engine. SAE Paper No. 831354. Hodgins, K.B., Gunawan, H., and Hill, P.G. (1992) Intensiﬁer –Injector for Natural Gas Fuelling Diesel Engines. SAE Paper No. 921553. Dumitrescu, S., Hill, P.G., Li, G., and Ouellette, P. (2000) Effects of Injection Changes on Efﬁciency and Emissions of a Diesel Engine Fuelled by Direct Injection of Natural Gas. SAE Paper No. 2000-01-1805. Felt, A.E. and Steele, W.A. (1962) Combustion Control in Dual Fuel Engines. SAE Paper No. 620555. Liu, B. and Checkel, D. (2000) Experimental and Modeling Study of Variable Cycle Time for a Reversing Flow Catalytic Converter of Natural Gas/Diesel Dual Fuel Engines. SAE Paper No. 2000-01-0213.

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9 Fuel Gas Applications in Industry Fernando C€orner da Costa 9.1 Introduction

A gaseous fuel to be considered as an industrial fuel gas must comply with the following characteristics: . . . .

availability on-site minimum quality reliable cost assured delivery.

The best known fuel gases are natural gas (NG) and liqueﬁed petroleum gas (LPG, LPGas) or propane, both available all over the world. Whereas LPG is usually distributed in the liquid state under pressure, NG can be delivered by pipeline or high-pressure cylinders in the gaseous state or also in the cryogenic liquid form as liqueﬁed natural gas (LNG). Natural gas occurs in the Earths crust associated or not associated with oil. The raw NG composition, from oil or gas ﬁelds, is mostly methane and ethane, but there are other hydrocarbons such as LPG and gasoline, and gases such as nitrogen, carbon dioxide, and hydrogen sulﬁde. Sometimes helium can also be found in NG compositions. After processing in gas plants, the LPG and gasoline are separated, the NG usually being distributed by pipeline to customers. The net heating value of NG varies in the range 34 000–40 000 kJ m3 STP. LPG can be produced from NG plants, oil reﬁneries or as a by-product from petrochemical processing. Its composition varies widely from country to country, but the main components are propane and butane together with minor components such as ethane and pentane. In cold weather, the blends are richer in propane due to its low boiling point, in order to have the gas available in the vapor phase for burning. The average net heating value of the LPG is 46 000 kJ kg1. In addition, there are various fuel gases usually applied in geographically restricted areas, such as coke oven gas (COG), blast furnace gas (BFG), landﬁll gas (LFG),

Handbook of Combustion Vol.3: Gaseous and Liquid Fuels Edited by Maximilian Lackner, Franz Winter, and Avinash K. Agarwal Copyright Ó 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32449-1

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producer gas, blue water gas, reﬁnery gas and dimethyl ether (DME) obtained by the technology known as gas-to-liquids (GTL). COG is a gas produced inside closed chambers at high temperatures – coke ovens or retorts – during the process whereby coking coal is transformed into coke by distillation. The coke thus obtained is mainly used in metallurgical processes such as blast furnaces and foundries. The COG is a medium heat value gas, in the region of 18 000 kJ m1 STP (net heating value), and its typical composition by volume is as follows [1]: . . . . . . .

hydrogen methane carbon monoxide hydrocarbons CnHm carbon dioxide oxygen nitrogen

54.0% 28.0% 7.4% 2.6% 5.2% 0.4% 4.0%.

COG applications are limited to the neighborhood of the coke plant due to the high transportation costs. It can be stored in low-pressure gasholders and distributed by pipelines. Its main applications are as a fuel gas to generate heat for several heating processes in steel mills and to generate electricity in on-site power plants. BFG is a by-product of a blast furnace during the ironmaking process from ores, being considered a lean gas: its net heating value is about 3300 kJ m3 STP. Its composition varies widely [1]: . . . .

carbon dioxide carbon monoxide hydrogen nitrogen

8–15% 23–33% 1.5–3.5% 50–60%.

Due to its low heating value, the use of BFG as a fuel is somewhat limited in steel mills, unless enriched with COG, NG, or LPG. Another alternative would be to burn BFG with oxygen-enriched atmospheres. Hydrogen has been considered the most important fuel for the future because its combustion does not liberate carbon compounds into the atmosphere, avoiding increasing the greenhouse effect. But there is no source of hydrogen in Nature; it must be produced from an industrial process such as water electrolysis or hydrocarbon steam reforming. The challenge is to produce hydrogen by a process without carbon emissions such as water electrolysis, when all the electricity is generated from an environmentally clean source such as hydropower plants or wind mills. DME is a fuel gas made from NG or methanol, its physical properties being similar to those of LPG such that both gases can be mixed inside the same pressure vessel. The main advantage of DME is its constant composition, but it has a 60% lower heat value by weight than LPG. There are also some other fuel gases such as acetylene and MAPP (methylacetylene and propadiene) usually applied nowadays in very speciﬁc applications such as

9.2 Industrial Heating Processes

carbon steel cutting and welding, non-ferrous brazing and special concentrated heating by using torches. However, both costs are much higher than with LPG or NG. The target of most fuel gas applications is to provide heat to equipment such as boilers, furnaces, kilns, process heaters, ovens, and dryers. In addition to heat, fuel gases can also be used as feedstock to produce active or neutral atmospheres, propellants, and cooling media.

9.2 Industrial Heating Processes

The applications of high-grade fuels such as NG and LPG in industrial heating processes have been changing the design techniques of thermal equipment since the 1950s. The heritage of large volumes in the combustion chamber, needed to assure complete combustion of solid fuels or producer gas, was a paradigm to be changed in addition to heat transfer concepts with less radiation and more convection. Cleaner combustion products allow the use of a low-density refractory such as ceramic ﬁber, reducing signiﬁcantly the heat storage in furnace structures [2]. The most common use of a fuel gas in industrial processes is heat generation. The ﬂow of heat in a process can be easily shown by a Sankey diagram. Figure 9.1 shows as an example a Sankey diagram of a water heater: the natural gas input (100%), the ﬂue gas losses (8%), the surface losses by convection and radiation (2%), the purging losses (0.2%), and the useful heat in water (89.8%). The Sankey diagram must be considered the most useful tool for understanding the behavior of the heat ﬂow in thermal processes. It allows us to calculate the burner power when replacing a heat source such as fuel oil or electricity by a fuel gas, and also to estimate the energy conservation potential of industrial processes.

Figure 9.1 Sankey diagram of a water heater [3].

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Industrial thermal processes can be considered under two main headings: . .

batch-type processes continuous-type processes.

The batch type is a process in which the feedstock or raw material is loaded into the thermal equipment one or more times, the process is carried out and the ﬁnal product is then delivered. This means that the process performs a cycle, for example, the melting of aluminum in a crucible furnace, where the cycle starts by loading the scrap and ingots into the furnace and the cycle ends by tapping the molten aluminum after melting and reﬁning. Then a new cycle begins, loading the furnace again. The continuous type is a process in which the feedstock is loaded while the ﬁnal product leaves the thermal equipment at the same time. An example is the cement rotary kiln: while the limestone (feedstock) is being continuously loaded into the kiln, the clinker (product) drops into the cooler. In most cases, continuous thermal processes allow one to achieve low-energy needs because batch processes spend fuel to reheat the furnace structure cycle by cycle. Sometimes the amount of fuel spent in reheating the structure is even higher than the heat absorbed by the product. In addition, continuous processes usually allow a better product quality due to an even operation. The heat transfer from a hot source such as a fuel gas ﬂame to an industrial process can be done in three ways: conduction, convection, and radiation. When replacing fuel oil, ﬁrewood, or electricity by a fuel gas, it is very important to take into account not only power and efﬁciencies but also the heat transfer behavior at the process. Usually the ﬂames from fuel oil and ﬁrewood are much more intense in radiation than gas ﬂames. On the other hand, if less radiation leaves a gas ﬂame, more energy becomes available by convection. This phenomenon can be observed when replacing fuel oil by natural gas in a ﬁre-tube boiler. In order to burn a fuel gas in industrial processes to release heat, it is necessary to have installed burner systems to mix the gas with oxygen, keeping the ﬂame under control. Air is the main source of oxygen for combustion, because it contains 21% oxygen on a volume basis. However, some high-temperature industrial processes need the use of oxygen at concentrations higher than that in air in order to increase the ﬂame temperature. There are three main divisions of gas burners: .

.

.

Air–gas burners, designed originally to ﬁre gas and air, but can also utilize light oxygen-enriched air according to its design. Most burners can operate with oxygen concentrations in air up to 25% and a few up to 30%. The problems that can occur on increasing the oxygen content in combustion air are burner overheating, due to increased ﬂame temperatures, and ﬂashback from the increased ﬂame speeds [4]. Oxygen–gas burners, designed to ﬁre gas and oxygen, in order to obtain high ﬂame temperatures and speeds. On the other hand, the volume of combustion products is very low compared with conventional air–gas burners. The oxygen ﬁring techniques are usually applied in high-temperature processes. Oxygen–air–gas burners specially designed to ﬁre gas with air and oxygen in a wide range of ratios.

9.3 Other Processes

A burner system consists of burners, ﬂow trains and supervising controls. The burners must provide ﬂame stability and ﬂame shape within the power range and the gas–air, the gas–oxygen, or the gas–air–oxygen ratios. The ﬂow trains control the pressures, the ﬂow rates at the proportioning requirements, and the light-up and shutdowns for burners and pilot burners. The ﬂame supervising control system is responsible for checking the ﬂame stability conditions and the utilities involved: ﬂows and pressures of fuel gas, air/oxygen, and, when necessary, cooling media (usually water). If an inadequate parameter occurs, the control system shuts down the burner or does not allow it to light up. The process supervising control is the interface between the burner system and the process, controlling temperatures, pressures, and other parameters.

9.3 Other Processes

A fuel gas can be used also for several processes besides heating processes, taking advantage of its chemical properties. Fuel gases, mainly NG, ethylene, and propylene, are also used as feedstock to the chemical and petrochemical industries. A wide range of products can be made, such as polytetraﬂuoroethylene (PTFE), polyethylene (PE), poly(vinyl chloride) (PVC), polystyrene (PS), polypropylene (PP) and ethylene oxide. NG can be used also as feedstock to produce hydrogen by steam reforming, sometimes called steam methane reforming (SMR). The ﬁrst reaction occurs at 875 C and 24 bar g in the presence of a nickel-based catalyst mass [5]: CH4 þ 2 H2 O ! CO2 þ 4 H2

However, about 10% of the carbon remains as carbon monoxide instead of carbon dioxide, a second reaction in a ferrochromium catalyzer being necessary [5]: CO þ H2 O ! CO2 þ H2

Therefore, 1 mol of methane produces 4 mol of hydrogen, with 1 mol of carbon dioxide as by-product. Another approach is the development of the gas-to-liquid (GTL) technologies, a way to transform natural gas into high-quality oil products such as DME, diesel, naphtha, and n-parafﬁns, to be used as sulfur-free and oleﬁn-free fuel or chemical feedstock. Many beneﬁts to the environment could be achieved by replacing the conventional fuels made in oil reﬁneries by the fuels obtained from GTL technologies. The products of incomplete combustion of fuel gas can be used for preparing several grades of atmospheres to react or protect industrial products. An example of a reactive atmosphere is the direct reduction of iron ore to sponge iron. As protective atmospheres, there are various processes that are needed to prevent or minimize oxidation mainly in the heat treatment of metals, such as annealing, bright annealing,

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and normalizing. Other steel heat treatments, such as carburizing, need a reactive gas to increase the carbon potential of the furnace atmosphere and change the metal characteristics. The most unusual application of incomplete combustion of fuel gases is the generation and projection of soot onto metallic surfaces in order to lubricate or to allow easy demolding in high-temperature processes such as in foundries, where conventional chemicals do not perform well. Therefore, the carbon content in the fuel gas must be as high as possible, acetylene being the most suitable. On the other hand, fuel gas may be used due to its physical properties such as pressure and boiling point. For example, butane and butylene could be used as physical foam-blowing agents in the production of extruded foamed thermoplastics. The main property of foaming agents is to remain mixed in the liquid state at the same temperature as the melted thermoplastic under pressure. When this mixture crosses the extrusion die port, the pressure drops and the foam-blowing agent boils, expanding the melt, also helping to cool it via the butane latent heat needs, becoming foam in the solid state. Another application of propane, n-butane, and isobutane blends is as a propellant in aerosol spray dispensers, also displacing the chloroﬂuorocarbons (CFCs), where the gas blend under pressure forces the liquid to release through the dispenser nozzle. Hydrocarbons such as butane started to replace CFCs the in the late 1970s, helping to reduce the destruction of the ozone layer in the stratosphere. More recently, DME has been playing the same role as a propellant and foamblowing agent for a wide range of products such as foods (creams, oils), hairsprays, air fresheners, spray paints, insecticides, deodorants, antiperspirants, tire inﬂators, and shaving foams.

9.4 Applications in Steel Mills

Steel mills can be divided into two groups: integrated and non-integrated. Integrated steel mills start from iron ore as the main feedstock. The iron ore is reduced to iron in a blast furnace and then processed to steel in a basic oxygen furnace (BOF). On the other hand, non-integrated steel mills start from steel scrap that is melted in electric arc furnaces. 9.4.1 Integrated Steel Mills

Returning to integrated steel mills, the fuel gases that must be considered are COG and BFG, both produced on-site, as by-products from the coke plant and the blast furnace, respectively, and NG, LPG, and acetylene delivered from gas companies. Gas applications will be considered according to the production chain, starting from the ﬁrst step, ironmaking. The main equipment is the blast furnace used to reduce the iron ore to pig iron, the feedstock being iron ore or pellets, metallurgical coke, and

9.4 Applications in Steel Mills

limestone. The use of charcoal instead of coke is restricted to small blast furnaces, but allows the production of the best quality pig iron due to the very low sulfur content. Nowadays, in order to reduce the coke rate and increase productivity, NG is injected into the blast furnace, in addition to other fuels such as pulverized coal, heavy fuel oils, and coking tars. In addition to NG, a few steel mills inject other fuel gases such as COG and LPG. The main advantage of using COG is that it is almost cost-free as a byproduct, it just being necessary to clean it, boost its pressure, and modify tuyeres. During periods of low demand in the steel mill, part of the COG is ﬂared. In some coke plants, the COG is used in power plants. The partial replacement of coke by fuel gases in blast furnaces depends on several factors, but rough ﬁgures [6, 7] are: . .

1 N m3 of NG per kg of coke 0.8 kg of LPG per kg of coke.

The injection of NG is common practice whereas the injection of LPG is somewhat limited due to its cost–beneﬁt value. The air for the blast furnaces, called hot blast, must be preheated in the range from 900 to 1300 C, usually in Cowper stoves. The main fuel burned in Cowper stoves is BFG, sometimes enriched with COG, NG, or LPG. The application of most molten pig iron produced is in steelmaking; just a small part is poured into ingots to be sold to foundries. The second step of the production chain is steelmaking. The main equipment is the BOF, where the molten pig iron is converted into steel by oxygen blow, it being necessary to add steel scrap in order to prevent the molten steel from overheating due to the heat liberated by the carbon burning. The application of fuel gases is just to dry and preheat the torpedo car (also called ladle car) and the BOF after a refractory repair or before receiving the molten pig iron. This preheating operation could be done with COG, NG, LPG, or BFG, the last one usually being enriched with a higher heating value gas. Sometimes oxygen-enriched atmospheres are used to increase the ﬂame temperature and reduce the heat-up time. From the BOF, the molten steel must be transported by ladles to be tapped into the tundish to feed the continuous casting machine (CCM), where its channels convert the metal into billets, blooms, and slabs. The ladles and the tundish must also be preheated after repairing or before operation when not sufﬁciently heated. The old process of pouring the molten steel into stationary molds to form ingots still exists in some steel mills, but the productivity in the CCM is higher. The use of BFG and COG instead of NG or LPG must take into account eventual contamination by the lowgrade gas residues. The continuous steel proﬁle leaving the casting machine must then be cut into suitable lengths by oxygen–gas torches. However, just high-grade fuel gases can be used for this purpose, such as NG, LPG, and acetylene. As mentioned, the old process, still existing in a few steel mills, is to cast the molten steel into ingots. The last step is the rolling mill, where the steel billets, blooms, and slabs acquire their ﬁnal shape. There are two ways to begin this step: hot direct rolling, by direct feeding from the CCM, which is the best process to save energy, or heating the cold or warm metal in reheating furnaces. The most common type is the walking beam

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furnace, but some pusher types and rotary hearth continuous furnaces are also found. The reheating furnaces can use low-grade fuels such as BFG and COG and also high-grade fuels such as NG, LPG, and fuel oils. The low heating value of the BFG can be compensated by enrichment with COG, NG, LPG, or fuel oil in dual fuel burners. Another alternative would be to enrich the combustion air with oxygen or to ﬁre with pure oxygen. It is also possible to install auxiliary oxygen–gas burners to boost production. When the steel is available in ingots, the most commonly used equipment is the pit furnace for reheating before rolling or forging. The fuel consumption in reheating furnaces depends mainly on the temperature of the steel pieces being fed into the furnace, the size of the furnace, and the combustion air temperature. Medium and large reheating furnaces usually recover the waste heat from the ﬂue gas to preheat the combustion air. As a rough ﬁgure, the speciﬁc fuel consumption of reheating furnaces would be about 2 GJ per ton of steel, which means 50 m3 of NG per ton of steel loaded at ambient temperature. 9.4.2 Non-Integrated Steel Mills

Non-integrated steel mills start the production chain by melting scrap in an electric arc furnace (EAF). The steel scrap must be conditioned before being fed into the EAF. Large pieces must be cut into suitable lengths by oxygen–gas torches or shears before pressing. The most commonly used fuel gases for oxygen cutting are NG, LPG, and acetylene. The application of the fuel gas in the EAF is effected by auxiliary oxygen–gas burners to boost production, reducing the melt down time, and, consequently, the tap-to-tap time, saving electricity and electrodes. The use of oxygen in this application is mandatory because the burners are lit at the beginning of the melting after loading each basket, when the furnace is completely full of scrap without any space available to establish a conventional air–gas ﬂame. The high-temperature oxygen–gas combustion products melt almost instantaneously all the scrap around the ﬂame. On the other hand, the ﬂue gas volumes are much smaller when burning with oxygen instead of air, allowing maximization of the power of the burners in spite of the difﬁculty for the combustion products to ﬂow throughout the scrap pile. Usually three, four, or ﬁve auxiliary burners can be installed in an electric arc furnace, at the cold spots and at the slag door. The efﬁciency of this application depends mainly on the possibilities of heat transfer from the ﬂame and its combustion products to the solid scrap. Therefore, the efﬁciency is as high as the amount of solid scrap inside the furnace, restricting the burners role to the ﬁrst half of melting down time for each basket. Oxygen–fuel burners can also be used to inject extra oxygen into the furnace when necessary. The replacement of electricity by oxygen–gas energy is within the following range: . .

1 m3 of NG þ 2.0 m3 of oxygen replaces 4–8 kWh; 1 kg of LPG þ 2.5 m3 of oxygen replaces 5–10 kWh.

9.5 Applications in Foundries

After being melted, the steel can be alloyed and reheated for tapping in the same EAF or in the ladle furnace (LF). The advantages of using the LF are optimization of the EAF productivity with just a melting machine and a reduction in the overall investment in the steelmaking considering the same steel production per year. The ladles must be preheated prior to receiving the molten steel from the EAF in reheating stations. In non-integrated steel mills, the fuels used to the ladle heating are NG, LPG, or fuel oils. The molten steel from the EAF or LF is then tapped into the CCM or into ingots, following the same production chain as in the rolling mill already shown. The fuel gas applications are the same, but BFG and COG are not available in a non-integrated steel mill. In the case of stainless-steel production, the molten metal must be treated using the argon–oxygen decarburization (AOD) or Creusot-Loire–Uddeholm (CLU) processes before casting. After shaping, some steel products must be treated in heat treatment furnaces. The furnaces can be heated directly or indirectly by the fuel gases, depending on the needs of controlled atmospheres. In direct heating, the combustion products come into contact with the steel product and any fuel gas available or its mixtures can be used such as BFG, COG, NG,or LPG. However, when a controlled atmosphere is needed, the combustion products must not be in contact with the load, it being necessary to keep the two separated by a heat-exchanging surface. The burner ﬂame and its combustion products are conﬁned inside heat-exchanging pipes or mufﬂes, which sometimes needs accurate combustion control to avoid damaging those elements. In this case, a high-grade fuel must be chosen such as NG or LPG, mainly in the case of heat-exchanging tubes. On the other hand, neutral or reducing protective atmospheres can also be generated by the combustion products (exothermic gas or exogas) or catalytic reaction (endothermic gas or endogas) of NG, propane, or butane with air, and also be obtained by mixing industrial gases such as nitrogen, argon, and hydrogen. The main products from a steel mill are proﬁles, bars, rails, wire rods, plates, hotrolled coils and sheets, cold-rolled coils and sheets, welded and seamless pipes, and steel castings.

9.5 Applications in Foundries

The deﬁnition of foundries in this chapter comprises melting and alloying of ferrous and non-ferrous metals to be poured into molds. 9.5.1 Molding

The molds are made from metal, usually water cooled, sand, or graphite. The main gas applications in metallic permanent molds are the following:

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. .

mold preheating in order to avoid losing the ﬁrst pieces due to the cold mold blackening of the mold by projecting soot onto the inner mold surface in order to ensure easy demolding after pouring the molten metal and cooling.

Preheating of the metallic mold usually uses NG or LPG ﬁring with air or oxygen. Sometimes a special burner design is needed to meet heating requirements such as ﬂame shape and heat distribution. An example is the mold for centrifugal casting of ductile iron pipes. If not preheated, the ﬁrst few pieces are lost until the mold reaches a suitable temperature, which could represent signiﬁcant losses of high-grade metal, time, and labor, as much as with a wider pipe diameter. The blackening of the metallic mold surface needs preferably a fuel gas with a high carbon content and fast molecular dissociation in order to maintain the soot concentration and projection speed. The gas that meets all requirements is acetylene. Sometimes it is also possible to use LPG, as rich as possible in oleﬁns. Sand molds are needed in some fuel gas applications: . . .

skin-dried sand mold, the inner surface of the mold being dried by manual torches sand cores, dried in small ovens sand recovery, usually a rotary drum kiln to evaporate and incinerate the sand additives such as phenolic resins.

9.5.2 Melting

In order to melt metals, various furnaces can be used. The main classiﬁcation of melting furnaces is related to the energy source for heat generation [8]: . .

combustion of fuels conversion of electricity into heat.

There are several different types of melting furnaces which use fuel gases as energy source. The main ones are: . . .

crucible or pot furnaces rotary drum furnaces reverberatory furnaces.

9.5.3 Crucible or Pot Furnaces

Crucible or pot furnaces are small furnaces used for melting, reﬁning, alloying, and holding non-ferrous metals such as lead, tin, zinc, aluminum, bronze, and brass. The crucible capacities vary from 150 to 1000 kg per batch, except for lead reﬁning, where melting pots of up 200 ton can be found [9]. There are two main types of crucible furnaces: semi-mufﬂe and full-mufﬂe furnaces. In the full-mufﬂe furnace, the combustion products cannot contact the molten metal, whereas in the

9.5 Applications in Foundries

semi-mufﬂe furnace, contact occurs just at the top of the pot. Usually the crucible furnaces have a single burner installed at the bottom of the combustion chamber, in a tangential position with respect to the annular space between the pot and the cylindrical refractory structure, to allow the combustion products to circulate around the pot, heating but not hitting. The fuel consumption of crucible furnaces depends on several parameters such as the initial and ﬁnal heat contents of the metal, the characteristics of the feedstock, the size of the equipment, the structural heat losses, and the time spent for the whole run (charging, melting, reﬁning/alloying, holding, and tapping). Another factor that changes the fuel consumption is how the furnace operates: continuously cycle after cycle, which means that the structure remains somewhat heated for the next run, or the furnace loses a lot of heat lying idle between runs. The average consumption of a crucible furnace just to melt aluminum is 150 m3 of NG per ton or 120 kg of LPG per ton, not taking into account alloying and holding times. Some crucible furnaces are used just for holding the molten metal delivered from another melting furnace while feeding the casting machines. 9.5.4 Rotary Drum Furnaces

Rotary drum furnaces are batch-type direct-ﬁred and therefore very efﬁcient melting machines due to the heat transfer from the ﬂame, the refractory, and part of the molten metal to the feedstock. The applications of rotary furnaces are not only to melt metals such as iron, copper, and aluminum, but also to recover lead and tin by metallurgical reduction from its oxides or low-grade scrap. Nowadays most rotary melting furnaces are equipped with oxygen–gas burners due to their high productivity and reduced environmental impact. On the other hand, these furnaces usually expend more alloying elements due to the metal surface area exposed very close to the ﬂame. The furnace capacities vary considerably, from small furnaces of 1 ton to large furnaces of over 60 ton per run. The most common furnace conﬁguration is a horizontal cylinder with two conical heads, the burner being installed at one end and the ﬂue stack at the opposite end. As any other batch-type melting furnace, the fuel consumption varies according to the metal, furnace, and operational features. The speciﬁc consumption of fuel varies also according to the size of the furnace, being higher in small than in large furnaces. For example, the speciﬁc consumption of gas in continuous runs for melt gray iron at 1450 C is about 120–125 m3 of NG per ton or 95–100 kg of LPG per ton burning with 230–240 m3 of oxygen per ton in a 1 ton rotary furnace. Now considering a 5 ton furnace, the gas consumption is reduced to about 70–80 m3 of NG per ton or 55–63 kg of LPG per ton, ﬁring with 140–150 m3 of oxygen per ton. However, it is not worthwhile to oversize the furnace capacity because it would increase the holding time and/or the idle time, while on the other hand increasing the fuel consumption.

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9.5.5 Reverberatory Furnaces

Reverberatory melting furnaces, most used for aluminum melting, can reach high capacities, over 80 ton per batch. These furnaces have the shape of a large box of refractory bricks externally lined with steel plates. The number of burners is usually from two to ﬁve, installed through the walls horizontally or slightly downwards in such a way as to prevent the ﬂames from overheating the metal. In some furnaces the burners are installed in the upper part of the walls but inclined downwards. Small furnaces have a single burner. The furnace design presents some variations such as wet-hearth and dry-hearth and movements such as tilting or partial rotation [7]. A typical gas consumption of a 20 ton reverberatory furnace, melting aluminum, is 113–126 m3 of NG per ton or 90–100 kg LPG per ton ﬁring air–gas burners. Figure 9.2 shows a gas burner ﬁring into an aluminum reverberatory melting furnace. Comparing rotary drum melting furnaces with reverberatory furnaces, the former are more efﬁcient as a melting machine whereas the latter are more suitable for alloying and holding molten metals. In the case of extrusion presses, the blackening of aluminum billets before extrusion prevents the remaining piece from sticking after pressing. Figure 9.3 shows the billet surface before and after blackening with a fuel gas, usually acetylene. 9.5.6 Cupola Furnaces

The most typical melting furnace used in iron foundries is the cupola furnace. Its original fuel is coke and the furnace has a cylindrical vertical body like a blast furnace, but the raw material is pig iron, iron scrap, steel scrap, and foundry returns, plus coke

Figure 9.2 Gas burner firing into an aluminum reverberatory melting furnace.

9.5 Applications in Foundries

Figure 9.3 Billet blackening.

and a small addition of limestone. An important operational parameter is the coke rate, the ratio between coke and iron. The average thermal efﬁciency of a cupola furnace can be considered as 30%, ranging from 20 to 25% in small furnaces up to 45% in larger units with a double row of tuyeres [10] The main problem with this type of furnace is the ﬂue gas emissions to the environment. Most countries have established strict rules to minimize emissions, which means that high investment is necessary. The aim of gas application in cupola furnaces is to replace part of the coke by NG or LPG, lowering the coke rate and ﬂue gas emissions. The cokeless cupola replaces totally the use of coke by NG, LPG, or fuel oil. The burners are installed in the crosssection of the shaft towards the center, below the water-cooled grate, ﬁring in understoichiometric condition to avoid oxidation, the addition of graphite powder also being necessary to correct the carbon content of the molten iron in the well. The absence of visible emissions from the ﬂue stack due to the very low emission of particulate matter is the main advantage to the environment, in addition to reduced carbon dioxide emission [9]. Rotary melting furnaces can also replace cupola furnaces, reducing emissions to the environment mainly if fume control devices are not installed. Gas-ﬁred rotary furnaces offer the beneﬁt of sulfur-free iron and combustion products. The ﬁnal cost must take into account the iron quality and the costs of fuel (coke and gas), oxygen, investment, and waste disposal. 9.5.7 Electric Melting Furnaces

The second group of melting furnaces is the electric furnaces, converting electricity into heat. The main types are [11]

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. . .

EAFs induction electric furnaces resistance electric furnaces.

EAFs are found only in large foundries for melting carbon steel, steel alloys, stainless steel and iron. As mentioned in Section 9.4, auxiliary oxygen–gas burners can be installed to boost production and reduce electricity consumption. The induction furnace ﬁnds widespread use in foundries. It is a versatile equipment to melt many metals and its alloys. There are two main types: pot or crucible induction furnace and channel induction furnace. The heat transfer from electricity to the solid metals is not efﬁcient at the beginning of the run, while there is no liquid metal in the pot. That is why a common practice is not to pour all of the liquid metal, leaving a liquid heel for the next run. However, it is not possible to do this if there is no metal compatibility in consecutive runs. With the pot induction furnace it is also possible to boost productivity and save electricity by installing an auxiliary gas burner or feedstock preheater. Due to the lack of free space in the pot induction furnace for it to act as a combustion chamber at the beginning of the run, when the pot is completely full of feedstock, it is mandatory to use an oxygen–gas burner. The productivity of the furnace increases because the burner almost instantaneously melts part of the solid metal, improving the heat transfer from the induction currents through the molten metal, reducing the melt down time. Therefore, the actual efﬁciency of oxygen–gas burners is limited just to the beginning of the run, mainly without the liquid heel practice. Resistance electric furnaces can be used up to temperatures as high as 1800 C. However, due to the lack of sufﬁcient space to establish a combustion chamber, it is very difﬁcult and costly to convert these furnaces to gas-ﬁred use. The best opportunities, when a signiﬁcant electricity replacement is aimed for, are the possibilities of implementing duplex processes, which means the combination of fuel melting furnaces with electric furnaces. The most common type is the rotary melting furnace to be used as a melting machine pouring the molten metal into the induction furnace for alloying, temperature conditioning and holding. This practice allows the molten metal production to be increased signiﬁcantly while reducing the speciﬁc electricity consumption. Usually the investment cost in a rotary melting furnace is lower than the investment in an induction furnace of the same capacity. 9.6 Applications in the Ceramic Industry

Ceramics manufacture was most probably the oldest industry of our developing civilization, starting its activities thousands of years ago to meet several basic needs such as storage of liquids and foods. The ceramic industry produces a very wide range of different products formed from clay and/or other similar substances. The main process can be summarized as extracting the components from Nature, preparing the components (sizing, cleaning,

9.6 Applications in the Ceramic Industry

concentrating, drying), mixing the components in a wet or dry way, forming, drying, and ﬁring. Some decoration processes are applied after forming or drying with just a single ﬁring being necessary. In other processes, the decoration is done after ﬁring, a second ﬁring then becoming necessary to burn the enamel. The main types of ceramic products are as follows: . . . .

coarse products made from rough clay such as bricks, roof tiles, drainpipes and earthenware refractory bricks of several shapes and qualities wall tiles and ﬂoor tiles for building and construction ﬁne ceramics such as porcelain and china for tableware, sanitary ware, ornamental articles, and technical products.

9.6.1 Coarse Products

The old intermittent kilns for ﬁring coarse products were originally designed to burn solid fuels, mainly coal and wood, and producer gas, presenting the features of updraft or down-draft systems. The ﬂow rate of ﬂue gases is high due to the large excess of air required, so the combustion chamber and the ﬂue path must be oversized compared with high-grade fuels such as NG and LPG. Nowadays the system still in use is the down-draft type, due to its better efﬁciency, such as the round beehive and rectangular Manchester kilns [12]. In countries where ﬁrewood is cheap and available with no environmental restrictions on its use, it is very difﬁcult to convert those kilns to NG or LPG due to the higher costs. On the other hand, the original design to handle high combustion product volumes is not suited to high-grade fuel gases which need a very low excess air level to achieve high efﬁciencies. Sometimes the low ﬂow of ﬂue gases cannot reach and heat efﬁciently all points in the kiln, reducing the quality of the product, if some design changes are not implemented. 9.6.2 Refractory Bricks

Refractory and insulating bricks are structural materials used to build a barrier to contain the heat inside a conﬁned space, minimizing the heat losses to the environment. Refractory bricks or ﬁrebricks must have chemical stability at high temperatures and meet the necessary insulation and mechanical strength requirements. Insulating bricks have the property of low thermal conductivity, consisting of high-porosity material, used as backing insulation of ﬁrebricks. In the ceramic industry, kilns are mainly responsible for fuel consumption. Dryers usually takes advantage of the warm ﬂue gases from the kilns, sometimes with a small addition of NG or LPG ﬁring to keep the temperature stable. Continuous kilns are also used. The fuel speciﬁc consumption in continuous kilns is much lower than in intermittent kilns. The inefﬁciency of conventional intermittent kilns is due to the fuel spent in reheating the kiln structure for each batch, which

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is aggravated if there is no device to recover heat from the ﬂue gases. The structural weight of the kiln is higher than the weight of the load in most cases. The main reason for still using intermittent kilns is that refractories need very long ﬁring cycles, such as silica that needs at least 8 days. In this case, a tunnel kiln to meet this requirement would be impractically long. Another reason is the small production of special articles that need a particular temperature proﬁle [13]. 9.6.3 Wall and Floor Tiles

The process to make wall tiles and ﬂoor tiles starts in the mass preparation by ball milling in a wet manner for all the components. The mass is then dried in spraydryers to reduce the moisture before being fed into the mold to be pressed, forming a biscuit. The biscuits are dried in vertical or horizontal chambers before being fed to the production line. The spray-dryers can be heated by NG, LPG, or fuel oil, whereas the dryer chambers cannot be heated by fuel oil. The old process was to make wall tiles and ﬂoor tiles in two steps: starting by making the biscuit in a ﬁrst burning before decoration, and then ﬁring again, both burnings being performed in tunnel kilns, most of them oil ﬁred, it being necessary to conﬁne the tiles inside mufﬂes to prevent contact with ﬂue gases, which would affect quality. Nowadays the process involves just a single ﬁring, NG and LPG being widely used in direct-ﬁred roller kilns. The average speciﬁc consumption is 58 m3 NG per ton of tiles or 46 kg LPG per ton of tiles. The maximum temperature of the process reaches about 1050–1250 C [9]. 9.6.4 Fine Ceramics

Fine ceramics such as porcelain for tableware and sanitary ware are also usually ﬁred in tunnel kilns, most of them gas ﬁred. However, the pottery industry uses intermittent kilns in addition to tunnel kilns, electrically heated or gas ﬁred. There is a wide variety of technical products, such as spark plugs, electric insulators and laboratory ware [12].

9.7 Applications in Glass Works

Glass works constitute a ﬁeld of industrial activities with plentiful fuel gas applications. Glass articles can be found in several industrial activities, such as packaging, building, transport, furniture, electronics, and optics. Houseware made with glass has been present in the human environment for more than 2000 years. The main raw materials for making glass are quartz sand and cullet. There are also many other components, such as soda ash, china clay, limestone, feldspar, dolomite,

9.7 Applications in Glass Works

red lead, and additives, according to the type and color of glass being produced: container, domestic, ﬂat, lead crystal, scientiﬁc, and glass ﬁber. Some small glass works use only cullet from an outside source as feedstock [14]. Glass processing requires the following steps: mixing the raw materials, melting, reﬁning, conditioning, forming, annealing, inspecting, and packing. 9.7.1 Glass Melting, Refining, and Conditioning

Glass works are intensive consumers of energy, as in many other high-temperature processes. The melting point of glass is not well deﬁned, but temperatures in the range 1350–1600 C soften the glass to a liquid with a viscosity below 10 cP [12]. That is why the design of melting furnaces must include a device to recover heat from the ﬂue gases. There are two main types of heat recovery: recuperative furnaces and regenerative furnaces. Regenerative furnaces preheat the combustion air by a twin chamber regenerator, packed with refractory pieces to store heat. While one chamber is being heated by the ﬂue gases, the other chamber heats up the combustion air. The change-over between chambers occurs every 20–30 min in order to maintain the combustion air temperature within an acceptable range, usually above 1200 C, achieving ﬂame temperatures of 1800–1950 C [12]. The most known continuous furnaces, according to the arrangement of the regenerators, are side-port furnaces (also cross-ﬁred), the largest ones, and end-port furnaces (end-ﬁred or horse-shoe ﬂame). The output of a side-port furnace can reach about 800 ton per day, whereas that of an end-port furnace cannot exceed much more than 200 ton per day [15]. Recuperative furnaces preheat the combustion air in a heat exchanger fed by the hot ﬂue gases after leaving the furnace. The preheated air temperature depends on the heat-exchanging design, always much lower than the temperatures obtained by regenerators. Recuperative continuous furnaces are medium-sized units and the melting area usually does not exceed 70 m3. There are also smaller furnaces such as day tanks (batch type) and direct-ﬁred continuous tank furnaces, most of them running with cold combustion air. The applications of fuel gases start at the stage of heating up the melting furnace, bringing the empty furnace from ambient up to the operating temperature when full of melted glass, this being the most critical stage of the furnace campaign [15]. High-grade fuel gases such as NG or LPG are the most suitable fuels to control the steps of the heating-up process, start drying the refractory material and then increase the temperature according to scheduled curves to accommodate the structure expansion. When the furnace reaches a safe level of temperature to guarantee spontaneous ﬂame ignition, usually above 1000 C, the gas burners could be replaced by heavy fuel oil burners if fuel gas is not aimed for [15]. Once a continuous furnace is in normal operation, all constituents of the raw material are melted and all chemical reactions are completed without leaving solid inclusions; then the molten glass is reﬁned, allowing time for the rise out of gaseous

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inclusions, and therefore must be conditioned at a suitable temperature for forming in the forehearth [2]. Even when the melting end runs on heavy fuel oil, the conditioning and sometimes the reﬁning must ﬁre NG or LPG in order to meet the accurate requirements. 9.7.2 Forming

The next step is forming, when the glass acquires its shape. If the glass article is obtained by pressing, the following fuel gas applications could be necessary: . .

. . .

Gob reheating to soften sharp edges from scissors that could leave traces after pressing. Mold preheating to allow the glass to slide inside the mold without increasing its viscosity in such way that it becomes impossible to reach all corners and further parts of the mold. Reheating of conveyor elements such as rotary bases and belts, to avoid thermal shock on touching hot articles. Blackening to lubricate conveyor belts, if the hot glass article must slide on the belt by a pusher, obtained by incomplete combustion of a fuel gas. Fire ﬁnishing of the seam indicating the line between the mold and the plunger, usually on the edge, by a row of laminar ﬂame burners, which softens the seam in order for it to become rounded.

For ﬁre polishing of glass surfaces in order to increase the brightness, usually by high-speed polishing burners, sometimes it is necessary to use a high-speed gas ﬂame such as hydrogen–oxygen or its blends with NG or LPG. If the forming is made by carousel machines such as the H-28/cut-off, the fuel gas applications are as follows: .

.

Blackening of the mold to prevent the article from sticking, by the incomplete combustion of acetylene, which permits a very thin and invisible layer of carbon. Cutting the unwanted part of the article in excess, usually drinking glasses, and edge ﬁnishing with a circular burner with a row of small narrow ﬂames towards the center.

In automatic IS (independent or individual section) machines the fuel gas application is just conveyor preheating. Some trials have been made to replace the conventional mold lubrication applied manually, a semi-ﬂuid graphite grease, by the automatic projection of soot from incomplete combustion of fuel gases. However, so far good results have not been reported. In the handmade glass industry, the main fuel gas applications are the following: . .

Cutting the unwanted part of the article in excess. Reheating the ware in glory holes to allow jobs that take a long time, keeping the article in a suitable temperature range.

9.7 Applications in Glass Works

Figure 9.4 Fire finishing of tableware. . .

Fire ﬁnishing and polishing stations (see Figure 9.4). Torches to reheat speciﬁc points.

9.7.3 Annealing and Hardening

The next step is glass annealing of the stresses generated by forming, which is performed in tunnel furnaces known as lehr. The glass articles from forming are placed on a conveyor belt for traveling across the furnace. This furnace can be direct ﬁred or semi-mufﬂed if the fuel is NG or LPG, but must be fully mufﬂed if ﬁring fuel oils or other fuels that contains sulfur, due to the formation of sodium sulfate on the glass surface, appearing like a light white cloud. The residence time and temperature for the annealing vary according to the glass composition and the thickness of the pieces [9]. Therefore, the speciﬁc gas consumption varies widely. Figure 9.5 shows the under-stoichiometric ﬂame of a laminar gas burner heating and blackening the lehrs conveyor belt, to avoid losses by thermal shock or wear when in contact with the heated glass pieces just arriving from forming. 9.7.4 Decorating

After inspection, some glass articles need to be decorated, such as soft drink bottles and tableware, by an automatic printing process, or painted manually, needing a

Figure 9.5 Laminar gas burner heating conveyor belt.

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return to the decorating Lehr to burn the enamel. In the lead glass industry, some crystal articles must be cut and polished, a nice handicraft job that adds a lot of value to the product. The same gas applications are applied to the decorating lehrs. 9.7.5 Packaging and Storage

The glass articles sometimes need heated air–gas burners for shrink packaging. Further, the engines of forklifts that handle the packages can also be gas operated. 9.8 Applications in Thermal Utilities

Thermal utilities can be deﬁned as all ﬂuids that can be used to exchange heat, meaning for heating and cooling purposes. There are many thermal utilities, but this chapter will describe the following types: . . . . .

cold water and cold air hot water and superheated water saturated and superheated steam thermal ﬂuids hot air.

9.8.1 Cold Water and Cold Air

Cold water and cold air are used in industrial processes to control temperatures, vapor pressures and chemical reactions, shifting solubility relationships to segregate components, and many other uses. Cold water can be used also to produce cold air in air conditioning systems in ofﬁces, laboratories, and rooms with atmosphere control. The most common way to produce cold water and cold air with fuel gases is to use a heat pump. There are two main types of heat pumps: compression chillers and absorption chillers. In compression chillers, the fuel gas can provide the mechanical energy by a gas engine, whereas in absorption chillers, the gas burning acts as the energy provider to drive the cooling process. The chillers can also generate simultaneously cold and hot water to allow important savings to be achieved when both utilities are needed. 9.8.2 Hot Water and Superheated Water

Hot water means water the temperature of which does not reach the boiling point, leaving a safety margin, at local atmospheric pressure. As a rule of thumb, the

9.8 Applications in Thermal Utilities

temperature is about 60–90 C. Although just a few industrial processes need such low temperatures, the main advantages are the following [16]: . . .

operation in atmospheric and low-pressure systems, allowing very thin wall thickness in piping and heat exchangers easy and accurate process temperature control, not exceeding the hot water temperature accidental contact with the skin does not cause severe burns.

However, the limitations of hot water are the following [16]: . .

large heat-exchanging areas due to the low temperature difﬁculty of operation in long circuits due to the heat losses, pressure drops, and low velocities.

Superheated water can be deﬁned as water the temperature of which is above the boiling point at local atmospheric pressure; therefore, to remain in the liquid state, it must be kept under pressure. As a safety margin is always recommended, this pressure must be at least slightly higher than the vapor pressure at the same temperature. Due to the pressure limitations, the maximum superheated water temperature is about 180–200 C. The main advantages of the superheated water are the following: . . .

reach saturated steam temperatures easy and accurate temperature control can operate in long circuits because the heat losses are acceptable.

On the other hand, superheated water can present some difﬁculties: . . .

hydraulic pumps needed, which expend energy piping with diameters larger than saturated steam for the same power, meaning higher costs of pipes, ﬁttings, and insulation accidental contact with the skin could cause severe burns.

The applications of fuel gases are in the water heater and in the boiler (superheated water), which generally are efﬁcient equipment. 9.8.3 Saturated and Superheated Steam

Saturated steam is generated by steam boilers or, more correctly, steam generators. Their construction designs vary considerably depending on the size and energy source. Basically, the boiler types can be classiﬁed in four main groups: . . . .

ﬁre-tube boilers water tube boilers waste heat boilers electric boilers.

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Fire tube and water tube boilers need fuels such as NG, LPG, fuel oils, ﬁrewood, or coal. In addition to costs, nowadays two main aspects must be taken into account: the fuel emissions and sustainability. NG and LPG are environmentally friendly in comparison with other fuels because they can be considered sulfur free and allow NOx levels to be easily reduced, which means less acid rain, generation of less carbon emissions and no emission of soot. Fuel oils and solid fuels in general need antipollution devices to clean the combustion products to meet environmental regulations in most countries. However, ﬁrewood from cultivated forests has the advantage of gaining carbon credits. Waste heat boilers takes advantage of a wasteful heat source such as hot ﬂue gases or lean fuel gases, but the investment required is higher than that for a conventional boiler of the same capacity. Although electric boilers do not themselves emit pollutants into the environment, the whole electricity chain must be taken into account such as the emissions from thermo-power plants and the losses in transmission and distribution lines. 9.8.4 Heat Transfer Fluids

Thermal ﬂuids can be used as heat-exchanging media for cooling or heating. Usually synthetic and thermal hot oils are used at temperatures out of the range of water and saturated steam, i.e. below 4 C and above 180–400 C. The main advantage of using synthetic and hot oils instead or water steam or superheated water at temperatures above 180 C is to avoid higher pressures. The thermal oils circulate in a closed circuit by pumping to bring heat to the equipment to be heated. The thermal oils are reheated in an oil heater, equipment similar to a water heater. 9.8.5 Hot Air

Hot air is mainly used for drying. There are several ways of heating an air stream: . . . .

direct heating from fuels, mixing the combustion products with air direct heating from electric resistances direct heating from quenching processes indirect heating, through a heat exchanger, the hot source being fed by combustion products, steam, hot water, or a heat transfer ﬂuid.

Direct air heating from fuels mixes the air with combustion products, changing the air composition somewhat. Higher temperatures mean a high content of combustion products, i.e. a high carbon dioxide content and less oxygen. If the heat source is a fuel oil, the heated air could be contaminated with sulfur compounds, heavy metals, soot, and other unburnt compounds. Direct air heating from NG or LPG meets most of the

9.9 Applications in the Rubber Industry

cleanliness requirements for the chemical, pharmaceutical, and food industries. The combustion products can sometimes be totally or partially delivered by the waste gas from another process, meaning good energy conservation practice. Direct air heating from electric resistances does not change the original air composition. However, if the electricity is generated by thermo-power plants, from the environmental point of view the efﬁciencies and the emissions of the power plant and transmission lines must be taken into account [3]. Direct heating from quenching processes is also good energy conservation practice, if otherwise the heat would be lost to the ambient air, for example, the quenching air from glass articles before leaving the lehr or from wall tiles leaving a roller kiln. However, sometimes this sort of source does not have enough heat, it being necessary to complement it with a reliable heat source such as NG or LPG burning. The indirect heating of an air stream through a heat exchanger allows high-quality hot air to be obtained, but the thermal efﬁciencies are lower than in a direct heating process.

9.9 Applications in the Rubber Industry

The rubber industry processes a wide variety of articles, from a simple eraser to sophisticated Formula One racing car tires. The main steps of the production chain are preparation of the rubber feedstock, shaping, curing, ﬁnishing, inspecting, and packing. The main heat sources for rubber processing are saturated steam and electricity feeding mixers, curing presses, and autoclaves. In the tire industry, about 70% of steam needs are consumed by curing presses. The main fuel gas application in the rubber industry is the steam boiler. The potential for energy conservation of the steam system can be summarized as follows [17]: . . . .

Optimize boiler operation by tuning all boilers to reduce excess oxygen levels in the ﬂue gas and also the unburnt carbon monoxide. Reduce the redundancy factors by load management when several boilers are kept operating at a time. Check if the boiler can reach its maximum power by studying the combustion air and ﬂue gas ﬂows rates, temperatures, and draft conditions. Verify the conditions of condensate return to the boiler; if a signiﬁcant amount of the condensates heat cannot return due to process contamination, a study to raise the make-up water temperature through a heat exchanger must be considered.

Another gas application is the replacement of electric resistances in autoclaves by a mufﬂed gas heater; this means the gas burner ﬁring into a metallic tube inside the autoclave close to the recirculating fan.

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9.10 Applications in the Food Industry

The food industry also utilizes many gas applications. The main advantage of using high-grade fuel gases, such as NG and LPG, is the cleanliness of its combustion products, the ease of handling in small burners, accurate temperature control, and very low maintenance needs. In this way, the combustion products can contact food products directly, reducing investment costs and increasing thermal efﬁciency, in addition to not contaminating the environment with sulfur compounds, soot, aldehydes, and other unburnt oil fractions usually present when combustion products are emitted into the atmosphere without anti-pollution systems. The heat from combustion products of high-grade fuel gases can be used directly in many applications such as cooking, baking, frying, drying, and sterilizing. Some examples are pans for cooking, ovens for bakeries, fruit dryers, and open can sterilization by direct ﬂame contact, and many others. The food industry also uses a lot of steam as heating medium in several processes such as pasta processing, yogurt, and similar biological cultures, in addition to almost all of the applications already referred to which utilize temperatures below 180 C. Some processes need steam as a moisturizing medium. 9.11 Applications in the Chemical and Pharmaceutical Industries

The most important fuel gas application in the chemical and pharmaceutical industries is steam generation as a heat source for processing. Chemical and biological reactions usually need very accurate temperature control, and saturated steam allows this due to its linear relationship between pressure and temperature. Another important application is drying processes, usually using heated air, the heating processes being described in Section 9.8.5. The hot air is then delivered to spray dryers, ﬂash dryers, tunnel dryers, and so on. A fuel gas can also be used to incinerate hazardous solid, liquid, and gaseous wastes in incinerators and ﬂares. The incinerators must be installed on-site or be mobile. The main incinerators are rotary kilns, liquid-waste incinerators, wastesludge incinerators (multiple-hearth furnaces, ﬂuid-bed incinerators, and infrared furnaces), retort-type multiple-chamber incinerators, and so on [18]. The use of afterburners is a common practice to incinerate combustible gas contaminants, including an auxiliary burner and combustion chamber. Incineration is very efﬁcient in reducing mass and volumes and destroys most hazardous products, but it can also generate some harmful compounds such as dioxins and furans. 9.12 Applications of Synthetic Natural Gas

Synthetic natural gas (SNG), also called propane–air, is a blend of LPG and air in suitable proportions to replace or complement NG delivery. The replacement of NG

9.12 Applications of Synthetic Natural Gas

by SNG could be necessary for several technical and commercial reasons such as a gas shortage, an accident in the gas pipeline, daily or wintry peak shaving, gas price variations and an interruptible gas contract. The main problem of replacing NG directly with LPG, without mixing air, would be the changes necessary to burners and controls, mainly in a critical situation when the gas delivery suddenly fails. Most industrial processes cannot be interrupted for more than a few minutes without business interruption. If it is a high-temperature process, the consequences could be catastrophic, with severe damage to furnaces. Therefore, SNG is a practical solution allowing a fast switch from NG. When replacing one fuel with another, such as SNG with NG, ﬁve aspects must be taken into account [19]: . . . . .

The The The The The

heat input rate range must be the same. ﬂow capacity of piping, valves, controls, burners and ﬂues. ﬂame stability conditions. heat transfer from the ﬂame. atmosphere in the combustion chamber.

The best known method to calculate the interchangeability of two fuel gases is by Wobbe index (WI) conservation. WI is deﬁned as WI ¼ HV=ðSGÞ0:5

where HV is the heating value of the gas and SG is the speciﬁc gravity of the gas (air ¼ 1.0). An example is give in Table 9.1. The WI values of NG and SNG are the same, but the gross HV of SNG by volume is 51% higher than that of NG. Therefore, the LPG–air blend (SNG) to replace NG must have the same WI as NG, but it can only meet the requirements of same heat input rate and ﬂow capacity when the burner systems are just controlled by pressures and oriﬁces. If the system is controlled by air and gas ﬂow rates linked to programmable logic controller (PLC), the software must be changed due to the new relationship between air and gas [20]. The three requirements of ﬂame stability, heat transfer, and combustion chamber atmosphere must be carefully studied for each case. Table 9.1 Properties of fuel gases.

Fuel gas NG

LPG

SNG

Property

Value

Gross HV SG WI Gross HV SG WI Gross HV SG WI

39 356 kJ m3 0.6340 49 427 kJ m3 109 234 kJ m3 1.8250 80 859 kJ m3 59 532 kJ m3 1.4506 49 427 kJ m3

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Figure 9.6 Electricity chain.

9.13 Replacement of Electrothermy by Fuel Gas

Electrothermy is the process that uses electricity to generate heat or to move heat from one place to another. The replacement of electrothermy by a fuel gas in industrial processes aims to reduce both the primary energy spent in thermopower plants and the environmental impact. Figure 9.6 shows the electricity chain from the primary energy to the useful heat. Considering an average efﬁciency of 40% from the fuel until the electricity leaving the generator and losses of 15% in the transmission and distribution lines up to the industry, the energy available as electricity would be just 34% of the primary energy. If the efﬁciency in the electric equipment were 89%, the useful heat would represent just 30% of the primary energy [3]. The fuel gas efﬁciency in low-temperature processes is always high. Considering the gas efﬁciency as 80%, the savings in primary energy would be very important: . . .

Electricity: Fuel gas: Savings:

useful energy ¼ 1.000 kW h > primary energy ¼ 3.333 kW h useful energy ¼ 1.000 kW h > primary energy ¼ 1.250 kW h 3.333–1.250 ¼ 2.083 kW h > 62.5%

This means in this exercise 62.5% less fuel gas and fewer emissions. However, unfortunately, many electrothermic processes cannot be replaced by a fuel gas for technical reasons. The author estimates that only about 30% of the processes could be converted into fuel gas ﬁring.

9.14 Conclusion

As shown in this chapter, the ﬁeld of fuel gas applications is beyond imagination. The challenge for the future is the development of new applications taking into account higher efﬁciencies, increased productivity, and improved quality with less impact on the environment and lower costs.

References

Every year many new applications are introduced in industry, requiring also the development of new materials and equipment such as tailor-made burners, heat recovery systems, sensors, controls, PLC software, and so on.

References 1 Ministry of Power (1958) The Efﬁcient

2

3

4

5 6

7

8 9

Use of Fuel, Her Majestys Stationery Ofﬁce, London. Pritchard, J.J. et al. (1977) Handbook of Industrial Gas Utilization, Van Nostrand Reinhold, New York. Costa, F.C. (2008) Replacing efﬁciently electrothermy by LPGas in the Brazilian industry. Presented at 21st LP Gas Forum, Seoul, South Korea. Saltin, L. (1981) Presented at Oxygen in Combustion for Heating and Melting, Liding€ o Sweden. Ahlberg, K. (1985) AGA Gas Handbook, AGA, Liding€o, Sweden. Lingiardi, O. et al. (2001) High productivity and coke rate reduction at Siderar blast furnace #2. Presented at 1st International Meeting of Ironmaking, 2001, Belo Horizonte, Brazil. Vertiola, S. et al. (1990) Conservaç~ao de Energia na Indústria Metalúrgica, IPT – Instituto de Pesquisas Tecnológicas, S~ao Paulo, Brazil. Trinks, W. (1951) Industrial Furnaces, vol. 1, John Wiley & Sons, Ltd, London. Cornforth, J.R. et al. (1992) Combustion Engineering and Gas Utilization, British Gas, E & FN Spon, London.

10 Baquero, A.A. (2000) Cubilote, Ediciones

UIS, Colombia. 11 Stasi, L. (1981) Fornos Eletricos, Hemus,

Brazil. 12 Williams, A.F. and Lom, W.L. (1974)

13

14

15 16 17

18 19

20

Liqueﬁed Petroleum Gases – Guide to Properties, Applications and Usage, Ellis Horwood, Chichester. Department of Energy (1978) Bulk Refractories Industry, Energy Audit Series No. 4, Department of Energy, London. Department of Energy (1979) Glass Industry, Energy Audit Series No. 5, Department of Energy, London. Doyle, P.J. (1979) Glass-Making Today, Portcullis Press, Redhill. Torreira, R.P. Fluidos Termicos, Hemus, Brazil. US Department of Energy (2008) Ofﬁce of Energy Efﬁciency and Renewable Energy, Report DOE/GO-102008-2520, revised May. Brunner, C. (1994) Hazardous Waste Incineration, McGraw-Hill, New York. Reed, R. (1986) North American Combustion Handbook, 3rd edn, North American Mfg. Co. Costa, F.C. (2003) Natural gas backup. Gas Brazil Newsletter, S~ao Paulo, Brazil.

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10 Overview of Liquid Fuels Oliver van Rheinberg 10.1 Introduction

Liquid fuels are used in all areas of society. They are applied in transportation, heating, and the chemical industries. This development is predominantly based on the fact that liquid fuels feature advantages over other energy carriers such as high energy density and storage stability. For this reason, liquid fuels will play a dominant role in transportation and for industrial products within the following decades. The rising demand for liquid fuels worldwide has led to increased production of primary energy carriers based on petroleum-based oil. The consistently increasing world population plus industrialization, especially in Asia and South America, and therewith rising energy consumption, inﬂuence the global climate. The overall energy consumption amounts worldwide to approximately 13 TW, of which 80% is covered by fossil energy carriers. This energy cohort comprises 43% oil, 40% coal and 17% gas. Carbon, which has been embedded over million of years, is released by their use in combustion and industrial systems. The released carbon dioxide (CO2) inﬂuences the measurable increase in CO2 concentration in atmosphere, which is clearly affected by human behavior. This was conﬁrmed for the ﬁrst time by the Intergovernmental Panel on Climate Change (IPCC) report in 2008. It is reported that the long-term consequences are not predictable at all, but that the current effects and the knowledge attained about the background will force us to act to reduce CO2 emissions immediately and in the future. The ICCP report assumes that a time frame of only two decades is left to minimize global warming to below 2 C, which is considered the temperature barrier for the preservation of the Greenland ice. Hence the main issue is a reduction in energy consumption, which includes the responsible use of energy and efﬁcient enhancement of technical applications. Furthermore the addition or substitution of renewable energy is essential. The European target for 2020 is to replace 10% of the total fuel consumption by renewable biofuels. For liquid fuels, the renewable constituents are biomass-based products which optimally should feature properties similar to or better than those of todays petroleum-based fuels. For the automotive sector, for example, it is essential to use

Handbook of Combustion Vol.3: Gaseous and Liquid Fuels Edited by Maximilian Lackner, Franz Winter, and Avinash K. Agarwal Copyright Ó 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32449-1

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energy carriers having a high energy density for extended use. A complete change to biomass-based fuels is not realizable from consideration of the current processes, such as the fermentation of ethanol, the esteriﬁcation of fatty acids and the Fischer–Tropsch synthesis (FTS) for biomass-to-liquid (BtL) products due to the limited availability of biomass. The main challenge consists in the technical compatibility of renewable fuels with existing infrastructure and constructions. Different chemical, physical, and application technology properties limit the addition of todays available renewable fuels to a minor percentage. Adjustment of the infrastructure and technical applications is very time- and cost-intensive. Thus the renewable fuels have to exhibit such properties that they are applicable in existing technical systems. Finally, the efﬁciency of biomass-based products has to be improved if such products are to be extensively introduced. For these reasons, this chapter deals with petroleum-based and biomass-based fuels in which especially their mixtures are considered. 10.1.1 Crude Oil and Refinery – Production of Petroleum-Based Liquid Fuels

In general, crude oils are complex mixture of constituents with a wide variety of properties. The quality of crude oils can be classiﬁed according to the hydrocarbon type, such as parafﬁnic, naphthenic, or aromatic, to the product fraction, to the distillation range, and to the sulfur content. These classiﬁcations are useful for the selection of suitable crude oils for given reﬁning structures [1]. For the ﬁnal use in technical applications they have to be treated in reﬁneries in order to provide products which meet certain quality requirements. The process scheme of a modern reﬁnery (Figure 10.1) demonstrates the complex assembly and interaction of different reforming processes for the production of advanced fuels, as described below. 10.1.1.1 Atmospheric Distillation Before entering atmospheric distillation, the crude oils are desalted. The most common desalting unit is the electrical desalter, in which an electric ﬁeld is applied to segregate water from the oil phase. Although crude oils are desalted at the production ﬁeld, small portions of water and salt remain and can cause corrosion, for example in the distillation column, or lead to erosion or sedimentation of salt in lines, pumps, and valves. In the distillation column, the crude oil is separated into fractions of different boiling ranges up to a temperature of 360 C. Higher temperature treatment, socalled cracking, leads to decomposition of the residue. A further fractionation can only be achieved by vacuum distillation at lower pressure. Atmospheric distillation divides the educts according to their major properties and they are classiﬁed as described in Section 10.1.2. Modern distillation columns work continuously and exhibit several side streams in addition to the top and bottom products [2–5]. The top product is condensed and separated into hydrogen sulﬁde (H2S), fuel gas (C1–C4 hydrocarbons) and liquid petroleum gas (LPG). The side streams comprise

10.1 Introduction

Figure 10.1 Process scheme and key processes in a modern refinery [2].

gasoline, petroleum or kerosene stream, and light and heavy gasoil. These streams need a ﬁnal treatment by hydrotreating processes. The atmospheric residue is applied as the feedstock for vacuum distillation. 10.1.1.2 Vacuum Distillation The operating pressure of vacuum distillation is usually 60–140 mbar. Under these conditions, vacuum gasoil (VGO) is obtained as the top product at approximately 150 C, lubricating oils (light, intermediate, and heavy) at 250–350 C and ﬁnally the residue at approximately 360 C. Normally the VGO feeds a fuel catalytic cracker (FCC) or hydrocracker unit (HCU) to produce a lighter hydrocarbon fraction. The residue of the vacuum distillation is used directly as bitumen or blended with heavy gasoil after visbreaking, thermal cracking, or coking to lighter hydrocarbon fractions. Mild vacuum distillation at 300–500 mbar is applied for the separation of highboiling naphtha (C8/C9 fractions) by simultaneously reducing the temperature of the distillation process [2–5]. 10.1.1.3 Thermal Cracking and Visbreaking The oldest and easiest conversion process is thermal cracking. At temperatures above 360 C, the bonds of the carbon molecules break and the compounds can be converted

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to lighter hydrocarbons. A temperature of 500 C and an appropriate residence time are adjusted to achieve high conversion and cracking. The products are dependent on the reaction conditions: gases, gasoline, middle distillates, and even residue. Visbreaking is a mild version of thermal cracking and is conducted at 460 C and 15 bar. By this process, the viscosity of the products, even of the residue, are improved so that they can be used as blends with middle distillates [2–5]. 10.1.1.4 Catalytic Cracking Catalytic cracking has several advantages over thermal cracking. At temperature of 480–540 C and pressures up to 1.5 bar, the feedstock, such as heavy and VGO or distillation residue, are decomposed to lighter hydrocarbon fractions for gasoline and the like. Lower cracking temperatures result in reduced production of dioleﬁns. Normally, catalytically cracked gasoline consists largely of isoparafﬁns, naphthenes, and aromatic components, which have higher octane numbers and greater chemical stability in comparison with mono- and dioleﬁns. Moreover, a lower production of C1–C2 hydrocarbons and an increasing yield of C3–C4 hydrocarbons result. These LPG streams are then predominately processed by alkylation and polymerization likewise to high-octane gasoline components. The middle fractions of the FCC unit are utilized as blending stocks for light heating and diesel and also heavy fuel oils. The catalysts are employed in bead, pellet, or powder form and are used in ﬁxed bed, moving or ﬂuidized bed arrangements. In the early stages, ﬁxed bed application and catalysts of natural clay materials were used. During World War II and after the 1960s, the development of catalytic cracking led to the introduction of synthetic crystalline zeolites and alumina catalysts, which are today predominantly used in ﬂuidized bed reactors. Nevertheless during catalytic cracking, coke is formed and blocks the active side of the catalysts. To avoid rapid degradation of the catalysts, they have to be regenerated continuously [2–5]. 10.1.1.5 Hydrotreating Hydrotreating in general is characterized by the reaction of the feedstock with hydrogen in the presence of certain catalysts such as cobalt–molybdenum–alumina, nickel oxide–silica–alumina, and platinum–alumina. The temperatures employed are 260–450 C with hydrogen partial pressures of 35–350 bar. The most important task of hydrotreating is the removal of sulfur [hydrodesulfurization (HDS)], the cracking of high-boiling products (hydrocracking), and the conversion of unstable components into stable products (hydrogenation) [2–5]. 10.1.1.6 Hydrodesulfurization HDS is the state-of-the-art method for the desulfurization of petroleum-based fuels. This heterogeneous catalytic conversion of organic sulfur compounds to hydrogen sulﬁde (H2S) takes place at temperature of 250–450 C and hydrogen pressures of 30–200 bar. An HDS unit is shown in Figure 10.2. Feed and hydrogen are mixed and heated to the appropriate operating temperature before entering the HDS reactor. In the reactor, the hydrogen reacts with the sulfur to H2S, whereas the hydrocarbons left are saturated simultaneously. Downstream of the HDS unit, the unused hydrogen,

10.1 Introduction

Figure 10.2 Hydrodesulfurization process and units in a modern refinery to achieve deep desulfurization of liquid fuels [6].

which can be recycled, and the fuel are separated. Finally, the desulfurized product is separated in a stripper from hydrogen sulﬁde (H2S), which is then converted to elemental sulfur typically by the Claus process [2–6]. 10.1.1.7 Hydrocracking Hydrocracking is a catalytic cracking process in the presence of hydrogen at temperatures of 260–450 C and partial pressures of 80–150 bar. The fed hydrogen will terminate many of the coke-forming reactions during the thermal reaction of the feedstock and additionally enhance the yield of the lower boiling components such as gasoline, kerosene, and jet fuel. Hydrogenation takes place simultaneously or sequentially [2–5]. 10.1.1.8 Hydrogenation Hydrogenation is generally used for the purpose of improving product quality without appreciable alteration of the boiling range. Under milder conditions, in comparison with hydrocracking, only the more unstable compounds are converted to stable products, which might lead to the formation of gums and insoluble materials, or act as coke precursors in combustion systems. The hydrogenation of aromatics will occur at higher operating conditions of 370 C and partial pressures of hydrogen of up to 150 bar [2–5].

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10.1.1.9 Isomerization and Catalytic Reforming The gasoline from atmospheric distillation has a low octane number of around 40–60, but to use the gasoline for spark ignition engines, the octane number must be 95–100. Isomerization and reforming processes have been developed to provide these highoctane constituents even in the lighter gasoline fraction. Therefore, n-parafﬁns are converted to their isomers under mild operating conditions. Many different catalysts are used, such as aluminum chloride, hydrogen chloride or supported metal and noble metal catalysts. The operating conditions vary widely from 40 to 480 C and from 10 to 68 bar, depending on the catalyst and feedstock [2–5]. 10.1.1.10 Alkylation Alkylation is the combination of oleﬁns with isoparafﬁns to give higher isoparafﬁns. The reaction is catalyzed by aluminum chloride, sulfuric acid, or hydrogen ﬂuoride. For example, in the reﬁnery isobutane reacts with 1-butene to form isooctane. Process conditions are temperatures up to 40 C and pressures of 1–10 bar, and for aluminum chloride up to 280 C and 68 bar. Lower temperatures are preferred to minimize undesirable side reactions such as polymerization of oleﬁns. The main beneﬁts of the alkylation are the much better properties of higher isoparafﬁns, resulting in higher octane numbers and clean combustion characteristics [2–5]. 10.1.1.11 Polymerization Gaseous oleﬁns such as propylene and butenes are generally products from cracking processes. During polymerization, these feedstocks are converted to liquid products suitable for gasoline. The conversion is carried out at temperatures of 150–220 C and pressures of 10–81 bar. The polymerization is supported by acidic catalysts such as sulfuric acid, copper pyrophosphate, and phosphoric acid [2–5]. 10.1.2 Classification of Liquid Fuels 10.1.2.1 Petroleum-Based Fuels The classiﬁcation of petroleum-based liquid fuels can be carried out according to their principle uses and furthermore according to the technical applications. Liquid fuels are predominantly used as transporting fuels, for heating purposes and for special applications in the chemical industries. Transporting fuels can be distinguished as automotive fuels, such as diesel and gasoline, aviation turbine fuels or kerosene, and marine fuels. For heating purposes, LPG and light and heavy gasoils are mainly used, where the light gasoil exhibits similar properties like diesel. Special products are bitumen, which is used in the building trade for water or corrosion protection and also in road construction, naphtha for the petrochemical industry, and ﬁnally lubricating oils. Fuels mainly used in combustion systems are gasoline and diesel for internal combustion engines, aviation fuels in turbines, and different types of gasoils for

10.1 Introduction

Figure 10.3 Boiling range of different liquid fuels, such as light and heavy naphtha, kerosen gas oil [1].

heating purpose. The following considerations are focused on these types of fuels and the current development of renewable fuels. The ﬁrst process step in reﬁneries is atmospheric distillation (see Section 10.1.1), which is performed in continuously operating fractionating columns. The crude oils are separated according to the boiling range. In Figure 10.3, typical boiling ranges of fuels are presented. Gasoline has a boiling range of 30–220 C and is used in internal spark ignition engines. Speciﬁcations are deﬁned in international directives, such as EN 228. Important speciﬁcations are octane number, density, volatility and stability. The reﬁnery streams commonly used are derived from crude oil distillation, alkylation, isomerization, cracking, catalytic reforming, hydrotreating, and oxygenates. The boiling range of kerosene is between 150 and 300 C. The main ﬁelds of application are jet fuels and aviation turbine fuels. The stringent quality control items are density, freezing characteristics, viscosity, thermal stability, conductivity, corrosion behavior, water content, aromatic and sulfur contents. The product kerosene can generally be distinguished into three groups: straight-run, cracked and other kerosene fractions. Straight-run kerosene is obtained from crude oil by atmospheric distillation and directly processed by hydrotreating to the ﬁnal product. Moreover, kerosene is obtained from petroleum feedstock by processes such as thermal, catalytic, steam and hydrocracking. Cracked streams can contain higher levels of

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aromatic compounds and oleﬁns. For this reason, these streams have to be treated by hydrogenation processes. Gasoil and diesel exhibit light and heavy gas oil fractions in the boiling range 150–360 C. For automotive diesel, the speciﬁcations are set according to EN 590 and for domestic heating applications in Germany according to DIN 51603-1. Straight-run gasoils are the major stream for the production of diesel, but secondary processing of heavier fractions is increasingly necessary in order to meet the product demand. Some of these reﬁnery streams are treated by HDS and hydrotreating processes. Heavy fuel oils consist of mixtures of residual oils from distillation and conversion reﬁnery processes. Most heavy fuel oils are currently based on short residues and residues from thermal and catalytic cracking operation. They are distinguished by high density, viscosity, and high sulfur content. Heavy fuel oils are currently used as marine fuels (bunker fuels), in power stations, and in industrial furnaces. Important characteristics are density, kinematic viscosity, ﬂash point, pour point, carbon residue, ash, water, and sulfur content. The high-viscosity residue components are normally blended with gasoils or similar lower viscosity fractions. In reﬁneries, catalytically cracked units and catalytically cracked cycle oils are common fuel oil diluents. 10.1.2.2 Renewable Fuels 10.1.2.2.1 First Generation of Renewable Fuels The ﬁrst generation of renewable fuels are bioethanol, biodiesel [ fatty acid methyl esters (FAMEs)] and pure vegetable oil. However, the latter is mainly applied in agricultural internal combustion engines. For diesel and middle distillates, the current commercially available substitute is FAMEs, known as biodiesel. Biodiesel is produced by transesteriﬁcation of vegetable oil. In Europe, biodiesel is mainly obtained from rapeseed oil [rapeseed methyl ester (RME)], in southern Europe additionally from sunﬂower-oil, in the United States predominantly from soybean oil and in Asia from palm oil [7]. To avoid competition with food products and furthermore to support the local economy, alternative sources such as jatropha oil, pongamia oil (India), argemone oil (Mexico) and others are also considered nowadays. Bioethanol is the current substitute for gasoline. Bioethanol is produced by fermentation of sugar and starch. In the United States and Europe, mainly maize and other crops are processed, in Brazil predominantly sugarcane. The conventional production is energy expensive, but nevertheless the CO2 balance is positive. 10.1.2.2.2 Second Generation of Renewable Fuels The main goal of the second generation of biofuel processes is to extend the amount of biofuel that can be produced sustainably by using different sources of biomass. This comprises nonfood parts of current crops, such as stems, leaves, and husk, other crops that are not used for food purposes, and industry waste such as wood chips and skins and pulp from fruit pressing. Second-generation biofuels are hydrogenated vegetable oil (HVO), lignocellulose-based bioethanol, BtL products (Shell–Choren process), biobutanol, and dimethyl ether (DME).

10.1 Introduction

Hydrogenated vegetable oil is produced at high temperatures and hydrogen pressures in a standalone unit or in co-processing of conventional petroleum-based oils. Neste Oil Corp. has developed this new technology and has called this biobased diesel fuel component NexBTL. The product is characterized as a high-grade liquid hydrocarbon. Other by-products are water and glycerine. Lignocellulose-based ethanol is still far away from being applicable. The BtL process using the FTS was developed for the substitution of diesel. Some pilot plants for the production of BtL are working, but nevertheless the commercial availability of BtL products is doubtful for the time being. Some sources assume marketability in the next few years, others estimate another 10 years yet. Biobutanol can be produced by fermentation. The infrastructure of existing ethanol installations can be used, except the distillation column, which has to be modiﬁed, causing marginal costs. The separation process is more complex and energy consuming than for ethanol in comparison. However, the better properties of butanol, namely higher density, lower vapor pressure and lower hygroscopic properties, in comparison with ethanol support the necessary investment by far. The production of higher alcohols such as butanol and fuels from algae are at the beginning of development and far away from marketability. Further, the use of DME in compression ignition engines is considered. A blend with diesel is not possible, because DME is gaseous at room temperature and pressure. Hence modiﬁcations of the fuel supply, similar to liqueﬁed gas in spark ignition engines, are necessary. Currently DME is produced from natural gas or coal. In the future, biogas, like biomethane, will be suitable. In the long term, DME should replace liqueﬁed gas. 10.1.3 Political Regulation and Directives

The introduction of cleaner fuels is a step-by-step process which is being pushed forward in many regions of the world. It is a very complex process that involves many stakeholders, requiring different improvements in technology and substantial investment. Due to the increasing consumption of fuels worldwide, together with air pollution and CO2 emissions, all countries are being forced to regulate fuel quality. In the past and even today the elimination of lead from gasoline is the ﬁrst step and is being continued with sulfur reduction even in transporting fuels. In the United States and Europe, there are regulations to increase the replacement of gasoline and diesel by renewable fuels. Moreover, other fuel properties such as aromatic content are also considered, although in some regions of the world lead and sulfur reduction is still an open issue. In view of the above, exhaust emission standards are prescribed by regulations of the European Union (EU) and the US Environmental Protection Agency (EPA). The European exhaust emission standard EURO V became valid in 2009 and regulates the limits of carbon monoxide (CO), nitrogen oxides (NOx), unburned hydrocarbons and particulate matter for internal combustion engines in automobile application.

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Table 10.1 Sulfur limits in liquid fuels in mg kg1 according to European Union regulations.

Category

1994

Gasoline

—

Diesel

2000

2005–2007a

2008–2010

<50 (<10 av.)

<10 From January 2009

Heating gasoil

<2000 CN >49 —

<150 RON >95 MON >85 <350 CN >51 <2000

<50 (<10 av.) —

Marine diesel

—

<2000

<1500

<10 From January 2009 <1000 (standard) <50 (low sulfur) From January 2008 <1000 From January 2010

a)

av. ¼ available, not required by fuel standard.

While the speciﬁcation of quality characteristics and speciﬁcation data originate from market requirements and technical applications, the increasing political inﬂuence, predetermined by the European Directives 98/70/EC and 2003/17/EC, has already led to drastic changes in fuel quality and characteristics. In addition, many different chemical and physical properties, especially the sulfur content in liquid fuels, have to be minimized towards the development of sulfur-free liquid fuels. All types of transporting fuels, such as automotive, marine diesel, and aviation fuels, and of heating fuels are included but still there are different sulfur limits depending on the region and application. An overview of the development and current state of the sulfur limits according to the EU and EPA regulations are summarized in Tables 10.1 and 10.2, respectively. Under these rules, all sulfur limits of automotive fuels are set to below 10 ppm (EU) as from 2009 and to below 15 ppm (EPA) as from 2010. Marine diesel and standard heating gasoil will have a maximum sulfur content of 1000 ppm, whereas in Europe a low-sulfur heating gasoil with 50 ppm of sulfur is available. Kerosene or jet fuel is an exception because the sulfur content has not been reduced in recent decades and 3000 ppm is still allowed even in 2009. Overall, it can be assumed that in future the sulfur content of all present types of fuels will be reduced to provide sulfur-free liquid fuels. The achievable sulfur levels in

Table 10.2 Sulfur limits in liquid fuels in mg kg1 according to EPA regulations.

Category

1988–1989

Gasoline Diesel Non-road diesel Jet fuel/kerosene

<1000 <5000 <20000 <3000

a)

1993

1995

2006a

2010

— <500 <5000 —

<330 — — —

<30 <15 (av.) <500 —

— <15 <15 —

av. ¼ available, not required by fuel standard.

10.1 Introduction

Figure 10.4 Current sulfur limits in automotive diesel according to region and continent [8].

Europe and the United States will become the standard for many other regions and countries worldwide. As shown in Figure 10.4, much higher sulfur limits are currently allowed in Australia, South America, Russia, Asia and Africa. The sulfur content varies from 50 ppm in Australia to 300–500 ppm in Russia and reaches 1000–12 000 ppm in some countries of South America and especially in South Africa and Asia. Hence large investments have to be made in the local reﬁnery industries. Probably many years will be necessary to reach guidelines similar to those in Europe and the United States. This development is comparable to the actual situation with lead-free gasoline. Even today, countries in North Africa, the Middle East and Asia apply tetraethyllead or similar hazardous octane boosters to improve the anti-knock behavior of gasoline. Due to the consideration of greenhouse gas (GHG) emissions, the adoption of renewable energy in almost every technical application is necessary. In this context, especially the European Directive 2001/77/EG should be mentioned, laying down a target of 20% of electricity production to be provided by renewable energy. The implementation of this target is supported by ﬁnancial support programs, reduction of bureaucratic barriers, and additional arrangements. Furthermore, the Directive 2003/30/EG, which regulates the addition of biofuels and other renewable fuels in the transportation sector, is the second fundamental proposal to reduce GHG emissions. The subordinate target of the replacement of 2% of transporting fuel by renewable fuels by 2005 has been missed and it is assumed that in 2010 the fraction will be at a level of approximately 4.2% in total. A new proposal by the EU Commission for the Directive 2009/28/EG to promote the use of energy from renewable energy sources

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Table 10.3 Target objectives for the adaptation of biofuels (biodiesel) in diesel for different countries: replacement (%) [9].

Australia Austria Canada China France Germany Japan Spain Sweden UK

2007

2008

4.3

5.75

2009

2010 1.00

2015

2020

3.50 3.00

10.00 5.75 6.25

3.00

7.00 6.75

8.00

10.00 9.00–12.00 5.00

100.00 5.00

includes the Directives 2001/77/EG and 2003/30/EG to some extent. The primary rearrangement is to shift the current regulations for 2010 to the end of 2011. The main intention of the Directive is to replace 20% of the overall energy consumption by renewable energy sources by 2020. The distribution in the electricity supply, heating, and cooling energy sectors and also transportation is left to the individual Member States. Nevertheless, it is recommended to replace 10% in transportation in order to reduce the enormous dependence on petroleum. For this reason, many countries in Europe and other regions of the world are working intensely on the adaptation of renewable fuels. An overview of some target objectives for automotive diesel is given in Table 10.3. The framework for the introduction of alternative and renewable fuels in gasoil for heating purpose is currently being developed and accompanied by appropriate technical investigations. In Europe, the norm EN 14213 has ﬁxed the requirements of so-called biodiesel for heating purposes. The recently introduced German prestandard DIN V 51 603 – Part 6, Alternative Gasoil for Heating Purposes, regulates the requirements and analytical methods for alternative and biofuels to ensure the reliable operation of commercial oil-burning heating systems at the national level. In the United States, the American Society for Testing and Materials (ASTM) has approved the inclusion of a 5% blend of biodiesel (B5) in heating oil [10]. This development is predictable for the near future in all European countries. Due to these political guidelines, the production of bioethanol and biodiesel in Europe has increased continuously and substantially from 1.5 million tons in 2003 to 5 million tons in 2006, as shown in Figure 10.5. In 2007, 6.9 million tons of biodiesel and 2 million tons of bioethanol were consumed. The physical and chemical parameters of fuels are likewise deﬁned in the corresponding fuel standards. The main parameters of each standard, such as boiling range, density, ﬂashpoint, and viscosity, will be kept constant. On their basis, technical applications will be developed and optimized and vice versa. Hence the adaptation of biofuels has to be well balanced with the established petroleum-based fuels so that they can be used without fundamental changes to the current technical applications. Although this has been considered, for example, by the addition of

10.2 Chemical and Physical Properties of Liquid Fuels

Figure 10.5 Development of annual biofuel production (bioethanol and biodiesel) in the European Union [11].

FAMEs to diesel, other parameters, which are not standardized, have changed the overall fuel properties and their applicability signiﬁcantly. The main issue in this process is to stabilize the product for storage and high-temperature use.

10.2 Chemical and Physical Properties of Liquid Fuels 10.2.1 Main Components of Petroleum-Based Fuels 10.2.1.1 Aliphatic Hydrocarbons Aliphatic compounds are chain-like linked hydrocarbons. Depending on their bond type and the degree of saturation, they are distinguished into alkanes (parafﬁns), alkenes (oleﬁns) and alkynes (acetylenes) according to Figure 10.6. Alkanes (parafﬁns) are completely saturated hydrocarbons with the molecular formula CnH2n þ 2. A branched molecule is called an isoalkane and a closed ring is called a cycloalkane (CnH2n) or naphthene. An aliphatic hydrocarbon is called an alkene or oleﬁn (CnH2n) if it is not saturated completely and has a C¼C double bond. Double bonds may occur in different and multiple locations within a molecule; thus dioleﬁns may be formed additionally to monooleﬁns. The amount of unsaturated compounds has decreased continuously in liquid fuels during recent decades due to increasing desulfurization and hydration and also alkylation in the reﬁning process. An aliphatic hydrocarbon with a CC triple bond with the molecular formula CnH2n 2 is called an alkyne. Alkynes are of minor importance in liquid fuels and are therefore not mentioned further.

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Figure 10.6 Basic structures of aliphatic hydrocarbons in liquid fuels.

10.2.1.2 Aromatic Hydrocarbons Aromatic hydrocarbons or so-called arenes are hydrocarbons containing an aromatic ring. Typical aromatic compounds are shown in Figure 10.7. Benzene (C6H6) is the simplest and most typical example, containing six carbon atoms and three double bonds. As benzene is highly carcinogenic, its content in gasoline is limited to 1 vol.% by EN 228. In general, an aromatic compound is a planar, cyclic molecule with conjugated double bonds whose molecules are sp2-hybridized and whose number of delocalized electrons complies with the H€ uckel rule. Compared with other double bond systems, they have a different and lower reactivity. Mono-, di-, and triaromatic compounds and polycyclic aromatic hydrocarbons (PAHs) are distinguished, according to the number of benzene rings. Compounds consisting of at least two benzene rings are called PAHs. Especially the di- and triaromatic compounds have a negative inﬂuence on the reactions during combustion and reforming of liquid hydrocarbons. Therefore, these compounds are listed separately in the determination of aromatic content according to EN 12 916. The properties of aromatic compounds can be modiﬁed signiﬁcantly by alkylated or substituted groups attached to the ring systems. The alkylated groups, which are

Figure 10.7 Basic structures of aromatic compounds in liquid fuels.

10.2 Chemical and Physical Properties of Liquid Fuels

called methyl (-CH3), ethyl (-C2H5) groups, and so on, may have a different length and may be present at different locations. This raises the boiling point, and therefore the more complex forms of these compounds are generally found at higher distillation temperatures. 10.2.1.3 Phenols Phenols are hydroxyl-substituted benzene compounds that are present in crude oil as trace elements. Phenols are also called aromatic alcohols, although the hydroxyl group (-OH) is too acidic for an alcohol. Thus phenol is a weak acid. Alkylphenols with a low molecular weight (C1–C3-alkylphenols) are the most common phenolic species in crude oil. The total phenol content may vary between 0.25 and 80 ppm. After the reﬁning process, phenols are still present in liquid fuels. Due to their polar characteristic they have a positive impact on lubricity and additionally act as radical interceptors, therefore having a positive impact upon thermal stability. On the other hand, there are negative aspects. Sterically uninhibited phenols may polymerize after oxidation and form insoluble residues called gum. The HDS during reﬁning also removes the phenols, hence the phenol content decreases with the sulfur content as a rule. 10.2.1.4 Physical and Chemical Properties of Aliphatic and Aromatic Hydrocarbons Due to the carbon chain length, molecule structure, and bonding characteristics, aliphatic and aromatic compounds feature different properties. Fuel characteristics such as heat value, density, boiling point, viscosity, and ignitability are essential to design combustion systems. In the following, these properties are explained on exemplary hydrocarbon structures and molecules. The heat value of liquid hydrocarbons is dependent on the carbon chain length and additionally on the molecule structure. The differences between parafﬁns and oleﬁns are negligible. The heat value decreases with increase in the carbon chain length for both structures and end asymptotically at values of approximately 44 MJ kg1. For gaseous fuels such as methane the heat value is 49.4 MJ kg1 and for pentane, which is the ﬁrst liquid hydrocarbon under standard conditions, it is approximately 45 MJ kg1. For aromatic compounds the heat value increases with increasing carbon number, from 40.2 MJ kg1 for benzene (C6H6) to 40.7 MJ kg1 for naphthalene (C10H8), and 42.12 MJ kg1 for anthracene (C14H10) [12]. The density of liquid hydrocarbons increases with increase in the carbon chain length. Aromatic compounds have higher densities than aliphatic compounds, which is constant for carbon numbers C6–C15 at approximately 0.86 kg l1. The boiling point of hydrocarbons increases with increase in the carbon chain length and is independent of the molecule structure. Exceptions are polyaromatics having two or more benzene rings, which feature a higher boiling point than corresponding parafﬁns of the same carbon number. For example, anthracene (C14H10) has a boiling point at 340 C and tetradecane (C14H30) 251 C. The viscosity increases with increase in the carbon chain length. Naphthenes have a higher viscosity than oleﬁns or parafﬁns.

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Figure 10.8 Cetane number of different fuel components and structures [13].

To deﬁne the ignition tendency of different fuels in internal combustion engines, technical parameters have been developed. Diesel fuels have to exhibit a good ignitability, expressed by the cetane number (CN), and gasoline a high residence to ignitability expressed by the research octane number (RON). Therefore, in Figure 10.8 the correlation of molecule structure and carbon number with the CN is shown. Generally, the CN increases with the carbon number, hence the ignitability increases. The effect of the molecule structure is as distinctive as that of the carbon number. Parafﬁns show the highest tendency to ignite, followed by oleﬁns and isoparafﬁns. Thus branched parafﬁns are less reactive than n-parafﬁns. Furthermore, the ignitability decreases for cyclic compounds such as naphthalene and especially for aromatics. Nevertheless, monoaromatic compounds can have CN numbers similar to those of isoparafﬁns at carbon numbers greater than C17. The lowest CN are represented by polyaromatic compounds [12, 13]. Fuel properties correlated with the molecular structure are summarized in Table 10.4. 10.2.1.5 Sulfur Species Sulfur is a natural component of crude oil, and the sulfur content depends strongly on the origin of the crude oil. The lowest sulfur contents can be found in crude oil from

Table 10.4 Properties of hydrocarbon groups.

n-Parafﬁns Isoparafﬁns Oleﬁns Naphthenes Aromatics a)

Ignition

Cold flow

Volumetric calorific value

Density

Smoking tendency

þ *

þ þ þ *

* þ þ

* þ

* * þ

þ þ High, þ good, * moderate, low, poor.

10.2 Chemical and Physical Properties of Liquid Fuels

Figure 10.9 Content of sulfur compounds depending on hydrocarbon chain length (C11–C31) and origin of the crude oil.

North Africa with an average of 0.1% (m/m) and the North Sea with 0.3% (m/m). Higher sulfur contents are found in North American oils with up to 1% (m/m), in oils from the Middle East with up to 2.5% (m/m) and in South American oils with up to 5.5% (m/m). The distribution of sulfur compounds depending on the fraction and the number of carbon atoms of an Arabic, a Russian and an African oil are shown in Figure 10.9. The sulfur content increases signiﬁcantly with the distillation range, hence most of the sulfur atoms are bonded in the high-boiling and complex hydrocarbons. In addition to the different sulfur contents, a Gaussian distribution of the sulfur compounds related to the corresponding fraction range becomes obvious. Sulfur compounds can be distinguished into aliphatic compounds and heterocyclic compounds (aromatic compounds and cyclic alkanes) according to their molecular structure. The basic structures of these compounds and their distillation ranges are given in Table 10.5. The sulfur is bonded to the hydrocarbon chain of aliphatic sulfur compounds. It is called a thiol if the sulfur atom is located on the end of the chain and a sulﬁde if the sulfur atom is located between two carbon atoms. The lighter fractions of middle distillates usually contain thiophenes, benzothiophenes, and dibenzothiophenes and the corresponding alkylations because the HDS in the reﬁnery removes most of the aliphatic compounds. In low-sulfur fuels, mainly highly alkylated dibenzothio-

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Table 10.5 Structures and boiling points of aliphatic and heterocyclic sulfur compounds.

Type

Aliphatic compounds

Compounds

Thiols

Distillation range ( C) >6

Sulﬁdes

>37

Disulﬁdes

>110

Heterocyclic compounds Thiophene

Chemical structure

SH

R S R

S

R

R

R

S

84

Methylthiophene

>110

Di-/trimethylthiophene

>130

Benzothiophene (BT)

220

Methylbenzothiophene

>230

Di-/trimethylbenzothiophene >240 Dibenzothiophene (DBT)

310

Methyldibenzothiophene (MDBT)

>315

Di-/tri-MDBT

>330

S

S

S

phenes, such as 4,6-DMDBT, are left and are the refractory species to be desulfurized as illustrated in Figure 10.10 [10, 14]. It has been calculated [15], considering current catalyst development, that the catalyst volume necessary for HDS in the reﬁnery is 2.1-fold in order to achieve a sulfur content js < 50 ppm, 3.5-fold for js < 10 ppm and 7-fold for js < 0.1 ppm in comparison with desulfurization levels of 350–500 ppm. The high costs of catalysts and the hydrogen necessary for the desulfurization make a deep desulfurization far below js ¼ 10 ppm most unlikely. The development of new catalysts may permit desulfurization to below js < 5 ppm. Research and development place the focus ﬁrst upon new substrates, such as TiO2, activated carbon, molecular sieves (usually Al2O3), second upon higher load and identiﬁcation of alternative active components such as Mo and W for hydrotreating reactions [16] and use of additives and also promoters (P, B, F, etc.) [15]; third upon the addition of base metals (Ni–CoMo, Co–NiMo, Nb, etc.) [16–18] and ﬁnally addition of noble metals (Pt, Rd, Ru, etc.) [19]. It is possible to adapt the reaction conditions during HDS to the increased demands. This includes raising the reaction temperature, raising the hydrogen partial pressure and the new design of counter-ﬂow reactors. Highly alkylated dibenzothiophenes require long residence times and high hydrogen partial pressures. To achieve a deep HDS, for example of diesel to below 10 ppm, a large catalyst volume and hydrogen consumption are necessary.

10.2 Chemical and Physical Properties of Liquid Fuels

Figure 10.10 Reactivity of sulfur compounds during hydrodesulfurization (HDS) in a refinery [14].

10.2.2 Gasoline and Common Renewable Fuels

Gasoline is a complex mixture of liquid hydrocarbons with a boiling range of 30–220 C and consists of C4–C12 hydrocarbons. The main components are parafﬁns, cycloparafﬁns, aromatic and oleﬁnic hydrocarbons. The more predominately parafﬁnic components are normal isomers, which dominate the branched isomers by a factor of two or more. The composition of the gasoline can vary over a wide range, even for those with the same octane number, and is dependent on the type and nature of the crude oil processed and the reﬁnery conﬁguration. The variation of all components and relevant characteristics of any individual crude oil allows the deﬁnition of gasoline and fuel in general only according to the overall physical and chemical properties. Gasoline has to comply with a large variety of speciﬁcations, among which the knock rating (octane number), volatility, boiling characteristics, density, oxidation stability, and lead content are of prime importance. Typical speciﬁcations for unleaded gasoline are deﬁned in EN 228 from 2004 (amended in 2006). These properties of gasoline are compared in Table 10.6 for methanol, ethanol and butanol as well as methyl tert-butyl ether (MTBE) and ethyl tert-butyl ether (ETBE). MTBE and ETBE are oxygenated compounds to increase the octane number of gasoline and are not considered as renewable fuels. The maximum oxygen content allowed according to EN 228 is 2.7 wt%. In addition to the speciﬁcation in Table 10.6, EN 228 deﬁnes that the oleﬁn content is limited to 18% and the aromatics to 35% by volume. Benzene is highly carcinogenic, hence its content in gasoline is limited to 1 vol.%. The sulfur content is set to a maximum of 10 ppm (mg kg1) and the oxidation stability must be a minimum of 360 min (EN ISO 7536). The balanced fuel volatility, expressed via the distillation behavior and vapor pressure, is important for safe operation. A certain fraction of volatile fuel components is

j283

Units

kg l1 MJ l1 kPa C C % v/v wt% g l1

Density at 20 C Heating value Vapor pressure at 20 C Boiling point Flash point RON Oxygen content Solubility in water at 20 C 0.63 32.2 — 25 40 95 — 50

Min. 0.775 32.9 60 250 — — 2.7 200

Max.

Gasoline

— — — 65 — 115 —

Min. 0.795 15.9 32 — — —

Max.

Methanol

— 0.78 21.1 21.7 — 16 78 — 12 — 106 130 — 35 Consolute

Max.

Ethanol Min.

Selected properties of gasoline, methanol, ethanol, butanol, MTBE, and ETBE.

Property

Table 10.6

— 26.9 — 118 35 94 —

Min. 0.81 27 6.7 — — — 22 90

Max.

Butanol

— — — — — — — —

Min.

42

0.74 26 54 55 28 114

Max.

MTBE

— — — — — — — —

Min.

0.74 27 30 73 19 117 16 13

Max.

ETBE

284

j 10 Overview of Liquid Fuels

10.2 Chemical and Physical Properties of Liquid Fuels

necessary for low-temperature performance. An excess of these components leads to problems with warm engines and ambient temperatures. Therefore, the distillation curve is adjusted due to seasonal and climatic variations. A high ﬁnal boiling point can cause deposit formation. Additives used in gasoline include anti-knock agents, antioxidants, corrosion inhibitors, metal deactivators, combustion chamber scavengers, detergents, and dyes. Ethanol (EtOH, C2H6O) is a clear, highly ﬂammable and pungent-smelling alcohol and is readily biodegradable. When ethanol is used as a substitute for gasoline, a denaturing medium is added to make it unpalatable to humans and it is taxed as gasoline and not as an alcoholic beverage. Ethanol is a knock-proof gasoline substitute exhibiting a RON of 104. The chemical structure and properties are independent of the production mode and the sources (fossil or biogenic). An overview of the major properties is given in Table 10.6. With addition of alcohols to gasoline, several effects can be determined that inﬂuence the properties of the blends signiﬁcantly. In Figure 10.11, the effects of methanol, ethanol, MTBE and ETBE on the vapor pressure are shown. The exceptional behavior of alcohols is evident. The greatest effect is caused by the addition of methanol. For ethanol, the distinctive effect of an increase in vapor pressure is caused in the range below 15wt %. A higher fraction leads to a decrease in vapor pressure as with all other alcohols and ethers. The change in the vapor pressure arises from intermolecular interactions, whereas fuels with a low aromatic content show only a minor change. In addition to the different vapor pressure characteristics, consequences on the boiling characteristics are also observed. In the middle area of the distillation curve, an increased volatility or a 50% lowered boiling point occur. For this reason, the T50 value is reduced for gasoline produced for E10 blends from 100 C down to 80 or 85 C. Furthermore, the RON is improved by the addition of ethanol more than the motor octane number (MON). This effect is independent of the gasoline itself. In Brazil, investigations on the oxidation stability of E5 to E25 blends have been conducted under ambient conditions and at 43 C. The presence of alcohols leads to more aging products than in the absence of alcohols. Ethanol is readily mixable with water. Hence the dissolving power of ethanol blends for water increases, so that for example E10 blends are able to adsorb 0.5% of water. However, with rising water content the dissolving power of gasoline for ethanol decreases. If the water content exceeds the limiting value, a phase separation into an upper hydrocarbon–alcohol and a lower alcohol–water phase occurs. To avoid s phase separation, the infrastructure has to be absolutely free of water. The corrosion potential of ethanol is much higher than that of other liquid hydrocarbons. The main problem at higher ethanol concentration is the swelling behavior and solubility of rubber and plastic. Without any adjustments, a maximum ethanol content of 5% can be used. As a consequence of its hygroscopic and corrosive properties ethanol cannot be used in the existing infrastructure for petroleum-based fuels. The distribution is carried out with special tank trucks. 1-Butanol (C4H10O) is an alcohol and a clear, uncolored ﬂuid, which is marginally soluble in water and mixable with gasoline to any extent. Hence butanol does not mix

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Figure 10.11 Vapor pressure by mixing of methanol, ethanol, MTBE, and ETBE in gasoline according to EN 228 [7].

with water and can be pipelined in existing systems. The heat value of 1-butanol (27 MJ l1) is higher than that of ethanol (21.7 MJ l1), whereas its density is similar to that of gasoline. 1-Butanol is fermented from reﬁned sugar or starch by microorganism such as Clostridium acetobutylicum. Crude materials include sugar cane, maize, wheat, and sugar beets. Current research is focused on the chemical conversion of cellulose to sugar, which is also to be fermented. The use of cellulose has several advantages such as better availability and does not compete with foodstuff sources. The end product shows up to 90% of the energy density of gasoline, which means that any fuel-injected engine and most carbureted engines are able to use butanol alone or in a mix with gasoline. Butanol is not corrosive or a solvent for polymers in fuel systems. Investigations with different elastomers and 20% butanol blends show negative effects on the swelling behavior only for Ethacryl and Nitril. The vapor pressure of butanol is much lower than that of ethanol and very similar to that of gasoline. By the addition of butanol to E5 blends the vapor pressure even decreases. Figure 10.12 demonstrates the effect of adding butanol to gasoline and to E5 blends. So far it has not been ﬁnally clariﬁed if the fatigue resistance of materials is inﬂuenced at all by butanol. However, up to now there is no indication of the opposite. Investigations on spark ignition engines have shown that up to 100% butanol can be used without any adjustments. Moreover, 30% diesel blends and 20% kerosene blends have been tested successfully. Engine and automotive tests using 16% butanol blends of high octane number exhibit a similar engine power to E10 blends. The vapor pressure and distillation curve for 16% butanol blends are almost identical with those for conventional gasoline.

10.2 Chemical and Physical Properties of Liquid Fuels

Figure 10.12 Influence of ethanol and butanol content on the vapor pressure [20].

10.2.3 Diesel and Common Renewable Fuels

Diesel and gasoil are a complex mixture of hydrocarbons with a carbon chain number of C11–C25 and a boiling range of 150–370 C. They are mainly used as automotive fuels for diesel engines and for heating applications (domestic and industrial). Diesel and gasoil contain n- and isoparafﬁns, cycloalkanes (naphthenes), aromatic hydrocarbons, mixed aromatic, cycloalkanes, and oleﬁns. Diesel is essentially the same as gasoil for heating purposes, but the proportion of cracked gasoil is usually lower since the high aromatic content of cracked gasoil reduces the CN signiﬁcantly (see Figure 10.8). The CN of diesel is one of the important issues for compression ignition engines. Additionally, the cold ﬂow performance, lubrication properties, viscosity and fuel stability are of importance. Typical speciﬁcations for automotive diesel fuel (according to EN 590 from 2004, amended in 2006) are listed in Table 10.7 compared with gasoil for heating purposes (according to the German standard DIN 51 603-1), RME, BtL/gas-to-liquid (GtL) fuels, and HVO. The addition of FAMEs in diesel for automatic application has been realized for a couple of years. In this period, a lot of experience on and changes to the fuel and to the application technique have been carried out to improve the properties of diesel– FAME mixtures and to implement them in the automotive infrastructure and application. FAMEs or colloquially biodiesel are produced from vegetable oil or triglycerides consisting of glycerine and three fatty acid molecules. The fatty acids are separated from the glycerine through the addition of methanol and catalysts during the production of FAMEs from vegetable oils as shown schematically in Scheme 10.1. This reaction is a chemical equilibrium reaction, meaning it can proceed in both directions. To achieve the desired esteriﬁcation grade of more than 98%, the equilibrium is shifted by a surplus of methanol.

j287

kg l1 cSt C MJ kg1 C — C mg kg1

Units

0.82 2.6 160 — 55 51 20 —

Min.

Diesel

0.845 4.5 360 43.4 — — 0 10

Max. — — 160 42.6 55 — 12 <1000 (<50)

Min.

Max. 0.86 6 (20 C) 350 — — — 10

Gasoil

Selected properties of diesel, gasoil for heating purposes, RME, BtL/GtL, and HVO.

Density at 20 C Viscosity at 40 C Boiling characteristic Heating value Flash point CN CFPP Sulfur content

Property

Table 10.7

— — 326 — — — — —

Min.

RME

0.833 4.83 366 37.3 170 53 3.6 10

Max. 0.77 3.2 260 — 70 73 26 —

Min.

0.785 4.5 330 44 — 81 0 —

Max.

BtL/GtL

0.775 2.9 260 — 72 84 30 —

Min.

HVO

0.785 3.9 300 44 — 99 5 —

Max.

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j 10 Overview of Liquid Fuels

10.2 Chemical and Physical Properties of Liquid Fuels

O H2C

O

HC

O

C

O R

H3C

OH

H2C

OH

H3C

OH

HC

OH

H3C

O

R

+

+ H3C O C R O

O H2C

O

C

Triglyceride

R

O

O C

C

R

H3C

OH

Methanol

H2C

OH

H3C

Free glycerine

O

C

R

FAME

Scheme 10.1 Reaction scheme of esterification of vegetable oil [21].

Alkali or earth alkaline bases such as NaOH, KOH, Ca(OH)2, and Mg(OH)2 can be used as catalysts. The bases are neutralized by inorganic acids so that the majority of the alkali and alkaline earth metal ions precipitate as salts. The glycerine is separated from the FAMEs. The oils used for transesteriﬁcation can result from different sources. In midEurope the production of FAMEs from rapeseed oil (RME) is prevalent. However, sunﬂower oil (sunﬂower methyl ester), soy oil (soy methyl ester), and palm oil (palm oil methyl ester) are also common. The properties of FAMEs as fuels are given in the European standards EN 14 213 and EN 14 214. The CN of blends is improved by the addition of FAMEs due to their higher CN. The ﬂash point increases from >55 C for diesel up to approximately 160 C for FAMEs. The cold ﬁlter plugging point (CFFP) for diesel, gasoil, and FAME is adjusted by suitable additive treatment depending on the season. For example, the CFFP varies for diesel from 20 C in winter to 0 C in summer according to EN 590. The CFFP for gasoil for heating purposes is not as distinctive as for diesel and is a maximum of 10 to 12 C. Pure FAMEs by nature have a lower or higher CFFP value than petroleum-based fuels, caused by the composition of saturated and unsaturated compounds. Nevertheless, to fulﬁll the CFPP requirements according to EN 590, an additive treatment of FAMEs is still necessary. Using such treated FAMEs in gasoil for heating purposes can lead to an improved CFFP and cloud point (CP). Low-sulfur and sulfur-free petroleum-based diesel has minor to bad lubricating properties as a consequence of the deep HDS and hydrogenation. The lubricity has an important impact on the life cycle of injection pumps, for example. The deeper the desulfurization, the lower is the lubricating capacity, so it has to be adjusted by speciﬁc additive treatment. FAMEs by nature have no or marginal sulfur components, but offers, due to the polarity of the structure and the oxygen content, a high lubricating capacity. Hence a low level of FAMEs in diesel upgrades the lubricating properties of the diesel signiﬁcantly. An addition of 1% of FAMEs to diesel is sufﬁcient to reduce the HFFR (high frequency reciprocating rig test – EN ISO 12 1561) to the required value of 460 mm. Nevertheless, higher FAME levels and higher temperatures over a longer period cause deposits in pumps and injectors, for example.

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Investigations on fuel stability concerning the deposit formation in injector systems and on oxidation stability have been reported [22, 23]. Petroleum-based sulfur-free diesel fuels possess a signiﬁcantly higher oxidation stability than FAMEs. An addition of 5% of FAMEs (B5) results in a minor decrease in oxidation stability. Diesel with 10% of FAMEs (B10) has a signiﬁcant inﬂuence on the oxidation stability, and 20% of FAMEs in diesel (B20) causes a general degradation of oxidation stability. The inﬂuence of the FAME quality for B10 and B20 blends is much more distinctive than the stability of the diesel fuel. Nevertheless, B5 and B10 blends have signiﬁcantly higher oxidation stabilities than pure FAMEs. The sludge formation, determined by a modiﬁed test according to IP 306, can be increased. It has been veriﬁed that a high induction period (better oxidation stability) results in less sludge formation and lower total acid number. Finally, it should be mentioned that for some blends the addition of FAMEs leads to increased oxidation stability according to the modiﬁed method DIN EN 14 112. A reason for this behavior has not yet been identiﬁed. The storage stability of B10 blends at 10 and 30 C for a period of 100 days revealed only a slight decline in the oxidation stability for both temperatures [23]. The additive treatment of FAMEs with antioxidants leads to a signiﬁcantly better stability of certain B5 up to B20 blends. Deposit formation at injector nozzles is predominantly dependent on the type of engine and operating conditions, and also the fuel quality. An engine with a pump–nozzle system of emission category 4 has been used [23]. It can be summarized that diesel fuels with 5% and 10% FAMEs do not show a higher risk of deposit formation than straight-run diesel fuel in general, although in some cases the reasons for greater deposit formation and increased performance could not be clariﬁed. The coking tendency for B10 blends is slightly increased, but still in the range of a coking degree of 1–4%. From analysis of deposits at the oriﬁce it can be estimated that certain fuels have a tendency for increased deposit formation. However, neither the raw materials of the FAMEs, the iodine number, nor the oxidation stability of the FAMEs provide an adequate explanation [23]. One year vehicle road tests have been performed in city cycles and durable road tests. The results with B10 blends show no inﬂuence on the emission and drivability compared with pure diesel fuel. Merely variations of the CO and particulate matter emissions within the regulatory limits were detected. Mainly the oil dilution using B10 blends was signiﬁcantly decreased and led to shorter oil change intervals. Due to the substitution of FAMEs and vegetable oil in gasoil for heating purposes, investigations on storage stability and deposit formation in domestic heating applications have been conducted [24–26]. The storage and oxidation stability of 5–20% FAMEs in low-sulfur gasoil for durations of 24 months were evaluated with regard to fuel properties such as peroxide, acids, oxidation stability, polymer content, water content, and thermal stability. In some cases copper coils are added to the fuel in order to reveal the inﬂuence of metals ions on the aging mechanism. At a storage temperature of 40 C, a B20 blend showed a signiﬁcant decrease in stability after 12 months. For B5 blends, the stability at 20 and 40 C was satisfactory even after 24 months of storage. This was conﬁrmed by fuel analysis and oil burner operation,

10.2 Chemical and Physical Properties of Liquid Fuels

which showed insigniﬁcant differences from petroleum-based fuels far lower than the regulatory deﬁned limit values [24, 25]. The use of copper leads to an acceleration of fuel aging and should be avoided by removal of copper or using special additives such as metal inhibitors. Nevertheless, during the operation of several ﬁeld units using B5 to B20 blends, no failure of the heating application due to the fuel occurred during two heating periods [26]. As a consequence, an additional additive treatment is essential for the use of 5% vegetable oil and a FAME fraction of 5–20% in domestic heating applications with typical storage durations of several years. Hydrogenated vegetable oil (HVO) is produced at high temperatures and hydrogen pressures in a standalone unit or in co-processing of conventional petroleum-based oils. The process converts biological triglycerides to non-oxygenated hydrocarbon biodiesel with similar chemistry and properties to synthetic GtL and BtL fuels. By-products are water and glycerine. HVO is basically a mixture of n- and isoparafﬁns and contains no sulfur, oxygen, nitrogen, or aromatics. According to the properties of parafﬁns, it has a high CN of approximately 90 and is used as a substitute for diesel or middle distillates [27] as shown in Table 10.8. The impact of HVO has signiﬁcant effects on the emissions of internal combustion engines [27]. A substitution of greater than 5% HVO leads to a decrease in CO, unburned hydrocarbons, and particulate emissions depending on the proportion of HVO. NOx emissions are almost identical with diesel according to EN 590 and are not reduced at all. Changes in fuel consumption or CO2 emissions are not detected. Although BtL fuels are so far not commercially available, they are of great interest. BtL fuels are generally synthesized from biomass. The ﬁrst process step is the gasiﬁcation of biomass to produce a synthetic gas, which in the second step is converted by the FTS to liquid hydrocarbons. This overall process is historically based on coal hydrogenation [coal-to-liquid (CtL)] and has now been adopted for biomass (BtL) and natural gas (GtL). BtL fuels are furthermore processed to diesel, gasoline, or

Table 10.8 Selected properties of 5, 20, and 85% NexBtL blends in diesel according to EN 590 [27].

Property

Density at 15 C Viscosity at 40 C CN Cloud point Sulfur Total aromatics Polyaromatics Distillation point Distillation 50% Final boiling point

Units

kg m–3 cSt — C mg kg1 % m/m % m/m C C C

NexBtL blend (%) 0

5

20

85

831 2.7 54 7 6 19 0.07 191 262 357

829 2.8 57 7 6 17.9 0.09 198 268 354

822 2.9 61 8 5 15.4 0.07 198 276 351

792 3.4 91 17 2 3.2 <0.03 229 293 334

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other fuels. For this reason, the fundamental chemical and even application technology properties can be transferred from GtL products and are currently being investigated in different research products and technical applications. The resulting synthetic fuel is free of sulfur and aromatic compounds. Mainly n- and isoparafﬁns with different carbon chain lengths can be adjusted by the process conditions. Hence GtL and BtL fuels are labeled as designer fuels. Several sources have described the advantages of GtL concerning engine operation [28–31]. GtL generally lowers all emission components compared with diesel fuel. The hydrocarbon and CO emissions are signiﬁcantly improved and also the NOx and particulate emissions, especially at low engine loads. The ignition delay is signiﬁcantly shortened with GtL fuels, resulting in more advanced and rapid combustion. The overall combustion is stabilized, so that higher EGR (exhaust gas recirculation) rates compared with conventional diesel fuel can be adjusted. Finally, the energy consumption with GtL is equal or slightly lower due to the higher CN. 10.2.4 Kerosene

Kerosene is the name for the lighter fraction of middle distillates. Kerosene contains C9–C16 hydrocarbons and the typical distillation range is from 120 to 300 C. The major components of kerosene are n-parafﬁns, isoparafﬁns, and naphthenes (cycloparafﬁns), comprising at least 70% by volume. Oleﬁns have to be avoided since they are in principle thermally unstable and can cause sludge and gum formation during storage or combustion. Accordingly, kerosene contains a maximum of 5% by volume of oleﬁns, whereas aromatic hydrocarbons are limited to 25% by volume. Kerosene for parafﬁn lamps has a very long tradition and is still widely used all over the world. Smoke point, ﬂash point, and volatility are important speciﬁcations. Nevertheless, the dominant application of kerosene is in aviation turbines and jet fuels. In Table 10.9, different commercial jet fuels and their properties are summarized. The extremely low temperatures at high altitudes can cause several fuel problems such as the precipitation of wax crystals and wax clusters. For this reason, the freezing point is deﬁned as the temperature up to which no wax crystals occur. The freezing point is one of the important fuel properties for aviation turbine fuels [1]. For civil aviation fuel Jet A is used exclusively in the United States. Its properties, such as a freezing point of 40 C, are equal to the military speciﬁcation of JP-1. For international civil aviation, the kerosene Jet A-1, equal to the military speciﬁcation JP1A or NATO-Code F-35, is used. The freezing point is 47 C, but this fuel has the same ﬂash point of 38 C and the same distillation range as Jet A. For military aviation, the codes JP-1 to JP-8 are common, but JP-2, JP-3 and JP 6 are older speciﬁcations that have became obsolete. JP-4 was developed for regions with extremely low temperatures. Its freezing point of 58 C is the lowest of all the fuels discussed. JP-4 kerosene is a wide-cut fuel, consisting approximately of 65% gasoline and 35% kerosene.

Freezing point

kg l1 mm2 s1 C % m/m % m/m C C

Units

0.775 — <205 — — 38 —

Min.

Jet A

0.84 8 300 0.3 25 — 40

Max. 0.775 — <205 — — 38 —

Min. 0.84 8 300 0.3 25 — 47

Max.

Jet A-1

0.751 — <100 — — — —

Min. 0.802 — 270 0.4 25 — 58

Max.

JP-4 (F-40)

Jet-fuel qualities of Jet A, Jet A-1, and JP-4 to JP-8 according to civil and military use [32].

Density at 15 C Viscosity at -20 C Boiling point Sulfur Aromatics ﬂash point

Property

Table 10.9

0.788 — <205 — — — —

Min. 0.845 8.5 330 0.3 25 60 46

Max.

JP-5 (F-44)

0.779 — 182 — — — —

Min.

JP7

0.806 8 288 0.1 5 60 43.3

Max.

0.775 — 186 — — — —

Min.

0.84 8 330 0.3 25 38 47

Max.

JP-8 (F-34/35)

10.2 Chemical and Physical Properties of Liquid Fuels

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JP-5 has an specially elevated ﬂash point of 60 C and a freezing point of 46 C at the maximum. This version has to assure higher security standards, for example on aircraft carriers. JP-7 has been deﬁned for aircraft for supersonic speeds. Due to the bulk temperature in the tank as a result of the air friction, the ﬂash point is as high as for JP-5 at 60 C but with better thermal stability. The freezing point is 43 C. JP-8 has been speciﬁed as a replacement for JP-4. The NATO-Code is F-34 or F-35. It is a less ﬂammable, less hazardous fuel for better safety and combat survivability, having a freezing point of 47 C and a ﬂash point of 38 C. JP-8 þ 100 is a special JP8 treated with additives to increase the thermal stability to above 38 C. As a substitute for kerosene, a fraction derived from coal CtL and FTS processes has been synthesized in South Africa by Sasol. The synthetic kerosene contains no aromatics and has to be blended with other product streams before it can be used as an aviation fuel. Furthermore, alcohols and esters have been examined as potential kerosene substitutes. However, the pure substances, such as methanol and ethanol, reduce the speciﬁc energy content signiﬁcantly, which is one of the important issues for aviation fuels to achieve long distance and low weights. 10.2.5 Marine and Residual Fuels

Bunker fuels or marine fuels are technically any type of fuel oil used aboard ships. There are two basic types of marine fuels: distillate and residual. A third type of marine fuel is a mixture of these two basic types, commonly called intermediate. Diesel fuel for marine use has the following types and grades: . . .

distillates: DMX, DMA, DMB, DMC (gasoil or marine gasoil) intermediate: IFO 180, 380 (marine Diesel fuel or intermediate fuel) residual: RMA–RML (residual fuel oil).

In the marine industry, distillate fuels are commonly called gasoil or marine gasoil, residual fuels are called marine fuel oil or residual fuel oil, and intermediate types are called marine diesel fuel or intermediate fuel oil (IFO). The fuel speciﬁcation of distillate marine fuel oil is given in Table 10.10. DMA is the common fuel for tugboats, ﬁshing boats, crew boats, drilling rigs, and ferry boats. Ocean-going ships that take residual fuel oil also take distillate fuels for use in auxiliary engines and for use in port. The common fuels are DMC, depending on the speciﬁc engines in service. DMB is infrequently speciﬁed, and is not available in all ports. In this case DMA, which is the most common compression ignition engine fuel for small- and medium-sized marine engines, is supplied as an alternative. DMB may have a certain limited amount of contaminants brought in during storage or transfer. DMC is manufactured from either heavier boiling fractions of straight-run distillate or is blended in marine fuel terminals from DMA and residual fuels. The speciﬁcations of DMX are very similar to those of automotive diesel, but density and distillation limits are not deﬁned [33].

10.3 Stability of Fuels Table 10.10 Specification of distilled marine fuel oil: DMX, DMA, DMB and DMC [33].

Property

Density at 15 C Viscosity at 40 C Water content Sulfur content Flash point Pour point Cloud point Cetane number

Units

kg m3 mm2 s1 % v/v % m/m C C C —

DMX

DMA

DMB

DMC

Min.

Max.

Min.

Max.

Min.

Max.

Min.

Max.

— 1.4 — — 43 — — 45

— 5.5 — 1 — — 16 —

— 1.5 — — 60

890 6 — 1.5 — 0 — —

— — — — 60 — — 35

900 11 0.3 2 — 6 — —

— — — — 60 — — —

920 14 0.3 2 — 6 — —

— 40

The most common IFO grades are called IFO-180 and IFO-380. The numbers are viscosity limits at the common fuel handling temperature of 50 C, and are equivalent to 25 and 35 cSt at 100 C. Residue fuel oils are complex mixture of high molecular weight compounds with a boiling range of 350–650 C, consisting of aliphatic, naphthenic, and aromatic compounds with a carbon number of C20–C50. Residue fuel oils also contain asphaltenes, which are highly polar aromatic compounds, having molecular masses of 5000–40 000 g mol1, and other heterocyclic compounds with large amounts of sulfur, nitrogen, and organometallic compounds. The most important trace element in residual fuel oil is vanadium. There are 15 different residual fuels listed in national and international speciﬁcations. Individual grades are designated by the letters A through H, K and L, with a number signifying the viscosity limit. The ignition performance is not speciﬁed, because in low-speed, long-stroke engines the combustion time is long enough to burn even components with a low ignitability. The carbon residue is measured to estimate the coke-forming and fouling tendency of the residue fuel. It is related to the asphaltene content in general, but there are differences in the relationship between different fuels [1]. The wide variety of intermediate and residual marine fuels reﬂects both the wide variety of residue and crude oils qualities as well as the design speciﬁcations of engine manufacturers. The latest quality standard is ISO 8217 from 2005. The ISO standards describe four qualities of distillate fuels and 15 qualities of residual fuels, as shown in Table 10.11. Over the years, the sulfur content has been limited due to environmental reasons.

10.3 Stability of Fuels

The requirements on liquid fuels in technical applications are continuously accelerating. Especially the mechanical and thermal stresses are increasing and challenge excellent fuel properties such as thermal, oxidation, and storage stability. The

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Table 10.11 Specification of different residual fuel oils: RMA–RMK [1].

Fuel

Carbon Ash Density at Viscosity at Water Sulfur Flash Pour 15 C 100 C content content point point: winter residue (% m/m) (kg m3) (mm2 s1) (% v/v) (% m/m) ( C) (summer) (% m/m) ( C)

RMA10 RMB10 RMC10 RMD15 RME25 RHF25 RMG35 RMH35 RMK35 RMH45 RMK45 RML45 RMH55 RMK55 RML55

975 981 981 985 991 991 991 991 1010 991 1010 — 991 1010 —

10 10 10 15 25 25 35 35 35 45 45 45 55 55 55

0.5 0.5 0.5 0.8 1 1 1 1 1 1 1 1 1 1 1

3.5 3.5 3.5 4 5 5 5 5 5 5 5 5 5 5 5

60 60 60 60 60 60 60 60 60 60 60 60 60 60 60

0.(6) 24 24 30 30 30 30 30 30 30 30 30 30 30 30

10 10 10 14 14 15 20 18 22 22 22 — 22 22 —

0.1 0.1 0.1 0.1 0.1 0.15 0.15 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

replacement of petroleum-based fuels by renewable fuels complicates the achievement of the given requirements in some cases. In this section, the aging mechanisms of petroleum- and FAME-based fuels are presented as an example to show the differences and difﬁculties. 10.3.1 Auto-Oxidation – Aging Mechanism of Petroleum-Based Fuels

The auto-oxidation of hydrocarbons has been investigated for several decades. The oxidation reactions are very complex, but in the last 20 years essential progress has been achieved. Several research programs cover a wide application area, including the chemical industry [34] and automotive [35] and turbine [36–38] applications. As a result of these studies, it is obvious that so called auto-oxidation reactions are responsible for deposits of hydrocarbons in premixed burner and in surface based vaporization systems. The auto-oxidation process itself is accelerated by high temperatures, metal ions and light. The global scheme in Figure 10.13 can be used for the description of deposit formation. The deposits depend on the wall temperature, the chemical composition, and the residence time on the heating surface. The fuel structure and the composition have a strong inﬂuence on the formation of carbonaceous residues. That means they inﬂuence the three parameters mass, formation temperature, and composition. A basic requirement for deposit formation is the presence of oxygen in the atmosphere. In experiments in nitrogen atmospheres, deposit formation could

10.3 Stability of Fuels

Figure 10.13 Basic global scheme for the deposit formation of mineral oils [39].

not be observed or only in very small quantities. In this case, the physically bound oxygen content is up to 0.8% and much lower than in the presence of air. For auto-oxidation reactions with oxygen, the supply of energy into the fuel is necessary. The auto-oxidation can be divided into several phases. As the ﬁrst step, a radical chain reaction takes place. In general, the initiation occurs through a metal catalyst and hydroperoxides are produced [34]. Initiation : .

.

.

RH þ O2 ! R þ OOH

R þ O2 ! RO2

.

ðfastÞ

ðslowÞ

ð10:1Þ ð10:2Þ

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The rate-determining step is the abstraction of a hydrogen radical (10.1) through thermal exposure. The resulting carbon radical quickly attaches to an oxygen molecule (10.2). Propagation :

.

ROO þ RH ! ROOH þ R

.

ð10:3Þ

The so-formed peroxide radical attacks neutral hydrocarbon molecules in a chain propagation reaction. In this reaction, a hydroperoxide and a new hydrocarbon radical are produced. Due to the different CH compounds in the molecule, the neutral hydrocarbon molecules differ signiﬁcant in reactivity. Tertiary CH compounds (Scheme 10.2) show at 120 C a relative high reaction rate of 50 in comparison with a reaction rate for peroxide radicals. This is higher than, for example, the radical chlorination (relative reaction rate: 11 at ¼ 0 C) of tertiary CH compounds. primary C-H

secondary C-H

tertiary C-H

RCH2 –H with ROO*

R2CH –H with ROO*

R3C –H with ROO*

Rel. reaction rate: 1

Rel. reaction rate: 9

Rel. reaction rate: 50

Scheme 10.2 Reactivity grading in terms of primary, secondary, and tertiary CH [39].

As a result of the chain branching reactions, the hydroperoxide dissociates into two new radicals: .

Branching : .

ð10:4Þ

.

ð10:5Þ

O2

RO þ RH ! ROH þ RO .

.

ROOH ! RO þ OH

O2

.

OH þ RH ! ROO þ H2 O

ð10:6Þ

2ROOH ðROOHÞ2 ðhigh concentrationÞ

ð10:7Þ

.

.

ðROOHÞ2 ! ROO þ RO þ H2 O

ð10:8Þ

In combination with the hydrocarbon radicals from the propagation reaction, alcohols, carbonyls, and water are produced. These are mainly formed from primary and secondary hydroperoxides. Beginning with the alcohols through intermediate products, ketones, carboxylic acids, and esters may be created. The hydroperoxide dissociation rate depends on the type of hydrocarbons. At high concentrations, it is also possible that the hydroperoxide, through hydrogen bonds, associates (10.7) and subsequently dissociates with radical production (10.8). Part of the carboxylic acids react to give hydroxyl and keto carbon acids (Scheme 10.3). Through polymerization and condensation, higher molecular weight substances (sludge and gum) can be formed [36]. In a second oxidation phase, the oils viscosity increases due to the polycondensation products from the ﬁrst phase. Here, for example, aldehyde and ketone react in

10.3 Stability of Fuels

Scheme 10.3 Aging mechanism of liquid hydrocarbons: branching into different reaction pathways.

an acid or base catalytic aldo reaction and dimers are produced. Some other polycondensation and polymerization reactions lead to high molecular weight intermediate products, which are no longer soluble in oil so that they precipitate as sludge and gum. Chain-breaking reactions [40]: .

2R ! RR .

2ROO ! ROH þ Ri CORj þ O2 .

.

ROO þ R ! ROOR

ð10:9Þ ð10:10Þ ð10:11Þ

All three chain-breaking reactions are possible; however, there are indications that in fact, only Equation 10.9 leads to chain breaking. The oxidation of dodecane was analyzed [40], and in the residues there were no traces of alcohols or ketones, as expected from Equation 10.10. In addition, no dialkyl peroxides (10.11) were found. 10.3.2 Aging Mechanism of Fatty Acid Methyl Esters

The reaction mechanism of FAMEs and deposit formation are likewise an autooxidation process. However, this process forms additional products to those from the petroleum-based oils due to fatty acids present in FAMEs. Depending on the vegetable oil (rapeseed, sunﬂower, etc.), the fatty acids have a different composition. In addition to saturated fatty acids (C18:0) they exhibit high percentages of single (C18:1), double (C18:2) and polyunsaturated (C18:3) compounds, so-called double bonds. These unsaturated compounds are the product weak spot and react to saturation initiated by metal traces, heat, light (UV radiation), and oxygen. The fatty acids tend to show radical formation signiﬁcantly more than petroleum-based oils (a factor of 10 per double bond). The auto-oxidation initiation starts at the carbon atom besides the double bond, where the hydrogen molecules are relatively weakly bonded and can easily dissociate by energy supply. The allyl radical formed reacts with the omnipresent oxygen and forms hydroperoxides. These molecules are not stable and easily split into peroxide radicals. In the next step, the peroxide radicals can react in two different ways, one possibility is a chain-breaking reaction. The peroxide

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radical becomes saturated and fatty acids are produced. If not, peroxide radicals may polymerize. Predominantly crosslinked structures such as gum are produced rather than linear polymers. How far and which polymerization products are formed depend strongly on the temperature, the medium (acid or base), and the residence time. Whereas the auto-oxidation of petroleum-based oils is based on the saturation of hydrocarbons, in contrast the aging mechanism of FAMEs can be distinguished into four categories of auto-oxidation: 1) 2) 3) 4)

auto-oxidation auto-oxidation auto-oxidation auto-oxidation

of of of of

simple unsaturated compounds non-conjugated polyunsaturated compounds conjugated polyunsaturated compounds saturated compounds.

The auto-oxidation of simple unsaturated linoleic acid, C18:0, is initiated at the carbon atoms C8 and C11 beside the double bound as shown in Scheme 10.4. In this position, the hydrogen molecules are relatively weakly bonded and they can be dissociated by energy supply. The so-formed allyl radicals react with the oxygen and form a mixture of 8-, 9-, 10-, and 11-hydroperoxide isomers. The isomers accumulate in different amounts. The 8- and 11-hydroperoxide isomers appear with 27% of the 9and 23% of the 10-hydroperoxide isomers [41].

Scheme 10.4 Auto-oxidation mechanism for oleate (C18:1) [41].

The oxidation rate of the non-conjugated polyunsaturated compounds is much higher. The reason for this is the stabilizing interaction of the methylene group between double bonds. The hydrogen separation takes place at the C11 atom and produces a pentadienyl radical. The oxygen attacks the end points C9 and C13 and both the 9- and 13-hydroperoxide isomers are formed in the same amounts [41]. In conjugated polyunsaturated compounds, the carbon chain is alternately attached to a double and a single bond. Conjugated trienes have a higher electron density due to the p-electron species. Thereby the oxygen attack on the double bond (1,2-addition) is much easier and so a diradical is formed. This reacts successively with other carbon double bonds and forms a dimeric diradical. This diradical can

10.3 Stability of Fuels

then in the last step polymerize to cyclic compounds. Through the trienes additional double bonds, not only a 1,2-addition but also 1,4- and 1,6-additions are possible. For these reasons, different dimers, trimers, and polymers can be formed. Hydroperoxides are produced in only a small amount. The auto-oxidation of saturated compounds has already been described for petroleum based fuels and is almost the same for saturated FAMEs. However, during the polymerization, not only linear polymerization chains are formed as in mineral oils, but also an increased production of crosslinked structures (gum) occurs. How many and which polymerization products are formed depend strongly on the medium temperature, the ambient conditions (acid or base), and the residence time. To produce a deﬁned polymer, a narrow temperature window of DT ¼ 2–3 K must be maintained. To improve the oxidation stability of fats and oils, natural or synthetic antioxidants are added. Most commercial antioxidant packages are based on the combination of two or more antioxidants (which are often complementary). Antioxidants are distinguished with regard to their reaction mechanisms. On the one hand there are chain-breaking or primary antioxidants (radical catcher) and on the other hand hydroperoxide disintegration or secondary antioxidants. Primary antioxidants are mostly steric hindered phenols. These substances can act as antioxidants by intercepting oxygen radicals, inhibiting pro-oxidative substances, and stabilizing hydroperoxides [42]. Therefore, antioxidants are capable of intercepting free alkyl and peroxide radicals and of stopping the radical chain reaction initiation. The favorable properties of antioxidants are based on their ability to split hydrogen atoms (H-donor) immediately and to saturate the radicals through bimolecular transfer. One example of this reaction is for 2,6-di-tert-butyl-4-methylphenol (butylated hydroxytoluene or BHT) as shown in Scheme 10.5.

Scheme 10.5 Reaction mechanism of sterically hindered phenols as an antioxidant [43].

Secondary antioxidants show a different behavior. The inhibitor group has the ability to react with hydroperoxides under oxidation of its own molecule. One example of this is the reaction of phosphites to give phosphonates. In the ﬁrst step, thioethers and hydroperoxides react to give sulfoxides, which are more effective antioxidants than the sulﬁdes. In the next step, sulfones are produced. Zinc alkyldithiophosphates are the most common hydroperoxide decomposer. Different studies demonstrate that the presence of natural vitamin E (a-, d-, c-tocopherols) in FAMEs increases the oxidation stability up to 10-fold. c-Tocopherol (C28H48O2) is the most effective antioxidant. Rapeseed and sunﬂower methyl esters already contain a lot of natural vitamin E and therefore exhibit better oxidation

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Scheme 10.6 Reaction of vitamin E with lipid hydroperoxide and alkoxy radicals [43].

stability than others. The effect of vitamin E is based on the fact that the peroxide radical protonates on vitamin E before another radical from the fatty acid moiety might emerge (Scheme 10.6). The vitamin E links the peroxide radical with the inhibited phenol so that subsequent reaction cannot take place. For the use of FAMEs as a substitute for diesel fuel, new additives and additive packages have to be developed in order to achieve the requirements of modern technical applications. As the higher aging rate of FAMEs even inﬂuences that of petroleum-based fuels, the stability of both has to be increased by natural and synthetic ingredients.

References 1 Elvers, B. (2008) Handbook of Fuels –

Energy Sources for Transportation, WileyVCH Verlag GmbH, Weinheim, ISBN 978-3-527-30740-1. 2 Nagy, G. and Hanscok, J. (2009) Key factors of the production of modern diesel fuel. In Fuels 2009, Mineral Oil Based and Alternative Fuels, 7th International Colloquium, 14–15 January 2009, ISBN 3924813-75-2. 3 Mineral€ olwirtschaftsverband (2003) MWV – Mineral€ol und Rafﬁnerien. Mineral€olwirtschaftsverband eV, Berlin. 4 Speight, J.G. (2008) Synthetic Fuels Handbook: Properties, Process and Performance, McGraw Hill-Handbooks, McGraw-Hill, New York, ISBN 978-0-07149023-8.

5 Onken, U. (1996) Arno Behr: Chemische

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7

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9

Prozesskunde, Georg Thieme Verlag, Stuttgart. Gary, J.H. and Handwerk, G.E. (1984) Petroleum Reﬁning Technology and Economics, 2nd edn, Marcel Dekker, New York, ISBN 0-8247-7150-8. Schmiedel, H.P. (2004) Biokraftstoffe – Eigenschaften und Erfahrungen bei der Anwendung. DGMK-Bericht 611-1, ISBN 3-936418-28-4. Szalkowska, U. (2009) Fuel quality – global overview. 6th International Colloquium on Fuels, Esslingen. Antoni, D., Schwarz, H.S., and Zverlov, V.V. (2007) Biofuels from microbes. Appl. Microbiol. Biotechnol., 77 (1), 23–35.

References 10 Crow, P. (2008) Big win for BioheatÒ :

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ASTM Approves 5% Biodiesel in Heating Oil. Oilheating, August 2008. M€ uller-Langer, F., Thr€an, D., and Kaltschmidt, M. (2008) Zuk€ unftige Biokraftstoffe – Stand und Perspektiven. Presentation at Innovative Kraft- und Schmierstoffe f€ ur die Mobilit€at von Morgen, ATZ/MTZ Conference 2008, Stuttgart, 11 November 2008. Pischinger, S. (2002) Verbrennungsmotoren – Band I, 23. Auﬂage, RWTH-Aachen, Aachen. Nagy, G. (2009) Key factors of the production of modern diesel fuels. Presented at the 6th International Colloquium of Fuels, Esslingen 2009. Torrisi, S. Jr, Remans, T., and Swain, J. (2002) The Challenging Chemistry of Ultra Low Sulfur Diesel – Part One. Process Technology – Catalysis, World Reﬁning, December 2002. Song, C. (2003) An overview of new approaches to deep desulfurization for ultra clean gasoline, diesel fuel and jet fuel. Catal. Today, 86, 211–263. Torrisi, S. Jr, Street, R., Remans, T., and Swain, J. (2003) The Challenging Chemistry of Ultra Low Sulfur Diesel – Part Two. Process Technology – Catalysis, World Reﬁning, February 2003. Reinhoudt, H.R., Troost, R., van Langeveld, A.D., Sie, S.T., van Veen, J.A.R., and Moulijn, J.A. (1999) Catalysts for second-stage deep hydrodesulphurisation of gas oils. Fuel Process Technol., 61. Shaﬁ, R. (2000) Hydrodesulfurization of hindered dibenzothiophenes: an overview. Catal. Today, 59, 423–442. Robinson, W.R.A.M. (1999) Development of deep hydrodesulfurization catalysts I. CoMo and NiMo catalysts tested with (substituted) dibenzothiophene. Fuel Process Technol., 61, 89–101. DuPont (2007) 1-Butanol as a Gasoline Blending Bio-component, Presentation 28 March 2007 to Mobile Sources Technical Review Subcommittee (MSTRS) of Clean Air Act Advisory Committee (CAAAC) of the US Environmental Protection Agency (EPA); www.epa.gov/air/caaac/mstrs/ March2007/Wolf.pdf.

21 Mittelbach, M. and Remschmidt, C.

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(2006) Biodiesel – the Comprehensive Handbook, 3rd edn, ISBN 3-200-00249-2, Martin Mittelbach, p.7. Loest, O., Ullmann, J., and Winter, J. (2006) DGMK – Research Report 639 – Investigation on the Addition of FAME to Diesel Fuel. DGMK, ISBN 3-936418-59-4. Loest, O., Ullmann, J., Winter, J., Leber, E., and Beerens, G. (2008) DGMK – Research Report 639-2 – Investigation on the Addition of 10% FAME to Diesel Fuel. DGMK, ISBN 978-3-936418-84-2. van Rheinberg, O., Dirks, H., Lucka, H., and K€ohne, H. (2008) Ablagerungsbildung von 5% FAME/VOBlends in Vormischbrennersysteme, DGMK-Projektbegleitung, October 2008. Dirks, H., van Rheinberg, O., Lucka, K., and K€ohne, H. (2008) Untersuchungen zur Produktqualit€at von Mischungen aus Heiz€ol EL und Fetts€auremethylestern bei der Langzeitlagerung. Report, October 2008, IWO – Institute for Economic Oil Heating. K€ohne, H., Lucka, K., van Rheinberg, O., K€ uchen, C., Jeromin, A., and Lucks, K. (2008) Alternative Brennstoffe f€ ur die efﬁziente Ölheizung, HKS-Fachjournal (Industrieverband Heizungs-, Klima- und Sanit€artechnik Bayern, Sachsen und Th€ uringen eV), Ausgabe 2008, pp. 102–108. Rantanen, L. and Linnaila, R. NExBTL – Biodiesel Fuel of the Second Generation. SAE-2005-01-3771. Munack, A., Herbst, L., Kaufmann, A., Ruschel, Y., Schröder, O., Bünger, J., and Krahl, J. (2005) Vergleich von Shell MittelDestillat, Premium-Dieselkraftstoff und fossilem Dieselkraftstoff mit Rapsmethylester. Report, December 2005. Leppenhoff, G., K€orfer, Th., Pischinger, S., Busch, H., Keppler, S., Schaberg, P., and Schnell, M. (2006) Potential of Synthetic Fuels in Future Combustion Systems for HSDI Diesel Engines. FEV, RWTHAachen, Daimler Chrysler, Sasol Chevron, SAE 2006-01-0232. Hermann, H.-O., Keppler, S., Botha, J.J., Schaberg, P., and Schnell, M. (2006) The potential of synthetic fuels to meet future Emission regulations. Daimler Chrysler,

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Sasol Chevron, 27th International Wiener Motorensymposium, 2006, vol. 2, p. 73. Geringer, B. (2006) Aktuelle und zuk€ unftige anforderungen an einspritzsysteme f€ ur otto- und dieselmotoren. CTI-Tagung Einspritzsysteme f€ ur Dieselmotoren Stuttgart, 29–30 March 2006. Exxon Mobil (2008) World Jet Fuel Speciﬁcation with Avgas Supplement, Exxon Mobil, Irving, TX. Environmental Protection Agency (1999) In-Use Marine Diesel Fuel, Engine Programs and Compliance Division, Ofﬁce of Mobile Sources, US Environmental Protection Agency, EPA 420-R-99-027. F€ urst, H. (1981) Autoxidation von Kohlenwasserstoffen, VLN 152-915/57/81, VEB Deutscher Verlag f€ ur Grundstofﬁndustrie, Leipzig. Hutﬂiess, M. (1994) Untersuchungen zum Einﬂuss des Kraftstoffes auf die Bildung von Ablagerungen auf Einlaßventilen von Einspritzmotoren, Dissertation, Universit€at Karlsruhe (TH), Lehrstuhl f€ ur Chemie und Technik von Gas Erd€ol und Kohle. Bothien, M.-R. (1987) Untersuchungen zum Verdampfen von Heiz€ol EL und Dieselkraftstoff bei der Gemischaufbereitung f€ ur

37

38 39

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Vorverdampfungsbrenner kleiner Leistung, Dissertation, RWTH Aachen. Brandauer, M. (1993) Grundlegende Untersuchungen zur Bildung von Ablagerungen in Brennr€aumen unter gasturbinentypischen Bedingungen, Dissertation, Universit€at Karlsruhe. Hazlett, R.N. (1991) Thermal Oxidation Stability of Aviation Turbine Fuels, ASTM. Fischoeder, A. (2006) Ablagerungsbildung durch Heiz€ol EL und 5% Heiz€ol EL – FAME – Blends bei der Verdampfung. Dissertation, RWTH-Aachen. Hazlett, R.N., Hall, J.M., and Matson, M. (1977) Reactions of aerated n-dodecane liquid ﬂowing over heated metal tubes. Ind. Eng. Chem. Prod. Res. Dev., 16 (2), 171–177. Frankel, E.N. (1984) Lipid oxidation: mechanism, products and biological signiﬁcance. J. Am. Oil Chem. Soc., 61 (12), 1908–1917. Dittmar, T., Ondruschka, B., Haupt, J., and Lauterbach, M. (2004) Verbesserung der oxidationsstabilit€at von fetts€auremethylester mit antioxidantien – grenzen des rancimat – tests. Chem.-Ing.Tech., 76 (8), 1167–1170. Zhang, X. (2004) Alterungsmechanismen €okologisch vertr€aglicher Druckﬂ€ ussigkeiten, Shaker Verlag, Aachen, ISBN 3-83223154-4.

j305

11 Hydrogen-Assisted Combustion and Emission Characteristics of Fossil Fuels Suresh K. Aggarwal 11.1 Introduction

A sustainable energy future will require progressively less reliance on fossil fuels, achieving signiﬁcantly higher efﬁciencies in the utilization of energy, increasing the role of renewable and bio-energy sources in a meaningful way, and moving towards a carbon-neutral economy. While such a scenario will depend upon the development of a variety of alternative/renewable energy sources and technologies, hydrogen can play a signiﬁcant role in this effort. However, hydrogen represents an energy carrier, and its production and storage become important considerations. Therefore, signiﬁcant fundamental research and technological developments are needed to make advances in its production, storage, and utilization. This chapter focuses on the utilization of hydrogen in transportation and power generation sectors. Various options of using H2 in the transportation sector may include hydrogen-powered internal combustion engines (ICEs) (H2 ICE) and fuel cells (FC) (H2 FC), hybrid systems (H2 ICE–H2 FC and H2 ICE–battery combinations), and H2 blended with existing and future fuels. Similarly, for stationary power generation, options may include hydrogen-powered gas turbines (H2 turbines) and H2 FC, preferably in an IGCC (integrated gasiﬁcation combined cycle) conﬁguration, and H2 blended with other fuels (hydrocarbons, biofuels, etc.). Using hydrogen versus electricity to power automobiles also remains an open issue, as the beneﬁts of generating electricity through the H2 route, such as H2 FC, or directly from biofuels are not well established at present. While it is not clear which option or options will emerge as the most viable for hydrogen usage, a blended fuel strategy using a mixture of hydrogen and fossil fuels (or biofuels) appears to be prudent. This is supported by considerations of cost, efﬁciency, reduction in greenhouse gases (GHGs) and other pollutant emissions, and the use of existing infrastructure. Moreover, a blended fuel strategy can address many of the challenges associated with the use of pure hydrogen, including safety, low energy per unit volume, and increased propensity for ﬂame instability and uncontrolled combustion such as explosions. Driven by these considerations, signiﬁcant

Handbook of Combustion Vol.3: Gaseous and Liquid Fuels Edited by Maximilian Lackner, Franz Winter, and Avinash K. Agarwal Copyright Ó 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32449-1

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research has been reported on the hydrogen-assisted combustion of fossil fuels, using both laboratory-scale and practical combustors. This chapter provides an overview of this research. Hydrogen has distinct thermo-transport properties, which make its combustion and emission characteristics notably different from those of hydrocarbons fuels. For instance, due to its high diffusivity and low ignition energy, hydrogen has superior ignition characteristics, much wider ﬂammability limits, and perhaps the highest laminar ﬂame speed of any hydrocarbon or biofuel. Important properties of hydrogen and other fuels are listed in Table 11.1. These characteristics have important consequences for fundamental combustion phenomena, such as ignition, extinction, ﬂame propagation, and stability, and also for practical devices using H2–hydrocarbon (HC) blends. For example, due to the low lean ﬂammability limit (wLFL), the addition of H2 to HC ﬂames can enhance their extinction characteristics, with signiﬁcant beneﬁts in terms of reduced NOx and improved lean blowout (LBO) behavior. Considerable research has been reported on the effects of hydrogen addition on the fundamental combustion and emission characteristics of HC fuels [1–3]. This chapter provides a review of this research. Due to limitations on the chapter length, the discussion is mostly restricted to laminar ﬂames. However, in order to highlight the potential beneﬁts of a blended fuel strategy for practical systems, research dealing with the performance and emission characteristics H2–HC-fueled internal combustion engines is discussed.

Table 11.1 Properties of H2, gasoline, and CNG at 298.15 K and 1 atm.

Property Density (kg m3) Melting point (K) Boiling point (K) Heat of fusion (kJ kmol1) Heat of vaporization (kJ kmol1) Lower heating value (MJ kg1) Heat of combustion (MJ kg1) Lean ﬂammability limit (wLFL) Rich ﬂammability limit (wRFL) Minimum ignition energy (mJ) Quenching distance (mm) Unstretched laminar ﬂame speed (SL ) (cm s1) Autoignition temperature in air (K) Adiabatic ﬂame temperature (K) at w ¼ 1.0 Speciﬁc heat capacity (kJ kmol1 K1) Thermal conductivity (W m1 K1) Diffusivity (DH2 air ) (m2 s1) 104

H2

Gasoline 730

CNG

0.0899 14.01 20.28 117 904 119.96 3.37 0.14 7.14 0.019 0.64 300 (w ¼ 1.8) 185 (w ¼ 1.0) 858 2483

0.72

308 800 44.79 2.83 0.7 4.0 0.24 2.0 37–43 (w ¼ 1.0)

45.8 2.9 0.4 1.7 0.28 2.1 38.0 (w ¼ 1.0)

550 2580

723 2214

28.84 0.1805 (300 K) 0.763 (273 K)

— — —

— — —

300–500

11.2 Theory and Applications in Research

The chapter is organized as follows. Studies on the fundamental combustion and emission characteristics of H2–HC–air mixtures are discussed in the next section. This is followed by a review of research dealing with the use of H2–HC blends in practical systems. Then the future scientiﬁc and technological advances needed are discussed, and a conclusion is provided in the last section.

11.2 Theory and Applications in Research

In this section, we discuss the effects of hydrogen addition on the fundamental combustion characteristics of HC fuels. Studies dealing with ignition, extinction, ﬂammability limits, burning velocity, ﬂame dynamics, stability, and emissions are reviewed. These aspects are important for designing combustor systems using H2–HC fuel blends. 11.2.1 Hydrogen-Enhanced Ignition of Hydrocarbon Fuels

The ignition characteristics of H2–air and HC–air mixtures have been extensively investigated. Studies dealing with the ignition of HC–air mixtures in laminar ﬂow have been reviewed by Aggarwal [4], and those dealing with ignition in turbulent ﬂows by Mastorakos [5]. Homogeneous ignition of H2–air mixtures has been investigated using shock tube [6, 7] and rapid compression machine (RCM) [8] methods. Shock tube and RCM studies have also been reported on the homogeneous ignition of various HC–air mixtures, including methane–air [9–11], methane– propane–air [12], methane–ethane–propane–air [13] and n-heptane–air mixtures [14, 15]. These investigations have reported data in terms of the ignition delay times as a function of important parameters, such as pressure, temperature, fuel/oxidizer/ inert concentrations, and equivalence ratio. The measured data have been used to develop or reﬁne the detailed mechanisms; see, for example, the mechanisms reported by Li et al. [7] and OConaire et al. [6] for H2 oxidation and by Petersen et al. [12] for methane–propane mixtures, Healy et al. [13] for methane–ethane– propane mixtures, and Dryer and co-workers [16] for n-heptane oxidation. In addition, the GRI-Mech 3.0 [17] for methane and methane–H2 mixtures and the NIST mechanism [18] for n-heptane have been used for examining the autoignition behavior. An important feature associated with the ignition of H2–air mixtures is the existence of reaction limits, which are analogous to the well-known explosion limits shown in Figure 11.1 in terms of the Z-shaped curve plotted with respect to the system temperature and pressure [19]. The three explosion limits can be explained by the dependence of key chain branching and termination reactions on pressure and temperature. The ﬁrst explosion limit is characterized by competition between chainbranching reactions and radical destruction (chain-termination) reactions due to radical species (H and OH) colliding with the walls of the vessel. Since wall collisions

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Figure 11.1 Explosion limits of a stoichiometric H2–O2 mixture. From Ref. [19].

become more dominant at lower pressures compared with molecular collisions, the ﬁrst limit is shifted to higher pressures as the temperature is decreased. The second explosion limit is due to competition between chain-branching (H þ O2 ! OH þ O being the main reaction) and chain-terminating (H þ O2 þ M ! HO2 þ M) reactions. Since the three-body reactions become more signiﬁcant at higher pressures, this limit shifts to higher pressures as the temperature is increased. At still higher pressures, the third limit is reached, as the HO2 concentration becomes signiﬁcant, leading to reactions HO2 þ H2 ! H2O2 þ H and H2O2 þ M ! 2OH þ M, consuming HO2 and producing radical species. Additional details can be found elsewhere [20, 21]. An important consequence of the explosion limits is the existence of ignition limits and reaction limits for H2–air mixtures, which are characterized by a non-monotonic variation of the ignition delay time and mass burning rate with pressure, respectively. The ignition limits have been extensively investigated by Law and co-workers using a counterﬂow conﬁguration [22, 23]. Similarly, the reaction limits, which are associated with premixed ﬂames, have been discussed by Tse et al. [24] in terms of the non-monotonic variation of the overall reaction order with respect to pressure. Briones et al. [25] demonstrated the existence of these limits in terms of the non-monotonic variation of mass burning rate with pressure for rich premixed and partially premixed ﬂames. The ignition and reaction limits are important in the context of characterizing the effect of H2 enrichment on the ignition and combustion characteristics of HC–air mixtures at high pressure. Compared with H2 and HC fuels, there have been fewer investigations on the ignition of H2–HC blends [26–29]. Most of these studies have examined the shock tube [26–28] and RCM [29] ignition of H2–CH4 mixtures and reported ignition delay

11.2 Theory and Applications in Research

Figure 11.2 Measured and predicted ignition delays for stoichiometric CH4–H2–air mixtures at 40 atm, with 0, 15, and 35% H2 by volume in the fuel. Symbols represent measurements and

lines represent predictions. A horizontal error bar indicates the typical uncertainty in temperature measurement. From Ref. [28].

time (both data and correlations) as a function of temperature, pressure, and H2 mole fraction in the blend. A representative result from Huang et al. [28] is shown in Figure 11.2, which plots the measured and predicted ignition delays for stoichiometric CH4–H2–air mixtures at 40 atm, with 0, 15, and 35% H2 by volume. As can be seen, the effect of H2 on the ignition delay becomes less pronounced at lower temperatures and higher pressures. The sensitivity study analysis indicated that the effect H2 is primarily related to the generation and consumption of H radicals. At high temperature (1300 K), the rapid decomposition of H2 molecules leads to the formation of H radicals, promoting the chain branching reaction H þ O2 ! OH þ O, and enhancing effect of H2 addition. However, at low temperature (1000 K), the increase in the H radical pool due to H2 addition becomes less signiﬁcant. The reduced effect of H2 on the ignition delay at higher pressures was attributed to the three-body chain-terminating reactions, such as H þ O2 þ M ! HO2 þ M, which become more signiﬁcant at higher pressures. Further, signiﬁcant differences were observed between experimental results and calculations, indicating a deﬁciency in the current reaction mechanisms used for the oxidation of CH4–H2–air mixtures at low temperatures and high pressures. In the cited study, a modiﬁed version of the mechanism [9] was employed. Some studies have also examined the effect of hydrogen on methane ignition under non-homogeneous conditions [22, 30, 31]. Ju and Niioka [30] performed a numerical study of the ignition of CH4–H2 mixtures in a supersonic mixing layer, and observed signiﬁcant ignition enhancement by the addition of hydrogen. They also examined the effect of methane on the hydrogen ignition, and identiﬁed three

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ignition regimes depending upon the amount of methane in the mixture: chainbranching inhibition regime, transition regime, and reaction competition regime. In the ﬁrst regime, corresponding to methane concentrations, the addition of methane signiﬁcantly increased the ignition time, primarily due to the scavenging of H radicals through the reaction H þ CH4 ! CH3 þ H2. Fotache et al. [22] reported an experimental and numerical investigation on the effect of hydrogen on ignition in non-premixed, counterﬂowing methane versus heated air jets. The addition of hydrogen was found to signiﬁcantly improve methane ignition due to the increased radical production and weakening of kinetic inhibition by diffusive separation of branching and termination reactions. They also identiﬁed three ignition regimes, namely, hydrogen-assisted, transition, and hydrogen-dominated, depending on the hydrogen concentration in the fuel jet. In the third regime, the ignition process was characterized by a competition between chain-branching (H þ O2 ! O þ OH) and chain-termination (H þ O2 þ M ! HO2 þ M) reactions, indicating the existence of second ignition limit, as discussed earlier. The second ignition limit was also observed in a recent numerical study [32] dealing with the effect of hydrogen on the ignition of HC–air mixtures in a constant-volume reactor. Hydrogen was found to have a signiﬁcantly more pronounced effect on methane ignition than on n-heptane ignition. In addition, for CH4–H2 mixtures, the ignition delay exhibited a nonmonotonic dependence on pressure as the amount of hydrogen was increased, indicating the dominance of hydrogen chemistry and the presence of second ignition limit, characterized by a competition between chain-branching and chain-termination reactions. This behavior was not observed, however, for n-C7H16–H2 mixtures. The comparison of ignition delay predictions with shock tube data also revealed deﬁciencies in the methane and n-heptane oxidation mechanisms used, especially at high pressures and low temperatures. In summary, the literature review indicates that the ignition behavior of H2–HC mixtures remains largely unexplored. Future work should focus on providing comprehensive shock tube data for different H2–HC blends, characterizing ignition behavior under non-homogeneous conditions, and developing reaction mechanisms for H2–HC blends. Such data and modeling capabilities are important for using such blends in ICEs [including homogeneous charge compression ignition (HCCI) engines], and gas turbine combustors. In addition, the ignition limits, which are well characterized for H2–air mixtures, should be investigated for H2–HC–air mixtures. 11.2.2 Effects of Hydrogen on Flammability Limits and Extinction of Hydrocarbon Flames

The effects of hydrogen on the ﬂammability limits and extinction of premixed hydrocarbon ﬂames are of fundamental interest in many applications, including gas turbine engines and mesoscale combustors. This is of particular interest for enhancing the lean combustion and emission characteristics of hydrocarbon fuels in the context of ultra-lean NOx technology. Recent studies on hydrogen-enriched lean swirl-stabilized CH4–air ﬂames [33, 34] have demonstrated that the hydrocarbon

11.2 Theory and Applications in Research

ﬂame stability and LBO limit can be enhanced through hydrogen addition. Moreover, lowering the lean ﬂammability limit without affecting the ﬂame stability and LBO characteristics could reduce NOx emissions signiﬁcantly. Whereas the ﬂammability limits of pure fuels are widely available, only a few studies have examined these limits for CH4–H2 blends [35, 36]. Van den Schoor et al. [36] systematically quantiﬁed the effect of H2 on the ﬂammability limits of methane using freely (planar) propagating ﬂames in a vertical tube and outwardly propagating spherical ﬂames in a constant volume combustor. The lower ﬂammability limit (wLFL) was deﬁned as the concentration of a non-ﬂammable mixture for which a 0.2 mol% richer mixture is ﬂammable, whereas the upper ﬂammability limit (wUFL) was deﬁned as the concentration of a non-ﬂammable mixture for which a 0.2 mol% leaner mixture is ﬂammable. Figure 11.3 presents the experimental and numerically obtained ﬂammability limits plotted versus the H2 mole fraction. As expected, measurements indicate that the hydrogen addition extends both the lower

Figure 11.3 Experimentally and numerically determined (a) upper and (b) lower flammability limits of CH4–H2–air mixtures plotted versus H2 mole fraction in the blend. From Ref. [36].

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and upper ﬂammability limits. For example, with 40% hydrogen by volume, wLFL decreases from 0.438 to 0.274 whereas wUFL increases from 1.787 to 2.128. Clearly, this offers signiﬁcant beneﬁts in terms of enhancing the lean combustion and emission characteristics of practical systems. Figure 11.3 also shows noticeable differences between the measured and predicted limits, revealing a deﬁciency in the numerical model in reproducing the lower ﬂammability limits at different H2 mole fractions. This implies that the chemistry and transport models need to be further validated for the combustion of H2–HC blends. An important point to note here is that although the methane mechanisms [12, 17] include hydrogen chemistry as a submechanism, there is a need for further validated/optimization of these mechanisms for H2–HC blends. Some recent studies have focused on this aspect [12, 28]. The extended ﬂammability limits with hydrogen addition imply that the extinction characteristics of hydrocarbon ﬂames may be enhanced using hydrogen, Studies on this aspect have considered hydrogen-enriched lean premixed methane–air ﬂames in a counterﬂow conﬁguration, and examined their extinction behavior near the lean ﬂammability limit by varying the stretch rate, equivalence ratio, and H2 mole fraction [37–39]. Jackson et al. [38] reported measurements in lean, premixed CH2–air ﬂames, and observed signiﬁcant enhancement in the lean ﬂammability limit and extinction strain rates with a relatively small amount of H2. This is illustrated in Figure 11.4, which plots the extinction strain rate as a function of w and H2 mole fraction in the blend (a). Note that the mixture was preheated to 300 C to establish ﬂames at high strain rates. As indicated in Figure 11.4a, not only does the H2 addition increase the extinction strain rate, it also extends the lean extinction limit. For example, at w ¼ 0.6, the extinction strain rate is increased from 2000 to 5000 s1 with 10% H2 by volume. Similarly, at a strain rate of 2000 s1, the lean extinction limit is extended from w 0.6 to w 0.45 with 10% H2. This has implications for enhancing the LBO and stable operating range of lean premixed combustors. The H2 addition also increases the stretched ﬂame speed, as shown in Figure 4 in [38], which implies that the ﬂame stabilization and ﬂashback behavior will be modiﬁed by H2. Flame speed–stretch interactions and cellular instabilities of HC ﬂames are also modiﬁed by H2. These aspects are discussed later in this chapter. Another interesting study was reported by Guo et al. [39], who examined the stretched-induced and radiation-induced extinction of premixed CH4–H2 ﬂames in a counterﬂow conﬁguration. A representative result from their numerical study is shown in Figure 11.5 in terms of the C-shaped curves [40]. The extinction strain rate exhibits a non-monotonic variation with w, indicating the existence of stretchinduced and radiation-induced extinction limits. These limits can be discussed in terms of the Damk€ohler number, as strain rate and w are varied. In the stretchinduced limit (high strain rates), as the strain rate is increased, it increases the scalar dissipation rate and the leakage of radicals, and these two effects decrease the Damk€ohler number, leading to ﬂame extinction. Therefore, for a given H2 mole fraction, as w is decreased towards the lean ﬂammability limit, it decreases the burning intensity and ﬂame temperature. Consequently, the extinction strain rate decreases to provide a longer residence time, and thus higher Damk€ ohler number, for the ﬂame to survive. On the other hand, in the radiation-induced limit, which

11.2 Theory and Applications in Research

Figure 11.4 Measured and predicted extinction strain rates plotted versus (a) w at fixed a and (b) H2 mole fraction at fixed w. From Ref. [38].

occurs at low strain rates, as the strain rate is decreased, the radiative heat loss increases due to the longer residence time. This decreases the ﬂame reactivity and the Damk€ohler number, leading to ﬂame extinction. Therefore, as w is reduced towards the lean ﬂammability limit, the extinction strain rate increases in order to reduce the radiative heat loss and sustain the ﬂame. The smallest w value on a given C curve deﬁnes the lower ﬂammability limit (LFL). The results in Figure 11.5 further indicate that with the increase in H2 mole fraction at a given w, the extinction stretch rate (Kext) increases in the stretch-induced limit and decreases in the radiation-induced limit. Thus, H2 addition enhances the ﬂame resistance to extinction in both the limits, and extends the lean ﬂammability limit to lower w values. This again offers the signiﬁcant advantage of reducing NOx in practical combustors, either by operating at leaner conditions or using dilution, such as exhaust gas recirculation (EGR) in ICEs, in order to reduce ﬂame temperatures. These aspects are discussed in Section 11.3.

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Figure 11.5 C-shaped curves for lean counterflow premixed CH4–H2–air flames, in terms of the extinction strain rate versus equivalence ratio at fixed H2 mole fractions. From Ref. [39].

The preceding results on the effect of H2 on the ﬂammability limits and ﬂame extinction illustrate that simpliﬁed ﬂame conﬁgurations can provide valuable information for practical combustors using H2–HC blends. However, additional studies are needed to provide data at high pressures and develop more reliable simulation capabilities for different blends. 11.2.3 Laminar Flame Speeds of Hydrogen–Hydrocarbon–Air Mixtures

Extensive data have been reported on the laminar burning velocities of H2–air and HC–air mixtures. Detailed chemistry and transport models have also been developed for computing laminar ﬂames, especially for H2–air and CH4–air mixtures. There have been only a few studies, however, for H2–HC–air mixtures. We ﬁrst provide an overview of laminar burning velocities and ﬂame–stretch interactions for pure fuels, and then discuss these aspects for H2–HC mixtures. Faeth and co-workers [41–43] and Law and co-workers [24, 44, 45] reported measurements of stretched and unstretched laminar burning velocities for H2 and CH4 ﬂames at different pressures. While these studies consider outwardly propagating spherical ﬂames, counterﬂow [46] and ﬂat ﬂame burners [47] have also been used. There also have been few studies reporting ﬂame speed data for H2–HC blends [48–52]. Representative results from the literature [25, 45] for H2 and CH4 ﬂames at different equivalence ratios and pressures are shown in Figures 11.6–11.8, and those for H2–CH4 and H2–natural gas (NG) blends are presented in Figures 11.9–11.11. Important observations are as follows:

11.2 Theory and Applications in Research

Figure 11.6 Measured and predicted unstretched laminar flame speeds versus w for (a) H2–air flames at 1 atm and (b) H2–O2–He flames at 5 atm, where O2/(O2 þ He) ¼ 0.125, and at 15 atm, where O2/(O2 þ He) ¼ 0.080. From Ref. [25].

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(a)

30

Laminar burning velocity (cm s-1)

25

20

2 atm

15

5 atm

10

2 atm - Present Work 2 atm - Hassan et al. (1998) 2 atm - Egolfopoulos et al. (1990) 5 atm - Present Work 5 atm - Gu et al. (2000)

5

0 0.5

0.6

0.7

0.8

0.9

1.0 φ

1.1

(b) 60

1.2

×

10 atm (17%O2 –83%He)

1.4

1.5

10 atm - CH4-air (Gu) 10 atm - CH4-air 20 atm - CH4-air

50 Laminar burning velocity (cm s-1)

1.3

10 atm - CH4-O2-He 20 atm - CH4-O2-He

40

30 20 atm (17%O2 –83%He)

20 10 atm (air)

10

× ×

× 20 atm (air)

0 0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

1.4

1.5

Figure 11.7 Laminar burning velocities of (a) CH4–air mixtures at 2, 5, 10, and 20 atm and (b) CH4–O2–He mixtures at 10 and 20 atm as a function of equivalence ratio. Symbols represent experimental data; lines represent calculation with GRI-Mech 3.0. From Ref. [45].

1)

Hydrogen ﬂames have much higher laminar burning velocities than methane ﬂames. This is expected due to higher diffusivity and reactivity of H2, as the pﬃ burning velocity varies as SL (D vi). The burning velocity plots also exhibit the wider ﬂammability limits of hydrogen compared with those of methane. Moreover, the peak in burning velocity for hydrogen ﬂames occurs under richer

11.2 Theory and Applications in Research

(a)

2.5 2HO2<->H2O2+O2

Overall reaction order

2.0

HO2+H<->H2+O2

OH+H2<->H2O+H 1 atm 10 atm 25 atm 50 atm 100 atm

1.5

1 atm 10 atm 25 atm 50 atm 100 atm

HO2+OH<->H2O+O2 HO2+H<->2OH

HO2+CH<->H2O+O2

φ = 3.00

OH+H2<->H2O+H

H+O2(+M)<->HO2+(+M)

H+O2(+M)<->HO2(+M) H+O2<->O+OH O+H2<->H+OH

φ = 0.56

H+O2<->O+OH

-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 S

S

1.0

0.5

φ = 3.00

0.0

φ = 0.56

0 (b)

20

40 60 Pressure (atm)

80

100

2.50 1 atm 5 atm 20 atm

CH+HO2<->O2+H2O 1 atm 5 atm 20 atm

2.25

φ = 0.9

HO2+CH3<->OH+CH3O

φ = 1.2

HO2-CH3<->CH+CH3O

OH-CO<->H+CO2

OH+CO<->H+CO2

H+CH4<->CH3+H2

H+CH4<->CH3+H2

Overall reaction order

2.00

H+CH3(+M)<->CH4(+M)

H+CH3(+M)<->CH4(+M) H+O2<->O+OH

H+O2<->O4+OH H+O2+H2O<->HO2+H2O

1.75

-0.2 -0.1 0.0

0.1

0.2

0.3

H+O2+H2O<->HO2+H2O

0.4

0.5

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

S

S Egolfopoulos & Law (1990) - φ = 1.0

1.50

φ = 0.9 φ = 1.0 φ = 1.2

1.25

1.00

0.75

0

5

10 Pressure (atm)

Figure 11.8 Overall reaction orders for (a) H2–air and (b) CH4–air premixed flames as a function of pressure for fixed equivalence ratios. The insets show the normalized sensitivity on

15

20

mass burning rate (S) for the most important elementary reactions at different w. From Ref. [45].

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2)

3)

4)

conditions compared with that for methane ﬂames, and the difference can be largely attributed to the higher diffusivity of hydrogen. As pressure increases, w corresponding to the peak burning velocity shifts to lower w values for hydrogen ﬂames, whereas it is relatively insensitive to pressure for methane ﬂames. In addition, for both hydrogen and methane ﬂames, the burning velocity decreases as pressure is increased; however, the sensitivity to pressure is different for the two fuels. Also note that for sub-atmospheric pressures the burning velocity for hydrogen ﬂames increases with pressure. The effect of pressure on laminar burning velocity is often expressed through an overall reaction order (n), given as n ¼ 2 þ 2[qln(SL0)/qln(p)] and plotted in Figure 11.8. For both the hydrogen and methane ﬂames, n exhibits a nonmonotonic variation with pressure, ﬁrst decreasing with pressure, reaching a minimum and then increasing. This non-monotonic behavior for H2 ﬂames isrelatedtothethreereaction(orexplosion)limitsforH2–O2 mixtures,asdiscussed earlier [20]. The initial decrease in n is related to the second limit, which corresponds to the increasing importance of the chain-termination reaction (H þ O2 þ M ! HO2 þ M) over branching reactions (such as H þ O2 ! OH þ O), whereas the subsequent increase in n is related to the third limit, which is due to the new branching pathways at high pressures involving consumption of HO2 and production of radical species. The non-monotonic variation of n with pressure for methane ﬂames has been discussed by Rozenchan et al. [45], who suggested an analogy with the explosion limits of H2–air mixtures. Using sensitivity analysis, they identiﬁed the main branching and termination reactions. The competition between the branching reaction (H þ O2 ! OH þ O) and the termination reactions (H þ O2 þ M ! HO2 þ M and H þ CH3 þ M ! CH4 M) causes the initial decrease in n with pressure, which reaches a minimum between 3 and 5 atm. Above 5 atm, the new branching reaction HO2 þ CH3 ! OH þ CH3O becomes active and contributes to the subsequent increase in n by supplying the ﬂame with OH radicals, which are further used by the chain-carrying step OH þ CO ! H þ CO2. The non-monotonic behavior of n is also related to the non-monotonic variation of activation energy (Ea) and Zeldovich number (Ze) with pressure. Both of these parameters are important in the context of cellular instabilities, as discussed in the next sub-section. It is also important to note that the reaction limits of H2–air mixtures have also been characterized in terms of the non-monotonic variation of mass burning rate with pressure [25]. However, this aspect has not been investigated for methane and other HC ﬂames. Comparison of the predicted and measured ﬂame speeds in Figures 11.6 and 11.7 reveals deﬁciencies in the reaction mechanisms under high-pressure conditions for both hydrogen and methane ﬂames. In particular, for the predicted laminar ﬂame speeds of H2–air mixtures, the Mueller mechanism [53] and the GRI-Mech 3.0 mechanism [54] show noticeable differences from the measured values. Similarly, ﬂame speeds predicted using the GRI-Mech 3.0 mechanism for methane–air mixtures exhibit discrepancies at higher pressures. This is not surprising, since these mechanisms were validated/optimized using targets at moderate pressures, generally 5–10 atm. This may be attributed to the non-availability of

11.2 Theory and Applications in Research

Figure 11.9 Measured unstretched laminar burning velocity versus equivalence ratio at fixed H2 mole fractions in NG–H2 blend at 1 atm. Data from Law for CH4 flames and from Lamoureux for H2 flames are also shown. From Ref. [71].

5)

6)

high-pressure data at that time, due to experimental difﬁculties resulting from cellular instabilities at higher pressures. However, recent studies [24, 45] have reported such data. Improved mechanisms for high-pressure combustion of H2–air [6, 7] and CH4–air mixtures [55, 56] have also been reported recently. The effect of H2 addition on the laminar burning velocity of natural gas and methane ﬂames is illustrated in Figures 11.9 and 11.10, respectively. The H2 addition (i) increases ﬂame speed due to the higher reactivity and diffusivity of H2 and (ii) shifts the peak ﬂame speeds to richer mixtures as the mixture diffusivity increases with H2. Furthermore, as indicated in Figure 11.10, the ﬂame thickness decreases with H2 addition, which enhances the ﬂame propensity to hydrodynamic instability [49]. Thus, the ﬂame susceptibility to cellular instabilities is also markedly inﬂuenced by H2 addition, as it modiﬁes the ﬂame–stretch interactions and Markstein number/length [1, 49, 52, 57]. In addition, the thermal–diffusive instability is strongly inﬂuenced by H2 addition due to its effect on the Lewis number. These aspects are discussed in the next section. Results in Figure 11.10 also indicate deﬁciency in the chemistry and transport models for simulating H2–HC premixed ﬂames. There have been few investigations on the effect of pressure on H2–HC premixed ﬂames. Since the laminar burning velocities of H2–air and HC–air ﬂames decrease with pressure, a similar trend can be expected for the effect of pressure on H2–HC premixed ﬂames. This is illustrated in Figure 11.11, which plots the laminar burning velocities versus equivalence ratio for CH4–H2–air mixtures at different pressures and two different mole fractions of H2. It is important to note, however, that the sensitivity to pressure, as indicated by the overall reaction order and mass burning rate, is different for H2 and HC ﬂames, which would lead to more complex behavior for H2–HC ﬂames. For instance, the effect of pressure on the mass burning rate of H2 ﬂames is strongly inﬂuenced by competition between

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Figure 11.10 Laminar burning velocity (a) and flame thickness (b) versus equivalence ratio for fixed H2 mole fractions in premixed CH4– H2–air flames at 1 atm. Symbols represent

experimental data. Lines in (a) represent calculations with CHEMKIN (solid line, a ¼ 0; long dashed line, a ¼ 0.1; and short dashed line, a ¼ 0.2). From Ref. [52].

the chain-branching and chain-termination reactions, manifested through the reaction limits [20, 25]. The H2–HC ﬂames may be expected to exhibit analogous behavior under certain conditions. These aspects have not been investigated in previous studies, which have been limited to moderate pressures, below 10 atm. 11.2.4 Flame–Stretch Interactions and Cellular Instability

Flame stretch and cellular instability represent two distinct but often coupled phenomena, which profoundly affect the structure, propagation, and dynamics of

Figure 11.11 Laminar burning velocity versus equivalence ratio for CH4–H2–air mixtures at different pressures and for 0.1 and 0.2 H2 mole fractions; a ¼ 0.1 (a) and 0.2 (b) Symbols represent the experimental data. Lines

represent calculations with CHEMKIN (solid line, P ¼ 0.1 MPa; long dashed line, P ¼ 0.3 MPa; and short dashed line, P ¼ 0.5 MPa. From Ref. [52].

11.2 Theory and Applications in Research

premixed ﬂames. Flame–stretch refers to the temporal rate of change of the ﬂame surface area, which may be caused by curvature, aerodynamic straining, and unsteady processes [1]. Cellular instability refers to the wrinkling of the ﬂame surface and may involve three types of instabilities: hydrodynamic, diffusional–thermal, and Rayleigh–Taylor [20]. Hydrodynamic instability is caused by the density difference across the ﬂame, represented by the density ratio of the burned to the unburned mixtures, and thus is present in all ﬂames [45, 58]. It is based on the hydrodynamic theory of Darrieus [59] and Landau [60]. Diffusional–thermal instability, which is also termed diffusive or thermal–diffusive instability, occurs as a result of the nonequidiffusive properties of the reaction mixture [61, 62]. It is characterized by the global Lewis number (Le), deﬁned as the ratio of the mixture thermal diffusivity to the mass diffusivity of the deﬁcient reactant, with Le < 1, Le ¼ 1, and Le > 1 indicating unstable, neutral, and stable situations, respectively. Rayleigh–Taylor instability is related to a negative density gradient in the direction of a body force such as gravity [1, 41]. The discussion in this chapter will focus on the diffusive and hydrodynamic instabilities. Stretched ﬂames are important from several considerations. First, the stretched laminar ﬂame speeds are now being recognized as fundamental as the unstretched ﬂame speeds. Second, ﬂame–stretch interactions involve rich physics with strongly coupled ﬂuid ﬂow, transport, chemistry, and transient processes [1]. Moreover, laminar ﬂame–stretch interactions provide important information to model ﬂame– turbulence interactions within the thin laminar ﬂamelet regime of premixed turbulent ﬂames [63]. Third, most practical ﬂames are subjected to a wide range of stretch rates, which inﬂuence the structure, extinction, and propagation characteristics of these ﬂames. For instance, the coupling between stretch and non-unity Lewis number modify the laminar and turbulent burning velocities, and thus their stabilization behavior. Finally, the ﬂame–stretch interaction affects both the hydrodynamic and diffusional instabilities. For example, a positive stretch tends to stabilize the hydrodynamic instability [45], whereas it tends to stabilize and destabilize the diffusional instability for mixtures with Le > 1 and Le <1, respectively. Cellular instabilities due to stretch and thermal–diffusion effects lead to the wrinkling of the ﬂame surface with regions of local extinction and robust burning [64]. Under certain conditions, the wrinkled ﬂames undergo self-acceleration [65], and may also transition to detonation. Therefore, it is important to characterize the effects of w, Le, and pressure on these processes in ﬂames burning H2–HC blends. Here we ﬁrst provide an overview of fundamental processes associated with ﬂame–stretch interactions, and then discuss the effects of hydrogen on these interactions. More fundamental analysis of this phenomenon can be found elsewhere [1, 61, 62]. The ﬂame speed response to stretch is expressed as [1, 41, 62] SL ¼ SL0 La K

ð11:1Þ

where SL and SLo are the stretched and unstretched laminar ﬂame speeds, respectively, K the stretch rate, and La the Markstein length. It is important to distinguish between the unburned (used in Equation 11.1) and burned Markstein lengths as discussed by Rozenchan et al. [45]. Following Aung et al. [66], the above equation can

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be written in terms of a non-dimensional stretch rate (Karlovitz number) and the Markstein number (Ma) as SL0 =SL ¼ 1 þ Ka Ma

with

Ma ¼ La =dD

and Ka ¼ KdD =SL

ð11:2Þ

where dD is the ﬂame thickness, which is often based on the laminar ﬂame speed and the mass diffusivity of fuel in the unburnt mixture, dD ¼ D/SL. The above two equations have also been obtained using asymptotic analysis [62], in the limit of small stretch. Although both of these equations have been employed to characterize the ﬂame speed response to stretch, it is preferred to report raw data using Equation 11.1, since La and K are more precisely deﬁned, whereas Ma and Ka require an estimate of ﬂame thickness. Thus, plotting SL versus K and using Equation 11.1 yields both the unstretched ﬂame speed and the Markstein length. Figure 11.12 from Law and Sung [1] demonstrates this approach for determining SL0 and La in counterﬂow H2–air and C3H8–air ﬂames. The slope of each curve for a ﬁxed w yields La, while the intercept with the y-axis (in limit of K ! 0) yields SL0. This procedure has been employed in numerous experimental and computational studies on stationary counterﬂow ﬂames and propagating spherical ﬂames. Figure 11.12 further indicates that for H2–air ﬂames, SL decreases (La > 0) and increases (La < 0) for rich (w¼ 3.0), and lean mixtures (w ¼ 0.6), respectively, representing diffusively stable and unstable conditions. Conversely, for C3H8–air ﬂames, the stable and unstable conditions correspond to lean (w ¼ 0.7) and rich mixtures (w ¼ 1.7), respectively. As discussed by Law and Sung [1], the instability is related to the non-unity Le for stretched ﬂames. If an initially planar ﬂame is perturbed into one containing alternating convex and concave segments towards the unburnt mixture, then for Le > 1 the burning is intensiﬁed at the concave segment and weakened at the convex segment, leading to smoothing of the wrinkles, that is, the ﬂame is cellularly stable. Conversely, for Le < 1 the ﬂame is cellularly unstable. In addition, as burning intensity in the concave region is reduced by ﬂame–stretch interaction, local extinction may also occur, leading to the formation of holes over the ﬂame surface. An example of local extinction is the tip opening in fuel-rich propane–air ﬂames established on a Bunsen burner [1]. There have been numerous studies on ﬂame–stretch interaction and diffusive instability of premixed ﬂames in different conﬁgurations. Faeth and co-workers [41–43, 66] reported extensive data on spherical ﬂames using H2–air and H2–air– diluent mixtures, and Law and co-workers [44, 45] investigated propagating spherical ﬂames and stationary counterﬂow ﬂames burning H2, CH4, C3H8, and various fuel blends. Figure 11.13 presents some representative results from Kwon and Faeth [42] in terms of the predicted and measured SL0/SL versus Ka for atmospheric H2–air ﬂames. For a given w, SL0/SL varies linearly with Ka with the slope yielding Ma, and demonstrating the validity of Equation 11.2. Moreover, Ma > 0 and Ma < 0 correspond to diffusively stable and unstable conditions for rich and lean H2–air ﬂames, respectively, which is consistent with the preceding discussion. For these positively stretched ﬂames, the diffusion of heat is defocused, whereas the diffusion of deﬁcient reactants is focused. Thus, for lean H2–air mixtures (Le < 1), the mass diffusion dominates, and the ﬂame speed increases with stretch, as indicated for w ¼ 0.6, and

11.2 Theory and Applications in Research

Hydrogen - air, 1 atm 1600

(a)

Sb (cm s-1)

1400

φ = 3.0

1200 (Sbº ) CFF

1000 800

φ = 0.6

600 0

500

1000

1500

2000

2500

3000

3500

4000

Stretch rate, κ (s

-1)

Propane–air, 1 atm 180 (b) 160

Sb (cm s-1)

140

φ=0.7

120

(Sbº ) CFF

100 80

φ=1.7

60 0

50

100

150

200

250

Stretch rate, κ (s-1) Figure 11.12 Computed stretched flame speed for counterflow hydrogen–air (a) and propane–air flames (b) , showing its linear variation with stretch rate, and the opposite response for lean and rich flames. From Ref. [1].

the ﬂame is diffusively unstable, implying propensity for cellular instability. In the context of H2–HC blends, this implies that H2 addition to lean hydrocarbon ﬂames will increase the unstretched and stretched ﬂame speeds, and promote diffusive instability. A few additional points are worth mentioning. First, there is a distinction between the unburned and burned Markstein lengths, which is related to the density ratio across the ﬂame and mass accumulation within the ﬁnite ﬂame thickness [45]. While these two Markstein lengths show similar trends with respect to w, pressure, and other parameters, it is important to make this distinction when reporting data

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Figure 11.13 Measured and predicted normalized laminar burning velocities versus Karlovitz number at different equivalence ratios for hydrogen–air flames at normal temperature and pressure. From Ref. [42].

on La or Ma. Second, for Le ¼ 1, the ﬂame speed is reduced for a convex segment and increased for a concave segment [1]. Consequently, the curvature tends to stabilize the ﬂame, and shift the stability boundary based on non-equidiffusive considerations away from Le ¼ 1 to smaller values of Le. The combined non-equidiffusive and pure curvature instabilities have been deﬁned as the thermal-diffusional instability. Third, for most HC–air mixtures, Le < 1 and Le > 1 for rich and lean ﬂames, respectively, implying that La < 0 (Ma < 0) and La > 0 (Ma > 0), respectively. Thus, lean H2–air and rich C3H8–air ﬂames exhibit a propensity for diffusive instability. Note, however, Le 1 for C2H4–air mixtures, and Le < 1 and Le < 1 (but close to unity) for rich and lean CH4–air mixtures, respectively. Fourth, the magnitude of La (or Ma) provides a measure of the ﬂame speed sensitivity to stretch. Thus, for La > 0 a decrease in La (or Ma) and for La < 0 an increase in its magnitude indicate an increased propensity for diffusive instability. In addition, the neutral stability condition corresponds to Le < 1, as discussed above. Finally, although no assumption was made in the original formulation about the relationship between SL and K (or between SL0/SL and Ka), being linear, most experimental and computational studies have observed this to be case for a wide range of conditions, away from extinction. This provides a convenient way of characterizing the effects of various parameters, such as equivalence ratio, pressure, and fuel composition, on ﬂame–stretch interactions in terms of the dependence of La

11.2 Theory and Applications in Research

Figure 11.14 Measured and predicted Markstein numbers versus equivalence ratio for H2–O2–He, H2–O2–Ar, and H2–air flames at normal temperature and pressure. Measurements from other investigators are also included. From Ref. [42].

(or Ma) on these parameters. For instance, Faeth and co-workers [42, 67] showed that Ma for H2–O2–N2 ﬂames can be modiﬁed by replacing nitrogen with different diluents. A representative result from their study [42] is shown in Figure 11.14, where Ma is plotted versus w for H2–O2 ﬂames diluted with argon and helium. Replacing nitrogen with argon has very little effect on the magnitude of Ma, since Le is hardly modiﬁed. On the other hand, helium has a signiﬁcant effect on Ma under fuel-lean conditions, as it stabilizes the fuel-lean ﬂames due to the enhanced diffusion of heat, which increases Le from below to above unity. In a similar way, the effects of pressure and hydrogen on the ﬂame–stretch interactions of HC–air ﬂames can be characterized in terms of its effect on La or Ma, as discussed next.

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11.2.5 Effect of Pressure on Flame–Stretch Interactions and Cellular Instability

Studies on ﬂame–stretch interactions at high pressure are relevant for numerous combustion systems, such as spark ignition engines, pulsed detonations, and accidental explosions. The effects of pressure on ﬂame–stretch interactions and cellular instability involve several coupled processes. As discussed earlier, the unstretched burning velocity exhibits a nonlinear dependence on pressure, due to the competition between chain-branching and chain-termination reactions. Moreover, as pressure is increased, ﬂames often exhibit cellular structures associated with the diffusive and hydrodynamic instabilities, which are related to the effects of pressure on the ﬂame structure and thickness [1, 45]. These effects can be analyzed by considering the following expression for the Markstein length [45]: La ZeðLe 1ÞdD ;

where

h i Ze ¼ Ea ðTb Tu Þ= RðTb Þ2

ð11:3Þ

where Ze is the Zeldovich number, dD the ﬂame thickness, Ea the activation energy, Tb the adiabatic ﬂame temperature, Tu the unburnt mixture temperature, and R the universal gas constant. This expression can be used to describe the effects of pressure and Le on La. According to this expression, the response of burning velocity to stretch is determined by three parameters, namely Le, dD, and Ze. The thermal–diffusion effect is represented by Le 6¼ 1, which determines the sign of La and hence the trend of the ﬂame response to stretch. Thus, for positively stretched ﬂames, Le > 1 (<1) yields La > 0 (<0), which correspond to diffusively stable and unstable conditions, respectively, as discussed earlier. The ﬂame thickness dD essentially characterizes the effect of pressure on La. Figure 11.15 from [45] presents the variation of dD (a) versus pressure for spherical CH4–air ﬂames at ﬁxed equivalence ratios. The ﬂame thickness for other fuels generally follows a similar behavior, and may be explained using the relation dD D/ SL. Although both D and SL decrease as pressure is increased, the effect on D dominates, and dD decreases with pressure. Moreover, dD determines the transit time of the reactants in crossing the ﬂame, and since the laminar burning ﬂux increases with pressure, dD decreases with pressure. Note, however, that SL exhibits a more complex dependence on pressure, since the burning rate is moderated due to the importance of the three-body termination reactions with increasing pressure [45]. Consequently, the effect of pressure on dD becomes moderated at higher pressures. Thus, according to Equation 11.3, the effect of pressure is to increase the ﬂame propensity to cellular instability. For instance, for diffusively unstable ﬂames (Le < 0), an increase in pressure will further increase the magnitude of La, whereas for diffusively stable ﬂames (Le > 0 and La < 0), it will decrease La, implying an increased propensity to cellular instability in both cases. As indicated in Figure 11.15, Ze has a more complex variation with pressure depending upon the equivalence ratio, indicating that it is strongly inﬂuenced by the combustion chemistry. However, as discussed by Rosenchan et al. [45], the effect of pressure on La appears mainly through dD, as the variation in Ze is relatively small compared with that in dD as

11.2 Theory and Applications in Research

Figure 11.15 Computed flame thickness (a) and Zeldovich number (b) plotted versus pressure for spherical CH4–air flames at fixed equivalence ratios of 0.7 (squares), 1.0 (triangles), and 1.3 (circles). From Ref. [45].

pressure is varied. Note, however, that Ze is an important parameter for characterizing the ﬂame tendency for self-acceleration and transition to detonation. It is also important to mention that although it is difﬁcult to isolate the effects of pressure and non-unity Lewis number, results indicate that hydrodynamic instability is more strongly inﬂuenced by pressure, whereas the diffusive instability is more strongly inﬂuenced by the Lewis number. Pressure effects on ﬂame–stretch interactions in ﬂames burning pure fuels have been extensively investigated. Most studies have considered outwardly propagating spherical ﬂames, although other conﬁgurations such as counterﬂow ﬂames have also been examined [44]. Aung et al. [41] reported data for H2–air ﬂames for pressures up to 4 atm, while Kwon et al. [65] and Rozenchan et al. [45] reported data for H2–air, C3H8–air, and CH4–air ﬂames for pressures ranging from 20 to 60 atm. We now present a few representative results from these studies in order to illustrate the relevance of Equation 11.3, and the effects of pressure on the Markstein length and cellular instability. Figure 11.16 from [41] shows the measured and predicted Markstein numbers (Ma) as a function of equivalence ratio for H2–air ﬂames at different pressures. As pressure is increased, Ma decreases and the neutral stability condition (Ma ¼ 0) shifts towards richer mixtures, with the implication that instability becomes more pronounced at higher pressures. Thus, even at these moderate pressures, there is a noticeable effect of pressure on Ma, which is related to the reduced ﬂame thickness (dD) at higher pressures. However, as noted by Kwon and Faeth [41], cellular structures at these pressures were related to diffusive instability,

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Figure 11.16 Measured and predicted Markstein numbers versus equivalence ratio for H2–air flames at pressures of 2.0. 3.0, and 4.0 atm. From Ref. [41].

since those due to hydrodynamic instability were observed at relatively larger ﬂame diameters (>60 mm). Figure 11.17 from [1] shows similar results in terms of the Markstein length plotted with respect to a normalized equivalence ratio at ﬁxed pressures for different ﬂame conﬁgurations. The normalized equivalence ratio (W) varies between 0 and 1, as w varies between 0 and 1, W < 0.5 (>0.5) corresponds to lean (rich) mixtures. These results are also consistent with the preceding discussion. Thus, as the pressure is increased, the Markstein length decreases since the ﬂame thickness is reduced. In addition, as the equivalence ratio is reduced towards leaner mixtures, the Lewis number decreases to a value below unity, increasing the ﬂame propensity to cellular instability. Law and co-workers reported comprehensive results on the cellular instability of outwardly propagating spherical ﬂames at high pressures for H2–air, C2H4–air, CH4–air, and C3H8–air mixtures. Figure 11.18 from [65] presents Schlieren images showing the morphology of spherically propagating H2 ﬂames at w ¼ 3.0 and p ¼ 10, 15, and 20 atm (Figure 11.18a) and C3H8 ﬂames at w ¼ 1.4 and 1.6 and p ¼ 5 atm (Figure 11.18b). For these rich mixtures, H2 and C3H8 ﬂames are diffusionally stable and unstable, respectively. Consequently, the cellular structures or surface cracks are related to hydrodynamic instability for H2 ﬂames, whereas both diffusional and hydrodynamic instabilities contribute to the development of cellular cells for C3H8

11.2 Theory and Applications in Research

Figure 11.17 Extracted Markstein lengths for hydrogen–air mixtures from three different flame configurations, namely counterflow (CFF) and outwardly (OPF) and inwardly (IPF) propagating spherical flames. From Ref. [1].

ﬂames. Therefore, it is interesting that the propensity to become unstable for H2 ﬂames is almost the same for the three pressures, since the ﬂame thicknesses were almost equal, being 0.033, 0.032, and 0.031 mm, respectively, for these three ﬂames. However, the activation energies (Ea) were determined to be 39, 48, and 54 kcal mol1, respectively. The larger Ea values at higher pressures indicate the ﬂame tendency to be more stable at higher pressures, as observed in the ﬂame sequences. Furthermore, the images indicate the enhanced tendency towards hydrodynamic instability as the ﬂame radius increases, and consequently the Karlovitz number decreases, rendering the ﬂame more prone to instability. For the two propane ﬂames in Figure 11.18b, the Le values were nearly the same, but the ﬂame thicknesses were 0.18 and 0.27 mm for w ¼ 1.4 and 1.6, respectively. Consequently, the ﬂame with w ¼ 1.4 is more unstable than that for w ¼ 1.6. Hence the images in Figure 11.18 demonstrate that a positive stretch and thicker ﬂame tend to delay the onset and development of hydrodynamic cells and that as the ﬂame becomes thinner, not only does it become destabilized earlier (i.e., for larger values of Ka), but also the cell size is smaller. Another important aspect is the onset of cellular instability or the ﬁrst appearance of cells. Clearly, this phenomenon is inﬂuenced by many parameters, including density ratio, laminar ﬂame thickness, stretch, Lewis number, and equivalence ratio. Bechtold and Matalon [68] and Addabbo et al. [69] analytically examined the transition to cellular instability for expanding spherical ﬂames, and obtained an expression in terms of a critical Peclet number (Pec) for this transition, with Pec deﬁned as the ratio of ﬂame radius at transition to laminar ﬂame thickness, Pec ¼ Rc/dD. Their analysis is supported by

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Figure 11.18 Schlieren photographs of (a) H2–15% O2–N2 flames with w ¼ 3.0 at 10, 15, and 20 atm and (b) C3H8–air flames with w ¼ 1.4 and 1.6 at 5 atm. From Ref. [65].

several experimental studies, including those by Bradley and Harper [70] and Kwon et al. [65] using different fuels. The following expression obtained from the linear stability analysis of Bechtold and Matalon [68] and with subsequent corrections by Bradley and Harper [70] provides the effects of various parameters on the critical Peclet number: Pec ¼ Pe1 ðsÞ þ ZeðLe1ÞPe2 ðsÞ

ð11:4Þ

As discussed by Jomass et al. [58], the ﬁrst term provides the effect of hydrodynamic instability[ through the density ratio for equidiffusive ﬂames, and the second term represents the inﬂuence of stretch and Lewis number for non-equidiffusive ﬂames. Law and co-workers reported extensive experimental veriﬁcation and discussion of this equation [58, 65, 70]. An important observation from their studies is

11.2 Theory and Applications in Research

that for near-equidiffusive acetylene ﬂames, the critical Peclet number has a nearly constant value for a range of conditions, whereas for non-equidiffusive hydrogen and propane ﬂames, it increases and decreases, respectively, with the equivalence ratio [65]. Therefore, for lean H2 ﬂames, Pec increases as w increases towards richer mixtures, indicating a delay in the onset of cellular instability, whereas for lean propane ﬂames, Pec decreases as the mixture becomes more fuel rich, indicating an earlier transition to cellular instability. Further discussion on the morphology of these ﬂames at different p and w can be found elsewhere [58, 65, 70]. 11.2.6 Effect of Hydrogen on Flame–Stretch Interactions and Cellular Instability

Whereas ﬂame–stretch interactions and cellular instability have been extensively investigated for pure fuels, there have been relatively few studies for fuel blends [2, 50, 52, 71]. Using Equations 11.3 and 11.4 and the preceding results for pure fuels, one can interpret the effect of H2 through its inﬂuence on ﬂame thickness (dD) and Lewis number (Le). The addition of H2 to HC ﬂames generally decreases dD, implying an enhanced propensity for hydrodynamic instability for these ﬂames. Note, however, that at high pressures, the mass burning rate and thereby the ﬂame thickness may exhibit more complex variations with pressure, especially as the H2 mole fraction in the blend becomes signiﬁcant. The effect of H2 on Le is also more complicated, and depends upon the HC fuel, equivalence ratio, and H2 mole fraction in the blend. In general, the addition of H2 to lean and rich HC–air mixtures would decrease and increase Le, respectively, implying an increased and reduced tendency for diffusive instability, sinceLe > 1 and < 1 forlean andrichmixtures,respectively, andtheopposite for H2–air mixtures. Thus, for lean HC–air mixtures, the addition of H2 would decrease the Markstein length (La) and increase the ﬂames propensity for diffusive instability. However, the actual effect depends on the type of HC fuel and the amount of H2 in the blend.Inthefollowing, wepresentresults fromvariousstudiestoillustrate theeffects of H2 on ﬂame–stretch interactions and cellular instability of HC ﬂames. Figure 11.19 from [52, 71] presents the experimentally obtained Markstein lengths plotted versus w for CH4–H2 and NG–H2 spherically propagating atmospheric ﬂames. Without H2, these ﬂames are diffusively stable for the entire w range, as indicated by the positive values of La, and their stability is further enhanced for fuel-rich conditions. The effect of H2 is to decrease La for the entire range of w investigated, with the implication that propensity for diffusive instability is increased for these ﬂames. Moreover, as discussed earlier (cf. Figure 11.10), the ﬂame thickness is reduced with H2 addition, indicating an enhanced propensity for hydrodynamic instability also. It is also interesting that for H2–NG blends with H2 mole fraction (XH2 ) 0.6, the stability of these ﬂames is essentially determined by H2, as indicated by the negative and positive values of La for lean and rich mixtures, respectively. The effect of H2 on the cellular stability of H2–HC ﬂames is illustrated in Figure 11.20 [2], which shows Schlieren images of hydrogen–propane–air ﬂames at mixing ratios (a) of 0.25, 0.50, 0.75, and 1.0, a pressure of 5 atm, and an overall equivalence ratio of 0.80. The hydrogen mole fraction (XH2 ), ﬂame thickness and

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Figure 11.19 Measured Markstein length versus equivalence ratio for CH4–H2 (a) and NG-H2 flames (b) at 1 atm. From Ref. [52].

critical ﬂame radius for each ﬂame are provided in Table 11.2. Here a is used to account for the vastly different stoichiometries of propane and hydrogen with respect to oxygen. Since for complete reaction 1 mol each of H2 and C3H8 require 0.5 and 5 mol of oxygen, respectively, a ¼ 5XHC/(0.5XH2 þ 5XHC), with a ¼ 0 and a ¼ 1 corresponding to pure H2 and C3H8 in the fuel–air mixture, respectively. The subscript HC indicates hydrocarbon (C3H8) fuel. As indicated in Figure 11.20, the C3H8 ﬂame (a ¼ 1) for these conditions is cellularly stable. As the amount of H2 in the blend is increased, the propensity for cellular instability is increasingly enhanced, and can be well described by the decrease in ﬂame thickness caused by H2 addition. The increased tendency to destabilize is also indicated by the decrease in the critical ﬂame radius for the appearance of cellular cells (cf. Table 11.2). Another important point to note is that for the conditions in Figure 11.20, the propane and hydrogen ﬂames are diffusively stable and unstable, respectively. However, even for the case of

11.2 Theory and Applications in Research

Figure 11.20 Schlieren images of hydrogen–propane–air flames for mixing ratio (a) ¼ 0.25, 0.50, 0.75, and 1.0 (propane–air) at 5 atm and at an overall equivalence ratio of 0.80. From Ref. [2]. Table 11.2 Properties of hydrogen–propane–air flames. From Ref. [2].

Pressure (atm)

5 5 5 5 5 1 2 5 5 5 From Ref. [2].

Equivalence ratio (w) 0.8 0.8 0.8 0.8 0.8 0.6 0.6 0.6 1.0 1.4

Mixing ratio

H2 mole fraction

1.0 0.75 0.5 0.25 0.0 0.5 0.5 0.5 0.5 0.5

0.0 0.77 0.91 0.97 1.0 0.97 0.97 0.97 0.97 0.97

Flame thickness (mm) 0.065 0.056 0.044 0.034 0.027 0.25 0.17 0.11 0.029 0.035

Rcrit (cm)

>2.4 2.24 1.19 0.73 0.48 >2.67 1.1–2.0 0.5–1.0 1.0–2.0 >2.0

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Figure 11.21 Schlieren images of hydrogen–propane–air flames (a ¼ 0.50) at 1, 2, and 5 atm and an overall equivalence ratio of 0.60. The mixing ratio is 0.5. From Ref. [2].

a ¼ 0.75, the H2 mole fraction in the blend is 0.77, implying that the cellular stability of these ﬂames is largely determined by H2, and the cellular structures depicted in Figure 11.20 are due to both the hydrodynamic and diffusive instabilities. The Schlieren images in Figures 11.21 and 11.22 [2] illustrate the effects of pressure and equivalence ratio, respectively, on the cellular stability of H2–C3H8 ﬂames. As pressure increases, these ﬂames exhibit an increased propensity to destabilize (cf. Figure 11.21), which can be attributed to the reduced ﬂame thickness at higher pressures. It is again worth noting that for a ¼ 0.5, the H2 mole fraction in the blend is 0.91, implying that the ﬂame dynamics are largely determined by H2. It is somewhat surprising, therefore, that a ﬂame at 1 atm does not exhibit cellular structures due to diffusive instability. The images in Figure 11.22 depict the effect of equivalence ratio on the cellular stability, and indicate that for a ¼ 0.5, the stability behavior of these ﬂames is largely determined by H2, as the lean and rich H2 ﬂames are diffusively unstable and stable, respectively. Further evidence is provided in Figure 11.23 from [2], which plots the critical Peclet number (Pec) as a function of w at a ﬁxed pressure. Pec increases monotonically as w is increased, indicating increasingly stable behavior that is representative of H2 ﬂames.

11.2 Theory and Applications in Research

Figure 11.22 Schlieren images of hydrogen–propane–air flames at 5 atm, and overall equivalence ratios of 0.60, 1.00, and 1.40. Mixing ratio ¼ 0.50. From Ref. [2].

Figure 11.23 Experimental critical Peclet number as a function of overall equivalence ratio for hydrogen–propane–air mixtures (a ¼ 0.50) at 2, 4, and 5 atm. From Ref. [2].

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Figure 11.24 Measured Markstein lengths as a function of SFHC (mole fraction of hydrocarbon fuel in H2–HC blend) for lean (w ¼ 0.6), stoichiometric (w ¼ 1.0), and rich (w ¼ 1.67) CH4-, C2H4-, and C3H8-substituted atmospheric H2–air flames. From Ref. [50].

Another aspect here pertains to the effect of adding HC fuels to H2–air mixtures. This is important from considerations of safety and other undesirable consequences of using H2, such as ﬂame stability, stabilization, and uncontrolled combustion. Law and Kwon [50] examined the effects of adding CH4, C2H4, and C3H8 to H2–air premixed ﬂames. Figure 11.24, from that study, shows the experimentally obtained Markstein lengths as a function of HC mole fraction in the blend for lean (w ¼ 0.6), stoichiometric (w ¼ 1.0), and rich (w ¼ 1.67) ﬂames at 1 atm. For fuel-rich conditions, La is positive, and increases with the addition of HC fuel, implying enhanced stability, consistent with the ﬂame morphology images in Figure 15 in their paper. However, the increase in La due to the addition of propane seems somewhat counterintuitive, since propane ﬂames exhibit diffusive instability for fuel-rich conditions. For stoichiometric conditions, for all three ﬂames, La changes from positive to negative with increasing HC addition, implying an increased propensity of these ﬂames for diffusivity instability. Finally, for fuel-lean conditions, while La values are negative for all three ﬂames, methane and propane addition tend to render the ﬂame more unstable and stable, respectively, whereas ethylene has no effect on stabilization. As noted earlier, for La < 0 and La > 0 there are fuel-lean and fuel-rich H2–air mixtures, respectively. Apart from the cited study, there has been little work on the effect of HC fuels on H2–air ﬂames. 11.2.7 Propagating Flames in Axisymmetric Coflowing Jets: Effect of Hydrogen

Previous studies dealing with ﬂame–stretch interactions and cellular instabilities in H2–HC ﬂames have considered simpliﬁed conﬁgurations. Although these studies

11.2 Theory and Applications in Research

have provided a wealth of fundamental information, it is important to examine these aspects using more realistic conﬁgurations. For instance, in order to capture multidimensional effects, such as ﬂame area variations caused by H2 addition, and ﬂame propagation and stabilization in a spatially nonuniform mixture fraction ﬁeld, a jet ﬂame represents a more relevant conﬁguration. Ko and Chung [72] and Qin et al. [73] investigated the propagation of CH4–air ﬂames using this conﬁguration. The ﬂame was initiated by creating an ignition kernel in the mixing layer of two coﬂowing jets, and its propagation and triple ﬂame characteristics were analyzed. Briones et al. [57] reported a numerical investigation on the effect of H2 enrichment on the propagation characteristics of laminar CH4–air ﬂames in the mixing layer of two coﬂowing jets. Figure 11.25 presents the temporal evolution of the ignition and ﬂame propagation processes, depicted in terms of heat release rate contours for the case without H2 enrichment. Following ignition, as the ﬂame propagates upstream in a nonuniform ﬂow ﬁeld, it exhibits a triple ﬂame structure at its leading edge, containing a rich premixed zone (RPZ), a non-premixed zone (NPZ), and a lean premixed zones (LPZ). The three reaction zones are indicated in the snapshot at t ¼ 48 ms. Figure 11.26 shows the computed displacement velocity (Vf) of the ﬂame edge as a function of the axial distance from the burner rim for 0, 25, 50, and 75% H2-enriched CH4–air ﬂames. As the H2 mole fraction is increased, Vf increases progressively due to the enhanced chemical reactivity and diffusivity, and preferential diffusion caused by H2 addition. Briones et al. investigated the effect of H2 addition

Figure 11.25 Simulations showing the temporal evolution of ignition and flame propagation in terms of heat release rate contours for a CH4–air flame. The rich premixed

(RPZ), non-premixed (NPZ), and lean premixed (LPZ) reaction zones are indicated in the snapshot at t ¼ 48 ms. From Ref. [57].

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Figure 11.26 Computed displacement velocity (Vf ) of the propagating triple flame versus axial distance from the burner rim for the 0, 25, 50, and 75% H2-enriched CH4–air propagating

flames. The measured Vf for a CH4–air propagating flame from Ref. [73] is also shown for validation of the numerical model. From Ref. [57].

on ﬂame–stretch interactions in these propagating triple ﬂames. Figure 11.27 plots the normalized local triple ﬂame speed as a function of the Karlovitz number (Ka) for different levels of H2 enrichment. With increasing H2 mole fraction in the blend, the triple ﬂame speed progressively increases. Moreover, the enhancement of its magnitude becomes increasingly larger compared with that of the (unstretched) laminar ﬂame speed as the amount of H2 in the blend is increased. The ﬂame speed–stretch interactions are also substantially modiﬁed by H2 addition. This is indicated by the progressive decrease in the Markstein number (Ma ! 0), implying an enhanced tendency towards diffusive instability, as the amount of H2 in the blend is increased. In a subsequent study, Briones et al. [74] examined the effect of H2 addition on entropy generation and second law efﬁciency in propagating CH4–air ﬂames. It was observed that there is no loss of exergy and the second law efﬁciency of the system remains nearly constant on blending H2 with CH4, since the increased irreversibilities due to H2 addition are compensated by the increase in ﬂow availability in the fuel blend. This implies that the thermodynamics of the combustion process may not be signiﬁcantly altered by blending H2 with CH4. In general, there has been very limited work on the effects of H2 on the propagation and emission characteristics of HC ﬂames. Future studies should consider such ﬂames under high-pressure conditions and burning different H2–HC blends.

11.2 Theory and Applications in Research

Figure 11.27 Normalized triple (local) flame speed as a function of Karlovitz number (Ka) for the flames discussed in the context of Figure 11.26. From Ref. [57].

11.2.8 Non-Premixed and Partially Premixed Hydrogen–Hydrocarbon Flames

Non-premixed and partially premixed ﬂames are commonly encountered in practical systems. However, there have been few studies on such ﬂames burning H2–HC blends [75–77]. Naha and co-workers [76, 77] numerically investigated the effects of blending H2 with methane and n-heptane on the structure and emission characteristics of counterﬂow non-premixed and partially premixed ﬂames. These two fuels are considered as the most representative gaseous and liquid fuels, and are also good surrogates for natural gas and diesel fuel, respectively. Simulations were performed using the OPPDIF [78] code in the CHEMKIN package [79]. The methane ﬂames were computed using the GRI-Mech 3.0 [17], whereas the n-heptane ﬂames were computed using Helds mechanism [80]along with theLi and Williams NOx model [81]. Fora fuel blend containing a mol of hydrocarbon (CxHy) fuel and b mol of H2, the mixture composition at the fuel boundary can be obtained using the equation aCx Hy þ bH2 þ cðO2 þ 3:76N2 Þ ! ðxaÞCO2 þ ðb þ ay=2ÞH2 O þ ð3:76cÞN2 ð11:5Þ

The mass balance yields c ¼ xa þ b/2 þ ay/4. For non-premixed ﬂames, specifying the mole fraction of hydrogen, b/(a þ b), yields the mole fraction of hydrocarbon fuel. For partially premixed ﬂames (for a given fuel stream equivalence ratio

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w), c on the right-hand side is replaced by (xa þ b/2 þ ay/4)/w. Then the mole fractions of CxHy, H2, O2, and N2 at the fuel boundary can be calculated for a given value of b/(a þ b). Figure 11.28 from [77] presents temperature and NO mole fraction proﬁles for non-premixed ﬂames established with CH4–H2 blends containing 10, 50, 70 and 90% H2 by volume, or 1.4, 11.1, 22.6 and 52.9% H2 by mass. The corresponding proﬁles for n-C7H16–H2 blends are shown in Figure 11.29. For both the methane and

Figure 11.28 Temperature (a) and NO mole fraction (b) profiles for non-premixed flames established at as ¼ 100 s1, with different CH4–H2 blends. From Ref. [77].

11.2 Theory and Applications in Research

Figure 11.29 Temperature (a) and NO mole fraction (b) profiles for non-premixed flames established at as ¼ 100 s1, with different n-C7H16–H2 blends. From Ref. [77].

n-heptane ﬂames, the H2 addition increases the ﬂame temperature, due to the higher heating value per unit mass of the fuel blend, as shown in Table 11.3. For CH4–H2 ﬂames, the peak ﬂame temperatures are 2027, 2130, 2199 and 2294 K for 10, 50, 70 and 90% H2 in the blend, respectively. It is also interesting to note from Table 11.3 that a CH4–H2 blend with 50% H2 by volume provides nearly the same amount of volumetric heat release rate as that of pure H2, but with a mixture density about four times than that of pure H2. This has implications for addressing the H2 storage

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Table 11.3 Heating value per unit mass and volumetric heat release rate for fuel blends.

Fuel blend

CH4–H2

n-C7H16–H2

Mole % of H2

Mixture density (kg m3)

0 10 50 100 0 10 50 100

0.646 0.589 0.363 0.081 4.036 3.640 2.058 0.081

HHV (MJ kg1)

55.65 56.89 65.34 142.92 48.53 48.49 50.39 142.92

Heat release rate 108 (J m3 s1) 7.00 5.75 3.50 3.47 7.90 6.50 4.50 3.47

requirements without reducing the energy output from the H2–HC blend. The results in Figures 11.28 and 11.29 further indicate that the H2 addition increases the ﬂame thickness and shifts the ﬂame towards the oxidizer side, due to the increased mass diffusivity. The effect of hydrogen addition on NO proﬁles in the two ﬂames is illustrated in Figures 11.28b and 11.29b, respectively. An important observation here is that whereas the H2 addition has a negligible effect on NO concentrations for methane ﬂames, it causes a signiﬁcant reduction in NO for n-heptane ﬂames. This difference can be explained by examining the contributions of thermal and prompt NO in the two ﬂames. The addition of H2 to either ﬂame decreases prompt NO, as it lowers the C2H2 and CH concentrations, but increases thermal NO due to the higher ﬂame temperature. These two effects essentially cancel each other, and H2 addition has an inconsequential effect on the amount of NO formed in methane ﬂames. On the other hand, most of the NO in n-heptane ﬂames is produced through the prompt mechanism, which is signiﬁcantly reduced by H2 addition, and, consequently, the total NO formed is signiﬁcantly reduced. This is demonstrated in Figure 11.30, which plots the thermal and prompt NO proﬁles for two different blends containing 50 and 70% H2 by volume. Thus, a practical implication from these results is while the addition of H2 to HC ﬂames is expected to increase thermal NO, one needs to look at other NOx formation mechanisms to assess the net effect on NOx emissions. This suggests further development and validation of NOx chemistry models under highpressure conditions. Furthermore, Naha and Aggarwal [76] observed a signiﬁcant reduction in CO2, CO, and C2H2 emissions with H2 addition for both the methane and n-heptane ﬂames. Since C2H2 has been identiﬁed as the most representative soot precursor in a variety of ﬂames [82, 83], the addition of H2 can be expected to reduce signiﬁcantly soot emissions from the combustion of HC fuels. Similar results were reported for the effects of H2 on the structure and emission characteristics of methane and n-heptane partially premixed ﬂames. Guo and Neill [84] examined computationally the effects of hydrogen/reformate gas on ﬂame temperature and NO formation in methane–air diffusion ﬂames. The H2 addition was found to increase the amount of NO formed, depending on the strain

11.2 Theory and Applications in Research

Figure 11.30 Thermal and prompt NO profiles for non-premixed flames with two different CH4–H2 (a) and n-C7H16–H2 (b) blends containing 50 and 70% H2 by volume. From Ref. [77].

rate and amount of H2 added. A representative result from this study is shown in Figure 11.31, which plots the N2 consumption rate versus H2 mole fraction at two different strain rates. The overall effect is consistent with the results reported by Naha and Aggarwal [76] in that the H2 addition has a negligible effect on NOx formation in methane ﬂames, as the increase in thermal NO is essentially balanced by the decrease in prompt NO. Figure 11.31 further indicates that the contributions of the NNH and N2O routes become more noticeable as the H2 concentration in the blend is increased. It is worth mentioning the correspondence between the fundamental results from laminar ﬂame studies and those obtained using practical conﬁgurations. For example, Choudhuri and Gollahali [85, 86] observed a reduction in CO and soot,

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Figure 11.31 Nitrogen consumption rates in counterflow hydrogen-enriched methane flames. From Ref. [84].

but an increase in NOx emissions from turbulent jet diffusion ﬂames established using H2–HC (NG and propane) blends. Similarly, Coppens et al. [87] reported measurements of burning velocity and NO formation in unstretched H2–CH4 ﬂames stabilized on a perforated plate burner at 1 atm. In lean ﬂames, enrichment by H2 had little effect on NO, whereas in rich ﬂames, it reduced the NO concentration signiﬁcantly. Since NO formation in lean ﬂames is predominantly due to the thermal mechanism, whereas in rich ﬂames it is due to the prompt mechanism, these results indicate that H2 addition does not affect thermal NO for lean conditions, but reduces prompt NO in rich ﬂames due to the reduction in CH radical species. These observations are consistent with those discussed for non-premixed and partially premixed laminar ﬂames. The effect of H2 addition on soot formation in HC diffusion ﬂames has also been investigated [88–91]. An important observation from these studies is that H2 addition causes a signiﬁcant reduction in soot production due to the dilution and chemistry effects. For instance, Du et al. [89] observed a substantial decrease in soot particle inception limit with H2 addition to ethylene, propane, and butane counterﬂow diffusion ﬂames. Guo et al. [91] examined the effect of H2 on soot formation in atmospheric coﬂow ethylene–air diffusion ﬂames. The numerical model employed

11.3 Applications in Industry

Figure 11.32 Normalized maximum soot volume fraction versus the fraction of diluent. From Ref. [91].

a detailed gas-phase reaction mechanism, which included aromatic chemistry up to four rings. The effect of H2 addition was examined through measurements and detailed chemical pathway analysis. As indicated in Figure 11.32, which shows the normalized maximum soot volume fraction plotted with respect to the mole fraction of He or H2 in the blend, the soot reduction involves both dilution and chemistry effects. It was suggested that the chemical effect on soot formation is due to the decrease in hydrogen atom concentration in soot surface growth regions and the higher concentration of molecular hydrogen in the lower ﬂame region. Choudhuri and Gollahali [92] reported an experimental–numerical study on the effect of H2 on pollutant emissions and volumetric soot concentrations in H2–NG jet diffusion ﬂames. It was observed that the axial soot concentration and EICO (emission index of carbon monoxide, deﬁned as the integrated production rate of CO normalized by the fuel consumption rate) decreases, whereas EINO (emission index of nitrogen oxide) increases with the increase in hydrogen content.

11.3 Applications in Industry

The preceding results from fundamental ﬂame studies clearly demonstrate the viability of using H2 in enhancing the combustion and emission characteristics of petroleum-based fuels. H2 addition signiﬁcantly extends the ﬂammability limits and enhances the ignition and extinction characteristics of HC ﬂames, with important consequences for enhancing the LBO behavior and emissions in practical combustion systems. For instance, the enhanced ignitability due to H2 addition can be used to

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improve the ignition performance of HCCI engines. The use of such blends is also well suited for SI engines, since a blend can be introduced through port injection or direct injection (DI). Moreover, the enhanced diffusivity and burning velocity through H2 addition can improve the charge homogeneity in the cylinder, and reduce the combustion duration and cycle-to-cycle variation, while the wider ﬂammability limits provide ﬂexibility in optimizing engine performance and emissions. Similarly, the presence of H2 can facilitate the engine starting process, while the presence of HC fuel can address the problems of low volumetric efﬁciency, preignition, and backﬁre associated with H2 IC engines [93]. In this section, we discuss studies dealing with these aspects. Extensive research has been reported on the performance of SI engines using blends of hydrogen with gasoline [94–97], methane [98–101], NG [102–109], compressed natural gas (CNG) [110, 111], biogas [112], methanol [113], and ethanol fuels [114]. Different engine conﬁgurations used in these studies include onecylinder research engines, four-cylinder engines, and automobiles. Many of these studies considered NG–H2 blends due to the abundant supply of NG and its cost advantage over gasoline and diesel fuels. Moreover, NG is cleaner than most petroleum fuels and has a higher octane rating than gasoline, hence it can be used in higher compression ratio (CR) engines leading to higher efﬁciency. Akansu et al. [115] and Dimopoulos et al. [116] have provided comprehensive reviews of studies concerning NG–H2 engines, and studies dealing with H2-fueled ICEs have been discussed by White et al. [117]. Other HC–H2 blends have also been considered in order to examine the effects of H2 addition on engine performance. Important observations from the various studies can be summarized as follows: 1)

2)

3)

The effect of H2 on engine emissions is characterized in terms of brake speciﬁc production of a pollutant species [g (kW h)-1]. In general, HC, CO, and CO2 emissions (or BSHC, BSCO, and BSCO2) decrease with H2 addition due to the replacement of HC fuel by H2, whereas NOx emissions increase. It is interesting that these results are consistent with those reported using laminar ﬂames [77]. A representative result from the experimental study of Kahraman et al. [118] is shown in Figure 11.33, which plots the CO2 and HC mole fractions versus excessive air ratio (EAR) for different H2 mole fractions. For a given EAR, the mole fractions of CO2 and HC decrease as the amount of H2 in the blend is increased. In addition, the lean operability limit is extended with H2 enrichment. NOx emissions in general increase with H2 addition at a ﬁxed w. However, the use of H2 permits operation with leaner mixtures without increasing the combustion duration, which reduces NOx emissions without sacriﬁcing engine output and efﬁciency. This has been observed in several investigations [115, 116]. For example, Shudo et al. [119] demonstrated that in a methane-fueled direct ignition engine, the leaner operation enabled by H2 addition leads to improved thermal efﬁciency and reduced HC and NOx emissions. Several studies have examined the effects of EGR and H2 addition on IC engine performance and emissions [120, 121]. With increasing amount of EGR, BSNOx decreases due to lower temperature, but BSHC increases due to reduced reaction

11.3 Applications in Industry

Figure 11.33 Variation of (a) CO2 and (b) HC with EAR for different H2 mole fractions in an NG–H2fueled SI engine. From Ref. [118].

4)

rates at lower temperature. However, with H2 addition, the amount of EGR can be increased, which reduces NOx without affecting HC emissions. The effect of H2 on engine efﬁciency depends on the compression ratio, EAR, spark timing, engine speed and load, and the amount of H2 in the blend. Due to the low ignition energy and high burning velocity associated with H2, the use of an HC–H2 blend accelerates ﬂame initiation and propagation, and reduces combustion duration. This leads to improved combustion efﬁciency, especially when the spark timing is retarded (optimized). Thus the addition of moderate amounts of H2 to HC-fueled SI engines can decrease BSFC (brake speciﬁc fuel consumption), especially at lean burning conditions and higher engine speeds.

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Figure 11.34 Brake fuel conversion efficiency versus EGR quantity at 2000 rpm and 4 bar bmep engine load (each point with efficiency optimal spark timing). From Ref. [116].

5)

Figure 11.34 from [116] illustrates the effect of H2 addition on the engine efﬁciency. As discussed in the cited study, each point on the curve has been optimized with respect to spark timing, and represents the maximum achievable engine efﬁciency for the tested engine. The ﬁgure indicates that increasing the H2 fraction above a certain value does not lead to higher efﬁciency, and in fact can reduce the engine efﬁciency. This behavior has been observed in several studies, and is attributed to the increased wall heat losses due to the higher in-cylinder temperature and smaller quenching distance. This indicates an optimum H2 mole fraction for a given blend, which yields the most beneﬁts in terms of improved efﬁciency, reduced NOx, CO2, CO, HC emissions, and addressing the problems of low volumetric efﬁciency, backﬁre, low-knock resistance, and fuel costs associated with H2. Some studies have also investigated the use of H2–HC blends in other combustion systems. Schefer [122] investigated the stabilization of hydrogen-enriched methane–air swirl-stabilized premixed ﬂames. It was shown that hydrogen addition reduces the lean stability limit, allowing stable burner operation at lower ﬂame temperatures, which is in turn beneﬁcial for achieving lower NOx emission. This is consistent with studies on lean-premixed combustors for stationary gas turbine applications [123–125], which show that a relatively small

11.4 Outlook

amount of H2 can extend the lean ﬂammability limits of CH4 turbulent ﬂames to lower equivalence ratios (w < 0.5), and reduce NOx emissions. Similarly, The addition of H2 to HCCI engines can signiﬁcantly enhance the ignition reliability, provide more homogeneous ignition, and expand the operation range of equivalence ratios and engine loads [126]. 11.4 Outlook

Results from fundamental ﬂame and engine studies clearly demonstrate the viability of a blended fuel strategy using H2–HC mixtures. Not only does this approach help reduce our dependence on fossil fuels, but it also increases the utilization of renewable and cleaner fuels. However, hydrogen being an energy carrier, there are signiﬁcant technological challenges with regard to its production, storage, and utilization. Major research efforts are needed to address these challenges and develop our fundamental understanding of the combustion and emission characteristics of H2–HC blends. Future work should focus on generating high-ﬁdelity data and developing reliable predictive capabilities for various H2–HC blends under realistic conditions. A variety of targets, such as ignition delay, jet stirred reactor data, laminar burning velocities, ﬂame structure, and emissions, should be used to develop reliable chemistry and transport models for a range of pressures, mixture ratios, and fuel compositions. Fundamental combustion theories are based on a single-fuel concept. These theories need to be modiﬁed for blended fuels. Thus the adiabatic ﬂame temperature, ﬂammability limits, ﬂame speeds, quenching distance, stretch effects, ﬂame stabilization, thermo-diffusive instability, and so on need to be revisited for blended fuels. Considerable effort is also required to develop more consistent or universal deﬁnition of important parameters, such as Zeldovich number and Lewis number, for fuel blends. For instance, Law et al. [2] deﬁned a weighted average Lewis number as Le ¼ 1 þ q1[qH(LeH 1) þ qC(LeC 1)], where q is the total heat release, qH and qC are the heat release associated with the combustion of H2 and HC, respectively, and LeH and LeC are the Lewis number of H2–air and HC–air mixtures at wH and wC, respectively. Previous studies on HC–H2 laminar ﬂames have mostly considered premixed ﬂames, and been limited to moderate pressures. Future research should examine ﬂame dynamics including ﬂame–stretch interactions and cellular instabilities at higher pressures. As noted earlier, the ﬂame sensitivity to pressure, as indicated by the overall reaction order and mass burning rate, is different for H2 and HC ﬂames. This should lead to more complex behavior for H2–HC blends. For example, the effect of pressure on the mass burning rate of H2 ﬂames is characterized by the competition between chain-branching and chain-termination reactions, manifested through the reaction limits [20, 25]. H2–HC ﬂames may exhibit analogous behavior under certain conditions, and should be investigated. Further research should also focus on ﬂames with blends of H2 and higher hydrocarbon fuels (C4 and higher) at elevated pressures.

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There has been very limited research on the structure and emission characteristics of non-premixed and partially premixed ﬂames with fuel blends. There has been little work on such ﬂames at high pressures. Similarly, few studies have considered turbulent ﬂames burning H2–HC blends [127–129]. Halter et al. [127] experimentally investigated the effect of hydrogen addition on the ﬂame front geometry and surface density of CH4–air turbulent premixed ﬂames. Many such studies should be performed under high-pressure conditions, and in more realistic conﬁgurations, such as swirl-stabilized ﬂames [129]. Turbulent non-premixed and partially premixed ﬂames burning H2–HC blends should also be investigated. Future work should also focus on performing engine experiments and examining novel strategies for optimizing the engine performance and emissions. Such strategies may include controlling the injection proﬁle, multiple injections, optimizing spark timing, amount of EGR, and controlling fuel composition and fuel–air ratio depending upon the engine operating conditions. In this context, the use of H2–HC blends in DI–SI, CI, and HCCI engines should be further examined.

11.5 Conclusion

This chapter has provided a summary of work dealing with the effects of hydrogen addition on the combustion and emission characteristics of hydrocarbon–air mixtures. Results from fundamental ﬂame studies and engine investigations have been discussed. An important observation is that a blended fuel strategy can synergistically combine the advantages of both fuels while mitigating the disadvantages of each. For instance, the ﬂammability limits and ignitability of HC–air mixtures can be markedly enhanced by H2 addition. Similarly, the use of HC fuel can address concerns about explosion hazards associated with H2–air mixtures, and enhance the knock-limited operation and power density of H2 ICEs. Results concerning laminar ﬂames indicate that the addition of H2 can increase their burning velocities, improve their extinction characteristics, and modify their ﬂame dynamics, including ﬂame speed–stretch interactions and stability. In addition, the emissions of GHGs and particulate matter in these ﬂames are signiﬁcantly reduced by H2 addition, while the emissions of NOx depend upon the ﬂame conditions and the amount of H2 fuel in the blend. Moreover, the addition of H2 considerably extends the lean ﬂammability limit, which offers signiﬁcant ﬂexibility in terms of lean operation, and further enhancement of combustor stability and emissions characteristics. Results from ﬂame studies are consistent with the observations regarding the effects of H2 addition on the performance of ICEs. For instance, the extended lean ﬂammability limit, higher burning velocities, and improved ignitability resulting from H2 addition can be used to enhance the performance and emissions of ICEs. The addition of H2 to HC-fueled SI engines improves the engines efﬁciency and lean burn capability, and decreases burn duration, cycle-to-cycle variation, and CO2, CO, and HC emissions. It can also lead to lower NOx emissions if the spark timing and the

References

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Acknowledgments

The author acknowledges the ﬁnancial support of NASA, NSF, and ANL/DOE during the past few years.

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combustion. Acta Physicochem. URSS, 19, 77. Sivashinsky, G.I. (1983) Instabilities, pattern formation, and turbulence in ﬂames. Annu. Rev. Fluid Mech., 15, 179–199. Clavin, P. (1985) Dynamic behavior of premixed ﬂame fronts in laminar and turbulent ﬂows. Prog. Energy Combust. Sci., 11, 1–59. Wu, M.S., Kwon, S., Driscoll, J.F., and Faeth, G.M. (1991) Preferential diffusion effects on the surface structure of turbulent premixed hydrogen/air ﬂames. Combust. Sci. Technol., 78, 69–96. Bradley, D., Sheppard, C.G.W., Woolley, R., Greenhalgh, D.A., and Lockett, R.D. (2000) The development and structure of ﬂame instabilities and cellularity at low Markstein numbers in explosions. Combust. Flame, 122, 195–209. Kwon, O.C., Rozenchan, G., and Law, C.K. (2002) Cellular instabilities and selfacceleration of outwardly propagating spherical ﬂames. Proc. Combust. Inst., 29, 1775–1783. Aung, K.T., Hassan, M.A., and Faeth, G.M. (1997) Flame stretch interactions of laminar premixed hydrogen/air ﬂames at normal temperature and pressure. Cornbust Flame, 109, 1–24. Qiao, L., Kim, C.H., and Faeth, G.M. (2005) Suppression effects of diluents on laminar premixed hydrogen/oxygen/ nitrogen ﬂames. Combust. Flame, 143, 79–89. Bechtold, J.K. and Matalon, M. (1987) Hydrodynamic and diffusion effects on the stability of spherically expanding ﬂames. Combust. Flame, 67, 77–90. Addabbo, R., Bechtold, J.K., and Matalon, M. (2002) Wrinkling of spherically expanding ﬂames. Proc. Combust. Inst., 29, 1527–1535. Bradley, D. and Harper, C.M. (1994) The development of instabilities in laminar explosion ﬂames. Combust. Flame, 99, 562–572. Huang, Z., Zhang, Y., Zenga, K., Liu, B., Wang, Q., and Jiang, D. (2006) Measurements of laminar burning

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84 Guo, H. and Neill, W.S. (2009) A

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94 Hoehn, F.W., Baisly, R.L., and Dowdy,

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105 Karim, G.A., Wierzba, I., and Al-Alousi, Y.

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(1996) Methane–hydrogen mixtures as fuels. Int. J. Hydrogen Energy, 21 (7), 625–631. Raman, V., Hansel, J., Fulton, J., Lynch, F., and Bruderly, D. (1994). Hythane – An Ultraclean Transportation Fuel. In: Proceedings of 10th World Hydrogen Conference Cocoa Beach, Florida, USA, 3, 1797–1806. Ma, F., Wang, Y., Liu, H., Li, Y., Wang, J., and Zhao, S. (2007) Experimental study on thermal efﬁciency and emission characteristics of a lean burn hydrogen enriched natural gas engine. Int. J. Hydrogen Energy, 32, 5067–5075. Ma, F., Wang, Y., Liu, H., Li, Y., Wang, J., and Ding, S. (2008) Effects of hydrogen addition on cycle-by-cycle variations in a lean burn natural gas spark-ignition engine. Int. J. Hydrogen Energy, 33, 823–831. Wang, J., Chen, H., Liu, B., and Huang, Z. (2008) Study of cycle-by-cycle variations of a spark ignition engine fueled with natural gas–hydrogen blends. Int. J. Hydrogen Energy, 33, 4876–4883. Collier, K., Hoekstra, R.L., Mulligan, N., Jones, C., and Hahn, D. (1996) Untreated Exhaust Emissions of a Hydrogenenriched CNG Production Engine Conversion. SAE Paper No. 960858. Das, L.M., Gulati, R., and Gupta, P.K. (2000) A comparative evaluation of the performance characteristics of a spark ignition engine using hydrogen and compressed natural gas as alternative fuels. Int. J. Hydrogen Energy, 25, 783–793. Porpatham, E., Ramesh, A., and Nagalingam, B. (2007) Effect of hydrogen addition on the performance of a biogas fuelled spark ignition engine. Int. J Hydrogen Energy, 32 (12), 2057–2065. Schafer, F. (1981) An Investigation of the Addition of Hydrogen to Methanol on the Operation of an Unthrottled Otto Engine. SAE Paper 810776. Al-Baghdadi, M.A.S. (2002) Hydrogen–ethanol blending as an alternative fuel of spark ignition engines. Renewable Energy, 28 (9), 1471–1478. Akansu, S.O., Dulger, Z., Kahraman, N., and Veziroglu, T.N. (2004) Internal

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combustion engines fuelled by natural gas–hydrogen mixtures. Int. J. Hydrogen Energy, 29, 1527–1539. Dimopoulos, P., Bach, C., Soltic, P., and Boulouchos, K. (2008) Hydrogen–natural gas blends fuelling passenger car engines: combustion, emissions and well-to-wheels assessment. Int. J. Hydrogen Energy, 33, 7224–7236. White, C.M., Steeper, R.R., and Lutz, A.E. (2006) The hydrogen-fueled internal combustion engine: a technical review. Int. J. Hydrogen Energy, 31, 1292–1305. Kahraman, N., Ceper, B., Akansu, S.O., and Aydin, K. (2009) Investigation of combustion characteristics and emissions in a spark-ignition engine fuelled with natural gas–hydrogen blends. Int. J. Hydrogen Energy, 34, 1026–1034. Shudo, T., Shimamura, K., and Nakajima, Y. (2000) Combustion and emissions in a methane DI stratiﬁed charge engine with hydrogen pre-mixing. JSAE Rev., 21 (1), 3–7. Heffel, J.W. (2003) NOx emission and performance data for a hydrogen fueled internal combustion engine at 1500 rpm using exhaust gas recirculation. Int. J. Hydrogen Energy, 28, 901–908. Heffel, J.W. (2003) NOx emission reduction in a hydrogen fueled internal combustion engine at 3000 rpm using exhaust gas recirculation. Int. J. Hydrogen Energy, 28, 1285–1292. Schefer, R.W. (2003) Hydrogen enrichment for improved lean ﬂame

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stability. Int. J. Hydrogen Energy, 28, 1131–1141. Nguyen, O.M. and Samuelson, G.S. (1999) ASME Paper No. 99-GT-359. Phillips, J.N. and Roby, R.J. (1999) Presented at the ASME International Gas Turbine and Aeroengine Congress and Exhibition, Indianapolis, IN, Paper 99-GT-115. Morris, J.D., Ballard, F.L., and Banti, A. (1998) Presented at the ASME International Gas Turbine and Aeroengine Congress and Exhibition, Stockholm, Paper 98-GT-359. Shudo, T. and Yamada, H. (2007) Hydrogen as an ignition-controlling agent for HCCI combustion engine by suppressing the low-temperature oxidation. Int. J. Hydrogen Energy, 32, 3066–3072. Halter, F., Chauveau, C., and G€okalp, I. (2007) Characterization of the effects of hydrogen addition in premixed methane/ air ﬂames. Int. J. Hydrogen Energy, 32, 2585–2592. Hawkes, E.R. and Chen, J.H. (2004) Direct numerical simulation of hydrogen-enriched lean premixed methane–air ﬂames. Combust. Flame, 138, 242–258. Kim, H.S., Arghode, V.K., Martin, B., Linck, M.B., and Gupta, A.K. (2009) Hydrogen addition effects in a conﬁned swirl-stabilized methane–air ﬂame. Int. J. Hydrogen Energy, 34, 1054–1062.

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12 Liquid Biofuels: Biodiesel and Bioalcohols George Skevis 12.1 Introduction

The development of alternative, renewable fuels is of global importance due to concerns regarding global warming, environmental protection and diversity, and security of energy supply (e.g., [1–4]). There is particularly an increasing demand for alternative, sustainable fuels in the transport sector. Internal combustion engines are responsible for almost one-quarter of the total CO2 emissions (e.g., [5] and references therein), with an increasing trend. Reductions in carbon dioxide [and other greenhouse gas (GHG)] emissions can be realized either through advances in technology (increases in engine efﬁciency coupled with reductions in fuel consumption) or through the use of new, alternative fuels developed in the context of either sustainability or security of supply [5]. The latter is of primary concern in view of limited reserves of crude oil – and fossil fuels in general –and their uneven geographical distribution. The combustion of conventional fossil fuels is also a major source of environmental pollution, mainly through emissions of harmful compounds, and any advances in fuels, coupled with conventional technology, should also preclude increases in the emitted levels of the latter. The term biofuel refers to any liquid or gaseous fuel that is predominantly derived from recently produced and harvested biological resources [6]. There are currently two major classes of biofuels: bioethanol – or bioalcohols in general – and biodiesel. The use of biofuels in general, and in the transport sector in particular, has signiﬁcant potential advantages (e.g., [4, 7–9]). Liquid biofuels can be produced locally in sustainable systems and can thus enhance security of primary energy resources, particularly for less developed nations. Further, the use of bioalcohols and biodiesel in internal combustion engines can appreciably reduce the demand for crude oil products. A strong argument in favor of biofuel use is their potential for GHG savings since they recycle carbon dioxide that was extracted from the atmosphere in producing biomass [8]. There are also potential environmental beneﬁts since they are relatively clean fuels with virtually no sulfur content and they can reduce waste by recycling. Vehicle performance may also improve since bioethanol and biodiesel are

Handbook of Combustion Vol.3: Gaseous and Liquid Fuels Edited by Maximilian Lackner, Franz Winter, and Avinash K. Agarwal Copyright 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32449-1

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known to possess higher octane and cetane numbers than conventional gasoline and diesel fuels, respectively. However, there are signiﬁcant negative effects that need to be carefully taken into account. Biofuel production and use are some times associated with poor energy balances where energy input for fuel processing can equal, or even exceed, energy output [7]. This is particularly the case for the so-called ﬁrst-generation biofuels. Production costs are currently very high, almost three times higher than those for gasoline and diesel fuel production [4]. There are also ethical and environmental issues related to the food versus fuels debate. In short, it is argued that energy crops used for biofuels production compete for fertile soil with the traditional agricultural production of food and that natural resources are not enough to sustain both food and fuels. Further issues of land pollution and decreased biodiversity may also arise (e.g., [10] and references therein). A critical well-to-wheels (WTW) assessment of future automotive fuels for the European Union with respect to GHG emissions, energy efﬁciency, and production costs can be found elsewhere [11]. Liquid biofuels (or rather blends of liquid biofuels with gasoline and/or diesel) provide one of the few options for fossil fuel replacement in the transport sector in the short term [3] and their use is actively promoted, particularly in the European Union. The EU Biofuels Directive (2003/30/EC) has set a target of 5.75% of all transport fuels to be derived from biomass by 2010. Whether a particular alternative fuel is suitable for use in existing engine technologies is largely a question of fuel–technology compatibility (e.g., [12]). Ideally, supplementary fuels should be used without signiﬁcant engine modiﬁcation and/or any substantial changes in the storage and transportation infrastructure. However, this primarily depends on fuel physical, chemical, and combustion properties. A further important issue is naturally environmental compatibility, since the alternative fuel (or fuel blend) should be less polluting than the conventional fuel that it replaces. Finally, issues relating to capital and maintenance costs should also be taken into account. Wider penetration and sensible use of biofuels and biofuel mixtures in internal combustion engines thus require, ﬁrst and foremost, a thorough understanding of their properties and the effect of their use in terms of engine efﬁciency and pollutant formation. This chapter aims to provide a state-of-the-art review of these important issues. First, a brief review of bioethanol and biodiesel production methods is presented. The physical and chemical properties of liquid biofuels are subsequently summarized and compared with those of conventional petrol, diesel, and aviation fuels. Progress in the combustion chemistry of bioalcohols and biodiesels is thoroughly reviewed. The chapter concludes with an assessment of the effect of liquid biofuels on combustion engine performance and emissions.

12.2 Biofuel Production and Processing

Liquid biofuels can be made from a variety of biomass sources utilizing diverse technologies. Depending mainly on the primary biomass source and also on the

12.2 Biofuel Production and Processing

production method, they can be classiﬁed as ﬁrst-, second-, or third-generation biofuels (e.g., [3]). First-generation biofuels are derived from food crops including starch (e.g., grains and cereals), sugars, and vegetable oils. Waste (cooking) oils and animal fats, although not directly derived from food crops, can also be classiﬁed as ﬁrst-generation sources. Second-generation biofuels are derived from crops with no food use. These mainly include lignocellulosic biomass with a high carbon content (e.g., grasses and trees) and agricultural and forestry waste products. Secondgeneration sources may also include organic household and municipal wastes. Production methods for second-generation biofuels have a better energy balance than their ﬁrst-generation counterparts (e.g., [4]) and a lower environmental impact, but they are not yet commercially feasible. Third-generation biofuels are produced from genetically engineered crops that, for example, contain enzymes capable of converting the plant material. The conversion of biomass into biofuels by microbes and the production of biofuels by algae also fall into this category which is still at a research level – see [13] for a very interesting review. The major biofuels produced and used globally are currently bioethanol and biodiesel. Bioethanol is by far the most widely used alternative fuel in the transportation sector and accounts for more than 94% of global biofuel production [14]. Bioethanol is produced either from sucrose-containing feedstocks, such as sugar beet and sugar cane, or from starch-based grains, mainly corn and wheat (e.g., [4, 6, 14–16]). In the former case, sucrose is squeezed out of the feedstock, puriﬁed and fermented with yeast into hydrated ethanol. The reaction proceeds in two stages. In the ﬁrst, the enzyme invertase catalyzes the hydrolysis of sucrose to glucose and fructose. In the second, the enzyme zymase converts glucose and fructose into ethanol. C12 H22 O11 ! C6 H12 O2 þ C6 H12 O2

ð12:1Þ

C6 H12 O6 ! 2C2 H5 OH þ 2CO2

ð12:2Þ

This is followed by distillation and dehydration to produce anhydrous ethanol. Bioethanol is produced from corn (and starchy material in general) by so-called (dry or wet) milling. The corn is ground and cooked and enzymes are added to convert the starch into simple sugars, which are in turn fermented by yeast into ethanol, as above. Due to the relatively high cooking temperatures and the need for large amounts of enzymes, starch-based ethanol is more expensive than ethanol obtained from sucrose. Lignocellulosic biomass is also a potentially attractive primary source for bioethanol production. Dedicated lignocellulosic crops in particular (such as coppiced trees and grasses) are a very promising feedstock since they are suitable for low-quality land not used for agriculture and they have a low environmental impact [14]. Lignocellulosic biomass consists of cellulose (glucose polymer) and hemicellulose (mainly xylose polymer) that can be converted to sugars and lignin that cannot. Cellulose generally comprises 40–50% of dry wood on a mass basis, with hemicellulose representing 15–25%. Lignin levels can be as high as 40%. Grasses are

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characterized by lower lignin and cellulosic and higher hemicellulosic content. Lignocellulosic material can be converted to ethanol through either biological or thermochemical conversion (e.g., [2, 6, 14, 17]). The ﬁrst process initially involves physical pretreatment of the raw material, including removal of lignin, followed by sacchariﬁcation (hydrolysis of cellulose into sugars) and fermentation (conversion of sugars to bioethanol). It should also be noted that, in the cellulose-to-ethanol conversion, process energy can be provided by any unused hemicellulose and lignin, thus providing very favorable energy balances and reduced GHG emissions. However, biological conversion has still not reached a commercial level, mainly due to the low yields and high costs associated with the hydrolysis process. The second process involves thermochemical gasiﬁcation of the cellulosic biomass material to synthesis gas (a mixture of carbon monoxide and hydrogen) followed by conversion of the latter – via fermentation or catalytic reactions – to ethanol. The process is currently not commercially viable but it can be used for the conversion of the lignin fraction of the raw material [14]. Several other alcohols have also been considered as potential alternative transportation fuels. Methanol is an important industrial chemical but sustainable methods for producing (bio)methanol are currently not viable (e.g., [18]. Although methanol has historically been produced from biomass (wood) pyrolysis, it is currently predominantly produced by steam reforming of natural gas. There are also safety concerns: methanol is highly toxic and burns with a nearly invisible ﬂame making ﬂame detection particularly difﬁcult. For these reasons, methanol production and use will not be considered further in this chapter. Propanol and butanol are signiﬁcantly less toxic and less volatile than methanol. Butanol in particular is increasingly considered as a potentially economically viable fuel. Biobutanol can be produced by the fermentation of starch, grains, or lignocellulosic biomass with processes very similar to those for the production of bioethanol. However, and mainly due to the toxicity of butanol to the fermenting microorganisms, production yields are currently very low – on average less than half of typical ethanol yields [19, 20]. Biodiesel refers to monoalkyl esters of fatty acids derived from vegetable oils or animal fats [21, 22] that can be used (in neat or blended form) as a fuel in diesel engines. There are three major types of biodiesel feedstocks: (i) edible vegetable oils such as soybean (mainly used in the United States), rapeseed (mainly used in Europe), sunﬂower, palm, peanut, and so on, (ii) animal fats such as tallow, lard, chicken fat and the by-products of the production of omega-3 fatty acids from ﬁsh oil, and (iii) waste vegetable (cooking) oils (e.g., [23]). Production of biodiesel from nonedible vegetable oils (e.g., [24]) and algae (e.g., [25]) is still at an experimental level. Vegetable oils are mainly triglycerides with a number of branched chains of different lengths and they can be converted to biodiesel by the transesteriﬁcation process, which is represented by the general equation shown in Scheme 12.1. Transesteriﬁcation refers to the reaction of a straight-chain alcohol, such as methanol or ethanol, with a fat or oil (triglyceride) to form glycerol (glycerin) and the (methyl or ethyl) esters of long-chain fatty acids (e.g., [26–28]). In Scheme 12.1, R1, R2, and R3 refer to long hydrocarbon chains, commonly known as fatty acid chains, and their structure depends on the particular feedstock. Fatty acid chains vary in the number of

12.3 Physical and Chemical Properties of Biofuels O H2C

O

C

O R

1

H2C

H3C

OH

O

O

C

R

2

+

H3C

OH

HC

OH

+

H3C

O

H3C

O

O

C

1

C

R

2

O

O H2C

R

O

O HC

C

R

3

H2C

OH

C

R

3

Scheme 12.1 Outline of the transesterification reaction.

carbon atoms and number of double bonds, but they usually fall in the range C14–C22 with one to three double bonds. Typical biodiesels consist of only a few methyl esters. For example, soybean biodiesel consists mainly of methyl linoleate (C19 – two double bonds), methyl oleate (C19 – one double bond), and methyl palmitate (C16 – saturated) with smaller levels of methyl linolenate (C19 – three double bonds) and methyl stearate (C19 – saturated) [12, 21]. Rapeseed biodiesel, on the other hand, consists mainly of methyl oleate and is highly unsaturated [12, 26]. Animal fat biodiesel is characterized by high levels of saturated components (methyl palmitate and methyl stearate) [12]. The transesteriﬁcation reaction is reversible and excess methanol is required to drive the reaction to the right; typical ethanol-to-triglyceride molar ratios are of the order of 6 : 1. The reaction is also generally slow so that an appropriate alkali catalyst – mainly sodium or potassium hydroxide – and temperatures up to 80 C are required for complete conversion. The efﬁciency of the transesteriﬁcation reaction using reﬁned vegetable oils can be as high as 98% [27]. Ethanol, and higher alcohols, can also be used in the transesteriﬁcation process but they are less reactive and less costeffective than methanol. Non-catalytic transesteriﬁcation is also possible but this would require supercritical conditions and/or high temperatures (e.g., [21, 22]). Several oils and animal fats contain substantial amounts of free fatty acids that cannot be directly converted to biodiesel. Whereas reﬁned vegetable oils contain less than 0.05% of free fatty acids, the corresponding ﬁgure for animal fat can be as high as 30%. The reaction of fatty acids with the alkali catalyst can lead to soap (and water) formation. High soap levels inhibit the separation of glycerol from the methyl esters and thus reduce process efﬁciency. In that case, a two-stage process is used where an acid catalyst is ﬁrst used in order to convert the free fatty acids to methyl esters. This is followed by transesteriﬁcation of the pretreated oil, as described above [28].

12.3 Physical and Chemical Properties of Biofuels

The suitability of liquid biofuels as an alternative transportation fuel depends on their physical and chemical properties and also their combustion and emission

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characteristics. These properties will determine, to a large extent, the compatibility of the alternative fuel both with the existing engine technology and with the conventional fuel that it (partly) replaces. It is therefore important to examine the physical and chemical properties of alcohols in comparison with those of gasoline and of biodiesel in relation to diesel. Physical and chemical properties of ethanol, butanol, biodiesel, gasoline and diesel are summarized in Table 12.1. It should be emphasized that diesel and gasoline are

Table 12.1

Comparison of properties of ethanol, butanol and biodiesel with gasoline and diesel.

Propertya)

Gasoline

Ethanol

1-Butanol

Diesel No. 2

Carbon content (wt%) Hydrogen content (wt%) Oxygen content (wt%) Density (g cm3) Stoichiometric air–fuel ratio (wt) Flammability limits (vol.%) Normal boiling point ( C)

85–88 12–15 0 0.72–0.78 14.7

52.2 13.1 34.7 0.794 9.0

64.9 13.5 21.6 0.81 11.2

84–87 13–16 0 0.85 14.7

1.4–7.6 27–225

4.3–19.0 78

1.4–11.2b) 82.7

1.0–6.0 180–340

Reid vapor pressure at 37.8 C (kPa) Kinematic viscosity at 40 C (cSt) Latent heat of vaporization (kJ kg1) Cloud point ( C) Pour point ( C) Flash point ( C) Iodine number Lower heating value (MJ kg1) Autoignition temperature (K) Octane number (RON) Cetane number

55–103

16

18.6

<1.5

299–346 (326–366) <0.5

0.5–0.6c)

1.5c)

3.64

1.3–4.1

4.08 (4.83)

350

921

585

235

254d)

— — 43 — 43.4

— — 13 — 27.0

— — 35 — 32.0

15–5e) 35–15e) 60–80 8.6e) 42.8

0.5 (4.0) 3.8 (10.8) 131 (170) 133.2 37 (37.3)

530

696

638

588

88–98 —

111f ) —

113 —

— 40–55

a)

b) c) d) e) f)

Biodiesel 77.2 11.9 10.8 0.885 (0.882) 13.8

— 50.9 (52.9)

Properties of gasoline, ethanol, and diesel have been taken from the NREL compilation (www. afdc.energy.gov/afdc/pdfs/fueltable.pdf, last accessed 4 February 2010) unless stated otherwise. Properties of 1-butanol have been taken from Gautam and co-workers [33, 34] unless stated otherwise. Properties of biodiesel have been taken from Graboski and McCormick [29] unless stated otherwise. In the last case, values refer to properties of soybean biodiesel with properties of rapeseed biodiesel in parentheses. Values adopted from Material Safety Data Sheet MSDS B5860 (http://www.jtbaker.com/msds/ englishhtml/b5860.htm, last accessed 4 February 2010). Data at 20 C. Data for rapeseed biodiesel from [35]. Data from [29]. Data from [30].

12.3 Physical and Chemical Properties of Biofuels

products of fractional distillation and mixtures of thousands of individual hydrocarbons with a wide carbon number distribution (C2–C14 for gasoline, C10–C22 for diesel), varying composition, and accordingly varying properties within accepted standards. On the other hand, bioethanol (and biobutanol) is a single-component fuel and typical biodiesels are mixtures of only a few components with much better deﬁned properties. An important difference between biofuels and conventional liquid fossil fuels in terms of chemical structure and elemental composition is their relatively high oxygen content, which is of the order of 10% for typical biodiesels, rising to over 35% for ethanol. Both bioethanol and biodiesels have very low sulfur levels and are virtually free of aromatic compounds. In contrast, the aromatic level of diesel fuels can be as high as 30%. The increased oxygen level results in a signiﬁcantly lower energy density for the oxygenated fuels. This is particularly the case for ethanol, which has only 60% of the gasoline energy content on a mass basis (slightly more on a volume basis due to the higher ethanol density). On the other hand, ignition quality is improved. The octane number of ethanol is signiﬁcantly higher than that of ordinary gasoline and cetane numbers of typical biodiesels are at the high end of the corresponding range of typical diesels. There are several more important differences between gasoline and ethanol and also between diesel and biodiesel fuels. The viscosity of biodiesel, although an order of magnitude less than that of the fatty acid feedstocks, is signiﬁcantly higher than that of diesel. Similarly, the viscosity of ethanol is more than double that of gasoline. Although the endpoint of the distillation curve is very similar for both diesel and biodiesel, biodiesel lacks low-boiling volatile compounds and this is naturally directly related to its drivability. Ethanol, on the other hand, is highly volatile. The effects of fuel volatility and viscosity on engine operation are discussed in Section 12.5. Biodiesel exhibits very poor low-temperature properties compared with diesel. It is interesting that both the pour and cloud points of biodiesel are almost 20 C higher than those of diesel. This necessitates the use of additives if biodiesel is to be used in cold climates. A further issue relating to biodiesel use is its poor storage behavior. This is related to its high degree of unsaturation, expressed empirically through the iodine number, and can lead to deposit formation and stability problems. As shown in Table 12.1, the iodine number of typical biodiesels is more than an order of magnitude higher than that of diesel fuel. On the other hand, both ethanol and biodiesel have substantially lower ﬂash points than gasoline and diesel, respectively, which makes them significantly less ﬂammable. The properties of biodiesel are largely dependent on the primary feedstock. In general, the physical and chemical properties of a particular biodiesel will be inﬂuenced by the fatty acid chain length, degree of unsaturation, and branching, and also on the nature of the alcohol used in the transesteriﬁcation process – see [29, 31] for very interesting reviews. It has thus been shown that cetane numbers increase with increasing degree of saturation and increasing chain length. As a result, biodiesel obtained from animal fats has a higher cetane number than vegetable oil-derived biodiesel [32]. Furthermore, ethyl esters have higher cetane numbers

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than methyl esters and there is evidence to suggest that the cetane numbers of propyl and butyl esters will be even higher [31]. The structure of biodiesel has an insignificant effect on energy content. However, it inﬂuences rheological properties, particularly at low temperatures, with animal fat, highly saturated methyl esters having much higher pour and cloud points than vegetable oil methyl esters. Similar trends are also observed in the case of viscosity. It should further be noted that branchedchain (such as isopropyl and isobutyl) methyl esters offer the potential of improved low-temperature ﬂow properties with no signiﬁcant viscosity increases [31]. Recently, the potential of butanol as an alternative fuel for internal combustion engines has also been considered. Butanol is characterized by a relatively high energy content, only 10% less than that of gasoline, while retaining the excellent ignition qualities of alcohols (its octane rating is generally slightly higher than the average gasoline octane rating). On the other hand, the viscosity of butanol is similar to that of diesel. Liquid biofuels are seldom used in neat form, mainly due to incompatibilities with current engine technologies and fuel distribution infrastructure. Instead, they are used as blends with conventional gasoline and diesel fuels in varying proportions. Low-percentage ethanol blends, such as E10 (10% ethanol and 90% gasoline, also referred to as gasohol) can be used in current vehicle engines without any modiﬁcation and can result in a modest octane rating increase. Higher ethanol levels, such as E85, can only be used in fuel-ﬂexible vehicles (e.g., [30]), where the fuel blend is stored in the same tank and fuel injection and spark timing are adjusted automatically according to the actual blend detected by electronic sensors. A particular problem with E85 is its low vapor pressure, lower than that of gasoline at low temperatures, which causes cold start problem and necessitates the use of fuel preheating. Ethanol is not miscible with diesel (it forms an emulsion) and its very low cetane number makes it difﬁcult to ignite in a compression ignition engine. However, there is currently active research into the potential of using low- or high-level ethanol blends in diesel engines (e.g., [4]). One potentially viable option would be the use of E-diesel, a diesel blend comprising up to 15% ethanol and up to 5% of solubilizing emulsiﬁers [36]. The addition of up to 15% ethanol seems to have only modest positive effects on viscosity but lowers proportionately the cetane number of the blend [36]. In order to overcome the solubility problems, an alternative approach has been adopted where E95 blends containing an appropriate ignition improver have been successfully used in truck engines. Biodiesel, on the other hand, is completely miscible with diesel fuel and can be used in current compression ignition engines in neat form (B100) or virtually in any blending ratio. Currently the most common blend is B20 (20% biodiesel–80% diesel), which can be used in heavy-duty engines with virtually no modiﬁcation [4]. The addition of up to 20% of biodiesel to diesel fuel appears to have no measurable effect on several fuel properties (density, viscosity, ﬂash and pour points) [37]. Biodiesel (or biodiesel blends) exhibit superior lubricity compared with conventional low-sulfur diesels – even a 1% biodiesel addition can increase fuel lubricity by 30% [4, 29]. However, due to its poor low-temperature ﬂow properties, fuel tank heating and/or suitable additives may be required in high-level biodiesel blends.

12.4 Combustion Chemistry of Biofuels

Finally, there is evidence to suggest that biodiesel can act as an emulsiﬁer for ethanol and it has been demonstrated that ethanol–biodiesel–diesel fuels blends (also referred to as EB-diesel) are characterized by enhanced stability, extending to subzero temperatures, and equal or superior fuel properties to diesel fuel [38].

12.4 Combustion Chemistry of Biofuels

The potential use of liquid biofuels (neat or blended with conventional fuels) in combustion engines requires a thorough understanding of their fundamental combustion properties. The increased oxygen content and lower energy density of biofuels may result in overall leaner combustion compared with conventional hydrocarbon fuels under similar conditions and will also seriously affect their emission characteristics: soot and particulate matter (PM) reductions are expected, coupled with possible increases in oxygenated intermediates and NOx. An accurate quantiﬁcation, both experimental and numerical, of the combustion chemistry can only be performed in well-controlled fundamental experimental conﬁgurations that closely resemble the operating conditions of practical combustion devices. Such conﬁgurations include shock tubes and rapid compression machines for ignition time delay measurements, combustion bombs and opposed-ﬂow diffusion ﬂames for laminar ﬂame speed determinations, and jet-stirred and ﬂow reactors and premixed ﬂames for species measurements. Laminar premixed ﬂames are particularly valuable since they closely resemble the ﬂow–chemistry interactions characterizing practical combustion devices. This chapter provides a thorough review of recent progress in experimental mapping and detailed kinetic modeling of the combustion of liquid biofuels in such fundamental conﬁgurations. There is a wealth of experimental investigations on ethanol pyrolysis and oxidation in homogeneous systems. Cooke et al. [39] were the ﬁrst to report ignition time delays in shock tubes for stoichiometric ethanol mixtures under high temperatures (1570–1870 K). Investigation of the ignition of ethanol–oxygen– argon mixtures in the temperature range 1300–1700 K, for pressures of 1.0 and 2.0 atm, and for equivalence ratios w ¼ 0.5, 1.0, and 2.0 was carried out by Natarajan and Bhaskaran [40], who were also the ﬁrst to propose a compact detailed kinetic mechanism for ethanol successfully reproducing their ignition time delay shock tube data. Ethanol ignition in shock tubes was also investigated by Simmie and co-workers [41–43] for equivalence ratios in the range w ¼ 0.25–2.0, pressures of 1.8–4.6 bar, and temperatures in the range 1080–1900 K. Experimental ignition time delays at higher temperatures were found to be about three times shorter than those measured by Natarajan and Bhaskaran [40] under the same conditions. A detailed kinetic mechanism of 30 species and 97 reactions was also developed through extensive rate constant optimization. The authors outlined the sensitivity of ignition time delays to the hydrogen abstraction reactions from ethanol and the CH3CHOH radical.

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G€ ulder [44] investigated laminar ﬂame speeds of ethanol–air gas mixtures in a constant-volume bomb for a wide range of pressures (p ¼ 1–8 atm), initial temperatures (T ¼ 300–500 K), and equivalence ratios (w ¼ 0.7–1.4). For atmospheric conditions at 300 K, the maximum ethanol–air laminar ﬂame speed (47 cm s1) occurred at an equivalence ratio of about w ¼ 1.1. The above dataset also constitutes the only high-pressure data for ethanol–air ﬂame speeds to date. Egolfopoulos et al. [45] used the counterﬂow twin ﬂame technique to measure laminar ﬂame speeds at atmospheric pressure conditions over the equivalence ratio range w ¼ 0.55–1.8 and at temperatures of 363, 428 and 453 K. More recently, Liao et al. [46] measured ethanol ﬂame speeds in a constant-volume bomb. Flame speeds of atmospheric pressure ethanol–air mixtures at 358 K with stoichiometries varying from w ¼ 0.7 to 1.4 were experimentally determined. The maximum ﬂame speed ulder [44] and Egolfopou(58 cm s1) was somewhat lower than that reported by G€ los et al. [45]. Ethanol oxidation has been studied by in the Princeton variable-pressure ﬂow reactor by Dryer and co-workers. Norton and Dryer [47] reported extensive species measurements from lean (w ¼ 0.61) to moderately rich (w ¼ 1.24) atmospheric pressure ethanol–oxygen mixtures at temperatures around 1100 K, while recently Li et al. [48] extended the above dataset to higher pressures (3–12 atm) and to lower temperatures (800–950 K). Proﬁles for fuel, major species, C1–C2 stable intermediates, including oxygenated formaldehyde and acetaldehyde, were obtained. Norton and Dryer [47] were the ﬁrst to provide a full representation of the three C2H5O isomers (a-hydroxyethyl, b-hydroxyethyl, ethoxy) chemistry and outlined their importance in ethanol combustion. Hydrogen atom abstraction reactions were assumed to lead to the C2H5O isomers, which in turns lead to the formation of C1 and C2 species. In particular, formaldehyde and the methyl radical are formed from ethoxy, whereas acetaldehyde and ethylene come from the b- and the a-hydroxyethyl radical, respectively. Additionally, C2H4OH decomposition to C2H4 and OH has been included and was found to be the major ethylene formation path. The a-hydroxyethyl radical was found to be the dominant product at lower temperatures, with the other isomers being more signiﬁcant at higher temperatures. The model ﬁnally reproduced the authors experimental data fairly satisfactorily. The ethanol abstraction reaction rates of Norton and Dryer [47] were subsequently adjusted by Curran et al. [43] in order to reproduce accurately their shock tube ignition time delay data. The ethanol sub-mechanism of Norton and Dryer [47] with some modiﬁcations was also used by Egolfopoulos et al. [45] in order to reproduce accurately their ﬂame speed data. In related work, ethanol oxidation in an atmospheric jet stirred reactor was studied by Dagaut et al. [49] under stoichiometric and rich (w ¼ 2.0) conditions and for temperatures in the range 1055–1077 K. Concentrations of fuel, major intermediates such as methane, acetaldehyde, and C2 species (C2H4 and C2H6), and also CO and CO2 were reported. Ethanol oxidation in an isothermal quartz ﬂow reactor at atmospheric pressure and in the temperature range 700–1500 K was also studied by Alzueta and Hernandez [50]. Ethanol pyrolysis in atmospheric pressure ﬂow reactors at high temperatures (>1000 K) has been studied by Rotzoll [51], who observed that, for temperatures near

12.4 Combustion Chemistry of Biofuels

1300 K, complete fuel conversion was attained at residence times of the order of 5–6 ms. In a related study, Li et al. [52] performed high-pressure (p ¼ 3–12 atm) ethanol pyrolysis experiments at a constant temperature of about 950 K in the Princeton variable-pressure ﬂow reactor. Interestingly, at these relatively low temperatures, ethanol conversion was incomplete even at residence times of the order of 1 s. Peg et al. [53] also obtained exhaust stable species measurements in an isothermal quartz ﬂow reactor for atmospheric ethanol pyrolysis at temperatures from 700 to 1200 K. Soot levels were also measured and were found to be lower than those measured in acetylene ﬂow reactors under similar conditions. A comprehensive detailed kinetic mechanism for ethanol oxidation has been developed by Marinov [54] and validated against ethanol laminar ﬂame speeds, ignition delay times, and species proﬁles from jet stirred and turbulent ﬂow reactors with signiﬁcant success. Pressure-dependent rates for four ethanol decomposition paths were proposed and ethanol abstraction reaction rates were derived either from theoretical calculations or from analogies with similar reactions. Extensive reaction path and sensitivity analyses were performed and it was found that under stoichiometric conditions in jet-stirred and ﬂow reactors, ethanol is primarily consumed by abstraction reactions to the C2H5O isomers. Among the latter, the CH3CHOH radical was reported to be dominant, leading to acetaldehyde formation. On the other hand, formaldehyde was principally formed through the ethoxy radical chemistry. Ethanol studies in ﬂames were initiated by Tanoff et al. [55], who used a continuous microprobe sampling mass spectrometry technique for analyzing a lean (w ¼ 0.5) low-pressure (p ¼ 30 mbar) ethanol laminar premixed ﬂame. Major species, including the stable H2 and CO intermediates, were reported. During the last few years, a substantial amount of data from ethanol combustion in burner-stabilized laminar premixed ﬂames has appeared in the literature. Kasper et al. [56] obtained comprehensive molecular beam mass spectrometry (MBMS) data sets from stoichiometric (w ¼ 1.00) and rich (w ¼ 2.57), low-pressure (p ¼ 50 mbar) ethanol laminar premixed ﬂames. They measured concentration proﬁles of major species and C1–C2 stable intermediates, including formaldehyde and C2H4O isomers. Their data, which uniquely include radical species (CH3, HCO, C3H3) and benzene proﬁles, are particularly valuable in assessing stoichiometric effects on the major ethanol consumption paths and on molecular growth processes. The laminar premixed ﬂame conﬁguration was also used by Taatjes et al. [57] for the investigation of the combustion chemistry of enols. They studied a moderately rich (w ¼ 1.96) ethanol–oxygen–argon ﬂat premixed low–pressure (p ¼ 35 Torr) ﬂame and obtained fuel, ethane, and ethanol mole fraction proﬁles by using an MBMS system. Saxena and Williams [58] investigated partially premixed and diffusion atmospheric counterﬂow ethanol ﬂames. They acquired species proﬁle measurements for fuel and major species in addition to C1–C2 stable intermediates. However, their dataset does not contain any information regarding oxygenated intermediates. More recently, Leplat et al. [59] used an MBMS system to analyze a stoichiometric, low-pressure, ﬂat premixed burner-stabilized ethanol ﬂame. The study was the ﬁrst to report quantitative OH measurements in ethanol ﬂames in addition to major and stable C1 and C2 intermediate species.

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Recent modeling efforts (e.g., [48, 58]) have been largely based on Marinovs description of the major C2H5OH and C2H5O reaction pathways up to acetaldehyde formation and destruction chemistry with some modiﬁcations to the rate and pressure dependence of the ethanol unimolecular decomposition reactions. In related work, Leplat et al. [59] performed a comparative numerical assessment of the detailed kinetic mechanisms of Dunphy and Simmie [42], Norton and Dryer [47], Dagaut et al. [49], and Marinov [54]. It was found that major species were satisfactorily reproduced by all four mechanisms but discrepancies were observed in predictions of minor species. In order to resolve at least partly such uncertainties and also to evaluate critically new kinetic and thermodynamic data in view of recent experimental targets, Vourliotakis et al. [60, 61] developed an updated comprehensive detailed kinetic mechanism for ethanol pyrolysis, oxidation, and combustion with particular emphasis on laminar premixed ﬂames. Novel features include a more complete description of the fall-off behavior of C2H5O decomposition reactions, isomerization reactions involving linear and cyclic C2H5O and C2H4O species, and an ethenol sub-mechanism. The mechanism was incorporated in a comprehensive C1–C6 hydrocarbon combustion mechanism [62–64] which has already been tested under a wide range of conditions in ﬂames and stirred and ﬂow reactors. Computations have been successfully compared against experimental data from shock tubes [40, 43], laminar ﬂame speeds [44–46], stirred and ﬂow reactors [47–49, 52, 53], and stoichiometric and rich burner-stabilized laminar premixed ﬂames and counterﬂow ﬂames [55–59], amounting to a total of 50 validation targets. It was shown that unimolecular decomposition reactions constitute major ethanol consumption paths in low-pressure ﬂames while abstraction reactions dominate under high-pressure and low-temperature conditions. The destruction chemistry of the ethoxy radical was shown largely to control the levels of key C1–C2 intermediates. The dynamics of molecular growth processes in the rich ethanol ﬂames were also assessed and it was shown that propargyl radical recombination is the dominant benzene formation path. There is also substantial evidence on the effects of blending ethanol with conventional hydrocarbons. Thus it has been shown that ethanol addition in laminar premixed propene ﬂames can lead to appreciable reductions in benzene and higher aromatics [56, 65]. In related work, McNesby et al. [66] demonstrated reductions in soot concentrations in ethanol-doped counterﬂow diffusion ethylene ﬂames. However, the opposite trend has also been observed depending on ﬂow and fuel conditions; McEnally and Pfefferle [67] reported enhanced soot formation in ethanol-doped ethylene non-premixed ﬂames. In either case, an increase in volatile organic compounds (VOC) (mainly formaldehyde and acetaldehyde) emissions is expected. More recently, Esarte et al. [68] experimentally studied the effect of ethanol addition in acetylene pyrolysis and found that soot formation is directly inhibited by the presence of ethanol. Further, B€ohm and Braun-Unkhoff [69] studied numerically the effect of ethanol addition on soot formation from benzene–argon mixtures under pyrolysis conditions. Soot formation was suppressed in the case of the addition of small ethanol levels but it was enhanced otherwise.

12.4 Combustion Chemistry of Biofuels

There have been relatively few experimental studies of the combustion of higher alcohols in reactors or ﬂames. In pioneering work, Norton and Dryer [70] studied the oxidation of C1 (methanol), C2 (ethanol), C3 [n-propanol, C2H5CH2OH, and isopropanol, (CH3)2CHOH], and C4 [tert-butanol, (CH3)3COH] alcohols in the Princeton atmospheric pressure ﬂow reactor and for intermediate temperatures (1020–1120 K). It was shown that the rate of fuel conversion decreased with increasing chain length and branching (isopropanol oxidation being considerably slower than that of n-propanol, with tert-butanol being the slowest). Primary (straightchain) alcohols were shown to produce more aldehydes but signiﬁcantly less alkenes than branched-chain alcohols and it was concluded that the combustion chemistry of branched- and/or long-chain alcohols resembles that of hydrocarbons. Sinha and Thomson [71] studied the chemical structures of isopropanol, propane, and isopropanol–propane atmospheric pressure counterﬂow diffusion ﬂames. Proﬁles of CO, CO2, and C1–C3 stable intermediates, including oxygenates, were reported. It was shown that propane and isopropanol ﬂames exhibited similar major species proﬁles but acetylene and benzene levels were signiﬁcantly lower in the latter case. The addition of isopropanol to propane resulted in only moderate synergistic effects. Li et al. [72] were the ﬁrst to characterize experimentally laminar premixed low-pressure n-propanol and isopropanol ﬂames. An impressive set of C1–C6 stable and radical species proﬁles was obtained in lean (w ¼ 0.75) and rich (w ¼ 1.80) ﬂames using isomer-speciﬁc tunable photoionization MBMS. Their results indicate that the n-propanol ﬂame exhibits higher aldehyde and lower propene levels than the isopropanol ﬂame, in agreement with similar observations in ﬂow reactors [70]. Benzene levels were identical in both ﬂames. In directly related work, Kasper et al. [73] used both electron ionization and photoionization MBMS to study stoichiometric (w ¼ 1.00), moderately rich (w ¼ 1.50), and nearly sooting (w ¼ 1.90) n-propanol and isopropanol low-pressure laminar premixed ﬂames. Good agreement between the two experimental methodologies was observed. The authors concluded that major species proﬁles were nearly identical in ﬂames of corresponding stoichiometry. It was further shown that the concentration of CHxO species was higher in the n-propanol ﬂame, whereas the concentration of the C2HxO species was higher in the isopropanol ﬂame. Similar benzene levels were recorded in the j ¼ 1.90 ﬂame for both fuels, although at values signiﬁcantly higher than in the determination of Li et al. [72] in very similar ﬂames. Experimental data on the oxidation and combustion of butanols are fast appearing in the open literature. In an early study, Roberts, as quoted in [74], measured laminar burning velocities of 1-butanol (CH3CH2CH2CH2OH)–air mixtures, for equivalence ratios in the range w ¼ 0.7–1.1 using shadowgraph images of a Bunsen ﬂame cone. Their measurements, indicating a maximum ﬂame speed of the order of 46 cm s–1, are in surprisingly good agreement with results obtained 50 years later using a constantvolume combustion bomb [75]. Ignition delay times for all four butanol isomers [1butanol, 2-butanol, CH3CH2CHOHCH3, isobutanol, CH3C(CH3)CH2OH, and tertbutanol] were determined by Moss et al. [76] in a shock tube for temperatures in the range 1200–1800 K and pressures of 1–4 bar. A detailed kinetic mechanism was also developed and validated against the experimental data. It was shown that 1-butanol and

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isobutanol were the most reactive isomers and that their destruction chemistry is dominated by fast, chain-branching reactions. On the other hand, the consumption of the least reactive 2-butanol and tert-butanol isomers takes place via dehydration, leading to alkene formation. The oxidation of 1-butanol in a jet-stirred reactor was studied by Dagaut et al. [77] at a pressure of 10 atm, in the temperature range 800–1150 K and for a wide range of stoichiometries (w ¼ 0.5–2.0). A detailed kinetic mechanism was also developed to model the above data with acceptable agreement. It was concluded, also in agreement with Moss et al. [76], that 1-butanol is mainly consumed by abstraction reactions. The data set was recently extended to atmospheric pressure and slightly higher (up to 1250 K) temperatures [75]. The ﬁrst study of butanol combustion in ﬂames was carried out by McEnally and Pfefferle [78], who doped methane–air co-ﬂowing ﬂames with n-butane, isobutane and all four butanol isomers. It was concluded that unimolecular decomposition reactions were the dominant butanol consumption pathways under all conditions studied. An interesting observation was that butanol addition to methane resulted in increases in benzene levels. The higher aromatic-formation formation of branchedchain alcohols (and alkanes) was also clearly demonstrated. Low-pressure laminar premixed butanol ﬂames were investigated by Yang et al. [79] using photoionization molecular beam mass spectrometry (PI-MBMS). However, the usefulness of the data set is limited since it contains only relative ionization spectra at a single ﬂame location. Extensive species proﬁle measurements from opposed-ﬂow 1-butanol–air ﬂame have also been reported by Sarathy et al. [75] and comparisons were made with species data from a similar n-butane–air ﬂame. It was shown that major species and intermediate C1–C3 hydrocarbon proﬁles are virtually identical, the only significant differences being the higher levels of acetaldehyde and 1-butene in the 1-butanol ﬂame. A provisional assessment of the emission characteristics of bioalcohols as compared with conventional hydrocarbon fuels can be made on the basis of key species levels in laminar premixed ﬂames. Comparisons are made for C1–C2 aldehydes (formaldehyde and acetaldehyde) and benzene. The latter is particularly important since it is the indicative species for polycyclic aromatic hydrocarbon (PAH) growth and soot nucleation (e.g., [62]). Although the evidence is fragmentary some conclusions can be drawn. Clearly, benzene levels in ethanol appear signiﬁcantly reduced compared with levels from C2 hydrocarbons. However, this does not appear to be the case for propanol ﬂames, which exhibit similar benzene levels (of the order of 10–40 ppm) to propane ﬂames operating under nearly identical conditions. Furthermore, switching from a hydrocarbon to an alcoholic fuel of the same rank appears to have no appreciable effect on formaldehyde levels. On the other hand, acetaldehyde levels are very much affected. Propanol ﬂames produce almost an order of magnitude more CH3CHO than propane ﬂames. The combustion chemistry of biodiesel is yet not adequately understood and is currently an issue of very active research. Experimental and computational studies of actual biodiesels are currently not feasible (e.g., [86, 87]), mainly due to their low volatility and the complexity of their molecular structure, respectively. Instead,

12.4 Combustion Chemistry of Biofuels Table 12.2 Chemical structures of typical biodiesel surrogates.

Name

Formula

Molecular structure

Methyl formate Ethyl formate Methyl acetate Ethyl acetate Methyl crotonate Methyl butanoate Ethyl propanoate Methyl isobutyrate Propyl acetate Isopropyl acetate Methyl hexanoate Methyl decanoate

C2H4O2 C3H6O2 C3H6O2 C4H8O2 C5H8O2 C5H10O2 C5H10O2 C5H10O2 C5H10O2 C5H10O2 C7H14O2 C11H22O2

CH3C(O)OH C2H5C(O)OH CH3C(O)OCH3 C2H5C(O)OCH3 CH3CHCHC(O)OCH3 C3H7C(O)OCH3 C2H5C(O)OC2H5 (CH3)2CHC(O)OCH3 CH3C(O)OC3H7 CH3C(O)OCH(CH3)2 C5H11C(O)OCH3 C9H19C(O)OCH3

research is focused on the investigation of the combustion chemistry of appropriately chosen surrogates. These generally fall into two categories (e.g., [88]). The ﬁrst includes small (generally up to ﬁve carbon atoms) alkyl esters which retain the functionality of the ester group without the complexity of the large carbon chain. The second approach assumes that the inﬂuence of the ester group is diminished as the carbon chain increases and thus models large alkyl esters as normal alkanes with the same number of carbon atoms. The chemical structure of typical biodiesel surrogates is presented in Table 12.2. A natural starting point involves the combustion chemistry of the smallest alkyl esters. Methyl acetate oxidation in stirred reactors was studied by Dagaut et al. [89]. Gasnot et al. [90] obtained temperature and species proﬁles measurements in lowpressure laminar premixed methane–air ﬂames doped with small amounts (up to 3% by volume) of ethyl acetate. Interestingly, peak levels of several oxygenated species, such as acetaldehyde, propanal, and acetone, were shown to decrease as ethyl acetate doping was increased. A detailed kinetic model was assembled and tested against the experimental data, with generally poor agreement, particularly for oxygenated species. Recently, Osswald et al. [85] obtained species measurements from lowpressure laminar premixed rich (w ¼ 1.82) methyl acetate and ethyl formate ﬂames. Major species proﬁles were found to be very similar for both isomeric fuels. It was shown that methyl acetate leads to formaldehyde formation, whereas ethyl formate leads to acetaldehyde formation. Benzene levels were signiﬁcantly higher in the ethyl acetate ﬂame but still signiﬁcantly lower than in similar hydrocarbon ﬂames, as also shown in Table 12.3. In related work, Westbrook et al. [91] developed a detailed kinetic mechanism in order to model the oxidation of four small alkyl esters (methyl formate, ethyl formate, methyl acetate, ethyl acetate) in laminar premixed ﬂames with reasonable success. Methyl butanoate has been widely used as the surrogate of choice mainly because it contains much of the essential chemical structure of real biodiesels and because its

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Table 12.3 Comparison of oxygenated species and benzene peak mole fractions in rich laminar premixed flames of hydrocarbon fuels and biofuels.

Fuel

C2H4 C2H6 C3H6 C3H6 C3H8 n-C4H10 C2H5OH n-C3H7OH n-C3H7OHb) iso-C3H7OH iso-C3H7OH CH3C(O)OCH3 C2H5C(O)OH

w

Pressure (mbar)

CH2O

CH3CHO

C6H6

Ref.

1.90 2.60 1.80 2.33 1.80 2.60 2.57 1.80 1.94 1.80 1.90 1.82 1.82

26.3 1000 50.0 50.0 39.5 1000 50.0 39.5 46.0 39.5 46.0 39.5 39.5

4.5 103 — 9.0 104 6.9 104 3.0 103 — 9.4 103 6.9 103 1.0 10_2 2.8 10-3 5.0 10-3 1.6 10-2 4.4 10-3

3.5 10–5 — — — 2.6 10-4 — 8.8 10-3a) 5.2 10-3 5.5 10-3 1.1 10-3 3.0 10-3 2.7 10-4 3.9 10-3

3.3 10–5 3.0 10–4 6.2 10-4 1.3 10-3 2.0 10-5 6.5 10-5 7.0 10-6 1.4 10-5 2.5 10-5 1.3 10-5 4.0 10-5 <1.0 10-6 4.0 10-6

[80] [81] [82] [82] [83] [84] [56] [72] [73] [72] [73] [85] [85]

a) Value refers to the total C2H4O signal, which may also contain contributions from ethanol. b) Values refer to PI-MBMS data for direct comparisons with [72].

combustion chemistry can be described by a kinetic mechanism of manageable size (e.g., [92]). In pioneering work, Fisher et al. [93] were the ﬁrst to develop a detailed kinetic mechanism for the combustion of methyl butanoate. Ga€ıl et al. [94] obtained species data in an atmospheric jet-stirred reactor (T ¼ 800–1350 K, w ¼ 1.13), an opposed-ﬂow diffusion ﬂame and in a high-pressure (p ¼ 12.67 bar) ﬂow reactor at low temperatures (T ¼ 500–900 K, w ¼ 0.35–1.5). A detailed kinetic model was also developed largely based on [93]. Methyl butanoate exhibited very low reactivity at low temperatures that was not reproduced by the kinetic model. The mechanism also failed to capture the fuel consumption rate in the opposed-ﬂow experiments. Methyl butanoate pyrolysis in a shock tube was studied by Huynh et al. [95], who also proposed a detailed kinetic mechanism again based on [93]. A signiﬁcant rebalancing of methyl butanoate combustion chemistry has been proposed by Dooley et al. [87]. The resulting mechanism was used to model the data of Ga€ıl et al. [94] with considerable success. Dooley et al. [87] also reported ignition delay times obtained in a shock tube (p ¼ 1–4 atm, T ¼ 1250–1760 K, w ¼ 0.25–1.5) and in a rapid compression machine (p ¼ 10–40 atm, T ¼ 640–949 K, w ¼ 0.33–1.0). The same group [96] also developed a detailed kinetic mechanism for the oxidation of ethyl propanoate with considerable success. In directly related work, Schwartz et al. [97] recorded temperatures and C1–C12 stable hydrocarbon species proﬁles in methane opposed-ﬂow diffusion ﬂames doped with ﬁve C5H10O2 isomers: methyl butanoate, ethyl propionate, methyl isobutyrate, propyl acetate, and isopropyl acetate. The work is of primary importance since it provides quantitative evidence of the effect of alkyl ester molecular structure on pollutant emissions. It was shown that all alkyl esters produce aromatic hydrocarbons

12.4 Combustion Chemistry of Biofuels

at higher rates that the undoped fuel. Methyl butanoate and ethyl propionate produced the least amount of aromatics and propyl acetate and isopropyl acetate produced the least amount of oxygenates. The effect of the degree of saturation on the combustion properties of methyl esters was quantiﬁed by Dagaut and co-workers [98, 99]. Sarathy et al. [98] studied the combustion of methyl butanoate and methyl crotonate in laminar counterﬂow diffusion ﬂames and concluded that although major ﬂame features, including formaldehyde and acetaldehyde levels, were nearly identical, the methyl crotonate ﬂame was characterized by signiﬁcantly higher concentrations of benzene and gaseous soot precursors. Jet-stirred reactor experiments conducted at lower temperatures (p ¼ 1 atm, T ¼ 850–1400 K, w ¼ 0.375–1.0) [98, 99] revealed that both fuels exhibit similar reactivity. It is worth noting that methyl butanoate releases substantially more carbon monoxide that methyl crotonate, at least for stoichiometric mixtures. Metcalfe et al. [100] studied the effect of fatty acid chain length on the ignition delay time of methyl butanoate and ethyl propanoate. Experiments were performed in a shock tube at pressures of 1 and 4 atm, temperatures in the range 1100–1670 K and a wide range of stoichiometries (w ¼ 0.25–1.50). It was found that, for stoichiometric mixtures, ethyl propanoate exhibited shorter ignition delay times than methyl butanoate, particularly for lower temperatures. The positive effect of pressure on autoignition was also demonstrated. In related work, Walton et al. [101] extended the ignition delay time measurements to lower temperatures (935–1117 K) and higher pressures (4.7–19.6 atm). Experiments were performed in a rapid compression machine for lean (w ¼ 0.3–0.4) mixtures. Again, ethyl propanoate exhibited faster ignition delay times than methyl butanoate. This is in agreement with the relative differences in the cetane number of the two compounds, as explained in the previous section. In a related interesting study, Farooq et al. [92] studied the pyrolysis of methyl acetate, methyl propionate, and methyl butanoate in shock tubes at high temperature (1260–1563 K) and moderate pressures (1.4–1.7 atm). Their measurements indicated that CO2 yields are largely independent of alkyl chain length. Experimental and numerical studies of larger methyl esters, more closely resembling the structure of actual biodiesel components, have recently appeared in the literature. Dayma et al. [102] studied the oxidation of methyl hexanoate in a stirred reactor at high pressure (10 atm) and in the temperature range 500–1000 K, whereas Herbinet et al. [88] developed a detailed kinetic model for the combustion chemistry of methyl decanoate. Finally, the oxidation chemistry of a rapeseed oil methyl ester (RME), having an average composition of C17.92H33O2, was studied by Dagaut et al. [103] at high pressure (1–10 atm) in the temperature range 800–1400 K. These studies clearly reveal that large methyl esters exhibit a strong negative temperature coefﬁcient (NTC) behavior, in marked similarity to normal alkanes (the NTC refers to a distinctive feature of the oxidation of some hydrocarbons where, in a certain lowtemperature region, the global reaction rate decreases with temperature). Actually, methyl hexanoate is the smallest methyl ester to exhibit NTC behavior [101]. This indicates that large n-alkanes are suitable surrogates for large methyl esters and in fact Dagaut et al. [103] assumed n-hexadecane to be an appropriate surrogate for RME.

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However, speciﬁc kinetic details, such as early CO2 formation, can only be captured by developing dedicated combustion chemistry models. Two recent detailed kinetic modeling studies are also worth mentioning. Dagaut and Togbe [104] used a mixture of isooctane, toluene, 1-hexene, and ethanol to simulate the oxidation of E85 in stirred reactors at intermediate temperatures (770–1220 K) and high pressure (10 atm) and for stoichiometries in the range w ¼ 0.3–2.0. Dagaut and Ga€ıl [105] studied the oxidation of an RME-Jet-A1 blend (20% RME–80% Jet-A1) in a stirred reactor under similar conditions (p ¼ 10 atm, T ¼ 740–1200 K, w ¼ 0.5–1.5). The addition of RME resulted in no signiﬁcant changes in the product distribution. A detailed kinetic model was also assembled, assuming n-decane and n-hexadecane to be appropriate surrogates for Jet-A1 and RME, respectively. It was shown that the fuel blend has slightly higher reactivity than neat Jet-A1.

12.5 Biofuel Combustion in Engines

The use of liquid biofuels in internal combustion engines in a sense predates that of conventional hydrocarbon fuels. One of the ﬁrst engines designed by Rudolf Diesel was operated on neat peanut oil as early as 1900 (e.g., [22]). However, the direct use of vegetable oils in diesel engines is not feasible since their very high viscosity (10–20 times higher than that of diesel) and low volatility seriously affect engine operation, mainly causing carbon deposit formation. The famous Ford Model T, released in 1908, was designed to run on ethanol. Currently, liquid biofuels are actively promoted for use in the transportation sector, mainly for their renewable nature and high oxygen content. However, being renewable and oxygenated does not necessarily guarantee efﬁcient and environmentally friendly operation. A correct assessment of their potential as vehicular fuels should be made on the basis of three major arguments: the effect on engine performance including efﬁciency and fuel consumption, the environmental impact in terms of air pollutant and emissions, and their environmental sustainability on a life cycle basis. Other issues are naturally important and include their compatibility with current engine technologies and fuel handling, storage, and distribution infrastructure. The effect of ethanol on spark ignition (SI) engine performance is mainly inﬂuenced by its high octane rating, its lower energy content, and its higher volatility at high temperatures, compared with gasoline fuel. A high octane number opens up the possibility of a higher compression ratio and increases in the spark advance that can result in a higher combustion pressure and potential increases in engine power and efﬁciency. The lower ethanol heating value will have a negative effect on engine power as the ethanol content in the gasohol blends is increased. However, this may be counterbalanced by the corresponding decrease in the air–fuel ratio of the mixture, which will allow for increased fuel intake at each cycle. The increased volatility is expected to result in a higher volumetric efﬁciency whereas the increased heat of vaporization will have a negative effect on peak cylinder temperature. However,

12.5 Biofuel Combustion in Engines

coupled with lower exhaust temperatures, the net effect may be a net increase in engine efﬁciency (e.g., [12]). It should further be noted that recent work indicates that both neat gasoline and ethanol–gasoline blends show very similar mixture formation and atomization patterns [106]. Experimental studies on spark-ignited engines of light-duty vehicles (LDVs) operating on gasohol blends seem to corroborate the above arguments. A concise review of the relevant literature was provided by Celik [107]. Most studies indicate that increasing the ethanol content in the fuel mixture, up to at least E30, increases both brake torque power and speciﬁc fuel consumption. Increases in compression ratio without knock occurrence can also be achieved. Depending on blend composition, optimum operating conditions can be speciﬁed for performance and emissions. Abdel-Rahman and Osman [108] demonstrated that for each gasohol blend in the range E0–E30 there is an optimum compression ratio for maximum power, for example, a compression ratio of 10:1 for E20. These observations are also supported by Topg€ ul et al. [109] for blends up to E60. Celik [107] also performed experimental studies on the determination of the optimum compression ratio for gasoline–ethanol mixtures (E0–E100) in a single-cylinder, four-stroke SI engine. It was shown that an E50 mixture at a 10 : 1 compression ratio resulted in increased engine power and reduced major pollutant emissions and speciﬁc fuel consumption at full load. There is also evidence to suggest that increasing the ethanol content of the fuel above a certain value has no appreciable positive effects on engine performance. It was thus shown [110] that an increase from E50 to E85, for the same compression ratio, resulted in virtually identical engine torque but with substantially (up to 20%) increased speciﬁc fuel consumption. There have been a large number of experimental studies on pollutants emissions from vehicles operating on ethanol–gasoline blends and their ﬁndings are summarized in a comprehensive review by Niven [111]. There are, however, signiﬁcant variations in emission data, sometimes with contradictory results. This can be attributed to the large variability of experimental conditions and to the relatively small number of vehicle–fuel pairs available. In order to overcome this problem partly, Graham et al. [112] performed an extensive statistical analysis of 43 vehicle– fuel pairs running on E10 and 11 vehicle–fuel pairs running on E85. Despite the large data spread, some general remarks can safely be made. Carbon monoxide tailpipe emissions are signiﬁcantly reduced in E10 (and E20) blends as compared with neat gasoline. Typical reductions can be as high as 20%. There appears to be no statistically signiﬁcant change in either NOx or total unburned hydrocarbon emissions, although evaporative losses due to ethanols higher Reid vapor pressure lead to increases in ethanol emissions. Benzene levels are also generally found to decrease [111]. The major cause of concern is clearly the very much increased acetaldehyde emissions – an average order of magnitude increase was reported by Graham et al. [112]. Interestingly, formaldehyde levels are not affected. There are important qualitative and quantitative differences in the case of E85 combustion. Acetaldehyde emissions are substantially increased – more than 25-fold compared with E0 – while an order of magnitude increase is recorded for formaldehyde. On the other hand, NOx, unburned hydrocarbons, and benzene

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emissions are signiﬁcantly reduced with no statistically important increases in CO levels recorded. It should be noted also that for all gasohol blends no signiﬁcant reductions in GHG emissions (CO2, CH4, N2O) were observed. The effect of the addition of ethanol (up to 10%) to diesel fuel on diesel engine performance and exhaust emissions has been studied numerically [113] and experimentally [114], indicating considerable advantages in soot and CO emissions with no adverse effects on NOx and speciﬁc fuel consumption. Ribeiro et al. [115] and Hansen et al. [36] have performed review studies on ethanol–diesel blends and reported that ethanol additions of the order of 15% can result in about a 30% reduction in PM emissions. However, aldehyde and unburned hydrocarbon emissions were found to increase. No clear trend could be established for CO and NOx emissions, although in general NOx emissions are expected to increase. Furthermore, the effect of ethanol addition to conventional diesel fuel was studied experimentally by Lapuerta et al. [116], who showed that there is indeed a signiﬁcant reduction in PM emissions, without any adverse effect on other gaseous pollutants. There is very limited evidence on the performance of vehicles operated on blends of higher alcohols. Yacoub et al. [33] studied the performance and emission characteristics of blends of C1–C5 alcohols (methanol, ethanol, 1-propanol, 1-butanol and 1-pentanol) with gasoline. Experiments were performed in single-cylinder SI engine and all alcohol–gasoline blends had the same oxygen mass content. Higher alcohol (C4–C5) blends were characterized by decreased engine power and efﬁciency and higher NOx emissions, but reduced CO and unburned hydrocarbon emissions, compared with neat gasoline. More recently, Karabektas and Hosoz [117] studied the performance and emission characteristics of a four-stroke direct injection compression ignition (CI) engine operated on isobutanol–diesel blends at a constant compression ratio. Decreases in engine power and efﬁciency, increases in speciﬁc fuel consumption, decreases in NOx and CO emissions, and increases in unburned hydrocarbon emissions were recorded in all cases. Biodiesel is in many respects an ideal (partial) substitute for diesel. Biodiesel is completely miscible with diesel and most physical and chemical properties of the two fuels are very similar. The energy content of biodiesel is approximately 10% lower than that of diesel, but its adverse effect on engine efﬁciency is compensated by the generally higher cetane number. However, there are two major drawbacks. The ﬁrst is the higher viscosity of biodiesel, which directly inﬂuences droplet atomization and spray penetration and may also have an effect on engine performance and emissions. The second is the extremely poor cold ﬂow properties of biodiesel. A ﬁnal point of concern is its high degree of unsaturation, which directly affects its stability and compatibility with current engine technology. Early work on the performance and emission characteristics of engines operated on biodiesel blends was summarized in a comprehensive review by Graboski and McCormick [29], and more recent data by Lapuerta et al. [118]. Although there are variations depending on engine design and fuel properties, some general conclusions can safely be drawn. Biodiesel does not cause any signiﬁcant loss of power in diesel engines except for full load operation, where modest reductions – less than the corresponding reduction in energy content of the blend – are

12.5 Biofuel Combustion in Engines

observed. This can be attributed to the higher viscosity, bulk modulus, and sound velocity of biodiesel, which cause an advanced start of injection and, coupled with the higher biodiesel cetane number, an advanced start of the combustion process [118]. Fuel consumption generally increases with increasing biodiesel content in the fuel blend in order to compensate for the loss of heating value [118]. In an extensive review by the US Environmental Protection Agency (EPA) [119], data from heavy-duty diesel engines without exhaust gas recirculation and after-treatment were used to obtain a linear relationship between biodiesel content and speciﬁc fuel consumption. A maximum increase of the order of 10% for B100 was quoted. No appreciable deterioration in engine efﬁciency compared with dieselfueled engines was observed. This can be attributed to the increased biodiesel lubricity, which reduces engine friction. The effect of biodiesel composition on engine performance has also been extensively studied. Graboski et al. [120] tested 28 neat biodiesels and four B-20 blends in a six-cylinder, four-stroke, direct-injected, turbocharged and intercooled heavy-duty 257 kW engine and found no fuel composition or structural effects on thermal efﬁciency. Sch€onbron et al. [121] studied the combustion performance of pure methyl esters and biodiesel blends in a single-cylinder diesel engine and found no appreciable variation in thermal efﬁciency. It was further concluded that ignition delay was strongly dependent on molecular structure and decreased with increasing fatty acid chain length, increased chain length of the alcohol moiety, and increased degree of saturation. These results are in qualitative agreement with similar observations from shock tube studies, as discussed in the previous section. Major trends in the emission characteristics of biodiesel fuels and biodiesel–diesel blends also appear well established and indicate signiﬁcant reductions in most regulated pollutants. Particularly important are the recorded decreases in PM emissions, a major pollutant in diesel combustion. The EPA report [119] proposes a linear decrease of PM emissions with biodiesel content reaching values of the order of 50% for B100. Such large reductions are mainly attributable to the total absence of aromatics and sulfur from biodiesel and also the increased oxygen content of the biodiesel molecule (e.g., [122]). Moreover, biodiesel soot is characterized by increased reactivity towards oxidation as compared with diesel soot [118, 123]. Further, Sch€onbron et al. [121] concluded that the degree of fatty acid chain saturation has a discernible positive effect on PM emissions. Similar reductions are also observed for carbon monoxide and unburned hydrocarbons [118, 119]. Apart from chemical reasons, such reductions can also be attributed to the advanced injection and combustion timing when using biodiesel. Biodiesel combustion in engines, on the other hand, is characterized by increases in NOx emissions. These generally are not excessive (the EPA report correlation [119] indicates a maximum increase in NOx emissions of the order of 10% for B100) and there are also indications to suggest that under certain engine operating conditions NOx levels can actually decrease. The chemistry of nitrogen oxide formation in biodiesel combustion is not yet completely understood, although it has been shown that increasing the length of fatty acid chain tends to increase NOx emissions.

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Two further issues regarding biodiesel combustion in engines are also worth discussing. The ﬁrst relates to biodiesel fuel spray atomization and dispersion. The increased biodiesel density, viscosity, and surface tension result in larger droplet size, slower droplet evaporation, shorter penetration lengths, and greater spray cone angles, adversely affecting fuel–air mixing and leading to the formation of locally fuel-rich mixture with detrimental effects on performance and emissions (e.g., [124]). The second relates to biodiesel compatibility with current engine technology. Biodiesel is an excellent solvent and this can cause degradation of engine elastomers and lead to fuel ﬁlter and injector plugging and fuel tank deposits (e.g., [29]). The potential of biodiesel use in gas turbines is also currently being explored. Bolczo and McDonell [125] investigated the operation of a 30 kW gas turbine engine with almost neat biodiesel (B99). Biodiesel exhibited inferior atomization and evaporation characteristics to diesel fuel. Nitrogen oxide levels were also found to increase. Improved fuel atomization resulted in reduced NOx and CO emissions but at levels higher than those of diesel. A comprehensive assessment of the energy and environmental impact of the use of liquid biofuels in the transportation sector requires a complete life cycle analysis (LCA) taking into account the complete fuel cycle (well-to-wheels) from biomass feedstock production (well-to-tank) to ﬁnal fuel consumption (tank-to-wheels) [life cycle analysis (or assessment) is a tool for the systematic evaluation of the environmental aspects of a product or system through all stages of its life cycle (e.g., [126])]. A critical review of relevant LCA studies is clearly beyond the scope of the present work. However, the potential of liquid biofuels in delivering carbon-neutral energy will be brieﬂy considered. There is a large body of LCA studies on GHG emissions from liquid biofuels, sometimes with conﬂicting results and conclusions, although it is generally agreed that signiﬁcant reductions in emissions and fossil energy consumption are to be expected. Excellent critical reviews have been published [126–128]. Biodiesel use generally reduces GHGs by a factor of 40–65% compared with conventional diesel in light-duty CI engines [4, 128]. However, this ﬁgure refers to neat biodiesel. For biodiesel–diesel blends commonly used in the transport sector, the reductions are naturally expected to be lower and there are indications that for B20 fuels GHG emissions may slightly increase compared with diesel [12]. The corresponding reductions in the case of ethanol are largely dependent on feedstock. Thus, for ethanol from corn or wheat, gains can be as low as 10–20% compared with gasoline, increasing to close to 80% when sugar cane is used as a feedstock. Even higher gains, in excess of 80%, may be expected in the case of lignocellulosic ethanol (e.g., [4]). A further crucial issue relates to the net energy ratio of the liquid biofuels. This can be expressed as the ratio of non-renewable (fossil) energy consumption for the generation of one energy unit [126]. Typical ratios for biodiesel are of the order of 0.40–0.70 whereas for ethanol produced from sugar cane they can be as low as 0.15. On the other hand, the net energy ratio for ethanol produced from corn can approach unity and there are even suggestions that it can be positive [127].

12.6 Conclusion

12.6 Conclusion

Liquid biofuels offer the potential of (partially) replacing conventional gasoline and diesel fuels in the transportation sector, thus making positive contributions towards conservation of energy resources, mitigation of GHG emissions, and environmental protection. The realization of such a potential, however, is very much dependent on the optimization of their production methodologies, physical and chemical properties, and their combustion performance and emissions in internal combustion engines. First-generation biofuels currently produced from food crops are marginally sustainable. They compete with food for their feedstock and fertile land, raising socioeconomic and ethical issues, and they are characterized by average to poor net energy input ratios. Second-generation biofuels are signiﬁcantly more attractive since no additional land is required when agricultural or forestry by-products or lignocellulosic material are used as feedstock and their net energy input and GHG gains are very much improved. Further research and development work is required to make second-generation biofuel production technologically and ﬁnancially feasible. Also, the use of waste materials as a feedstock for biodiesel production should be actively promoted. A further important issue relates to the properties of biofuels. The physical and chemical properties of ethanol and biodiesel are fairly similar to those of conventional gasoline and diesel, respectively, with some notable exceptions – namely boiling point incompatibilities and low heating value for ethanol and poor cold ﬂow properties and stability for biodiesel. The development of fuel blends, such EBdiesel and tailor-made biodiesels, with optimized properties, is an active current area of research. Current engine designs can generally be operated on certain ethanol–gasoline and biodiesel–diesel blends without any performance deterioration. Although preliminary evidence from the use of butanol in internal combustion engines and biodiesel in gas turbine engines is not particularly encouraging, further work is required in order to draw reasoned conclusions. The blending of liquid biofuels with conventional fuels generally has a positive impact on PM emissions. However, no clear trend can be established for unburned hydrocarbon, carbon monoxide, and nitrogen oxide emissions. Some of these uncertainties are clearly attributable to the variability of engine and operating conditions, and naturally more experimental investigations under well-controlled conditions are required. However, the role of combustion chemistry is clearly crucial. Although signiﬁcant progress in the combustion chemistry of bioalcohols and biodiesel has been made in the last 10 years, several issues remain unresolved. A particularly important issue that merits further investigation is to what extent the functional group (OH in the case of alcohols and O(C¼O) in the case of alkyl esters) determines combustion performance and emissions. Furthermore, the dynamics of benzene, PAH, and soot formation and the NOx formation and destruction pathways during the combustion of liquid biofuels have not yet been

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systematically studied. Experimental and numerical investigations of neat or blended alcohols and biodiesel in well-controlled fundamental experimental conﬁgurations that closely resemble the operating conditions of practical combustion devices are needed.

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13 Combustion in a Spark Ignition Engine Alexey A. Burluka 13.1 Introduction

A modern spark ignition (SI) engine is an incomparably more sophisticated device than the ﬁrst in-cylinder compression engine made by Nikolaus Otto in 1876. Todays SI engine is a mature and well-established prime mover for hundreds of millions of passenger cars and tens of millions of diverse other applications, ranging from simple single-speed two-stroke engines in lawnmowers and portable electric power generators to complex four-stroke engines used in power boats and light airplanes. At present, the main fuel for an SI engine is petrol derived from crude oil reﬁning; however, the advent of renewable energies and fuels will not lead to the demise of the SI engine, which can be adapted to a wide range of liquid fuels with no or very little modiﬁcation. An indication of the fuel ﬂexibility is that a commercially sold passenger car can be run without modiﬁcations on petrol including as much as 10% of bioethanol; this introduction of a bio-derived component has recently been legislated in the European Union. Regardless of the fuel used, the main combustion mode in an SI engine is turbulent premixed combustion; the combustion process within any one given cycle occurs over a wide range of pressures and temperatures. Furthermore, the combustion is affected by the heat losses to the chamber walls and the piston; there are also mass ﬂows into crevices, such as the top land crevice, which can eventually result in some part of the charge leaking through into the crankcase, resulting in decrease in efﬁciency. This chapter aims to describe the main features of these phenomena and to provide some methods for their mathematical description. To a large extent, it is based on research carried out in the University of Leeds for the last 30 years at both fundamental and applied levels. Combustion in an internal combustion engine is a complicated phenomenon; this immediately becomes clear if one tries to deﬁne it. An appropriate deﬁnition would be unsteady development of strongly exothermic chemical reactions in turbulent ﬂow in a variable-volume non-adiabatic chamber in the presence of heat and mass exchange with the surroundings. In addition to these factors, one should add the

Handbook of Combustion Vol.3: Gaseous and Liquid Fuels Edited by Maximilian Lackner, Franz Winter, and Avinash K. Agarwal Copyright 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32449-1

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process of the ﬂame initiation by a spark in an SI engine. Therefore, in applied engine modeling, an appropriate balance must be found between the complexity of the mathematical description and the fact that this engine modeling should remain usable by design engineers, especially for the so-called parametric studies of an engine which require a large number of computer simulations. Although great progress has been achieved with computational ﬂuid dynamics (CFD), describing the in-cylinder processes in terms of the Favre-averaged transport equations for concentrations of reactive species, velocity, and turbulence properties, it should be noted that a typical one-cycle simulation time in the best current practice ranges from several hours to several months. A serial production engine cylinder geometry usually lacks symmetry, for example, as is the case with the commonly used pent-roof cylinder head and proﬁled piston crown. Modeling a ﬂame inside such a geometry would therefore require a solution of a set of unsteady three-dimensional equations of turbulent motion. Moreover, when modeling of an even relatively small sub-part of the engine, such as the injection line, is performed, its boundary and initial conditions are rarely known to any acceptable degree of accuracy. Hence, although a CFD calculation of the engine combustion remains a prospective research tool, its complexity and uncertainty in input required justify, at least for the time being, the use of simpler methodology such as that considered here [1]. For the modeling of combustion, and related to it processes inside the combustion chamber, an alternative to the CFD approach, known as thermodynamic or zonal modeling [1, 2], is based on abandoning all attempts to resolve the unsteady turbulent ﬂow details. Instead, this method represents the in-cylinder ﬂow turbulence in terms of a few simple parameters, such as the root mean square (rms) velocity and time or length integral scale. As will be shown below, these parameters are important for the determination of the combustion heat release; unlike the CFD approach, information about them is a required input to the model. The term zonal originates from the idea that the in-cylinder charge can be described as consisting of a few distinct zones; for example, a two-zone model would consider the charge as composed of fresh reactive mixture and fully burned gas, both being in the state of chemical and thermodynamic equilibrium. In this approach, the integral average rate of heat release from combustion is expressed from the mass burning rate, for the calculation of which there exist a number of alternative expressions [2–4]. The mass burning rate is determined by the ﬂame propagation, which is inﬂuenced in engines by several factors simultaneously. The studied factors include engine speed determining the turbulence strength, mixture equivalence ratio, compression ratio, and the spark advance. Because of the large number of physical processes involved in the engine operation and limitations of both the modeling and experiments used for its support, some potentially important effects (factors) have been left out of consideration. The included agents comprise the gas exchange ﬂow pattern, for example, such as obtained with a variation in valve arrangement, charge inhomogeneity, or stratiﬁcation, and the change in the chamber geometry. The question of how to distinguish effects of the individual above-mentioned factors calls for experimental evidence obtained in well-deﬁned and well-controlled

13.2 Thermodynamic Modeling: Principles and Components

conditions. Such evidence is very difﬁcult to obtain in a serial production engine; hence, for the purpose of the modeling assessment, several extensive experimental datasets have been collected from the measurements of single-cylinder four-stroke ported research engines. Among them, the Leeds University Ported Optical Engine (LUPOE) provides excellent optical access to the combustion chamber [5, 6]. Measurements obtained in this engine [6] are used throughout this chapter as the benchmark of simulations and their underlying models.

13.2 Thermodynamic Modeling: Principles and Components

Undergraduate engineering thermodynamics textbooks commonly assume that combustion in a spark-ignition engine is so fast that the operation of the latter may be described with an ideal Otto cycle. The thermodynamic efﬁciency g of the Otto cycle with a compression ratio rv is g ¼ 1rv1c

ð13:1Þ

where c ¼ cp =cv is the ratio of the speciﬁc heats. While the textbook analysis is oversimpliﬁed, and a real engine cycle will look more like Figure 13.1, the conclusion

Figure 13.1 Illustrative diagram for an SI engine. TDC, top dead center; BDC, bottom dead center.

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that the efﬁciency increases with the compression ratio holds true, and while the ﬁrst engines had rv 3, a modern naturally aspirated engine would operate at rv 11, resulting in combustion at very high temperatures and pressures. Furthermore, it is not only efﬁciency which matters, and the outcome of modeling of the combustion event in an engine should include engine torque and power deﬁned by the dependence of the in-cylinder pressure on the crank angle, composition of exhaust, including pollutants, deﬁned by the dependence of the mass fraction burned on the crank angle, and the probability of the knock onset determined by the two abovementioned dependences. In turn, the pressure for the closed part of the cycle is determined by rates of combustion and heat losses to the cylinder walls Q_ wall , head, and piston crown. For combustion in a closed volume, the pressure rise is proportional to the proportion of mass burnt: xb ðtÞ ¼

mburnt pðtÞ p0 ¼ mtotal peq p0

where p0 and peq are the initial and adiabatic thermochemical equilibrium pressure, respectively. However, the link between the pressure and the mass fraction burned is far from straightforward in an engine because of (i) the variable volume due to the piston motion, (ii) heat losses dependent on ﬂame conﬁguration, and (iii) eventual mass losses through the piston rings, that is, blow-by. Also, what fraction of the total trapped mass is burned depends on the rate of not only combustion but also mass exchange between the cylinder and the space where combustion is impossible, such as the top land crevice, inter-ring space and the crank case. This latter mass exchange is commonly termed the blow-by ﬂow. To complicate things further, variable speciﬁc heats and moving chemical equilibrium in the burned gas make it necessary to employ an iterative procedure. This section provides details of modeling of these interacting processes. The approach adopted in this work can be described as follows. Initially, the compression process is modeled from the moment when the intake process is ﬁnished. After the ignition, the entire in-cylinder charge is split into two zones: one composed of the fresh mixture, referred to with a subscript u, and the other composed of the burned gas, properties of which are denoted with a subscript b; see Figure 13.2. The ﬂame is the separation boundary of these two zones. Each zone is characterized with the same pressure p, but its own distinct mass m, temperature T, volume V, area of contact with the kth cylinder wall, or piston crown, termed the wetted area Ak, and chemical composition given by the species molar fractions xi which are found from the rates of dissociation set of equations; see for example, [7] or Vol. 1 Ch. 2. In fact, an instantaneous onset of the equilibrium is assumed for all species except CO and NO. The formation of the carbon monoxide is found from the rate of the one-step reversible reaction CO þ OH > CO2 þ H, while the nitrogen monoxide concentration is calculated from the extended Zeldovich mechanism [8]. Fresh gas may obviously contain combustion products from either trapped residuals or exhaust gas recirculation (EGR).

13.2 Thermodynamic Modeling: Principles and Components

The transfer of the mass from the fresh to the burned zone is determined by the propagation of the ﬂame. In this chapter, the ﬂame is assumed to grow spherically from the spark position until it meets the chamber walls; however, the assumption of a spherical ﬂame can be relaxed. The speed of the turbulent ﬂame propagation, related to the turbulent mass burning rate, is the key variable and it is perhaps fair to say that every other in-cylinder process is affected either directly or indirectly through alteration of temperature or pressure. The ﬂame speed is unsteady; ﬁrst, the ﬂame accelerates [3, 4] after the ignition, whereas in later stages, the proximity of the walls decelerates it [1, 2, 6]. Finally the simulations stops with the beginning of the exhaust process, thus effectively leaving the engine breathing out of consideration. All the variations in engine breathing are simply represented here with changes in the charge temperature, pressure, and composition; this is done here to simplify the modeling as the emphasis is placed on the accurate representation of combustion. Variations of the speciﬁc molar heats for individual species with temperature, cpi(T ), are important; these are calculated with the polynomial functions of temperature [9]; the same polynomials are used to obtain values of the speciﬁc enthalpies hi(T ) and entropies si(T ). Their real-gas pressure dependence is neglected in the results shown below; however, it is considered important for strongly super- or turbocharged engines, see, for example, Section 2.2.7 in [10]. The molecular transport coefﬁcients, such as viscosity, heat, and mass diffusivity, are calculated with the expressions derived in the gas kinetic theory; see, for example, [11] or Vol. 1 Ch. 3. 13.2.1 Thermodynamic State of Burned and Unburned Gases

As has already been said, the charge is separated into two zones, burned and unburned, having the same pressure. The use of ideal gas dependences for speciﬁc heat requires iterative procedures for ﬁnding how the temperature, volume, and composition of these zones is changed by various processes. These procedures determine a total change for a time step, ﬁrstly applying various processes consecutively and, secondly iterating for new temperature and pressure satisfying a given constraint. These latter iterations involve recalculation of the chemical composition in the burned gas. For the sake of conciseness, Table 13.1 presents the constraints adopted for calculation of the thermodynamic changes induced by a given process. The adopted approach consists in splitting of a time step into several pseudo-steps with individual physical processes, in the following sequence: blow-by ﬂow, heat exchange, combustion, thermal expansion of the products, and ﬁnally the piston motion. The last three steps employ an iterative procedure to ﬁnd new volumes and temperatures of fresh and burned zones in addition to the new pressure. 13.2.2 Equations Describing the State of the Charge

The main equation describes the energy conservation of the entire in-cylinder charge; in its most general form it states

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Table 13.1

Constraints adopted for calculation of the thermodynamic changes induced by a given

process. Fresh charge

Process

Burned gas

Piston motion Constant-volume combustion

Isentropic compression Total internal energy is constant and the burned zone internal energy is increased in proportion to the amount of the mass burned Total internal energy is constant

Thermal expansion of products – pressure equalization Heat loss

Blow-by

_ þ U_ þ Q_ ¼ W

The internal energy of the corresponding zone is reduced by a known amount For mass loss – constant temperature and the decrease in mass by a known amount; for the mass gain occurring at later stages in the working stroke – the returning gas has the wall temperature and the fresh charge composition

X

_ in Uin m

X

Constant temperature

Isentropic compression

Constant temperature, the mass is decreased by a known amount

_ out Uout m

ð13:2Þ

_ ¼ pðtÞdV=dt is the power of the work done by the where Q_ is the heat transfer rate, W _ charge on the piston, U ¼ dU=dt is the rate of change of the charge internal energy, _ out are the mass ﬂow rates in and out of the cylinder of the components _ in and m m or species with internal energy Uin and Uout , respectively. Volume is a known function of crank angle q: 1 Vswept 1 þ Vswept Cr þ 1 cos q Cr2 sin2 q 2 ð13:3Þ V ðq Þ ¼ rv 1 2 Similarly to Equation 13.2, equations can easily be written for any distinct zone, burned, or fresh gas, denoted with the subscripts u and b, respectively: u;b þ p V_ u;b u;b m _ u;b þ mu;b U Q_ w;u;b ¼ U ð13:4Þ where bar over U denotes the speciﬁc, per unit mass, internal energy. 8 9 Tðu;b < = X 1 xi Uu;b ¼ X xi Ui0 T 0 þ cpi ðT Þ R dT ; Wi : i T0 W i i where xi , Ui0 , and Wi are the molar fraction, internal energy of formation, and weight X xi ¼ of the ith species, respectively. The mean molecular weight is simply W Wi i

13.2 Thermodynamic Modeling: Principles and Components

Figure 13.2 Illustration of the thermodynamic approach to combustion in an SI engine.

For the closed part of the cycle, the rate of change of mass of the unburned zone _ c , and a ﬂow into the top land crevice in Equation 13.4 is caused by combustion, m _u¼m _ c þm _ bb . The top land _ bb ; see Figure 13.2: m and further down the ring packs m _ c . In order to _b¼m crevice is typically located furthermost from the spark, hence m obtain a relationship between pressure increase in the engine and the rate of combustion, Equation 13.4 for the fresh gas may be combined with the differential relationship derived from the ideal gas equation of state: Rg _ u Tu þ mu T_ u pV_ u þ Vu p_ ¼ m W

so that p_ ¼

1 _ u cp;u Tu þ mu c p;u T_ u Q w;u m Vu

ð13:5Þ

where h is the speciﬁc enthalpy. In Equation 13.5, the last two terms describe the evolution of pressure during the motoring cycle. The same derivation when applied to the burned products zone results in

1 _ b hb þ mb h_ b Q w;b ð13:6Þ p_ ¼ m Vb Equations 13.5 and 13.6, supplemented with the statement that there are only two zones Vu þ Vb ¼ VðqÞ, form the governing set of equations [1]. The crux of the matter _ bb , and Q_ w;b . _ c; m is in providing speciﬁc expressions for the quantities m

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13.2.3 Models of Heat Transfer

Representation of the time-averaged heat ﬂux from the charge is commonly made via deﬁnition of some average Nusselt number, Nu. Widely used, and judging from a large number of applications, equally successful models of heat transfer are due to Annand [12] and Woschni [13]. In the former, the rate of heat exchange is the sum of convective and radiative contributions: 4 Nul ðTav Tw Þ þ Es Tav Tw4 Q_ w ¼ SðqÞ D

ð13:7Þ

where SðqÞ is the chamber inner surface area, l ¼ rcp K is the molecular thermal conductivity, D is the engine bore, E 0:58 is the charge emissivity, s ¼ 5:6705 108 J m2 s1 K4 is the Stefan–Boltzmann constant, and Tw is the corresponding wall temperature. Note that the empirical character of this model is shown by the necessity to increase the emissivity to E 0:76 for diesel engines, and it is likely that a similar correction will also have to be applied to direct-injection spark ignition (DISI) engines. The Nusselt number Nu in Equation 13.7 is expressed in terms of Reynolds number deﬁned from the mean piston speed: Nu ¼ ð0:35 7 0:8ÞRe0:7 ¼ ð0:35 7 0:8Þ

Upiston D 0:7 n

ð13:8Þ

The Woschni model [13] is structurally similar to the simpler Annand [12] model, but it employs extra adjustable constants with different values for compression, expansion and gas exchange parts of the cycle. 13.2.4 Flow into Top Land Crevice and Blow-By

_ bb ðp; Tu Þ escaping the combustion is based on a Calculation of the mass loss m representation of the ﬂow path as a series of communicating reservoirs, consecutively numbered in the inset in Figure 13.2. Making the assumption that the ﬂow between _ 0 ] is compressible _ bb ðp; Tu Þ ¼ m the regions m and m þ 1 [so that in this notation m and isentropic, one can write 8 2 3912 1 c1 CD Am Pm Pm þ 1 c < 2c 4 Pm þ 1 c 5= _ m ¼ pﬃﬃﬃﬃﬃﬃﬃﬃﬃ m 1 :c1 ; Pm Pm RTm

ð13:9Þ

if the ﬂow is not choked, and cþ1 2ðc1Þ CD Am Pm 1 2 _ m ¼ pﬃﬃﬃﬃﬃﬃﬃﬃﬃ c2 m c þ 1 RTm

ð13:10Þ

13.2 Thermodynamic Modeling: Principles and Components

for choked ﬂow. Equations 13.9 and 13.10 require knowledge of the pressures and temperatures along the ﬂow path. The pressure in the crankcase is taken as the atmospheric pressure, whereas the pressure Pm in the region m is found from dPm RTm _ mÞ _ m1 m ¼ ðm dt Vm

_ m. The temperature Tm is solved simultaneously with Equations 13.9 and 13.10 for m taken as equal to the prescribed cylinder wall lining temperature Tw , usually the same as in Equation 13.7. The passage areas Am, that is, area of the ring gaps and so on, and the volumes Vm are determined to a large extent by manufacturing tolerances; they need to be measured accurately in the cold engine and an allowance has to be made for the thermal expansion of the rings. The discharge coefﬁcients CD can been found from adjusting the calculated motoring pressure trace to the measured one. Following this procedure, it has been found [6] that, for the LUPOE, a signiﬁcant proportion, up to 20%, of the total trapped mass may render itself unavailable for the combustion escaping to the top-land crevice. This mass partially returns to the main volume, late in the expansion stroke where the in-cylinder pressure drops; its combustion is henceforth late with an obvious detrimental effect on the engine performance. This type of loss is smaller but not negligible for a serial production commercial engine with higher rotational speed. 13.2.5 Combustion Part of the Cycle

The burned gas zone is initiated at the spark discharge instance as a sphere of diameter equal to the spark plug gap, about 2 mm, ﬁlled with combustion products. After that, a three-zone model [14] is used to calculate the progression of the ﬂame. In addition to Equations 13.5 and 13.6, this model introduces a notion of an entrainment front and assumes that the combustion mass rate is proportional to the mass of the fresh gas me behind this front: dmb me mb ¼ dt tb

ð13:11Þ

where mb is the total mass of burned gas, me is the mass of the entrained gas, both burned and unburned, and tb is the characteristic burning time. The entrained mass is then found identifying the entrainment front with the leading boundary of the ﬂame brush; the leading boundary is assumed to propagate normal to itself with a speed ute : dme ¼ ru ute Ae dt

ð13:12Þ

where Ae is the area of the leading edge of the ﬂame brush. If the ﬂame is a sphere truncated by the walls, see Figure 13.2, then expressions for the ﬂame area Ae and volume Ve in terms of the ﬂame radius rte are used:

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drte ¼ ute dt

Ae Ae ðrte Þ

Ve Ve ðrte Þ

In these equations, the ﬂame speed ute , the key quantity determining the combustion rate, depends on the turbulence, ﬂame size and position, and the thermodynamic state of the fresh gas. The following sections specify these dependences and also the method of calculating the ﬂame area Ae . It is important to emphasize that the adopted assumption of a spherical ﬂame introduces two different ﬂame radii as, in addition to the entrainment radius rte , the burned product zone also has its own radius rtr . The difference rte rtr is the measure of the turbulent ﬂame brush thickness. rte should be compared with the ﬂame radius obtained from ﬂame visualization with either natural light or Schlieren imaging corresponding to the leading edge of the ﬂame. rtr may be derived from measurements of pressure inside the cylinder provided that an independent estimation of the heat and mass losses is available; assuming that the instantaneous ﬂame thickness including both reaction and preheat zones is very small, then laser sheet image yields rtr . 13.2.6 Flame Geometry

Flame geometry simulation is an important part of the engine modeling as it allows the calculation of both the active ﬂame area and the wetted areas of the chamber walls. The ﬁrst step in representation of the ﬂame geometry consists in a discretization of the entire surface of the chamber walls into discrete triangular elements, for example, as can be seen in Figure 13.3. This procedure is the same as is commonly employed in the representation of a surface in the stereo-lithography (STL) CAD ﬁle formats. Subsequent to this, the entire volume of the chamber is subdivided into an ensemble of tetrahedral (pyramidal) elements, the bases of which are the surface elements. All these volume elements have the common apex located at the mid-point between the spark electrodes. An example of the subdivision of an entire chamber with a discshaped head is shown in the Figure 13.4. It should be mentioned that this geometry description has the limitation that any straight line from the spark position should have a unique intersection with the chamber walls; this might not necessarily be the case with a fore-chamber ignition engine. The propagating ﬂame is characterized with sums of volumes, surface, and wall wetted areas from every individual volume element. After the discretization has been performed, the known volume of the burned gas is used to derive rtr at any given instant in time. At the same time, the entrainment radius rte is calculated explicitly and the entrained volume is found from it. Currently, these contributions are found assuming that the ﬂame is a truncated sphere; whereas the assumption of a spherical ﬂame front can easily be relaxed, this has rarely, if ever, been attempted. It is worth mentioning that the accuracy with which the ﬂame can be assumed to be spherical has been investigated [15] and it was concluded that this assumption may over-predict the ﬂame area, especially in early stages; nonetheless, the error decreases for later stages of combustion.

13.2 Thermodynamic Modeling: Principles and Components

Figure 13.3 Discretization of a combustion chamber with a disk-shaped head. K. Liu (2003) unpublished.

Figure 13.4 Ratio of the volume-averaged turbulence rms velocity to the mean piston speed.

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13.2.7 Burning Rate

A number of different models have been proposed for the turbulent mass burning rate utr or, related to it, the turbulent entrainment velocity ute; a comprehensive review of these models can be found, for example, in [4]. The relationship between utr and ute is not straightforward [16] and depends on the details of the ﬂame geometry; under engine conditions, this relationship is complicated because the ﬂame brush thickness ﬁrst increases after ignition, but decreases when the ﬂame approaches the walls. Also, the thermal expansion of the combustion products affects this relationship for combustion in a closed volume, however, this fact is ignored in all available theoretical models which derive the ﬂame speed for the hypothetical case of a constant density ﬂow. Of course, it is possible to determine the ﬂame speed or burning rate from the ﬂow simulation where a transport equation is solved for the enthalpy, or temperature, or the progress variable, and one or more reactive species as done in any CFD simulation. In this approach, the burning rate is determined by adopted expression(s) for the average reaction rate(s); these expressions are unknown, no commonly agreed theory, or model, exists, and all models proposed so far have been either based upon, or derived from, the data obtained in experiments performed at atmospheric pressure. How well such models represent the burning rates under high-pressure and -temperature engine conditions is an open question; indeed, the analysis in [4] showed that with very few exceptions models fail to predict the very weak pressure dependence of ute observed in experiments. In other words, in thermodynamic modeling a suitable a priori dependence for ute is taken as a departure point, whereas a CFD approach takes a suitable a priori dependence for the average reaction rate, deriving ute from it. Nevertheless, one should not assume that the additional complexity of the latter approach makes it any more reliable or accurate. A number of models can be used for ﬁnding the rate of entrainment ute ; the models used here are brieﬂy described in the following sections. 13.2.8 Zimont–Lipatnikov Model

The Zimont–Lipatnikov model [4, 17–19] is based on the assumption that the turbulent ﬂame tends to an equilibrium state at which its speed equals its burning velocity and is given by 1

ut0 ¼ cZ u0 Da4

ð13:13Þ

where u0 is the turbulence rms velocity, cZ is the model constant and Da is the Damk€ohler number, deﬁned as the ratio of the turbulent integral time scale tt ¼ lt =u0 to the characteristic chemical time scale tch ¼ K=u2n : Da ¼

tt 1 ¼ lt u2n K1 u0 tch

13.2 Thermodynamic Modeling: Principles and Components

where K is the molecular thermal diffusivity and un is the laminar mass burning rate. Although in reality the thermal diffusivity increases with temperature and changes with the local gas composition, commonly used is the value of K in the cold reactants. Equation 13.13 gives the value of the turbulent ﬂame speed when the equilibrium is already attained; however, it is well known [1–4] that, under engine conditions, the ﬂame speed increases following the ignition. This ﬂame acceleration is described in this model [18, 19] invoking an analogy between the ﬂame kernel and the Taylor dependence for diffusion of a patch of an inert contaminant in a homogeneous turbulence: 1=2 tt tt e t 1 ute ¼ un þ ut0 1 þ ð13:14Þ t 13.2.9 Leeds Models

the K model, originally proposed in [3], is based on a large compilation of experimental data presented in the form of two separate diagrams relating the burning rate of an unsteady developing turbulent ﬂame with the time elapsed from ignition. The burning rate depends on the Lewis number Le ¼ Dm =K ¼ rcp Dm =l, that is, the ratio of the thermal diffusivity to the mass diffusivity of a deﬁcient species, Le < 1.3 or Le 1.3. Turbulence effects on the burning rate are expressed in terms of a dimensionless Karlovitz number, deﬁned as [20] 0 2 1 u K ¼ 0:157 Ret 2 ð13:15Þ un where Ret is the turbulence Reynolds number, Ret ¼ lt u0 =n, and n is the kinematic viscosity in the cold reactants. The Karlovitz number is essentially the product of the rms value of the strain rate and the chemical time-scale tch . The original model [3] was formulated in terms of diagrams which had to be tabulated as look-up tables for numerical simulations of an engine; following the same approach, and aiming at a better description of the Lewis number effects, subsequent work [21] has expressed the ratio ute =un as a function of the effective turbulence intensity u0k and the dimensionless product K Le: ute ¼ CKLe u0k ðK LeÞ0:3

ð13:16Þ

where CKLe is a constant of the order of one [21, 22]. The effective rms velocity u0k takes into account the ﬂame development with the time t elapsed since ignition: 8 2 39 3 = < 4 t 0 0 5 uk ¼ u 1exp40:28 ð13:17Þ : ; tt Equations 13.16 and 13.17 are referred to as the K-Le model. It should be mentioned that while the Lewis number enters K-Le model explicitly, the current

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understanding [22, 23] is that its effects are much stronger than what Equation 13.15 would predict, and a correlation of ute with the Markstein number is sought [23]. The ﬁnal remark is that both the Zimont–Lipatnikov model, Equations 13.13 and 13.14, and the Bradley–Lawes K-Le model, Equations 13.16 and 13.17, describe the propagation of an unconstrained ﬂame away from a wall. For engine applications, an additional factor therefore has to be introduced into expressions for ute in order to take into account the deceleration of a ﬂame approaching the chamber side wall [24]. In order to obtain an expression for the deceleration, use is made of the self-similar spatial proﬁle of the mean ﬂame progress variable cðr; tÞ [4], the validity of which under engine conditions can be derived from the experiments in [25]: cðr; tÞ ¼

1 rrte 1erf 2 df ðtÞ

We assume that the approaching wall cuts the self-similar ﬂame brush so that the mass burning rate is decreased by a wall situated at Rw by a factor of ute Rw rte 1 ð13:18Þ ¼ 2 erf df ðtÞ u0te compared with an unconstrained ﬂame. The dependence of the ﬂame brush thickness df ðtÞ in Equation 13.18 is taken following the analysis in [4]: df ðtÞ ¼

1 pﬃﬃﬃ t t 2 2lt 1 þ exp tt tt

ð13:19Þ

13.2.10 Burn-Up Time Scale

In the adopted model, there is another parameter that strongly inﬂuences the rate of combustion in addition to the rate of entrainment, namely the characteristic time scale for combustion of the entrained mass tb in Equation 13.11. There are two alternative expressions for this time scale: tb ¼ cb

lt un

ð13:20Þ

tb ¼ cb0

ll un

ð13:21Þ

or

where ll is the Taylor length scale of turbulence, ll ¼ cl lt Ret0:5 , and cl is a model pﬃﬃﬃﬃﬃﬃﬃﬃﬃ constant for which the value of cl ¼ 40:4 has been suggested [20]. The only difference between the two expressions above is in the choice of a suitable length scale; however, there is a large difference in the corresponding values of the coefﬁcients cb and c 0 b and the overall rate of heat release is very sensitive to these.

13.2 Thermodynamic Modeling: Principles and Components

13.2.11 Laminar Burning Velocity

The laminar burning velocity un is required for the determination of both the entrainment rate and the burn-up time. For high temperatures and pressures relevant for engine conditions, a commonly used expression due to [26] is un ðT; pÞ ¼ un0 ðT0 ; p0 Þ

a b T P 1xb0:77 T0 P0

ð13:22Þ

where the reference temperature and pressure T0 , P0 are commonly taken as the normal conditions of 298 K and 1 atm, respectively; xb is the mole fraction of residual combustion products in the unburned gas. Different fuels are described with different exponents a and b; for the fuels used in this work they are as follows: isooctane:

gasoline (indolene)

a ¼ 2.18 0.8(j 1) b ¼ 0.16 þ 0.22(j 1)

a ¼ 2.4 0.271 j3.51 b ¼ 0.357 þ 0.14 j2.27

where j is the equivalence ratio. The reference laminar burning rate un0 ðT0 ; p0 Þ is taken as a quadratic polynomial of the equivalence ratio: un0 ¼ Bm þ Bw ðw wm Þ2 with the coefﬁcients Bm , Bj , jm listed in Table 13.2. The laminar burning rate and ﬂame thicknesss dn then determine the chemical time-scale as tch ¼

dn K ¼ un u2n

The inﬂuence of the chemical structure of fuel on the laminar burning velocity has been studied [27]; the results indicate that this inﬂuence is far from negligible, and has to be taken into account when results obtained with primary reference fuels (PRFs) are extrapolated to gasolines. In particular, it was found that straight-chain hydrocarbons burn faster than branched-chain isomers, and the presence of unsaturated bonds increases the combustion rate. A similar study [28] found a strong, nearly two-fold, difference in measured burning rates in laminar ﬂames of oxygenated isomers.

Table 13.2 Coefficients in the expression for un0 ðT0 ; p0 Þ [26].

Fuel

wm

Bm (cm s1)

Bw (cm s1)

Isooctane Gasoline

1.13 1.21

26.3 30.5

84.7 54.9

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When the laminar ﬂame speeds measured under laboratory conditions are used for engine modeling, one has to exercise some degree of caution. This is because of the propensity of laminar ﬂames to form a cellular front, which is well documented in experiments [29, 30]; this propensity is augmented with the increase in pressure because of decrease in K, the corresponding thinning of the ﬂame, and consequently to widening of the range of unstable wavelengths of Darrieus–Landau, or hydrodynamic, instability. This effect is so strong that even at a modest, compared with engine conditions, pressure of 10 atm no smooth ﬂame observation has ever been reported in the literature. At present, it is unclear whether cellular laminar ﬂames have a deﬁnite steady propagation speed and thickness and the available measurements, for example [30, 31], show that they do not. This implies that the laminar ﬂame speed and time deﬁned above should be understood as simply the characteristic scales for chemical reactions rather than the measurable properties of ﬂames.

13.3 Turbulence Properties

The turbulence inside the cylinder is mostly generated by the shear motion and decay of the intake jet-like ﬂow to which may add the motion towards the center from the squish margins [31]. The deﬁnition of what is a proper turbulence in an engine is complicated by the facts that the ﬂow is inherently unsteady and, in addition, the mean ﬂow pattern may vary from one cycle to another; see for example, the particle image velocimetry (PIV) study in [32], which showed that the average ﬂow switches between three different patterns for low-swirl unshrouded valves, and while the mean ﬂow has the appearance of one large vortex when shrouded valves are used, the intensity of that vortex ﬂuctuates cycle-to-cycle. The complexity of the problem is further illustrated by an application of the principal orthogonal decomposition [33] method to a simple ﬂow inside a chamber with a moving piston; even in the absence of combustion, at least four modes are required to account for the main part of velocity ﬂuctuations [34]. It is worth noting that, regardless of whether they constitute the turbulence proper, these large-scale ﬂow features affect fairly strongly the rate of combustion in engines [35, 36]. Often, a distinction is made between tumble and swirl, that is, large-scale rotations around an axis orthogonal to and aligned with the cylinder axis, respectively [36]. A common approach to the deﬁnition of in-cylinder turbulence is based on the assumption that the velocity spectrum components the frequency of which is below some threshold fm constitute the mean velocity; the rest of the spectrum with f > fm forms the turbulence [35–38]. However, whatever the method of separating turbulence from the mean motion is used, they all exhibit signiﬁcant cycle-to-cycle variations (CCVs) which have been shown to be one of the main contributing factors to the engine cyclic variability [24]. Prediction of the turbulence in engines remains a challenge [36, 38]; while some promising results for a cold ﬂow have been obtained with the LES approach [39], typically the combustion CFD modeling still requires initial and boundary conditions from the simpler thermodynamic modeling [40].

13.3 Turbulence Properties

The thermodynamic engine modeling does not resolve the ﬂow details inside the cylinder; hence, at this level of approximation, it requires only turbulence properties averaged over the entire chamber volume as a function of crank angle. These may be determined with zero-dimensional versions of the k–e model developed in [41, 42], where the turbulence generation and dissipation are expressed in terms of the mean piston speed and the energy ﬂow rates through valves. In this approach, the integral length scale of turbulence is usually taken as proportional to the instantaneous clearance height, H: lt ¼ cl H, where the typical value of the proportionality factor is cl ¼ 0.15–0.2. A sample of predictions of the turbulent rms velocity u0 ðqÞ with such a model is shown in Figure 13.4 for different engine speeds. Very strong turbulence is generated by the intake ﬂow but its intensity is reduced by a factor of ﬁve or more by the instant of ignition, near top dead center (TDC). The second sharp increase in u0 seen approximately at the end of combustion period is, in all likeliness, non-physical and is caused by use of rapid distortion theory dependencies, which predict that the thermal expansion will produce turbulence [42]. Knowledge of u0 and lt allows the deﬁnition of the integral time scale of turbulence as tt ¼ ct

lt u0

with a constant value of ct ¼ 0:6 [6]. 13.3.1 Cyclic Variability

CCVs are essentially differences in instantaneous combustion rates between different cycles at nominally identical operating conditions, illustrated in Figure 13.5. These variations are an impediment to improving the performance of an engine [1], because under operating conditions at the knock boundary, the octane number requirements, maximum compression ratio, and spark timing are all limited by the propensity for autoignition to occur in the fastest burning cycles. Furthermore, the spark timing for a given running condition is typically optimized for the heat release proﬁle of the most frequent cycle, hence any deviation from this optimum will entrain penalties in terms of lost power and efﬁciency. A strong CCV, such that the variation in indicated mean effective pressure (IMEP) is greater than 10%, is noticeable to the driver as a deterioration in vehicle drivability [1]. A review [43] suggests that total elimination of CCVs would result in a 10% increase in brake power output for the same fuel consumption. Unfortunately, the current trend in engine design favors an increase in the amount of exhaust gas recirculation, which, together with lean combustion, leads to increased cyclic variability. In particular, CCVs in early ﬂame development restrict lean operation for any particular fuel [44]. There are a number of potential sources of CCVs [1, 24, 43], including the variability in: (i) charge motion and turbulence during combustion; (ii) the trapped amounts of fuel, air, and residual and/or recirculated exhaust; (iii) uniformity of the mixture composition within the cylinder, especially near the spark plug, associated with imperfect mixing between the air, fuel, and residual or recirculated exhaust; and

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45 40 35

Reference condition CR=7.6/ 1500rpm/ φ=1.0/ IT=20ºbTDC Filmed and non-filmed cycles Ensemble average

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Crank angle (º) Figure 13.5 Crank-resolved in-cylinder pressure of 100 cycles. Reproduced from [6].

(iv) spark discharge characteristics, such as breakdown energy and initial ﬂame kernel random displacement. Some of the above-mentioned factors are of much greater importance than others [24]. An attempt to describe CCVs in the framework of thermodynamic modeling has been made [24] and it was found that imposing a 10% variation in the rms velocity u0 at the moment of ignition allows a fairly good estimation of CCVs of peak pressure; see Figure 13.6. Variation of the equivalence ratio of the charge has been identiﬁed as the second main cause of CCVs, producing a spread of values of the maximum pressure occurring at the same crank angle. 13.3.2 Model Assessment

To qualify as a design tool, a numerical model must be predictive and be assessed as such for its ability to simulate the engine performance under other operating conditions without any adjustment of its constants. The approach presented in this chapter was used for different engines running at different speeds, compression ratios, fuels, and equivalence ratios [24, 45, 46], for all available experimental datasets. As the mathematical models use several constants, the values of which are not known in advance, for example, the discharge coefﬁcients in Equations 13.9 and 13.10, the approach adopted for the model assessment here is as follows. First, the parameters of the blow-by model are found so that the measured motoring pressure agrees well with predictions. Second, using measured ﬁred data at one particular set of conditions, referred to as the reference conditions in what follows, the values of the

13.3 Turbulence Properties

Figure 13.6 Comparison of predicted (GT-LU) and measured (Experiment) peak pressure versus crank angle of its occurrence. Reproduced from [24].

combustion model constants are found so as to obtain the best possible agreement of predicted ﬂame radii and in-cylinder pressure with values measured for an average cycle. It is important, especially for reliable knock detection [45], to obtain an accurate prediction of both pressure and ﬂame position simultaneously; this is because the same pressure may correspond to different fresh gas volumes and temperatures if the magnitude of heat losses varies. Therefore, an experiment suitable for modeling assessment would provide both pressure and ﬂame radius measurements, for example, as was done in several studies [5, 6, 36]. For the present model, this reference set of conditions is provided by the LUPOE with a compression ratio of 7.6 running at 1500 rpm, using a skip-ﬁred residuals-free stoichiometric fuel–air mixture with an ignition timing of 20 before TDC optimized for the highest brake mean effective pressure (BMEP) [6]. The reference conditions were simulated [6] with varying constants for the entrainment ﬂame velocity: cz in Equation 13.13, and CKLe in Equation 13.16; also varied was the proportionality constant cb for the burn-up time, Equation 13.20. It is worth emphasizing that the latter constant affects pressure history only while an entrainment velocity constant affects both ﬂame radius and the pressure. For the Zimont–Lipatnikov model, Equations 13.13 and 13.14, excellent agreement with the measured ﬂame radii and pressure trace was obtained at the reference conditions with cz ¼ 0:8 and cb ¼ 0:6, Equations 13.13 and 13.20; the so found values are well within the spread of literature data [6]. It was found that the optimum value of the constant in the K–Le correlation is CKLe ¼ 1:4, Equation 13.16; with these values, the two models for the entrainment ﬂame velocity produced nearly identical results for the reference conditions. In addition to the varied proportionality factors, the burning rate is sensitive to the laminar burning velocity un, rms turbulent velocity u0 and integral length scale lt .

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Under engine conditions, measurements of these are subject to uncertainty and, for the last two, cyclic variations. To investigate the sensitivity of the model to these parameters, the effects of a 10% variation in each parameter were considered at the reference conditions [6]. A 10% variation of un resulted in a 10% variation of peak pressure magnitude with an alteration of 1.5 CA (crank angle) of the peak occurrence; maximum temperatures varied by 15 K and 25 K for fresh and burned gas, respectively, with similar variation of 1.5 CA of their onset. This change also led to a variation of approximately the same 1.5 CA of the combustion duration and an approximately 8% change in the turbulent burning rate. Very nearly the same magnitude of changes was induced by a 10% variation in u0 ; however, the effects caused by varying the integral scale were much smaller. In every case, these variations were much smaller than the amplitude of cyclic variability at nominally identical conditions; see Figure 13.5. Once the model constants had been found using the reference conditions, they were used unchanged for the simulations of other operating conditions and engines, and conclusions were drawn about the predictive ability of the model to reproduce the experimentally observed response to changes of the engine geometry, operating conditions, and so on. A typical outcome of this procedure is shown in Figures 13.7 and 13.8. Figure 13.7 shows the predicted response to an increase in the engine speed. Compared with the reference case, the pressure trace corresponds to cycles the maximum pressure of which is higher than the average (see Figure 13.7a), while the predicted ﬂame position agrees well with the average. This comparison indicates that the heat losses are under-predicted slightly; however, the overall level of agreement is very good. Even more encouraging is that the same model, Equations 13.13, 13.14 and 13.20, with the same constants applied to a radically different engine conﬁguration in a predictive mode [46], yields an equally good agreement; see Figure 13.8.

13.4 Outlook

Mainstream SI engine technology currently employs the so-called port (or manifold) injection, that is, fuel injection into intake runners or the back side of the inlet valves. Current R&D with this technology [46] is concentrated on engine downsizing, which allows pumping loss and friction to be reduced; to have the same power and torque a downsized engine requires heavier super/turbo-charging, and better than currently available ﬂexibility with the valve timing and lift. From the combustion point of view this means ever higher pressures and temperatures, higher concentrations of residual gas or stronger EGR dilution, or both, whereas the mainstream combustion research until very recently used near-stoichiometric combustion at atmospheric pressure. Nonetheless, there are no fundamental reasons why the thermodynamic modeling approach outlined in this chapter cannot be applied successfully under the new, more challenging engine conditions provided that it is based on sound and well-tested combustion models; indeed, it has been widely used both in industry

13.4 Outlook 40 35

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Crank angle [º] Figure 13.7 Experiments versus model predictions for the LUPOE running at 2000 rpm: (a) in-cylinder pressure; (b) visible (entrainment) flame radii; (c) turbulent burning velocity. Reproduced from [6].

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Figure 13.8 Comparison of calculated and measured effects of engine speed on in-cylinder pressure for a turbo-charged four-cylinder engine. Reproduced from [46].

and in academia for the last 35 years since the seminal work of Blizard and Keck [14]. The same approach can also be applied in a very straightforward manner to sparkassisted homogeneous charge compression ignition (HCCI) engines with the ﬂames near the lean limit of propagation; the main question for this application is how this limit is affected by turbulence. Direct injection, spark-ignition (DISI) engines, in addition to possible improvements available to ported injection engines, offer a possibility of signiﬁcant gains in

13.5 Conclusion

efﬁciency through raising the compression ratio to values comparable to the diesel, that is, compression ignition, engines [48]. When a liquid fuel is injected directly into the combustion chamber, there appears the need to describe the charge preparation, that is, atomization and vaporization of the liquid spray and the turbulent mixing of the fuel vapor with the rest of the charge [49]. Mixture preparation was always a concern for diesel engines, where there is no propagating ﬂame and combustion models are typically simpler [10]. Modeling of a DISI engine should therefore combine methods developed for mixture preparation in diesel engines with a description of a ﬂame propagating in a potentially strongly stratiﬁed charge. Perhaps the greatest difﬁculty for combustion theory applied to a DISI engine would be the (highly relevant) question of transition from premixed to diffusion-controlled combustion.

13.5 Conclusion

The modern SI engine is a mature and well-established prime mover for hundreds of millions passenger cars and tens of millions of diverse other applications, ranging from simple single-speed two-stroke engines in lawnmowers and portable electric power generators to complex four-stroke engines used in power boats and light airplanes. The main combustion mode in an SI engine is turbulent premixed combustion where a ﬂame propagates through a mixture of homogeneous chemical composition; the combustion process within any one given engine cycle occurs over a wide range of pressures and temperatures. While a computational ﬂuid dynamics calculation of the engine combustion remains a prospective research tool, and owing to its complexity and the uncertainty of input, the use of a simpler methodology such as the thermodynamic or zonal modeling considered in this chapter is desirable. Crucial to the engine performance, the net rate of heat release is determined by several processes, the principal of which is ﬂame propagation. This chapter has reviewed applications of modern premixed combustion models to SI engine conditions. Measurements of turbulent ﬂame velocity and observations of the ﬂame structure were compared with existing models. Results obtained with the described approach were presented and the relative importance of various sub-models has been discussed. It has been shown that a very simple modiﬁcation of existing models allows one to predict the magnitude of the CCVs in engine performance. Finally, a brief outline of the latest trends in SI engine development and the challenges that it may pose for the combustion research was given.

Acknowledgments

Many stimulating discussions with colleagues, past and present, are gratefully acknowledged. Special thanks are due to Prof. C.G.W. Sheppard, Prof. D. Bradley, and Drs M. Lawes, G. Sharpe, K. Liu, T. Hattrell, A. Cairns, and E. Abdi Aghdam.

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Engine Fundamentals, McGraw-Hill, New York Blumberg, P.N., Lavoie, G.A., and Tabaczynski, R.J. (1979) Phenomenological models for reciprocating internal combustion engines. Prog. Energy Combust. Sci., 5, 1233–1267. Abdel-Gayed, R.G., Bradley, D., and Lawes, M. (1987) Turbulent burning velocities: a general correlation in terms of straining rates. Proc. R. Soc. London A, 414, 389–413. Lipatnikov, A.N. and Chomiak, J. (2002) Turbulent ﬂame speed and thickness: phenomenology, evaluation, and application in multi-dimensional simulations. Prog. Energy Combust. Sci., 28, 1–74. Hicks, R.A. (1994) Turbulent Flame Structure and Autoignition in Spark Ignition Engines. PhD thesis, University of Leeds. Abdi Aghdam, E. (2003) Computational and Experimental Study of Flame Propagation in Radial Stratiﬁed Charge SI Engines. PhD thesis, University of Leeds. Desoky, A.R.A.A. (1981) An Experimental and Theoretical Study of the Combustion Process in a Divided Chamber Spark Ignition Engine. PhD thesis, University of Leeds. Lavoie, G.A., Heywood, J.B., and Keck, J.C. (1970) Experimental and theoretical study of nitric oxide formation in internal combustion engines. Combust. Sci. Technol., 1, 313–326. Burcat, A. and Ruscic, B. (2005) Third Millennium Ideal Gas and Condensed Phase Thermochemical Database for Combustion with Updates from Active Thermochemical Tables. Technical Report ANL 05/20; also TAE Report No. 960, Technion, Haifa, available from http:// www.osti.gov/bridge and ftp:// ftp.technion.ac.il/pub/supported/aetdd/ thermodynamics. Stiesch, G. (2003) Modelling Engine Spray and Combustion Processes, Springer, Berlin.

11 Bird, R.B., Stewart, W.E., and Lightfoot,

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E.N. (1960) Transport Phenomena, John Wiley & Sons, Inc., New York. Annand, W.J.D. (1963) Heat transfer in the cylinder of reciprocating internal combustion engines. Proc. Inst. Mech. Eng., 177 (36), 973–990. Woschni, G. (1967) A Universally Applicable Equation for the Instantaneous Heat Transfer Coefﬁcient in the Internal Combustion Engine. SAE Paper 670931. Blizard, N.S. and Keck, J.C. (1974) Experimental and Theoretical Investigation of Turbulent Burning Model for Internal Combustion Engines. SAE Paper 740191. Witze, P.O. (1994) Interpretation of headgasket ionization-probe using a two-zone spherical ﬂame model. Proceedings of International Symposium COMODIA 94, pp. 453–458. Lipatnikov, A.N. and Chomiak, J. (2000) Transient and geometrical effects in expanding turbulent ﬂames. Combust. Sci. Technol., 154, 75–117. Zimont, V.L. (1979) Theory of turbulent combustion of a homogeneous fuel mixture at high Reynolds numbers. Combust. Exp. Shock Waves, 15, 305–311; translation of Fiz. Gor. Vzryva, 1979, 15 (3), 23–32. Lipatnikov, A.N. and Chomiak, J. (1997) A Simple Model of Unsteady Turbulent Flame Propagation. SAE Paper 972993. Lipatnikov, A.N., Wallesten, J., and Nisbet, J. (1998) Testing of a model for multi-dimensional of turbulent combustion in spark-ignition engines. Proceedings of Fourth International Symposium COMODIA 98, JSME, Tokyo, pp. 239–244. Abdel-Gayed, R.G., Al-Khishali, K.J., and Bradley, D. (1984) Turbulent burning velocities and ﬂame straining in explosions. Proc. R. Soc. London A, 391, 393–414. Bradley, D., Lau, A.K.C., and Lawes, M. (1992) Flame stretch rate as a determinant of turbulent burning velocity. Philos. Trans. R. Soc. London A, 338, 359–387.

References 22 Bradley, D. (2002) Problems of predicting

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turbulent burning rates. Combust. Theor. Model., 6, 361–382. Bradley, D., Haq, M.Z., Hicks, R.A., Kitagawa, T., Lawes, M., Sheppard, C.G.W., and Woolley, R. (2003) Turbulent burning velocity, burned gas distribution, and associated ﬂame surface deﬁnition. Combust. Flame, 113, 415–430. Abdi Aghdam, E., Burluka, A.A., Hattrell, T., Liu, K., Sheppard, C.G.W., Neumeister, J., and Crundwell, N. (2007) Study of Cyclic Variation in an SI Engine using Quasi-Dimensional Combustion Model. SAE Paper 2007-01-0939. Gillespie, L., Lawes, M., Sheppard, C.G.W., and Woolley, R. (2000) Aspects of Laminar and Turbulent Burning Velocity Relevant to SI Engines. SAE Paper 2000-01-0192. Metghalachi, M. and Keck, J.C. (1982) Burning velocities of mixtures of air with methanol, iso-octane and indolene at high pressure and temperature. Combust. Flame, 48, 191–210. Farrell, J.T., Johnston, R.J., and Androulakis, I.P. (2004) Molecular Structure Effects on Laminar Burning Velocities at Elevated Temperature and Pressure. SAE Paper 2004-01-2936. Burluka, A.A., Harker, M., Osman, H., Sheppard, C.G.W., and Konnov, A.A. (2010) Laminar burning velocities of three C3H6O isomers at atmospheric pressure. Fuel, doi: 10.1016/j.fuel.2010.02.004, to appear. Bradley, D., Hicks, R.A., Lawes, M., Sheppard, C.G.W., and Woolley, R. (1998) The measurement of laminar burning velocities and Markstein numbers for iso-octane–air and iso-octane–n-heptane– air mixtures at elevated temperatures and pressures in an explosion bomb. Combust. Flame, 115, 126–144. Bradley, D., Lawes, M., Liu, K., Verhelst, S., and Woolley, R. (2007) Laminar burning velocities of lean hydrogen–air mixtures at pressures up to 1.0 MPa. Combust. Flame, 149, 162–172. Tabaczynski, R.J. (1976) Turbulence and turbulent combustion in spark ignition engines. Prog. Energy Combust. Sci., 2, 143–165.

32 Reuss, D.L. (2000) Cyclic Variability of

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Large-scale Turbulent Structures in Directed and Undirected IC Engine Flows. SAE Paper 2000-01-0246. Sirovich, L. (1989) Chaotic dynamics of coherent structures. Physica D, 37, 126–145. Fogleman, M., Lumley, J., Rempfer, D., and Haworth, D. (2004) Application of the proper orthogonal decomposition to datasets of internal combustion engine ﬂows. J. Turbulence, 5, 023. Daneshyar, H. and Hill, P.G. (1987) The structure of small-scale turbulence and its effect on combustion in spark ignition engines. Prog. Energy Combust. Sci., 13 (1), 47–73. Hill, P.G. and Zhang, D. (1994) The effects of swirl and tumble on combustion in spark-ignition engines. Progr. Energy Combust. Sci., 20 (5), 373–429. Semenov, E.S. (1963) Studies of turbulent gas ﬂow in piston engines, in Combustion in Turbulent Flow (ed. L.N. Khitrin), Israel Program for Scientiﬁc Translations, Jerusalem, pp. 122–147. Moriyoshi, Y., Kamimoto, T., and Yagita, M. (1993) Deﬁnition of turbulence in in-cylinder ﬂow ﬁelds. JSME Int. J. Ser. B, 36 (1), 172–177. Celik, I., Yavuz, I., Smirnov, A., Smith, J., Amin, E., and Gel, A. (2000) Prediction of in-cylinder turbulence for IC engines. Combust. Sci. Technol., 153, 339–368. Linse, D., Hasse, C., and Durst, B. (2009) An experimental and numerical investigation of turbulent ﬂame propagation and ﬂame structure in a turbo-charged direct injection gasoline engine. Combust. Theor. Model., 13 (1), 167–188. Lumley, J.L. (1998) Engines: an introduction, Cambridge University Press, Cambridge. Morel, T., Rackmil, C.I., Keribar, R., and Jennings, M.J. (1988) Model for Heat Transfer and Combustion in Spark Ignited Engines and its Comparison with Experiments. SAE Paper 880198. Ozdor, N., Dulger, M., and Sher, E. (1994) Cyclic Variability in Spark Ignition Engines: a Literature Survey. SAE Paper 940987.

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44 Aleiferis, P.G., Hardalupas, Y., Taylor,

A.M.P.K., Ishii, K., and Urata, Y. (2004) Flame chemiluminescence studies of cyclic combustion variations and airto-fuel ratio of the reacting mixture in a lean-burn stratiﬁed-charge spark-ignition engine. Combust. Flame, 136, 72–90. 45 Liu, K., Sheppard, C.G.W., Smallbone, A.J., and Woolley, R. (2004) The Inﬂuence of Simulated Residual and NO Concentrations on Knock Onset for PRFs and Gasolines. SAE Paper 2004-01-2998. 46 Hattrell, T., Sheppard, C.G.W., Burluka, A.A., Neumeister, J., and Cairns, A. (2006) Burn Rate Implications of Alternative Knock Reduction Strategies for

Turbocharged SI Engines. SAE Paper 2006-01-1110. 47 Beretta, G.P., Rashidi, M., and Keck, J.C. (1983) Turbulent ﬂame propagation and combustion in spark ignition engines. Combust. Flame, 52, 217–245. 48 Takagi, Y. (1998) A new era in sparkignition engines featuring high-pressure direct injection. XXVII Symposium (International) on Combustion, pp. 2055–2068. 49 Lebas, R., Menard, T., Beau, P.A., Berlemont, A., and Demoulin, F.X. (2009) Numerical simulation of primary breakup and atomization: DNS and modelling study. Int. J. Multiphase Flow, 35, 247–260.

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14 Diesel Combustion Öivind Andersson 14.1 Introduction to the Diesel Engine

In contrast to earlier engine inventors, Rudolf Diesel (1858–1913) had a deep theoretical knowledge of thermodynamics. His ﬁnal grades from the Technische Hochschule M€ unchen were the highest ever given at his graduation in 1890. He spent the next 10 years in Paris running the Swiss company Sulzers agency for ice machines. During this period, he thought about how Carnots thermodynamic theories could be used in a combustion engine. His ﬁrst engine concept used the same medium as the ice machines; ammonia. Despite spending all of his spare time and a large part of his private money on this engine, it never became useful [1]. In 1890 he moved to Berlin. At the same time, he shifted from ammonia to air as the working medium. According to Carnot, efﬁciency would be maximized if heat was added at a constant high temperature, and removed at a constant low temperature. Theoretically, an efﬁciency of 75% could be obtained. Diesel ﬁled a patent application for an engine working at two constant temperatures in 1892. The engine used pure air during compression and added the fuel after compression. His former employer, Sulzer, was not interested in investing in the development of this engine. Instead, he built the ﬁrst functioning prototype of the diesel engine in cooperation with Maschinenfabrik Augsburg AG in 1893. The company was later merged with Maschinenbau AG N€ urnberg and is today known as MAN. His engine was the ﬁrst to ignite the fuel by the heat of compression of the in-cylinder air. At an early stage, he started to doubt the principle of working at two constant temperatures, due to the technical limitations involved. His notes from 1893 rule out isothermal combustion in favor of isobaric combustion, which he believed was the right way forward. After this, continuous improvements were made to the prototype until 1897, when a brake efﬁciency of 30.2% was recorded. This was over 35% higher than the otto engines of the time [1]. Today, a diesel engine typically uses 25% less fuel than a gasoline engine in the same vehicle, if they have the same cylinder conﬁguration and displacement volume. This difference in fuel consumption is partly due to diesel fuel being denser than

Handbook of Combustion Vol.3: Gaseous and Liquid Fuels Edited by Maximilian Lackner, Franz Winter, and Avinash K. Agarwal Copyright Ó 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32449-1

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gasoline, containing about 10% more energy per unit volume, but also to an intrinsically higher efﬁciency of the diesel combustion process. Taking the energy densities of the fuels into account, a diesel car is about 17% more energy efﬁcient than its gasoline counterpart. These ﬁgures are based on ofﬁcial fuel consumption ﬁgures for European cars in 2008. The difference in efﬁciency is due to the diesel engine being operated at higher compression ratios and with a surplus of air, increasing the efﬁciency of the thermodynamic cycle. The part load pumping losses of gasoline engines add to the difference. These arise from regulating the load of gasoline engines by throttling the intake air ﬂow (see Vol. 3 Ch. 14). Comparisons between diesel and otto engines are most easily made in the European car market, as the share of diesel-powered cars in the European Union now exceeds 50% [2]. In view of increasing demands on fuel economy, due to increasing fuel prices and expected regulatory measures to cut CO2 emissions, an increased interest in diesels can be expected in other parts of the world also. The main drawback of the diesel engine has traditionally been its emissions, especially those of NOx and particulate matter (PM). However, the difference in emissions between diesel and gasoline engines decreases with each new European legislation level. By 2014, the Euro 6 standard will bring an almost complete convergence of diesel and gasoline emissions [3]. As shown in Figure 14.1, the European diesel car standards have approximately halved the emissions at each step during the last decade. This continuing development calls for the constant introduction of new technologies on a range of engine systems. The most important emissions from diesel engines are oxides of nitrogen (NOx) and PM. PM contains a solid part, in addition to soluble organic material and sulfuric acid. The solid part consists of carbon (soot) from the combustion process and ash compounds from lubrication oil additives and engine wear. Particles leaving the engine are, however, primarily soot particles formed during the combustion

Figure 14.1 Development of the European emissions regulation for cars from 2000 (Euro 3) to 2014 (Euro 6).

14.1 Introduction to the Diesel Engine

process [4], and this is the only part of PM that will be treated in this chapter. NOx emissions can arise through a number of mechanisms. In the combustion of fuels not containing nitrogen, oxidation of atmospheric nitrogen by the so-called thermal mechanism is the major source [5]. This mechanism is characterized by relatively slow reaction rates and a strong temperature dependence. On time scales typical of engines, signiﬁcant amounts of NOx are generally not produced until temperatures exceeding 1800–2000 K are reached. A classic dilemma in diesel combustion development is the trade-off between soot and NOx. Decreasing one of these emissions usually leads to an increase in the other. This is explained in textbooks by the different temperature dependences of these emissions. NOx formation is promoted by high local temperatures, conditions during which soot is oxidized; see, for example, [6]. This is, however, a simpliﬁed description that does not account for details in the formation mechanisms of these emissions. Figure 14.2 shows results from a computational ﬂuid dynamics (CFD) simulation of a typical light-duty diesel combustion cycle [7]. Soot and NOx concentrations apparently develop in different ways as a function of time. The NOx curve is dominated by a steep increase in the early parts of the cycle. A mixture that burns early is later compressed to higher temperatures and has time to form NOx via the relatively slow thermal mechanism. Thermal NOx is formed in stoichiometric or slightly lean mixtures. The soot curve, on the other hand, shows both a steep increase at the beginning of the cycle and a slower, but large, decrease during the expansion stroke. This is because soot is ﬁrst formed in fuel-rich zones under high-temperature conditions, and later oxidized in stoichiometric zones. As soot and NOx are formed through different mechanisms in different zones, trends in these emissions can, in principle, be decoupled. Apart from NOx and PM, the emissions regulations include unburned hydrocarbons (UHCs) and carbon monoxide (CO). These are traditionally less challenging

Figure 14.2 In-cylinder soot and NOx concentrations as a function of crank angle position. The data were obtained from CFD simulation of a typical light-duty diesel combustion cycle [7].

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for diesel engines than for gasoline engines. However, tightening NOx requirements call for decreases in the combustion temperature, for example, by using exhaust gas recirculation (EGR). At lower temperatures, UHCs and CO tend to increase, as these are the result of incomplete oxidation of the fuel. UHCs mainly arise from four sources: rich zones, where the oxygen deﬁcit impedes the oxidation process; zones too lean for complete oxidation; cold zones near walls; and fuel from the sac volume of the injector. CO mainly becomes problematic if the ﬁnal combustion stages have not been completed before the bulk temperature drops below approximately 1500 K in the expansion stroke. The oxidation of CO to CO2 then cannot be completed [6, 8]. Diesel engines are used in a wide range of applications, from large ship engines to small, portable power plants. Due to the large span in size and demands on these engines, their designs vary signiﬁcantly. To maintain a reasonable scope, the descriptions in this chapter focus on light duty engines (for cars) and heavy duty engines (typically used in trucks and construction machines), as they represent the largest share of the market.

14.2 Combustion System Characteristics

The diesel combustion process commences by injecting fuel into an atmosphere that has been compressed to a temperature high enough for self-ignition to occur. Today, fuel is always injected directly into the combustion chamber. Previously, so-called pre-chamber systems were common. These systems introduced the fuel into a small cavity adjacent to the main combustion chamber. As turbulence was generated when combustion spread into the main chamber, low injection pressures could be used. This was favorable when using mechanical injection systems. Pre-chamber systems were mainly used in light duty engines to reduce cost and engine noise. Since they used 10–15% more fuel than direct injection (DI) diesel engines, DI systems are now exclusively used despite their higher demands on fuel injection technology [9]. Modern diesel combustion systems consist of a cavity in the piston, where the main part of combustion takes place, and a multi-hole fuel injector mounted in the cylinder head. The injector nozzle is normally placed on the cylinder centerline and the fuel jets emanating from the nozzle holes are directed into the cavity. The intake ports are sometimes considered to be an important part of the combustion system, since the gas motion in the cylinder is of central importance to the combustion process. Peripheral systems for supercharging and cooling the intake air and EGR also have an important effect on combustion. This chapter focuses on the processes taking place between the intake and exhaust strokes. Therefore, these external systems are not treated here. A brief description of combustion system characteristics will be given, mainly covering combustion chamber types, the effects of the bore to stroke ratio and the compression ratio, the so-called k-factor, and a few words about gas motion. There are several reasons for placing the combustion chamber in the piston. One is that it allows a ﬂat cylinder head surface. This increases the mechanical strength,

14.2 Combustion System Characteristics

Figure 14.3 Schematic sections through the pistons in re-entrant (a) and open (b) combustion chambers: (1) bowl; (2) squish volume; (3) pip; (4) lip. The open combustion

chamber is drawn larger to indicate that it is predominantly used in heavy duty engines, whereas the re-entrant chamber dominates in light duty engines.

which is favorable for withstanding the high peak cylinder pressures prevalent at high loads. The cavity also has an important role in generating a gas motion that supports the combustion process. The combustion chamber has a number of characteristic features that are described schematically in Figure 14.3. The area above the piston top, outside the cavity, is denoted the squish volume. Inside the cavity – often called the bowl – there is a central pip below the nozzle. If the entrance to the bowl has a smaller diameter than the maximum bowl diameter (i.e., if it has a lip), it is called a re-entrant combustion chamber. If there is no lip, it is called an open combustion chamber. The re-entrant chamber is the dominant one in light duty engines, but both types may be encountered in both light and heavy duty engines. The re-entrant combustion system was invented in Switzerland by Hippolyt Saurer. His patent from 1934 describes a combustion system that is still surprisingly representative of most modern diesel engines, with an axisymmetric, re-entrant combustion chamber, a centrally mounted injector for direct injection, and a gas-motion typical of modern engines [10]. Saurers combustion system is depicted in Figure 14.4. Other deﬁning characteristics of the combustion system are the bore to stroke ratio and the compression ratio. Increasing the bore to stroke ratio at a ﬁxed cylinder

Figure 14.4 An image from Saurers patent from 1934, describing the principles of the re-entrant combustion system [10].

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volume allows larger inlet valves. This allows higher intake air mass ﬂow rates and higher engine power. The main effect of the resulting shorter stroke is decreased friction due to lower mean piston speeds. A larger bore allows a wider bowl. This could be advantageous at high engine speeds [11]. It should be noted that the bore to stroke ratio cannot be chosen freely as it affects the height and length of the engine, which are usually limited by vehicle constraints. Even differences of a couple of centimeters may be critical for ﬁtting an engine into a vehicle. Increasing the compression ratio increases the theoretical efﬁciency of the engine. It also increases the heat losses around top dead center (TDC) due to a larger surface to volume ratio. Reasonable variations in compression ratio are therefore expected to have a limited impact on efﬁciency [6]. However, lowering the compression ratio allows greater speciﬁc power at a given peak cylinder pressure [11]. This is the main reason for the current decreasing trend in diesel engine compression ratios. In 2009, 16:1 is a typical compression ratio, which is about four units lower than a decade ago. The lower compression ratio limit is set by demands on cold starting performance. For example, in light duty engines the limit steadily decreases as new glow plug technology becomes available. Diesel combustion chambers are sometimes evaluated using the so-called k-factor, deﬁned as the ratio between the volume inside the bowl and the total volume at TDC [11]. A high k-factor increases the portion of the air inside the bowl and is considered to be beneﬁcial for the air utilization. This is because air outside the bowl, for example in the squish volume, is assumed to be unavailable for the combustion process and for the oxidation of soot. Air utilization is, however, governed by complex transport phenomena in the bowl that make the practical relevance of the k-factor doubtful. As will be seen in Section 14.7, two well-optimized bowls that have exactly the same k-factor may still produce different smoke values at exactly the same operating conditions [7]. The intake air in diesel engines often has an organized, rotating motion about the cylinder axis, which is called swirl. Engines without swirl are said to have quiescent combustion systems. The latter are common in heavy duty engines employing open combustion chambers. Most light duty engines use swirl in combination with reentrant chambers, as described in Saurers patent from 1934 [10]. Saurers basic idea was to aid fuel–air mixing by injecting the fuel directly into a swirling air charge. By forcing the swirling air into the combustion chamber through a throttle (the narrower lip region), the ﬂow was supposed to accelerate. Swirl is still commonly described as an aid for fuel–air mixing [6, 9]. However, in modern diesel engines, the momentum in the spray is typically three orders of magnitude greater than that in the swirling gas ﬂow. Moreover, fuel–air mixing mainly occurs close to the nozzle, before the fuel starts to react. Rotational velocities are low in this region, and swirl cannot be expected to have a major effect on the air entrainment. Swirl can be expected to affect the late-cycle mixing needed to oxidize soot after the end of injection. This late cycle process is enhanced by a complex injection–swirl interaction earlier in the cycle [12], which will be described in Section 14.7. In quiescent combustion systems, there are, ideally, no energy losses associated with setting up the swirl motion. This often gives a higher volumetric efﬁciency but it

14.3 Diesel Fuel Injection

also increases the demands on the fuel injection system. For example, higher fuel pressures and smaller nozzle holes can be needed [9]. Another advantage of quiescent combustion systems is minimized heat transfer losses, since swirl enhances heat transfer to the combustion chamber walls by increasing the turbulence.

14.3 Diesel Fuel Injection 14.3.1 Mechanical Systems

The fuel injection system is of major importance for the diesel combustion process. For many decades, the injection pumps were the central elements of the system. The nozzle was in principle a passive, spring-loaded valve, opening and closing at certain pressure levels provided by the pump. In the 1990s, the market was dominated by inline pumps and distributor pumps. In-line pumps incorporated a camshaft driven by the engine, and one pumping element for each engine cylinder. Figure 14.5 shows a photograph of an inline diesel fuel injection pump. Each pumping element was connected to its nozzle through a long high-pressure pipe. They provided fuel pressures of up to 1150 bar and were mainly used in commercial vehicle engines

Figure 14.5 Inline diesel fuel injection pump from Delphi. Connections for high-pressure fuel pipes are situated on the top, above the pumping elements and the cam shaft (not visible).

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Figure 14.6 Rotary mechanical diesel fuel injection pump (DP310), a distributor-type pump from Delphi. Connections for high-pressure pipes are situated at the front.

developing about 35 kW l1 (the unit kW l1 refers to the speciﬁc power, that is, the ratio of the maximum rated power to the displacement volume) [9]. Distributor pumps were used in applications demanding compact and lightweight installations. They incorporated only one pumping element, regardless of the number of engine cylinders. The camshaft was replaced by a rotating cam plate or cam ring and fuel was delivered to the outlet ports of the pump using a distributor element. Figure 14.6 shows a photograph of a distributor-type pump with connections for high-pressure pipes at the front. Also here, long high-pressure pipes connected the outlet ports to the nozzles. They provided fuel pressures of up to 700 bar and were mainly used in car engines developing about 50 kW l1 [9]. In 2009, typical speciﬁc powers for light duty diesel engines are 55–65 kW l1. 14.3.2 Electronic Systems

Over the last decade, electronically actuated injection systems have become increasingly common. Common rail fuel injection systems now dominate in the car market as they offer many advantageous features. They consist of a high-pressure

14.3 Diesel Fuel Injection

Figure 14.7 The latest generation common rail injection system from Delphi, the so-called Direct Acting Diesel Common Rail System. The rail with the injectors is shown as (b) and the high-pressure pump as (c). (a) The electronic control unit and (d) a fuel filter.

accumulator (rail) mounted on the cylinder head. The rail is fed by a high-pressure pump driven by the engine, providing a constant supply of fuel at the desired pressure. Electronically actuated injectors are connected to the rail through short high-pressure pipes. Figure 14.7 shows a recent common rail system from Delphi. With common rail systems, the injection pressure is decoupled from the engine speed and the injected fuel quantity, as opposed to the older pump systems. Due to the constant pressure level, the fuel delivery is uniform and approximately proportional to the energizing time of the injector. Common rail systems produce higher injection pressures than the older, mechanical systems. They are ﬂexible in terms of the timing and number of injections, and allow free matching of the injection pressure to the engine operating mode [13]. Since the accumulator makes high pressure available throughout the cycle, very late injections are possible, as is required by certain exhaust gas aftertreatment technologies. At the time of this writing, commercial common rail systems deliver up to 2500 bar for heavy duty and 2000 bar for light duty engines. Current development of common rail systems focuses on decreasing the response time of the injector to increase the number of injections possible during a cycle, and to decrease the dwell time between them. Injectors with piezoelectric actuators have started to replace the original solenoid type. This, in combination with lighter injector needles, reduces the response time. As in solenoid injectors, the piezoelectric actuator still acts on a servo valve that opens the needle by the fuel pressure. However, direct-acting piezoelectric injectors are beginning to emerge on the market,

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Figure 14.8 Cutaway drawing of the direct acting diesel fuel injector from Delphi. The large element at the center is the piezoelectric stack which acts directly on the needle through a hydraulic coupling.

where the piezoelectric element acts directly on the needle [14]. This allows a completely free choice of dwell times between injections. Figure 14.8 shows a cutaway drawing of the direct-acting diesel fuel injector from Delphi. Another type of innovative injection system is the so-called unit injector. This combines the injection pump and the injection nozzle in a single unit, screwed directly into the cylinder head. The pumping element is driven by the engines cam shaft. A high-speed solenoid valve controls the start and end of injection electronically. The main advantages of these injectors are the potential for higher peak injection pressures than with common rail systems and the absence of external highpressure fuel pipes. Unit injectors are mainly used in heavy duty diesel engines [9]. Since the pressure is generated by a cam, high pressure is only available during a limited part of the cycle, limiting the use of multiple injections. New systems are under development that combine the unit injector with a pressure accumulator, allowing multiple injection events at extreme timings [15].

14.4 Diesel Engine Heat Release

In engines, the combustion process is often monitored by the rate of heat release as determined from the cylinder pressure trace. The heat release is normally divided into four phases, delimited by vertical lines in Figure 14.9. The ﬁrst period, between needle opening and the start of combustion, is called the ignition delay period. During this time the injected fuel is atomized into ﬁne droplets, heated and vaporized

14.4 Diesel Engine Heat Release

Figure 14.9 A typical rate of heat release diagram for a heavy duty diesel engine.

by mixing with hot air until a combustible mixture is formed. The length of the ignition delay period is affected by the ambient temperature and the quality (cetane number) of the fuel [16]. The second phase is the premixed burn, typically deﬁned to begin when the heat release rate turns positive. It is characterized by a sudden, steep increase in the cylinder pressure, which is the origin of the characteristic diesel sound. The combustible mixture prepared during the ignition delay now burns in a premixed volumetric reaction zone where the combustion rate is limited by the rate of the chemical reactions. When a large portion of this mixture has been consumed, the heat release rate temporarily drops, indicating the end of the premixed burn period. The amount of premixed burn depends on the amount of mixing during the ignition delay [16]. It takes place in fuel-rich zones in the leading portion of the developing diesel jet [17]. The phase from the end of the premixed burn until the end of fuel injection is the main mixing-controlled burn period. A turbulent diffusion ﬂame has now formed around the jet where combustion products from the rich, premixed reactions are consumed [17]. The rate of heat release rate is limited by the rate of mixing between air and these products. Detailed studies of the combustion process have revealed that premixed and mixing controlled combustion occur simultaneously in different parts of the jet [17], making the division into these different periods less exact than previously thought. The heat release rate merely indicates which type of combustion is dominating at different times. After the end of injection, remaining fuel or combustion intermediates burn in a diffusion ﬂame. This fourth period is called the late mixing-controlled burn. The main difference to the preceding period is that there is no spray to drive the mixing. The heat release rate therefore drops rapidly. Due to the expanding motion of the piston, the mixture cools, which may lead to poor combustion efﬁciency if the oxidation rate is too low [16].

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14.5 Some Useful Theoretical Concepts 14.5.1 Conceptual Model of Burning Diesel Jets

During the late 1990s, the picture of burning diesel jets changed drastically. Laser diagnostic techniques applied to an optical engine provided completely new information about the structure of the jets in DI diesel combustion systems. In an extensive imaging study of combustion species, and also of liquid and vapor fuel zones, a new picture emerged that refuted earlier descriptions. The resulting conceptual model is now well established and will be brieﬂy described as it provides a useful mental image of diesel combustion. A detailed description of the measurement techniques used can be found in [17] and references therein. The older descriptions had been based on spray studies under conditions that were not representative of modern diesel engines. Differences from these descriptions were found in the autoignition process and in the distribution of droplets and soot in the jet. The differences can be summarized as follows. First, it was found that autoignition occurred in fuel-rich mixtures throughout the leading portion of the jet. It was earlier believed to occur under stoichiometric conditions. Second, no fuel droplets were present in the reaction zones. It was earlier believed that combustion took place around individual fuel droplets. Third, soot was present throughout the downstream region of the reacting jet. Some earlier models described soot as forming primarily at the diffusion ﬂame around the jet periphery. Lastly, soot particles encountered in the upstream region of the jet were much smaller than those in the head vortex. This suggested a start of soot formation in the upstream region with formation and growth continuing as the soot moved through the jet [17]. The distributions of liquid, vapor, soot, and various reaction zones are depicted for a fully developed jet in Figure 14.10.

Figure 14.10 A conceptual model of a burning diesel jet. Adapted by J. Dec from [17].

14.5 Some Useful Theoretical Concepts

During the early spray development, it was found that the liquid fuel penetrated up to a certain distance. This distance decreased with increasing temperature, but was independent of injection pressure. As fuel vapor penetrated beyond this point, autoignition occurred in the leading portion of the jet, in a fairly uniform vaporphase region with fuel–air equivalence ratios ranging from 2 to 4. The process took place almost uniformly throughout this region, without ﬂame propagation, and was accompanied by a rapid rise in the heat release. Shortly after the fuel broke down, small soot particles formed throughout the fuel-rich downstream region of the jet. About midway through the premixed burn (as determined from the pressurederived heat release rate), a diffusion ﬂame formed at the jet periphery. In this same region, larger soot particles started to appear. The diffusion ﬂame extended around the jet periphery and back to a point close to the liquid penetration length. By the start of the mixing controlled heat release, the head vortex region had higher soot concentrations and larger particles. The concentrations and sizes were smaller upstream, as can be seen in the image of the fully developed jet in Figure 14.10. This suggested continuous formation and growth of soot particles in the central reaction zone [17]. In summary, the developed jet consisted of a liquid region close to the nozzle, a fuel-rich premixed reaction zone in the central region, and a turbulent diffusion ﬂame at the periphery. Soot formation and particle growth took place in the premixed zone, whereas soot oxidation and NOx formation occurred in the diffusion ﬂame. The earlier descriptions of diesel combustion agreed that the premixed combustion was stoichiometric and the major source of thermal NOx. The newer results showed that it could not be an important source of thermal NOx since it was fuel rich. Earlier descriptions also pointed to the diffusion ﬂame as the primary source of soot, which was also refuted by the new model. Soot and NOx are still considered to be the most important emissions from diesel engines. Thus, the conceptual model has added valuable knowledge about the formation mechanisms of these pollutants. Finally, it should be noted that Dec suggested that the model might change under different conditions, for example, if signiﬁcantly smaller nozzle holes were used [17]. 14.5.2 Air Entrainment

As we have seen, the rich premixed reaction zone in the center of the jet produces signiﬁcant amounts of soot. It has been suggested, and demonstrated experimentally, that a leaner premixed zone would produce less or no soot [18, 19]. This means that more air entrainment into the spray will decrease the soot formation rate. A brief account of the limiting factors for the air entrainment is given below, since it clearly is a parameter of central importance. The description presented here follows the analysis by Naber and Siebers [20]. They used an idealized model jet to derive a relationship for the air entrainment. The jet is assumed to be isothermal, incompressible, to have no velocity slip between the injected fuel and the ambient air, and to have a constant spreading angle, a. The jet is also assumed to have radially uniform velocity and fuel concentration proﬁles. This is

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Figure 14.11 Idealized model jet. Adapted from [21].

clearly a highly idealized model of a real jet. However, the analysis focuses on the most fundamental physics of the spray; the conservation of mass and momentum. As a consequence of these general principles, some factors are necessarily limiting for the air entrainment process. The idealized model jet is depicted in Figure 14.11. Fuel enters through the oriﬁce on the left and is assumed not to vaporize. As the fuel droplets reach the rightmost boundary of the control volume (dashed), a certain amount of air has been entrained (white arrows). The mass ﬂow of fuel at the oriﬁce is given by _ f ¼ rf Að0ÞUð0Þ m

ð14:1Þ

where rf is the fuel density, A(0) is the effective oriﬁce area, and U(0) is the injection velocity. The air mass ﬂow rate at x, the rightmost boundary of the control volume, is given by _ a ¼ ra AðxÞUðxÞ m

ð14:2Þ

where ra is the ambient air density, A(x) is the jet cross-sectional area, and U(x) is the jet velocity at x. The downstream location, x, is assumed to be large enough that the area occupied by the fuel droplets is negligible compared with A(x). The fuel mass ﬂow over the control volume boundary at x is the same as the mass ﬂow at the oriﬁce, but the entrained, quiescent air slows the jet. The jet velocity at x is given by the conservation of momentum: h i rf Að0ÞUð0Þ2 ¼ rf Að0ÞUð0Þ þ ra AðxÞUðxÞ UðxÞ

ð14:3Þ

where the left-hand side is the momentum ﬂux at the oriﬁce and the right-hand side is the momentum ﬂux at x. This can be solved for U(x) and inserted in Equation 14.2. An expression for the fuel to air mass ﬂow ratio at x is obtained by dividing Equation 14.1 by Equation 14.2. With some algebraic manipulation, this analysis gives a powerful scaling relationship for the mean equivalence ratio in the jet crosssection:

14.5 Some Useful Theoretical Concepts

2fs ¼ pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ ﬃ W 1 þ 16~ x 2 1

ð14:4Þ

where fs is the stoichiometric air-to-fuel mass ratio for the fuel and ambient air composition. The coordinate x~ is a non-dimensional axial distance from the oriﬁce: x~ ¼

x xþ

ð14:5Þ

where x is the physical distance from the oriﬁce and x þ is a characteristic length scale: rﬃﬃﬃﬃﬃ rf df r a ð14:6Þ xþ ¼ tanða=2Þ where df is the effective nozzle diameter (adjusted for cavitating ﬂow) and a=2 is half the spreading angle of the model jet, as deﬁned in Figure 14.11. Although Equation 14.4 was developed for a non-vaporizing spray, it also applies to vaporizing sprays. Naber and Siebers found that the differences in penetration speed and spreading angle between vaporizing and non-vaporizing sprays were small [20]. As the jet deceleration depends on the air entrainment – through the conservation of momentum – this indicates very similar air entrainment in the two cases. A number of interesting conclusions can be drawn from Equations 14.4–14.6. The decreases with increasing distance from most obvious one is perhaps that W the nozzle, that is, more air is entrained over a longer distance. Equation 14.6 also implies that . . .

Air entrainment increases strongly with decreasing oriﬁce diameter. Air entrainment has a weaker dependence on the density ratio between fuel and ambient gas. There is no explicit dependence on injection pressure.

The last point is contrary to a belief that seems to be common among engineers. The spreading angle, a, depends mainly on oriﬁce ﬂow effects, such as cavitation, and on the density ratio between fuel and ambient gas [21]. A larger jet spreading angle reﬂects more air entrainment, as more air is present inside a wider cone. In summary, the nozzle hole diameter and the ambient gas density are the main factors affecting the air entrainment. 14.5.3 Flame Lift-Off

Fuel jets in diesel engines burn as lifted ﬂames. As previously described, the ﬂame consists of a rich, premixed reaction zone at the center and a turbulent diffusion ﬂame at the jet periphery. The lift-off length is deﬁned as the distance between the nozzle oriﬁce and the upstreammost part of the diffusion ﬂame. For example, in Figure 14.10 the lift-off length is approximately 18 mm. A detailed treatment of ﬂame lift-off can be found elsewhere (see Vol. 3 Ch. 5).

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Air is entrained over the full length of the jet. Downstream from the lift-off length, however, most of the entrained air is consumed in the diffusion ﬂame. The portion of the fuel energy that is released in the central, fuel-rich zone is thereby largely limited by the amount of air entrained upstream of the lift-off length. Factors affecting the air entrainment and the lift-off length will therefore affect the extent of the premixed reaction. Fuel concentration measurements and air entrainment estimates suggest that about 20% of the air required to burn the fuel is entrained upstream of the lift-off length [22]. As previously mentioned, the amount of air present in the premixed reaction zone is of central importance for soot formation. The equivalence ratio in the premixed zone depends both on the air entrainment rate and on the lift-off length, since a longer lift-off length allows air entrainment over a longer distance. Numerous investigations of the lift-off length have been made in optical engines and spray chambers. The diffusion ﬂame is often imaged using natural chemiluminescence from hydroxyl radicals, OH, which are abundant in the high-temperature diffusion ﬂame (see Vol. 2 Ch. 1). A comprehensive experimental database shows that the lift-off length increases as the injector oriﬁce diameter or the injection pressure increases. More speciﬁcally, it scales with the square root of the pressure drop over the nozzle, that is, the lift-off length has a linear relationship with the injection velocity, U. Furthermore, the lift-off length decreases when . . .

the ambient temperature increases the ambient density increases the ambient oxygen concentration increases. The lift-off length, H, scales with these parameters according to H / Ta3:74 r0:85 d0:34 UZst1 a

ð14:7Þ

where Ta is the temperature of the ambient gas, ra is the ambient density, d is the nozzle oriﬁce diameter, U is the injection velocity, and Zst is the stoichiometric mixture fraction, that is, the ratio of the fuel mass to the total mass of fuel and ambient gas in a stoichiometric mixture. These dependences have been established empirically in a constant-volume combustion vessel [23], but a fundamental understanding of the underlying mechanisms is still lacking. Further research is also needed to clarify the effects of fuel parameters on the lift-off length, such as cetane number. Furthermore, in an engine, effects of adjacent jets on multi-hole injectors, effects of the piston bowl, and effects of the in-cylinder ﬂow may become important.

14.6 Heavy Duty Combustion Systems

Customers buying trucks tend to look upon them as on any other investment. A main criterion is the total cost of ownership. There are high requirements on the operating costs, including fuel economy, and also on durability, ease of maintenance, and high

14.6 Heavy Duty Combustion Systems

up-time [24]. For the engine and its combustion system, this means that efﬁciency and robustness are of major importance. Another factor driving the development of truck engines is emissions legislation. New and upgraded engines tend to be launched when new legislation levels are introduced. Heavy duty diesel engines tend to be operated at high loads where the long injection durations produce a combustion process that is largely spray driven. A useful picture for understanding emissions formation in these engines is that of a lifted ﬂame on a stationary jet. This is because the diesel spray can be considered to behave as a quasi-stationary jet during a large part of combustion. The descriptions in the preceding section are therefore useful for understanding the combustion and emissions formation processes in these engines. We have, however, only considered single jets. In engines, several jets burn in a conﬁned space and interact with the combustion chamber wall and also with each other, as can be seen in Figure 14.12. As the burning jet impinges on the bowl wall, it is deﬂected and continues to expand along the bowl perimeter as a wall jet. Eventually, the wall jets of adjacent fuel jets will meet. The ﬂame will then be pushed back towards the nozzle between the two jets, along with hot combustion products. It has been shown that when combustion products interact with the incoming jet, the lift-off length is shortened and soot formation increases [25]. This may partially explain the observation of shorter lift-off lengths in engines compared with those of single, free jets in large combustion chambers [26].

Figure 14.12 Photograph of the quasi-stationary spray phase in an optical diesel engine. Eight sprays emanate from the centrally mounted nozzle, and the interaction between neighboring sprays is evident around the periphery. Photograph courtesy of Lund University, Sweden.

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The wall interaction may also have a direct effect on soot formation. In an experiment comparing free jets with jets impinging on walls, it was shown that soot formation downstream of the impingement location was reduced by a factor of two compared with the free jet. This could be explained by the cooling effect of the wall, which would reduce the soot formation rate, or by increased air entrainment due to the impingement [25]. Another experiment has shown that impingement indeed may increase mixing in the wall jet, especially at higher injection pressures. In the free part of the jet, air entrainment was unaffected by the wall. At the impingement position, air entrainment was reduced compared with a free jet but increased again in the wall-jet region. Using an injection pressure of 1500 bar, the overall mixing was the same as in a free jet, but at 2000 bar it was increased [27]. In short, the wall interaction in itself seems to reduce soot formation. The in-cylinder ﬂow has been shown to have an effect on the lift-off length. In an engine with a swirl ratio, Rs, of 0.5, the measured lift-off length was on average 7% longer on the windward, or upswirl side of the jets [26]. In engines, there are also cycle-to-cycle variations in the lift-off length that may reduce the practical potential of concepts that attempt to reduce soot formation by increased mixing upstream of the lift-off position. In the engine with Rs ¼ 0.5 the lift-off length varied by 20–30% on a cycle-to-cycle basis [26], whereas in an engine with Rs ¼ 2.18 it varied by only 5–10% [28]. The swirl number in itself does not seem to explain the extent of the variations and it is unclear by what mechanism they are induced. One possible explanation is that lower swirl levels decreases the stability of the ﬂow structures and induces variation. The combustion chambers of heavy duty diesel engines are often open, that is, there is no lip at the entrance to the bowl. Using less complex bowl geometries reduces the heat transfer by reducing the area to volume ratio at TDC. The combustion system is also often quiescent, which further reduces the heat losses compared with a swirl-supported system. This is beneﬁcial for efﬁciency and for reducing the heat load at the piston top, which can be substantial in high load operation. The function of the lip in re-entrant, swirl-supported systems is to trap the rotating air in the bowl. Without the lip, the centrifugal force would drive this air into the squish volume during expansion, thus impeding the late-cycle mixing within the bowl. The air motion in heavy duty engines is dominated by the kinetic energy of the spray. This reduces the need for swirl and, thus, for re-entrant combustion chambers.

14.7 Light Duty Combustion Systems

Cars are consumer products and the demands on light duty engines are thereby more complex than those on heavy duty engines. Performance in terms of rated power, torque characteristics, and engine response is important for many customers. Driving comfort may also be important, for example, in terms of vibrations and combustion noise, or adequate low-end torque to reduce the need for kick-down during acceleration. With increasing fuel prices, fuel economy becomes increasingly

14.7 Light Duty Combustion Systems

important. However, while the cost of ownership may be important in certain segments of the car market, it may be a low priority to other customers. In summary, the car market is driven more by a multi-faceted and subjective experience of ownership than by the objective considerations characterizing the heavy duty engine market. Although these customer demands certainly affect the requirements on the combustion system and its supporting systems, the development is ﬁrst and foremost driven by the emissions regulations. The duty cycle of light duty diesel engines is weighted toward light loads and urban driving. Injections thereby tend to be short and the picture of a quasi-stationary jet is no longer adequate. In fact, the major part of the heat release may take part after the end of injection. Accordingly, processes supporting the late-cycle oxidation are more important for the engine-out PM emissions than in heavy duty engines. A typical heat release rate in a light duty diesel engine has a long tail after the main heat release, well after the end of injection. This is the slow, mixing-controlled burn of combustion products formed earlier in the cycle, for example, soot. If this tail were shifted toward TDC, the thermodynamic efﬁciency would increase. Emissions would decrease, since enhanced oxidation permits less partially oxidized products to survive into the exhaust port. The turbulence generated during fuel injection is located in the direct vicinity of the spray and decays rapidly after the end of injection. Therefore, the spray cannot deliver all the kinetic energy needed to drive the late-cycle mixing. However, swirlsupported, re-entrant combustion systems have a mechanism by which a fraction of the kinetic energy of the fuel injection can be stored in the swirling ﬂow. This energy can later be released as turbulence to promote the late-cycle mixing [12, 29, 30]. 14.7.1 Enhancing Late-Cycle Mixing

The mechanism can be understood using a simpliﬁed picture. Consider a case where the swirling air motion in the bowl is described by a solid-body rotation about the cylinder centerline. The effect of the fuel injection event is treated very simply: a portion of the air at the center is entrained into the jets and transported to the periphery, as shown in Figure 14.13. The remaining air is thereby displaced towards the center. Due to the rotational motion, this redistribution involves performing work against the centrifugal force. After the redistribution, both air masses still display solid-body rotations but, since the angular momentum of the masses is conserved, they rotate at different rates. If the fraction of the air displaced by the jets is denoted f, it can be shown that the redistribution increases the mean ﬂow rotational kinetic energy, Ek, according to Ek;before f3 ¼ þ ð1 þ f Þ2 Ek;after 2f

ð14:8Þ

There is clearly a signiﬁcant increase in kinetic energy even for moderate amounts of displaced air.

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Figure 14.13 Simplified picture of the fluid redistribution due to injection. Fluid with low angular momentum is transported to the bowl periphery, forcing high angular momentum fluid inward [12].

A more detailed description of this interaction between injection and swirl is given in Figure 14.14, showing a vertical section through the bowl from the center to the cylinder wall. Assuming solid-body rotation, the air close to the cylinder wall has the highest tangential velocity during the compression stroke. Air on the cylinder centerline has the lowest tangential velocity. As the piston approaches TDC, the air in the squish volume is displaced into the bowl. It remains in the outer regions of the bowl due to the centrifugal force associated with its rotation (black zone in Figure 14.14a). The central parts of the bowl still contain air with low tangential velocity (light gray zone). The fuel injection changes the picture, since air with low tangential velocity is entrained and transported by the jets to the bowl perimeter. The high tangential velocity air is thereby displaced downward-inward along the combustion chamber ﬂoor. After injection, the outer region of the bowl thus contains low tangential velocity ﬂuid. The high tangential velocity ﬂuid resides closer to the center, as can be seen in Figure 14.14b [7, 12]. There are three major consequences of this process. First, the interaction of the fuel jets with the ﬂow swirl creates a toroidal vortex in the lower, outer portion of the bowl that transports partially oxidized fuel inwards along the bowl ﬂoor, then

Figure 14.14 A more detailed picture of the fluid redistribution in the bowl due to injection. Half the bowl region is depicted, from the cylinder centerline to the cylinder wall. The black regions correspond to gas with high tangential

velocity. The lighter gray the color, the lower the tangential velocity of the gas. Parts (a) and (b) correspond to the situations before and after injection, respectively.

14.7 Light Duty Combustion Systems

upwards along the bowl pip (lower arrow in Figure 14.14b). Second, an additional vortex forms in the upper central region of the bowl that transports fresh air from the central parts of the bowl toward the partially oxidized fuel. Thus, a turbulent stagnation plane is formed where the combustion intermediates and soot are oxidized in a diffusion ﬂame [7, 12]. Third, at the location of the stagnation plane, ﬂow deformation due to steep gradients in the tangential velocity, and also gradients in the vertical and radial velocities formed by the vortex pair, increases the kinetic energy of the turbulence, which further promotes rapid oxidation. This is the mechanism by which the kinetic energy stored in the rotational ﬂow is released as turbulence [12]. In summary, the interaction between the injection and the swirling ﬂow inﬂuences the combustion in two important ways. First, large-scale ﬂow structures are formed that transport unburned or partially burned fuel to regions where sufﬁcient O2 is available to complete the combustion process. Second, ﬂuid deformation due to the large-scale structures generates additional turbulence, which accelerates the mixing of the partially burned fuel with the fresh air. The enhanced mixing, in turn, promotes more rapid combustion and oxidation of particulates. These mechanisms are of crucial importance for the performance of light duty, swirl-supported diesel combustion systems. 14.7.2 Effects of Swirl and Bowl Geometry

The complexity of the late-cycle ﬂow processes is illustrated by Figure 14.15, which shows numerical simulation results for two swirl ratios, Rs. The vectors represent the

Figure 14.15 Bulk flow structures formed in the bowl of a typical light duty diesel engine. At the higher Rs a dual vortex structure is formed (b) whereas at the lower Rs only one vortex is formed (a) [31].

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Figure 14.16 Effect of the bowl geometry on the air utilization. The interaction between the secondary (upper) vortex and regions rich in O2 [red areas in (b) and (d)] creates a larger interface between O2 and soot when using a flatter pip [7].

in-plane ﬂow-ﬁeld. At the higher Rs, that is, in (b), the dual vortex structure is formed as previously described. It creates a stagnation plane between partially burned fuel (represented by CO) and O2. At the lower Rs, the structures fail to form. This impedes the formation of a stagnation plane and, thereby, the late-cycle oxidation process [31]. The reason that the secondary (upper) vortex is not formed is that the lower Rs decreases the centrifugal force acting on the rotating ﬂuid. The primary vortex then becomes larger and the secondary vortex vanishes. The effect of the bowl geometry on the ﬂow structure is illustrated by the simulation results in Figure 14.16. Images (a) and (b) show a combustion chamber with a pronounced central pip, whereas in (c) and (d) the bowl has a ﬂatter pip. These two bowls set up slightly different ﬂow patterns in the bowl. The images in (a) and (c) show tangential velocity in color and in-plane velocities as arrows. Dual vortex structures are present in both bowls. (b) and (d) show O2 in color and soot as iso-contours. With the ﬂatter pip [(c) and (d)], the secondary vortex has interacted with the O2-rich regions to form an extended interface with the regions containing soot. This is beneﬁcial for soot oxidation. The bowl in (a) and (b) is less successful at forming this interface and most of the soot resides far from the O2. There are no ﬂow structures present to further promote mixing between O2 and soot, and the late-cycle oxidation is impeded [7]. The simulations were made using identical operating conditions, apart from the bowl geometries. The two geometries further

14.8 Means of in-Cylinder Emissions Control

Figure 14.17 In-cylinder soot mass as function of crank angle position for the two bowl geometries in Figure 14.16. The improved late-cycle oxidation with the flat pip is explained by the extended interface between O2 and soot shown in Figure 14.16 [7].

had the same compression ratio, bowl volume, and, hence, the same k-factor (see Section 14.2). Still, the air utilization is different in the two bowls. This demonstrates how a simpliﬁed measure such as the k-factor can be misleading if used without caution. Figure 14.17 shows the total in-cylinder soot mass obtained from the simulations of the two geometries in Figure 14.16. The enhanced late-cycle oxidation in the bowl with the ﬂatter pip is readily visible [7].

14.8 Means of in-Cylinder Emissions Control

One of the most striking trends in diesel engine technology is the constant increase in peak injection pressures. This is a well-established means to decrease PM emissions. Whether it affects the formation or the oxidation of soot is a complex question. As seen in previous sections, an increased injection pressure does not increase the air entrainment in the free part of the jet. It does increase the lift-off length, which may lead to a leaner premixed reaction zone on quasi-stationary jets [22, 23, 32]. This could explain the PM trends by decreasing soot formation rates. Another plausible explanation is that the decreased residence time in the jet that follows from higher injection velocities decreases the time for soot formation [23, 31, 33]. Increased jet velocities may also increase the turbulent mixing in the downstream regions of the jet, enhancing the soot oxidation [28]. This is supported by results showing increased air entrainment in the wall-jet region of impinging fuel jets when the injection pressure is increased [27]. A common side effect of elevated injection pressures is increased NOx emissions, due to increased rates of heat release.

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According to the discussion about emissions in the Introduction to this chapter, there seem to be at least two possible methods to decrease soot independently of NOx. First, we note that soot and NOx are formed in different portions of the charge. If the richest portions of the charge were removed, soot production would decrease independently of NOx production. Second, we note that the soot formed is consumed in the late cycle. If the late-cycle oxidation were enhanced, soot emissions would also decrease independently of NOx. The ﬁrst method is employed in so-called low-temperature combustion (LTC) concepts, for example, in [34–38]. A long ignition delay allows a longer mixing period, thus decreasing the peak local equivalence ratios. In these systems, which are still rare in production vehicles, long ignition delays are produced using high levels of cooled EGR. This also decreases NOx, but it often slows the late-cycle burn and can lead to poor efﬁciency. Another side effect of this strategy is increased emissions of CO and UHCs. This is due to the lower temperatures during combustion induced by the diluted charge and, in some cases, late combustion phasing. A detailed treatment of LTC is given in (Vol. 5, Ch. 2). Other ways of reducing the peak equivalence ratios during combustion are described in the discussion about air entrainment. Using smaller diameter oriﬁces in the nozzle enhances the air entrainment rate into the spray. This also affects the lift-off length in a direction that enhances the effect on the equivalence ratio. To maintain the maximum fueling rate (i.e., full load performance) with the smaller oriﬁces, one can increase the injection pressure, or simply increase the number of oriﬁces in the nozzle. There is a practical limit to the minimum oriﬁce diameter caused by the increased risk for deposits; see for example, [39]. In addition to the oriﬁce diameter, the TDC density also has a signiﬁcant effect on air entrainment. However, it also affects the lift-off length in a way that may slightly counteract its effect on the equivalence ratio [28]. A different strategy for decreasing soot emissions is to enhance the late-cycle oxidation, for example, by modiﬁcations to the combustion system geometry. The aim is to assure that the soot formed is exposed to levels of oxygen and turbulence that are adequate for fast oxidation. This approach offers additional advantages through its potential to increase the efﬁciency of the engine without increasing NOx [7]. It can be applied to both light and heavy duty combustion systems. Soot emissions may also be decreased by injecting a small amount of fuel after the main injection, a so-called post injection. Although the post-injection increases the temperature when it burns, the enhanced soot oxidation is probably a result of increased turbulence and mixing [40]. Post-injections can be applied without losing efﬁciency. In light duty engines, they are usually most effective at light to medium loads. In heavy duty engines, they can be applied at higher loads as the lower engine speed provides time for the post-injection to be effective during the late cycle. The analysis of diesel combustion is challenging in many different ways. We are faced with non-equilibrium thermodynamics, chemical kinetics, unsteady ﬂuid dynamics, multi-phase ﬂows, and turbulence – all coupled in one problem. In the limited spaceof a single chapteronecanonlyhope to drawa briefsketch. Hopefully, this chapter can serve as an inspiration for further studies into this complex and intriguing ﬁeld.

References

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Society of Automotive Engineers, Warrendale, PA. Smolinska, J. (2008) EU Economic Report February 2008, ACEA, Brussels. European Commission (2007) Regulation (EC) No. 715/2007 of the European Parliament and of the Council. Majewski, W.A.Diesel Particulate Matter, DieselNet Technology Guide; http:// www.dieselnet.com/tech/dpm.html. Miller, J.A. and Bowman, C.T. (1989) Mechanism and modelling of nitrogen chemistry in combustion. Prog. Energy Combust. Sci., 15, 287–338. Heywood, J.B. (1988) Internal Combustion Engine Fundamentals, McGraw-Hill, New York. Andersson, Ö., Somhorst, J., Lindgren, R., Blom, R., and Ljungqvist, M. (2009) Development of the Euro 5 Combustion System for Volvo Cars 2.4 l Diesel Engine, SAE Paper 2009-01-0167. Kim, D., Ekoto, I., Colban, W.F., and Miles, P.C. (2008) In-cylinder CO and UHC Imaging in a Light-duty Diesel Engine During PPCI Low-temperature Combustion, SAE Paper 2008-01-1602. Beer, A. (ed.) (1994) Diesel Fuel Injection, Robert Bosch, Stuttgart. Saurer, H. (1934) Improvements in and relating to internal combustion engines of the liquid fuel injection type, Patent GB421101. Fasolo, B., Doisy, A.-M., Dupont, A., and Lavoisier, F. (2005) Combustion System Optimization of a New 2 Liter Diesel Engine for EURO IV, SAE Paper 200501-0652. Miles, P.C., RempelEwert, B.H., and Reitz, R.D. (2003) Squish-swirl and injection-swirl interaction in directinjection diesel engines. Proceedings of ICE 2003 – 6th International Conference on Engines for Automobile, Capri. Dietsche, K-.H. and Crepin, J. (eds) (1999) Diesel Engine Management, Robert Bosch GmbH, Stuttgart Sch€ oppe, D., Hardy, M., Z€ ulch, S., and Geurts, D. (2008) Exceeding customer expectations with the innovative direct

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acting diesel fuel injection system: unique advantages of a breakthrough technology. 29. Internationales Wiener Motorensymposium. Tullis, S., Greeves, G., Draper, D., Milovanovic, N., and Zuelch, S. (2007) Advanced Hybrid Electronic Unit Injector with Accumulator for Enhanced Multiple Injection and Ultra High Injection Pressure Capability, SAE Paper 200701-1895. Hsu, B.D. (2002) Practical Diesel Engine Combustion Analysis, Society of Automotive Engineers, Warrendale, PA. Dec, J.E. (1997) A Conceptual Model of DI Diesel Combustion Based on Laser Sheet Imaging, SAE Paper 970873. Pickett, L.M. and Siebers, D.L. (2004) Non-sooting, Low Flame Temperature Mixing-controlled DI Diesel Combustion, SAE Paper 2004-01-1399. Chartier, C., Aronsson, U., Andersson, Ö., Egnell, R., Collin, R., Seyfried, H., Richter, M., and Alden, M. (2009) Analysis of Smokeless Spray Combustion in a Heavy Duty Diesel Engine by Combined Simultaneous Optical Diagnostics, SAE Paper 2009-01-0286. Naber, J.D. and Siebers, D.L. (1996) Effects of Gas Density and Vaporization on Penetration and Dispersion of Diesel Sprays, SAE Paper 960034. Siebers, D.L. (1999) Scaling Liquid-phase Fuel Penetration in Diesel Sprays Based on Mixing-limited Vaporization, SAE Paper 1999-01-0528. Siebers, D.L. and Higgins, B.S. (2001) Flame Lift-off on direct-injection diesel sprays under quiescent conditions. Trans. SAE, 110 (3), 400–421. Pickett, L.M., Siebers, D.L., and Idicheria, C.A. (2005) Relationship Between Ignition Process and the Lift-off Length of Diesel Fuel Jets, SAE Paper 2005-01-3843. Dollmeyer, T.A., Vittorio, D.A., Grana, T.A., Katzenmeyer, J.R., Charlton, S.J., Clerc, J.C., Morphet, R.G., and Schwandt, B.W. (2007) Meeting the US 2007 Heavyduty Diesel Emission Standards – Designing for the Customer, SAE Paper 2007-01-4170.

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Jet–Wall Interaction Effects on Diesel Combustion and Soot Formation, SAE Paper 2005-01-0921. Musculus, M.P.B. (2003) Effects of the In-cylinder Environment on Diffusion Flame Lift-off in a DI Diesel Engine, SAE Paper 2003-01-0074. Bruneaux, G. (2005) Mixing Process in High Pressure Diesel Jets by Normalized Laser Induced Exiplex Fluorescence Part II: Wall Impinging Versus Free Jet, SAE Paper 2005-01-2097. Aronsson, U., Chartier, C., Andersson, Ö., Egnell, R., Sj€oholm, J., Richter, M., and Alden, M. (2009) Analysis of the Correlation Between Engine-out Particulates and Local F in the Lift-off Region of a Heavy-duty Diesel Engine Using Raman Spectroscopy, SAE Paper 2009-01-0284. Miles, P., Megerle, M., Hammer, J., Nagel, Z., Reitz, R.D., and Sick, V. (2002) Latecycle Turbulence Generation in Swirlsupported, Direct-injection Diesel Engines, SAE Paper 2002-01-0891. Miles, P.C. (2008) Turbulent ﬂow structure in direct-injection, swirlsupported diesel engines, in Flow and Combustion in Reciprocating Engines (eds. C. Arcoumanis and T. Kamimoto), Springer, Berlin, Chapter 4. Miles, P.C., Hildingsson, L., and Hultqvist, A. (2006) The inﬂuence of fuel injection and heat release on bulk ﬂow structures in direct-injection, swirlsupported diesel engines. 13th International Symposium on Applications of Laser Techniques to Fluid Mechanics, Lisbon, Portugal, 26–29 June, 2006. Pickett, L.M., Caton, J.A., Musculus, M.P.B., and Lutz, A.E. (2006) Evaluation of the equivalence ratio–temperature region of diesel soot precursor formation using a two-stage Lagrangian model. Int. J. Engine Res., 7, 349–370.

33 Pickett, L.M. and Idicheria, C.A. (2006)

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Effects of ambient temperature and density on soot formation under high EGR conditions. Paper presented at Thermo- and Fluid-Dynamic Processes in Diesel Engines: THIESEL, Valencia, Spain. Kimura, S., Aoki, O., Kitahara, Y., and Aiyoshizawa, E. (2001) Ultra-Clean Combustion Technology and Premixed Combustion Concept for Meeting Future Emission Standards, SAE Paper 200101-0200. Akihama, K., Takatori, Y., Inagaki, K., Sasaki, S., and Dean, A.M. (2001) Mechanism of the Smokeless Rich Diesel Combustion By Reducing Temperature, SAE Paper 2001-01-0655. Walter, B. and Gatellier, B. (2002) Development of the High-power NadiSt Concept Using Dual-mode Diesel Combustion to Achieve Zero NOx and Particulate Emissions, SAE Paper 2002-01-1744. Weissb€ack, M., Csató, J., Glensvig, M., Sams, T., and Herzog, P. (2003) Alternative combustion, an approach for future HSDI diesel engines. MTZ Motortech. Z., 64, 718–727. Hasegawa, R. and Yanagihara, H. (2003) HCCI Combustion in a DI Diesel Engine, SAE Paper 2003-01-0745. Argueyrolles, B., Dehoux, S., Gastaldi, P., Grosjean, L., Levy, F., Michel, A., and Passerel, D. (2007) Inﬂuence of Injector Nozzle Design and Cavitation on Coking Phenomenon, SAE Paper 2007-01-1896. Helmantel, A., Somhorst, J., and Denbratt, I. (2003) Visualization of the effects of post injection and swirl on the combustion process of a passenger car common rail DI diesel engine. International Combustion Engine Division (ICES03) Spring Technical Conference of the ASME, Salzburg, Austria.

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15 Oil Outlook Torsten Clemens 15.1 Introduction

About 36% of the world energy consumption is fueled by oil [1]. This share is expected to slightly decrease in the future. Oil is by far the most important energy carrier for transportation, accounting for almost 70% of the fuel used (see also Vol. 3, Ch. 10)]. However, oil is inevitably a ﬁnite resource. It has been generated during millions of years and consumed for the last 150 years. From 1998 to 2008, oil prices increased rapidly from less than $10 per barrel (bbl) to more than $140/bbl. This sharp increase in price triggered discussions concerning peak-oil. Peak-oil refers to a forecast of a maximum oil production due to the limited amount of oil available. From this point in time onwards, oil production will decrease and oil prices increase accordingly. This peak in oil production will inevitably come. However, large differences exist concerning the expectation of when this maximum oil production will occur [2, 3]. This chapter sheds light on oil resources and production. It is based on the analysis of the International Energy Agency (IEA) for the oil production forecast and other sources for the oil reserves, resources, and technologies to produce oil. The next section describes how oil is formed and how it migrates. Section 15.3 deals with classiﬁcation of oil into resources and reserves. Then in Section 15.4 oil resources are described in more detail, followed by a section on oil production and reserves history (Section 15.5). Section 15.6 covers an oil production forecast. The next section (Section 15.7) illustrates the impact of CO2 emissions, then the oil price outlook is given (Section 15.8), and the chapter is concluded with a discussion and summary section (Section 15.9).

15.2 Formation and Migration of Oil

Hydrocarbons such as oil and natural gas are formed from organic material. This material, the remains of plants and animals that lived tens to hundreds of millions of years ago, sank to the bottom of the sea. There, the material was covered with mud, Handbook of Combustion Vol.3: Gaseous and Liquid Fuels Edited by Maximilian Lackner, Franz Winter, and Avinash K. Agarwal Copyright Ó 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 978-3-527-32449-1

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sand, and other mineral deposits. This rapid burial prevented the immediate decay which would normally occur if organisms remained exposed on the sea ﬂoor. The lack of oxygen in the sedimentary layers caused the organisms to decay slowly into carbon-rich compounds. These compounds became mixed with sediments deposited at the same time and formed carbon-rich sediments. These carbon-rich sediments were buried by other sediments. Due to continuous subsidence of the organic-rich sediments and lack of oxygen in these layers, the temperature and pressure increased and the organic matter changed chemically into kerogen. The organic-rich sediments were transformed into so-called source rocks, typically ﬁnegrained carbon-rich shales. For oil to be formed, the shales have to reach a certain temperature. Below this temperature, oil remains trapped in the form of kerogen. Above the maximum temperature, oil is cracked further into natural gas. The range of temperature at which oil is formed is called the oil window. Typically, this oil window is at depths between 4 and 6 km. Under these conditions, oil is lighter than water. Hence, over geological times, the generated oil moves upward until it reaches either the surface or is trapped by impermeable rocks (e.g., tight shales, salt). In order to be producible, the rocks in which the oil is trapped must have a high enough permeability to allow economic oil production rates of the wells. In particular, if the oil was accumulated in shallow traps (at a depth from the subsurface of less than 1000 m), the oil is often chemically altered by bacteria. This alteration leads to highly viscous oil (heavy oil, bitumen). The lighter components were used by the bacteria and the heavier components of the oil remain. This type of oil is called unconventional oil and is found in large quantities in Canada [4] and Venezuela [5]. The next section describes how the oil is classiﬁed into reserves and resources. The subsequent paragraphs build on this classiﬁcation.

15.3 Oil Reserves Classification

A large amount of hydrocarbons are present in the Earths crust. Accumulations of hydrocarbons are classiﬁed into reserves and resources based on the commercial viability of producing these quantities. Recently, a number of world-wide active societies deﬁned a standard for the classiﬁcation of such hydrocarbons into resources and reserves. This standard is called the Petroleum Resources Management System (PRMS) [6] and was developed by the Society of Petroleum Engineers (SPE), American Association of Petroleum Geologists (AAPG), World Petroleum Congress (WPC), and Society of Petroleum Evaluation Engineers (SPEE). It has become an internationally widely used standard. For reporting reserves to the ﬁnancial markets, companies often adhere to the requirements set by the Securities and Exchange Commission (SEC) in the United States. In addition, the United Nations (UN) is developing a new standard.

15.3 Oil Reserves Classification

Due to the wide use of the PRMS, the following paragraphs describe this standard in more detail. Petroleum is deﬁned as a naturally occurring mixture consisting predominantly of hydrocarbons in the gaseous, liquid, or solid phase. Petroleum may also contain nonhydrocarbon compounds, common examples of which are carbon dioxide, nitrogen, hydrogen sulﬁde, and sulfur. In the further description of resources and reserves in this chapter, the focus will be on oil. Figure 15.1 provides a graphical representation of the PRMS classiﬁcation framework. The classiﬁcation clearly distinguishes between hydrocarbons in the subsurface and those hydrocarbons which can be produced economically. Figure 15.1 has two axes: on the vertical axis the commercial certainty impact on the classiﬁcation is shown, and the technical uncertainty range is shown on the horizontal axis. The life cycle of hydrocarbon accumulation classiﬁcation begins with an estimate of hydrocarbon volumes in the subsurface, mainly driven by geological and geophysical investigations. These volumes are qualiﬁed as Prospective Resources. Once a well has been drilled and it has been proved that hydrocarbons are present, these volumes are shifted into the Contingent Resources category. At this stage, it is not clear if the hydrocarbons can be produced economically. After deﬁnition of projects to

Proved

Probable

Possible

Contingent Resources

Potentially Commercial

Prospective Resources

Increasing Commercial Certainty

Commercial Sub-Commercial

Discovered Initially In Place

Reserves

Unrecoverable Undiscovered IIP

Total Petroleum Initially-In-Place (IIP)

Production

Unrecoverable Range of Technical Uncertainty

Figure 15.1 Petroleum resources management system reserves classification A clear distinction between resources and reserves is made. Reserves are those quantities of petroleum which can be produced economically under current conditions. It should be noted that the sizes of the boxes for the resources and reserves do not reflect the

volumes of the hydrocarbons in these categories. After discovery of a new hydrocarbon field, usually the recoverable volumes are classified as contingent resources. After it has been shown with reasonable certainty that the volumes can be produced, they can be shifted into the reserves category. Modified after [6].

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recover the hydrocarbons and having reasonable certainty that these projects will be performed, these volumes can be moved into the Reserves category. The deﬁnition of reserves is very stringent: Those quantities of petroleum anticipated to be commercially recoverable from known accumulations from a given date forward under deﬁned conditions [6]. Technological improvements or increasing anticipated prices for hydrocarbons lead to increasing reserves. An example is the Canadian oil sands. The recovery methods have improved and oil prices have increased substantially within the last 10 years. Hence some of the oil sand deposits were shifted into the reserves category. It should be noted that the great majority of the hydrocarbons in the Earths crust are classiﬁed as resources and not as reserves. The horizontal axis of Figure 15.1 shows the uncertainty range in estimating reserves. As the detailed distribution of the properties of the formations containing hydrocarbons is not known (the distance from one well to the next is hundreds to thousands of meters), there are uncertainties concerning the volume and recoverability of the hydrocarbons. In PRMS, in the reserves category, these ranges of technical uncertainty are covered by the terms Proved Reserves for a 90% chance that at least these volumes can be recovered, Probable Reserves for a 50% chance for recovery of at least these volumes, and Possible Reserves for a 10% chance that at least these volumes of hydrocarbons can be produced. The PRMS classiﬁcation strictly separates this technical uncertainty from the commercial maturity of production of hydrocarbons. The PRMS classiﬁcation uses the term resources for potentially recoverable hydrocarbons from hydrocarbon-bearing formations. That means not the hydrocarbons in place but the fraction of hydrocarbons in place which can potentially be produced. In the following paragraphs, the term reserves will not be used according to the classiﬁcations in PRMS. The reason is that for an oil production outlook, insufﬁcient data is available to classify oil accumulations according to PRMS. For a number of countries, the Proved Reserve numbers are according to PRMS, but for others – for example, Middle East countries – the number would be different if PRMS was applied strictly. Since most of the oil in the subsurface is in the resources category, this category is discussed in the following section in more detail.

15.4 Oil Resources

According to the PRMS system, in order to assign resources of any class, a project needs to be deﬁned in some form of technically feasible development plan. Even for Prospective Resources, the estimates of recoverable quantities must be stated in terms of the produced and sold quantities assuming successful discovery and commercial development to fulﬁll the requirements for resources according to PRMS. At this stage, major uncertainties exist. Hence the development program will not be of great detail and is largely based on analogous projects.

15.4 Oil Resources

In the literature concerning future oil production, usually the term resources is not used in the way deﬁned in PRMS. Sometimes, oil in place is confused with recoverable oil volumes. The PRMS system is very systematic and strict concerning the classiﬁcation of hydrocarbon volumes in the reserves but also in the resources category. A large amount of recoverable hydrocarbons in the subsurface exists which is likely recoverable; however, not even notional projects have been deﬁned in sufﬁcient detail to qualify under the PRMS system as resources. For an outlook on oil production, the PRMS classiﬁcation which is based on projects would be too conservative. In this chapter, the term resources is used for recoverable oil from petroleumbearing rocks. For a large part of the resources, no speciﬁc project to recover oil has been deﬁned. However, based on analogues, a certain recovery factor of the oil present in the rocks can be assumed. For this oil production to become economically viable, in a number of cases, signiﬁcantly higher oil prices or improved technology is assumed (e.g., oil shales). Oil of various qualities has been discovered. The most easily producible oil is called conventional oil. This oil has so far been produced in by far the largest proportion of oil in place compared with oil of other quality. Heavier, more viscous oil is present in larger quantities in the subsurface than conventional oil. Oil with an API gravity of less than 20 API (more than 0.934 g cm3) or a viscosity in the reservoir of more than 1000 cP (mPa s) is called heavy oil. Oil with an API gravity of less than 10 API (more than 1 g cm3) or a viscosity in the reservoir of more than 10 000 cP (mPa s) is called bitumen or tar (Figure 15.2). In the case of oil production from oil shales, the shale has to be heated in order to produce oil. The oil present in the subsurface can be envisaged in a triangle (Figure 15.3). Conventional oil is present in signiﬁcantly smaller amounts than heavy oil, bitumen or oil that can be generated by heating oil shales. The red areas in Figure 15.3 indicate the amount of oil of the respective categories which has been produced to date. Estimates are that about 30% of the recoverable conventional oil has been produced. As has been shown in the previous section on oil reserves, there is a large range of uncertainty concerning the recoverable volumes from oil reservoirs. The reason is that the properties of the reservoir (e.g., porosity and permeability) and

Conventional oil 1000 cP

20°API Heavy oil

10000 cP

10°API Bitumen

Figure 15.2 Classification of oil as conventional oil, heavy oil and bitumen. Oil is classified based on gravity and/or viscosity at reservoir conditions. The higher the viscosity, the more difficult it is to produce the oil.

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Increasing production costs

Produced until now Heavy oil

Bitumen

Oil shales

Figure 15.3 Resources triangle. The diagram illustrates the amount of the various oil types present in the subsurface. Conventional oil is present in the subsurface in much smaller quantities than heavy oil, bitumen, or oil which can be produced from oil shales (not to scale).

Increasing technology requirement

Conventional oil

The red areas indicate how much of the respective oil type has been produced so far. Whereas about one-third of conventional oil has been produced, only very small fractions of the other oil types have been recovered.

of the ﬂuids within the reservoir at reservoir conditions are not known sufﬁciently. Only a limited number of wells are drilled into reservoirs. Distances between wells can be hundreds to thousands of meters. In between the wells, the properties of the oil reservoirs are inferred from geostatistics and seismic and other geophysical properties. In addition, technology continuously progresses, changing the ultimately recoverable oil from an oil ﬁeld. Examples in the past are the advent of three-dimensional (3D) and four-dimensional (4D) seismic, horizontal wells or CO2 enhanced oil recovery. All these technologies increased the ultimately recoverable oil from known reservoirs. Heavy oil and bitumen have only been produced in small amounts with respect to the total potentially recoverable volumes. Oil production from oil shales has been negligible so far. Production of these types of oil requires greater investments than conventional oil production. Viscous oil and oil shale has to be heated to be able to produce oil. Also, the heavy oil and bitumen produced are of lower quality than conventional oil. Therefore, this oil has to be upgraded to reach the same quality as conventional oil or is sold at a lower price. Another consideration is the additional technology and ﬁeld optimization required compared with conventional oil production. The amounts of recoverable oil and the price of oil which is necessary to produce the respective oil types are shown in Figure 15.4. The most cost-effective oil production is from Organization of Petroleum Exporting Countries (OPEC) Middle East ﬁelds. Production of oil from oil shales requires the highest oil price. Within each of the different types, oil can be produced at different costs. Heavy oil production requirements ranges from about $20/bbl to almost $80/bbl.

15.5 Oil Production History and Reserves Development

Economic price 2004 (US$/bbl)

90 80

Super deep

70

Arctic

Oil shales

60 50

Heavy oil/ bitumen

Deep water

40 30

EOR

20

OPEC Middle East

Already produced

10 0

0

1

Other conv. oil

2

3

4

5

6

Available oil (trillion barrels)

Figure 15.4 Oil price required for economic oil production from different types of oil fields. The oil price which is required for production of OPEC Middle East oil is much lower than the oil price necessary for commercial production of oil

from oil shales. However, the quantity of oil which could be produced from oil shales is larger than the additional recoverable oil from OPEC Middle East oil. Modified after IEA.

It should be noted that oil can be produced from Middle East countries at substantially lower costs than from other areas of the world. Currently, oil production is the major source of income for the national budgets of some of these countries. Lower oil prices result in less funding being available to combat natural oil ﬁeld decline and to maintain oil production from these countries. Hence lower oil prices lead to decreasing production from these countries in the mid-term, a factor which has to be taken into account in the global supply and demand approximations. The following paragraphs show how much oil has been produced in the past and how the reserves developed in history. This section is followed by an oil production outlook.

15.5 Oil Production History and Reserves Development

The world oil production increased from less than 60 million barrels per day (bbl/d) in 1982 to more than 81 million bbl/d in 2007. Figure 15.5 shows how the production increase is distributed over the different areas. Oil production from North America declined slightly whereas more oil could be produced in the other areas of the world. The increase in oil production in Europe and Eurasia beginning in the late 1990s mainly originated from the former Soviet Union countries. The share of oil production from the Middle East has increased significantly within the last 20 years. Figure 15.6 shows the proven oil reserves and the distribution of the reserves over different areas.

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Oil production (million bbl/d)

80

80 Asia Pacific

70 60

70 60

Africa

50

50

Middle East

40 30

40 30

Europe & Eurasia

20

20

South and Central America

10

10

North America

0 82

85

90

95

00

05

07

Figure 15.5 Oil production from 1982 until 2007 The largest increase in oil production was seen from the Middle East. The increase in oil production from Europe and Eurasia from the mid-1990s came mainly from former Soviet Union countries. Modified after BP [1].

The ﬁgures shown here were not generated by using estimates of reserves based on the PRMS system. In a number of Middle East countries, no details of the ﬁeld evaluations are known outside those countries. Figure 15.6 shows that the worlds total proven reserves in 1987 were 910 billion bbl. From 1987 to 2007, these volumes increased by 320 billion bbl to 1237 billion bbl. This increase in the ﬁgures occurred even though more than 500 billion bbl have been produced in the same period. In addition, 152 billion bbl of proven reserves can be added for the Canadian Oil Sands [1].

Proven oil reserves (billion bbl)

800 700 600 500 400 300

1987 2007

200 100 0 North America

South & Europe & Central Eurasia America

Middle East

Figure 15.6 Proven oil reserves in 1987 (first column) and 2007. The largest increase occurred in the Middle East. Only in North America did reserves decrease – not taking oil

Africa

Asia Pacific

sand reserves in Canada into account. The total worlds oil reserves were 910 billion bbl in 1987 and amount to 1237 billion bbl in 2007. Modified after BP [1].

Ratio production / proven reserves in years

15.6 Oil Production Forecast

90 80 70 60 50 40 30 20 10 0 North America

South & Central America

Europe & Eurasia

Middle East

Africa

Asia Pacific

Figure 15.7 Distribution of the oil production/proven oil reserves ratio over the different areas in the year 2007. On a world scale, the ratio of oil production/proven oil reserves is about 40. Modified after BP [1].

The largest increase by far in reserves was seen in the Middle East countries. In this region, reserves were increased by almost 190 billion bbl. The reserves in the Middle East were increased sharply between 1987 and 1990. Subsequently, they remained constant. The ratio between oil production and oil reserves remained at around 40 on a world-wide scale. The increase in production was offset by an increase in reserves. Figure 15.7 shows the distribution of the oil production/oil reserves ratio over the areas. The Middle East countries have the longest lifetime of oil reserves and North America the shortest. In the past, it has been difﬁcult to predict the increases in reserves and effects on oil production. This indicates that current predictions will also have a large range of uncertainty. The next section gives an outlook on oil production based on data gathered by the IEA [7].

15.6 Oil Production Forecast

As has been seen from many failed attempts in the past, forecasting future oil production is notoriously difﬁcult. This section refers to the oil production forecast given by the IEA [7]. The IEA came up with several scenarios which are based on population growth, growth of gross domestic product, and other political energy policies that inﬂuence the usage of oil. Here, the reference scenario of the IEA will be discussed in more detail. Figure 15.8 shows the oil production reference scenario forecast.

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100 Natural gas liquids

80

Non-conventional oil

60

Crude oil –additional EOR Crude oil –fields yet to be found

Crude oil – fields yet to be developed

40 20 0 1990

Crude oil – currently producing fields

2000

2010

Figure 15.8 Oil production forecast according to the reference case scenario of the IEA [7]. The decreasing production from currently producing fields due to the limited amount of oil present in these fields and the increase in

2020

2030

demand has to be offset by bringing already discovered fields on-stream, finding new fields, enhanced oil recovery, and non-conventional oil. Modified after IEA [7].

In the subsequent paragraphs, Figure 15.8 will be described in more detail starting with production from currently producing ﬁelds upwards. 15.6.1 Oil Production Based on Currently Producing Fields

As described in the section above, proven oil reserve ﬁgures have increased continuously over the last 20 years. This was achieved despite the production of signiﬁcant amounts of oil in the same period. This shows that it is not sufﬁcient to base a prediction of future oil production on the current proved reserves numbers. One reason for this is that uncertainties exist concerning the size of the current ﬁelds. According to the PRMS deﬁnitions, there is a 90% chance that more oil than the proven reserves can be recovered. A substantial part of the worlds proven reserves is located in countries where PRMS is applied, hence upward revisions of these numbers are expected. In addition, adding the 90% chance numbers of statistical distributions does not result in a 90% chance of the total distribution but in a lower number. This aggregation systematically underestimates proven reserves on a global scale. Also, in the past it has been seen that oil recovery factors (oil produced/total oil originally in place) of ﬁelds can be improved by applying new technology. In Figure 15.8, production from existing ﬁelds is shown in blue. In the past, the production-weighted average decline rate increased (Figure 15.9). The reason is the production of increasing amounts of oil from smaller oil ﬁelds and from off-shore. Smaller oil ﬁelds have a tendency to provide less upside potential than larger oil ﬁelds. Off-shore ﬁelds have to be produced faster than on-shore ﬁelds to recover the costs of

15.6 Oil Production Forecast

Average oilfield decline rate (%)

16 14

OPEC Non-OPEC

12 10 8 6 4 2 0 Pre-1970s

1970s

1980s

Figure 15.9 Average observed oilfield decline rate by year of first production. Typically, initially fields are produced at a plateau production rate. The next phase in oil field production is declining oil production due to an increase in water production or lowering of the field pressure. The diagram shows that fields that

1990s

2000-2007

were discovered later are declining faster. The reason is that from the 1980s to 2007, more smaller fields and off-shore fields were brought into production than in earlier years. These fields typically show higher decline rates. Modified after IEA [7].

the large upfront capital spending for the platforms faster. The worlds overall production-weighted decline rate in 2007 was 6.7%. The IEA expects this decline rate to increase to 8.6% in 2030. This decline rate has been taken into account to generate the estimates shown in Figure 15.8. 15.6.2 Oil Production from Discovered Fields that Will be Developed

Most oil was discovered before 1965. These oil ﬁelds are to a large extent already in production. The gray volumes in Figure 15.8 refer to oil production coming from already discovered ﬁelds that have not yet been developed. For larger scale oil production projects, a 5–10 year gap between discovery of the ﬁeld and ﬁrst oil production is not uncommon. The reasons are the substantial investments associated with such developments, resulting in extended planning periods and long construction times associated with large-scale projects. 15.6.3 Oil Production from Yet to be Found Fields

Figure 15.10 shows that more than 100 billion barrels oil equivalent (boe) were added within the last 10 years. The diagram also indicates that the average discovered ﬁeld

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900

Rebound in global oil demand 1998

800 700 600

1991 Deep-water exploration beginning Gulf War

Revolution in Iran 2nd oil price shock

500

1979

400 1st oil price shock

300 200

2005

1973

100 1965

0

0

10

20

30

40

Cumulative new field wildcats Figure 15.10 Cumulative added volumes of oil discovered as a function of cumulative amount of wildcats (wells drilled into potentially oilbearing structures) drilled from 1965 onwards.

50

60

in 1000 wells

Until 1979, more exploration in the Middle East was performed. Due to the larger field size in the Middle East, larger fields were discovered. Modified after IEA [8].

size decreased signiﬁcantly after 1979. The reason is the decreasing exploration activity after 1979 in the Middle East, where ﬁeld sizes are the largest. New technology such as 3D seismic methods boosted the average ﬁeld size discovered. In particular after 1998, the oil price increased sharply, leading to a higher activity level and use of advanced technology. The light red area in Figure 15.8 illustrates additional oil production from ﬁelds that are expected to be found in the near future. Due to the long lead time between discovery and ﬁrst production, production from these ﬁelds will be negligible in the next 10 years. 15.6.4 Oil Production Due to Additional Enhanced Oil Recovery (EOR) and Unconventional Oil Production

The easiest way to produce oil is by depletion. Oil production by depletion means that no ﬂuid (water or gas) is injected into the reservoir. Hence the pressure decreases with time. Therefore, oil production decreases with time until no economic oil production can be achieved from the porous medium. Often, water from an underlying water-ﬁlled porous sandstone or limestone is ﬂowing into the oil reservoir, keeping the reservoir pressure up. If this support is not sufﬁcient, water can be injected to increase oil recovery (Figure 15.11) [9]. Typical recovery factors of oil by depletion without water inﬂux are less than 10% of the oil originally in place, that is, 90% of the oil remains in the reservoir. In the case of water injection, recovery factors can be increased to 30% and even 65%.

15.6 Oil Production Forecast

Figure 15.11 Improved oil recovery by water injection. To increase the recovery of oil, the pressure in the oil fields is kept at a higher level by water injection. Without water injection, the

recovery is much lower. The pressure in the field decreases without water injection until no economic flow of oil to the surface can be achieved.

The remaining oil in the reservoir is trapped by capillary forces between oil and water on a pore scale. A number of technologies exist to increase oil recovery. These technologies are called enhanced oil recovery (EOR) methods. Depending on the properties of the oil ﬁelds (permeability and porosity) and of the oil which is contained in the ﬁelds, and also the depth of the reservoirs, a technique to increase oil recovery can be chosen [10, 11]. Cost for these methods are higher than producing oil by depletion or water injection. Figure 15.12 shows the application envelope of EOR methods. Currently, mainly three of these technologies are applied. Steam injection for production of oil with a high in situ viscosity, hydrocarbon or CO2 injection for recovery of oil with low in situ viscosities and polymers for medium-viscous oil recovery. The application envelope of the various technologies is determined by the physical process resulting in increased oil production and economic considerations. Steam injection is used for very viscous oils. Steam injection leads to heating of the reservoir, which results in a decrease in the oil viscosity by orders of magnitude. Hence the oil is able to ﬂow and can be produced. For oil with lower viscosities at reservoir conditions, this effect is less pronounced. Therefore, the incremental oil by steam injection compared with water ﬂooding for oil with lower viscosities is too little to be economically attractive. By injecting polymers, the viscosity of the injected water is increased. This leads to less ﬁngering of the water through medium-viscous oil and better oil displacement and production. Gas injection for oil with a low in situ viscosity changes the displacement process. Whereas water injection traps oil by capillary forces, gas injection results in gravitydominated so-called ﬁlm ﬂow of the oil at the edges of the pores.

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Insitu oil viscosity (cP)

Depth below surface (m)

0

1

5

10

50

100

1,000

50 500 1000

2000

5,000

10,000

100,000

Steam injection CO2 injection / hydrocarbon gas injection

Polymer injection

Insitu combustion

Surfactant injection / alkali surfactant polymer injection N 2 injection /

3000 air injection

Figure 15.12 Application envelope of various enhanced oil recovery methods (not to scale). Various methods to enhance oil recovery exist. Depending on the depth of the fields and viscosity of the oil at reservoir conditions,

different technologies can be used. Currently, gas injection, polymer injection, and steam injection are the most widely used technologies to enhance oil production.

Typically, incremental oil recovery for polymer and gas injection processes is 5–15% of the oil originally in place. For steam injection, incremental oil recovery can be much higher due to the very low recovery of highly viscous oil without heating it. Unconventional oil (green area in Figure 15.8) refers to oil with very high in situ viscosities. This oil is called heavy oil if the in situ viscosity is above 1000 cP or the API gravity less than 20 (more than 0.934 g cm3) or bitumen if the in situ viscosity is above 10 000 cP and the API gravity less than 10 (more than 1 g cm3). The main recovery mechanism for these oils is by steam injection as described above. A special case of unconventional oil production is recovery from oil shales. Oil shales contain kerogen, which can be converted into oil by using heat. Oil shales can either be extracted by mining and then treated at the surface or be produced in situ [12, 13]. So far, only pilot tests for extracting oil from oil shales in situ have been performed. Production costs are still high. Since the amount of recoverable oil from oil shales could be several trillion barrels and oil prices are higher than in the late 1990s, there is renewed interest in this resource. 15.6.4.1 Oil Production from Natural Gas Liquids Gas production is forecast to increase in the coming decades. By processing the gas, condensate is produced which is added to oil production. In addition, gas can be converted to liquids. Currently, some plans for converting large gas reserves into liquids exist. If these projects materialize, then the oil production from gas will increase. 15.6.4.2 Implications of Increased Oil Demand/Production Oil demand is assumed to increase from 2007 to 2030. The split of the forecast oil consumption increase is shown in Figure 15.13. The largest contribution of the oil

15.7 Impact of CO2 Emissions

China Middle East India Other Asia Latin America E. Europe/Eurasia Africa OECD North America OECD Europe OECD Pacific -2

0

2

4 6 million bbl/d

8

10

12

14

Figure 15.13 Increase in oil consumption per region from 2007 to 2030. Modified after IEA [7].

consumption increase is expected from China, and a decrease is expected for a number of Organization for Economic Cooperation and Development (OECD) countries. To meet this increase in oil consumption, oil production has to be increased. As shown above, oil production from existing ﬁelds has to be offset, already discovered ﬁelds have to be developed, new ﬁelds must be discovered, and new technology to recover more oil and oil from more difﬁcult ﬁelds has to be applied. To be able to meet the oil demand, large-scale, capital-intensive projects have to be performed. The IEA estimated that about $6.3 trillion needs to be invested in oil-related infrastructure from 2007 to 2030. About 80% of this investment has to go into the upstream sector (oil exploration and production). Production from OPEC countries is expected to increase until 2030. The largest portion of the increase from OPEC countries will come from Saudi Arabia. The output from non-OPEC countries will increase and then stay constant. This reﬂects the lower production costs from OPEC countries compared with other areas and the large amount of oil reserves in OPEC countries. Therefore, the share of oil production from OPEC countries will increase from 40% in 2005 to 51% in 2030 [7]. Burning hydrocarbons generates CO2. Increasing production of hydrocarbons will increase CO2 emissions. This has implications for the attempts to decrease greenhouse gas emission and the required capital spending. The next section describes some of the impacts of increasing CO2 emissions.

15.7 Impact of CO2 Emissions

Due to the forecast increasing consumption of energy, CO2 emissions are expected to increase. In the reference scenario of the IEA [7], emissions would increase from

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CO2 emission (Gt)

45

550 Policy scenario

40

450 Policy scenario CCS

Reference scenario

Nuclear 550 Policy scenario

35

Renewables & biofuels Energy efficiency

30 450 Policy scenario

25 20 2005

2010

2015

2020

2025

2030

Figure 15.14 Energy-related CO2 emissions for different scenarios. To be able to reduce the CO2 emissions into the atmosphere, a variety of different technologies have to be applied. Modified after IEA [7].

28 Gt in 2006 to 41 Gt in 2030 (Figure 15.14). This scenario, which assumes no change in governmental policies, would lead to a concentration of 1000 ppm in the atmosphere and a forecast increase in temperatures by 6 C by the end of the century. To stabilize the level of CO2 in the atmosphere at 550 or 450 ppm, a combination of cap-and-trade systems, sectoral agreements, and national measures have to be pursued. To be able to achieve the 550 scenario, additional investments of $4.1 trillion compared with the reference scenario have to be made. Also, $17 per year per person needs to be spent on more efﬁcient cars, appliances, and buildings to meet this reduction target [7] The 450 scenario requires more substantial changes. The assumption for this scenario is faster deployment of yet unproven technologies and faster growth of renewable energy. Global energy investment of $9.3 trillion in addition to the reference scenario is expected. Technologies concerning capturing of greenhouse gases are discussed in Vol. 2 Ch. 14 and geological storage of CO2 to reduce greenhouse gas emissions in Vol. 5 Ch. 19. Increasing the costs for CO2 emissions by a cap-and-trade system will have an impact on oil production from unconventional resources. Oil production from such ﬁelds requires more energy because water has to be converted into steam to permit production of this highly viscous oil. Hence the CO2 emissions per barrel are higher than for conventional oil production. This means that the marginal costs for the development of such resources will increase if a cap-and-trade CO2 emission reduction scheme is implemented. Estimates for this increase are about $2/bbl additional operating expenditure.

15.8 Oil Price Outlook

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15.8 Oil Price Outlook

In general, three main factors will inﬂuence the development of the oil price in the medium term [14]: . . .

population growth the reliance of the world on liquid fuels for transportation the development costs of marginal resources.

Short-term ﬂuctuations due to perceived or real short-term shortages or surpluses and speculation lead to high volatility of the oil price. Also in the past the oil price was characterized by high volatility. Numerous political events and perceived shortages and surpluses of oil resulted in large ﬂuctuations in the oil price. Figure 15.15 shows the oil price in money of the day and 2007 values. In late 2008, the oil price dropped to a level of $40/bbl due to an economic crisis. According to economic theories, oil ﬁelds should be developed in order of their marginal production costs. However, in the past, ﬁelds in the Middle East with lower development costs than most of the other ﬁelds have not produced. One reason is that the income from oil production in a number of these countries is crucially important for their national budget. Lower oil prices lead to a reduction in investment in oil production and accordingly lower oil production in the medium term. Currently, Saudi Arabia has additional capacity to manage supply and demand. In the medium term, output from Middle East countries has to increase and their capacity to manage supply and demand is expected to diminish.

Pennsylvanian oil boom

Sumatra production began

Growth of Discovery of Venezuelan the spindletop production Texas Fears of shortage in US

East Texas field discovered

Loss of Iranian supplies

Post war reconstruction

Asian financial crisis Netback pricing Iraq invaded introduced Kuwait Suez crisis Iranian Invasion revolution of Iraq Yom Kippur war 110

90 80 70

$ 2007

60 50 40 30 20

$ Money of the day 1861-69

1870-79

1880-89

1890-99

10 1900-09

1910-19

1920-29

1930-39

1940-49

1950-59

1960-69

1970-79

1980-89

1990-99

Figure 15.15 Oil prices from 1861 to 2007. The oil price peaked in the 1970s due to political events. In late 2008, the oil price decreased to a level of $40–50/bbl. Modified after BP [1].

2000-07

Crude oil prices (US$/bbl)

100

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About 45 million bbl/d of oil production capacity has to be built to meet the oil production decline even in the scenario that oil consumption remains ﬂat. This capacity cannot be provided only by ﬁelds with low production cost. Hence it is likely that the oil price will increase to above $70/bbl in the medium term since ﬁelds with higher costs have to contribute. Short-term ﬂuctuations will be large due to political and other factors distorting supply and demand for a short period. Also, speculation exaggerates oil price trends. The economic turbulences which started in the fourth quarter of 2008 will for some time lead to low oil prices due to an economic slowdown. However, it might exaggerate an upturn in oil prices due to a delay in necessary investment. Another inﬂuencing factor is the replacement of oil by another source of energy at a lower cost. Currently, such an alternative is far from being commercially available.

15.9 Discussion and Conclusion

Oil is a ﬁnite resource. At some point in time, oil production will decline. This decline might be triggered by either technological advances of energy carriers replacing oil or by the limited availability of oil. In recent years, the high oil price has triggered a lot of discussions concerning the timing of peak oil production. A number of authors expected the high oil price to be the ﬁrst sign of oil supply shortage and peak oil production. Within the last 20 years, the worlds oil reserves have increased despite the fact that a substantial amount of oil has been produced during that period of time. The oil production/proved oil reserves ratio remained constant although more and more oil is being produced. It should be noted that the deﬁnition of proved reserves is ambiguous and data about reserves are not always reliable. Recently, a new system was introduced for the deﬁnition of proved reserves, the petroleum resources management system. However, this system is not used by all the countries for deﬁning proved reserves. The proved reserves volumes, according to the PRMS deﬁnition, will be in more than 90% of the cases exceeded. This is one part of the explanation for why the oil production/proved reserves ratio remained constant during the last 20 years. With time, more information was gathered about the oil ﬁelds, and oil volumes remaining in the ground were moved from the probable reserves category (at least a 50% chance that more oil will be produced) to the proved reserves category. Other major factors for the oil production/proved reserves ratio remaining constant are the advances in technology for improving the amount of oil which can be produced from oil ﬁelds and increasing oil prices, allowing production of formerly economically unattractive ﬁelds. Future oil demand is expected to increase by 1% per year until 2030. This increase in demand can be met by summing up oil production from (i) continuing production from existing ﬁelds, (ii) bringing already discovered new ﬁelds on-stream, (iii)

References

discovering new ﬁelds, (iv) enhancing oil recovery, (v) production from unconventional oil, and (vi) production of gas condensates. It should be noted that very large volumes of oil exist in the subsurface. The main part of this oil is so-called unconventional oil having very high viscosities or deposited in the form of oil shales. Production of oil from these resources is only economically attractive at oil prices higher than $70/bbl. To be able to produce sufﬁcient oil to meet demand, large investments are necessary. Estimates are that about $6.3 trillion is required until 2030. With the recent downturn of the economy and credit ﬁnancing, it will be more difﬁcult to achieve the growth targets – at least in the short term. If CO2 emissions have to be reduced, an additional $4–9 trillion is required to ﬁnance capital expenditure related to the necessary reduction programs. Oil production investment will be in competition for some of these funds. Regarding the oil price, high volatility has to be expected. Typically, larger oil ﬁeld developments have a lead time between the decision to fund a project and the ﬁrst (incremental) oil of 5–10 years. This contributes to times of tight oil supply and oil supply surplus. A number of large-scale projects will be delayed at times of lower oil prices, resulting in higher oil prices in the medium term. In addition, political factors and speculation exaggerate short-term trends. In the medium term, oil prices above $70/bbl are required for the development of the unconventional resources containing trillions of barrels of oil. In the medium term, the oil price will rise above this level unless technologies replacing oil as an energy carrier are developed.

References 1 BP (2008) BP Statistical Review of World

2

3

4

5

Energy, June 2008; http://www.bp.com/ productlanding.do? categoryId¼6929&contentId¼7044622. Campbell, C. (2005) Oil Crisis, Multi Science Publishing, Brentwood, Essex, p. 456. Kawata, Y. and Fujita, K. (2001) Some Predictions of Possible Unconventional Hydrocarbons Availability Until 2100. Society of Petroleum Engineers Paper SPE 68755. Larisse, I. (1999) Heavy Oil Production in Venezuela: Historical Recap and Scenarios for Next Century. Society of Petroleum Engineers Paper SPE 53464. Towson, D.E. (1997) Canadas Heavy Oil Industry: a Technological Revolution. Society of Petroleum Engineers Paper SPE 37972.

6 SPE, AAPG, WPC, SPEE (2007)

7

8 9

10

11

Petroleum Resources Management System; http://www.spe.org/spe-app/ spe/industry/reserves/prms.htm. IEA (2008) World Energy Outlook 2008; http://www.worldenergyoutlook. org/. IEA (2006) World Energy Outlook 2006; http://www.worldenergyoutlook.org/. Craig, F.F. (1973) Engineering Waterﬂoods for Improved Oil Recovery. Society of Petroleum Engineers Paper SPE 4425. Taber, J.J., Martin, F.D., and Seright, R.S. (1997) EOR Screening Criteria Revisited – Part 1: Introduction to Screening Criteria and Enhanced Recovery Field Projects. Society of Petroleum Engineers Paper SPE 39385. Taber, J.J. Martin, F.D., and Seright, R.S. (1997) EOR Screening

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