Computational Investigation of Oxygen Concentration Effects on a

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Computational Investigation of Oxygen Concentration Effects on a Soot Mechanism with a Phenomenological Soot Model of Acetone−Butanol−Ethanol (ABE) Zhichao Zhao,†,‡ Han Wu,§ Mianzhi Wang,‡ Chia-Fon Lee,*,‡,∥ Jingping Liu,† Jianqin Fu,† and Wayne Chang‡ †

State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, Hunan 410082, People’s Republic of China ‡ Department of Mechanical Science and Engineering, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States § School of Automobile, Chang’an University, Xi’an, Shanxi 710064, People’s Republic of China ∥ Center for Combustion Energy and State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, People’s Republic of China ABSTRACT: A phenomenological soot model of acetone−butanol−ethanol (ABE) was proposed with modification of the fuel pyrolysis process, and the oxidation effect on soot number density was also included. Using KIVA-3 V Release 2 code coupled with this ABE soot model, multi-dimensional computational fluid dynamics (CFD) simulations of ABE spray combustion in a constant volume chamber were conducted to investigate the effects of ambient oxygen concentrations on combustion and soot emission characteristics. Validation experiments were also conducted in an optical constant volume combustion chamber under 1000 K initial temperature with varying oxygen concentrations (21, 16, and 11%). The results demonstrated that predicted timerelated soot mass showed good agreement with experimental data qualitatively. The total yield of soot mass initially increased with decreasing oxygen concentrations from 21 to 16% and then decreased to the lowest level at 11% ambient oxygen. The non-monotonic ABE soot behavior with a decreasing ambient oxygen concentration was related to the transition of the dominant effect from the suppressed soot oxidation mechanism to the suppressed formation mechanism.

1. INTRODUCTION Nowadays, the diesel engine is still the main power source of both vehicles and other power machineries because of its high thermal efficiency, reliability, and durability. To reduce the dependence of diesel refined from petroleum, the world never stops searching for renewable energy sources and viable alternative fuels. The emissions emitted from diesel engines can also cause various problems, such as climate change and human diseases. In recent years, the studies of biofuels and oxygenated hydrocarbons have gained significant political and scientific interest, owing to concerns about the energy and environmental crises. Alcohols, such as methanol, ethanol, and butanol, have been widely considered as alternative fuels for diesel engines.1−5 Among them, ethanol has been commercially used as a fuel additive. However, longer chain alcohols, such as butanol, have received increasing attention in recent years because of their higher heating value, cetane number, lower vapor pressure, lower latent heat of vaporization, and much greater miscibility with conventional fuels.5−7 Meanwhile, butanol can also be produced by alcoholic fermentation of biomass feedstock, which makes it as an attractive renewable energy source. The acetone− butanol−ethanol (ABE) fermentation, which is one of the major methods to produce butanol using biomass, can be tracked back to the period of World War I pioneered by Chaim Weizmann.8 Since then, several improved methods with new strains and process solutions of ABE fermentation were proposed to reduce the cost of substrate and butanol toxicity of the fermenting microorganisms.9−11 © XXXX American Chemical Society

Despite the increasing interests in butanol, the costs for ABE separation from dilute fermentation broth are so high that the industrial-scale production of bio-butanol has thus far been prohibited.12 On the basis of this consideration, it would be ideal if the ABE mixture could be directly used as an alternative fuel or fuel additive. Zhou et al.13 conducted an experimental investigation of ABE−diesel blend combustion using laser diagnostics in a constant volume chamber. A similar study was carried out by Chang et al.14 in a diesel engine with ABE−diesel and water−ABE emulsion−diesel blends. The results from both studies indicated that the usage of ABE as a fuel additive in a spray combustion engine not only reduced soot emission but also improved the thermal efficiency. Soot, which is one of the major pollutants generated in diesel engines, will not only cause serious human health problems but has significant influence on engine performance and in-cylinder behavior. In appearance of soot within the flame area, the radiative heat loss increases because of the enhancement of flame emissivity by soot particles;15,16 thus, the thermal efficiency will decrease. To pursue an in-depth understanding of the soot behavior and combustion process in diesel engines, multidimensional simulations have become an essential part in the modern engine design processes. Soot models can be classified as purely empirical, phenomenological semi-empirical models and Received: November 4, 2014 Revised: February 3, 2015

A

DOI: 10.1021/ef502433d Energy Fuels XXXX, XXX, XXX−XXX

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detailed soot models.17 Among them, detailed soot models include detailed fuel gas-phase chemistry, polycyclic aromatic hydrocarbon (PAH) formation reactions, and particle dynamics, which are believed to provide most accurate quantitative information on soot behavior. At present, the mechanisms of combustion, oxidation, and pyrolysis for ABE mixtures are still not completely understood. To the knowledge of the authors, studies on this topic are scarcely found in the literature. On the basis of previous experimental measurements in a pyrolysis reactor, Van Geem et al.8 developed a detailed mechanism for the combustion and pyrolysis of ABE mixtures. After validation against flame and pyrolysis experiments, the model showed the feasibility of capturing the trends of most of the products, except for butanal. Although further experimental verification is needed to validate this mechanism, it is thus far considered as a mechanism of great potential for future modeling of oxygenated hydrocarbon blends, such as the ABE mixture. However, there are still many challenges in using detailed chemical kinetics models for practical engine simulations. First, the detailed model for ABE mixtures is still in its infant stage because it is validated only for simple combustion systems. The comprehensive reaction mechanisms describing the fuel pyrolysis and oxidation for engine conditions could be more complicated. Second, it is computationally demanding when a detailed chemical kinetics soot model is used in multi-dimensional engine modeling. The high cost mostly stems from interactions between the detailed chemistry and turbulent mixing on a sub-grid level.18 Third, uncertainties existing in other sub-models used in practical engine simulations, such as the turbulence model and spray model, may minimize the advantages of using a detailed soot model. Over the past few decades, a variety of empirical and semiempirical soot models have been proposed for engine simulations. In 1983, Hiroyasu et al.19 proposed a representative empirical model, which was known as the Hiroyasu− Nagle/Strickland-Constable (HNS) soot model. This two-step model had low computational cost and extended the understanding of the in-cylinder soot behavior, which had been widely used in practical engine simulations. However, the twostep model by Hiroyasu oversimplified the soot formation process, so it was not able to provide details of the relevant intermediate species during the soot formation and oxidation process. Moreover, the predicted results could not capture the trends of measured soot behavior in a low combustion temperature environment. After that, Cheng et al.20 improved the model by Hiroyasu by taking into account the soot concentration transport with air flow and the turbulence effect on chemical reactions. Although improvement was achieved in predicting the soot concentration distribution, information which was important to understand the soot behavior still could not be provided, such as evolution of soot relevant intermediate species, particle number, and particle size. Therefore, phenomenological models seem to be a preferred or compromised choice in multi-dimensional simulations. Because most important particle dynamics and chemical kinetics of soot evolution are included, phenomenological models can provide in-depth and detailed information on the physical and chemical processes of soot evolution. In addition, they can be easily implemented into computational fluid dynamics (CFD) programs, such as KIVA and STAR-CD, because of the ignorance of intermediate branching chain reactions. In recent years, several representative multi-step phenomenological (MSP) soot models were proposed, and great improvements had been

achieved in soot modeling.21−24 Fusco et al.22 developed an eight-step model, which included the processes of fuel pyrolysis, soot inception, surface growth, coagulation, and oxidation. Over a wide range of temperatures and equivalence ratios, the predicted results demonstrated the feasibility of using the model to reproduce the trends of soot behavior observed in other literature. After that, on the basis of the model by Fusco et al., Tao et al.21 presented a nine-step model validated over wide diesel engine operation conditions. In this model, a semiempirical approach was adopted, in which the process of soot precursor formation from acetylene was employed instead of evaluating precursor formation directly from vapor-phase fuel. In addition, the OH-related oxidation by Neoh et al.25 was also included in the model. Qualitative and even semi-quantitative agreements between predicted and measured engine-out soot could be achieved within a certain range of engine-operating conditions. Recently, Bi et al.26 further improved the model by Tao et al. by modifying the reaction step related to the soot mole concentration and adopted the revised model to simulate the diesel spray combustion and soot behavior in a constant volume chamber with different oxygen concentrations. In another study, Bi et al.27 extended the usage of this kind of model to investigate the temperature effect on biodiesel combustion and soot behavior. On the basis of the detailed chemical kinetic model for biodiesel, the C18 chain methyl ester was used as the fuel surrogate, while CO and CO2 were added to the fuel pyrolysis reaction as major products besides C2H2. In a parallel study, Xiao et al.28 adopted a similar soybean biodiesel phenomenological soot model to explore the effect of oxygen concentrations on soot behavior. The predicted results of these studies were in good agreement with the measurements over wide operation conditions. Furthermore, it also confirmed the feasibility of using this kind of phenomenological soot model for alternative fuels. In the present study, a phenomenological soot model for ABE mixtures was proposed on the basis of the structure of the model by Tao et al.21 and the fuel pyrolysis reaction was modified according to the detailed chemistry. In addition, the oxidation effect on soot number density is included in the current model. Experiments were conducted in an optical constant volume chamber under 1000 K initial ambient temperature with different oxygen concentrations (21, 16, and 11%). The measured chamber pressure and time-related soot mass concentration were used to validate the ABE soot model. To bring further insight of the soot evolution, the calibrated model was used to predict transient behavior and spatial distributions of soot-relevant species, such as acetylene, soot precursor, and OH radicals.

2. EXPERIMENTAL SECTION The optically accessible constant volume chamber (CVC) is a cylindrical chamber with a bore of 110 mm and a height of 65 mm. A fused silica window of 130 mm in diameter and 60 mm in thickness is installed at the top of the chamber, which has high ultraviolet (UV) transmittance down to 190 nm. A hydraulic-actuated electroniccontrolled unit injector is mounted at the center of the chamber head. The injector has six symmetrical holes of 0.145 mm orifice diameter, with a 140° spray angle. The injection pressure and duration are controlled at 130 MPa and 3.5 ms. A quartz pressure transducer (Kistler 6121) embedded in the chamber wall is used to record the chamber pressure. Before the experiment, the chamber wall was first heated to 380 K by eight heaters to mimic the wall temperature of a diesel engine and to avoid water condensation. Then, a premixed gas mixture of pure C2H2, N2, and air is filled into the chamber and ignited by a spark plug and a plasma coil to generate a high-temperature and high-pressure B


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environment, which approximates the typical diesel engine in-cylinder conditions toward top dead center (TDC). The oxygen concentration of the after burned mixture in the chamber was varied from 21 to 11%, which corresponded to different exhaust gas recirculation (EGR) rates on the diesel engine. In this study, the ABE solution is prepared with a volume fraction of 3:6:1 (acetone/butanol/ethanol), which is a typical composition of the product of ABE fermentation. The properties of individual fuels are listed in Table 1. The forward-illumination light-extinction (FILE)

Table 1. Properties of Individual Fuels16 property

acetone

molecular formula cetane number octane number oxygen content (wt %) density at 15 °C (g/mL) autoignition temperature (°C) flash point at closed cup (°C) lower heating value (MJ/kg) boiling point (°C) stoichiometric ratio latent heat at 25 °C (kJ/kg) flammability limit (vol %) saturation pressure at 38 °C (KPa) viscosity at 40 °C (mm2/s)

C3H6O

27.6 0.791 560 17.8 29.6 56.1 9.54 518 2.6−12.8 53.4 0.35

Figure 2. Schematic of the FILE method. butanol

ethanol

C4H9OH 25 96 21.6 0.813 385 35 33.1 117.7 11.21 582 1.4−11.2 2.27 2.63

C2H5OH 8 108 34.8 0.795 434 8 26.8 78.4 9.02 904 4.3−19 13.8 1.08

instead of the soot concentration. On the basis of the Rayleigh approximation, it can be expressed as Cv L =

⎛I ⎞ λ ln⎜ 0 ⎟ 2K a ⎝ I ⎠

(2)

where λ is the wavelength of the monocolor light and Ka is the dimensionless absorption constant, which is equal to 5.47. This value is obtained from the validation of Rayleigh approximation for soot measurements with primary soot sizes below 50 nm and laser wavelengths of 511 nm. The total time-related soot mass can be obtained by summing all of the local soot mass at each pixel, which can be expressed as m=

∑ ρs CvL(Δri)2

(3)

In eq 3, it is assumed that the area represented by each pixel is square with dimension Δri, the mean soot mass density ρs is 2.0 g/cm3,30 and i is the index of the pixel.

29

method developed by Xu and Lee is used to explore the soot distribution and provide a two-dimensional time-resolved quantitative soot measurement. According to the FILE method, a light source and camera are placed on the same side of the flame through the same fused silica, as shown in Figure 1. The light diffuser located behind the

3. SIMULATION MODELS 3.1. Phenomenological ABE Soot Model. As mentioned above, the ABE phenomenological soot model retains the main chemical reaction mechanisms of the model by Tao et al.21 However, the fuel pyrolysis process is modified, which contains CO formation and C2H2 formation reactions. In addition, the oxidation effect on the soot number density is considered. The schematic of the ABE phenomenological soot model is presented in Figure 3. There are 10 steps used to describe the

Figure 1. Schematic of the experimental setup. flame is to ensure that sufficient reflected light is collected by the camera. As shown in Figure 2, the incident light will be attenuated when it goes through the soot cloud and is reflected by the diffuser. Therefore, the changes in reflected light intensity caused by the presence of soot follow Lambert−Beer’s law I = I0 exp(−

∫0

2L

Kext dx)

(1)

Figure 3. Schematic of the 10-step phenomenological soot model.

where I and I0 are the reflected light intensities with and without the presence of the soot cloud, respectively, Kext is the extinction coefficient for a soot cloud, and L is the thickness of the soot cloud. Because the thickness of the soot cloud is usually unknown, therefore, what is obtained from the experiment is the line of sight,

soot behavior, including (1) CO formation from fuel pyrolysis, (2) acetylene formation from fuel pyrolysis, (3) generic soot precursor formation from acetylene, (4) soot particle inception, C


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Step 3: Soot Precursor Formation.

(5) bigger mature soot particles formed through coagulation of incipient soot particles, (6) incipient soot particles growing through surface growth in an acetylene-rich environment, (7) mature soot surface oxidation via oxygen, (8) mature soot surface oxidation via OH radicals, (9) O2-related acetylene oxidation, and (10) OH-related soot precursor oxidation. Because of the different fuel structures of ABE blends and change in the model structure, the rate coefficients of some steps have been adjusted or proposed on the basis of the measurements. The detailed descriptions are presented as below. Step 1: CO Formation from Fuel Pyrolysis. ω1

fuel → αCO:

ω1 = αk1[fuel]

ω3

C2 H 2 →

(4) 3

CH3COCH3 → CH3COCH 2 → CH 2CO → CH 2O → CO (5) M

CH3COCH3 → CH3CO → CH3 + CO

ω3 =

2 k 3[C2H 2] β

(8)

where k3 = 1.0 × 109 exp(−2.0 × 104/T) with the unit of s−1. The rate coefficient is reduced by 2 orders of magnitude compared to that proposed by Tao et al.21 Soot precursors are commonly considered as gaseous species and related to the detailed chemical mechanism of PAHs. According to the review by Tree and Svensson,35 although many studies have been conducted to investigate the PAH formation mechanism, uncertainties still exist in the description of pre-particle chemistry. Previous studies were mainly focused on the detailed chemical mechanisms of small PAHs, such as the first benzene ring, which is believed to be the bottleneck toward forming heavier PAHs.37,38 However, the chemical pathways of soot nucleation from higher PAHs are still elusive, and only a few aspects of these phenomena have been detected and established.26 Therefore, small PAH molecules (up to seven rings) are popularly considered as the soot precursors, which are used in diffusion combustion simulations with simplified models. Saffaripour et al.39,40 conducted a series of studies of Jet A-1 in a laminar sooting co-flow diffusion flame and assumed that soot is formed by the dimerization of pyrene, a PAH molecule with 16 carbons and 4 aromatic rings. After that, in another study,41 benzopyrene (C20H12) with five aromatic rings was adopted for nucleation instead of pyrene because pyrene molecules were not thermodynamically favored to dimerize.42−45 In some recent studies, PAHs larger than coronene (C24H12 with seven rings) were also detected in soot formation zones,46,47 which indicated that soot precursors can be larger PAHs. Furthermore, Jia et al.48 reported that ignorance of heavier PAHs can lead to a considerable loss of soot mass. As suggested by Tao et al.21 and Bi et al.,26,27 fullerene (C60) is adopted as a soot precursor species in the present model based on the observation of heavier PAHs,46,47 which contains 60 carbon atoms (β = 60) and 20 aromatic rings with a soccer structure. A one-step global reaction is adopted to describe the precursor formation. Step 4: Soot Particle Inception.

where k1 = 5.5 × 10 exp(−6.1 × 10 /T) with the unit of s−1, α is the number of carbon atoms in the fuel molecule, and [fuel] is the molar concentration of fuel (mol cm−3). On the basis of the detailed chemical mechanism of each single component in the ABE mixture and observations reported in a previous investigation,8 CO, methane, and ethylene were the major species generated during the early ABE pyrolysis process. In 2011, Harper and Pyl et al. conducted an experimental study of ABE pyrolysis in a reactor. Pyrolysis was observed to occur in the reactor when T > 823 K based on which activation energy (J mol−1) was estimated, and conversion reached more than 98% when the mean temperature reached 1100 K. As the major product of the ABE pyrolysis reaction, CO has a mass fraction around 30%. For alcohols, such as ethanol and butanol, the aldehyde group (−CHO) will be formed via the dehydrogenation reaction. Then, CO will be generated because of decomposition of the aldehyde group. For acetone, the dominant reaction paths for decomposition are listed below.31−34 2

2 precursor: β

(6)

In general, if carbon has been taken away from fuel in the form of CO, carbon will no longer evolve into a soot particle even if entering a fuel-rich zone.35 Therefore, in comparison to diesel, ABE has a lower sooting tendency because less fuel could be convert to species that are precursors or building blocks for soot. Step 2: C2H2 Formation from Fuel Pyrolysis. ω2 α α fuel → C2H 2 : ω2 = k 2[fuel] (7) 2 2

ω4

precursor → β Csoot :

ω4 = βk4[precursor]

(9)

where k4 = 1.41 × 10 exp(−2.52 × 10 /T) with the unit of mol−1 cm3 s−1. The rate coefficient is increased by a factor of 2.82 × 102 compared to that proposed by Tao et al.21 Soot consisted of various kinds of compounds, such as phenol, cyclo-propyl carbinol, ethyl cyclo-propane carboxylate, etc. Moreover, the morphological structure of soot is extremely complex and closely related to factors, such as temperature, fuel structure, etc.49 However, in general terms, it is considered as a solid substance consisting of roughly eight parts carbon and one part hydrogen. Incipient soot particles have the highest hydrogen content with a C/H ratio as low as 1, while the hydrogen fraction decreases as soot becomes mature.35 The density of soot is reported to be 1.84 ± 0.1 g/cm3,50 and most other authors adopted a slightly lower value.35 In the present model, it is assumed that incipient soot particles solely contain carbon with a density of 1.81 g/cm3. The incipient soot particles are treated as spheres with a diameter of 1.28 nm, which is the same as that in the model by Tao et al.21 10

where k2 = 4.0 × 108 exp(−2.5 × 104/T) with the unit of s−1. The rate coefficient is reduced by a factor of 4.0 × 10−2 compared to that proposed by Tao et al.21 Although the fuel pyrolysis products include several unsaturated hydrocarbons, polyacetylenes, and PAHs, it is widely accepted that acetylene is the most abundant species detected in the soot formation reaction area.36 To improve the computational efficiency, acetylene is assumed as the sole species related to soot formation during the ABE pyrolysis process. On the basis of the ABE pyrolysis investigation,8 CO formation is observed much earlier than C2H2 with the increase of the temperature from 900 to 1100 K, which indicates that the CO formation reaction has lower energy barriers. Therefore, the fuel pyrolysis process is separated into two reactions with different activation energies in the present model, with which both low- and high-temperature pyrolysis regimes might possibly be covered. D

4


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Step 5: Soot Particle Coagulation. ω5

xCsoot → Csoot :

ω5 =

1 k5Ns 2 2

contains several semi-empirical oxidation reactions. On the surface of a soot particle, there are two kinds of reactive sites, which are significantly more reactive sites and less reactive sites. The detailed description of the NSC model can be found in ref 53. Step 8: OH-Related Oxidation.

(10)

In the present study, soot particles combine via coagulation. Generally, coagulation occurs when particles collide and coalesce, thereby decreasing the soot number density without changing the combined mass of the soot particles. During coalescence, two roughly spherically shaped particles combine to form a single spherical particle. In eq 10, x (x ≥ 2) is a symbolic number indicating the number of soot particles involved in the coagulation process, k5 is the collision frequency constant, and Ns is the soot number density. In many previous research works,24,36 soot particles were normally assumed to be spherical and in the freemolecular regime, and the collision frequency can be given by

k fm = 4α

ω8

Csoot + OH → Csoot + CO +

(11)

⎛ 6m ⎞1/3 s ⎟⎟ d p = ⎜⎜ π ρ N ⎝ s s⎠

(12)

where k8 is the OH-related soot oxidation rate constant in the OH oxidation model by Neoh et al.25 It has been proven that OH radicals have a significant effect on soot oxidation. The OH radical is the dominant soot oxidant under fuel-rich and stoichiometric conditions, while soot can be oxidized by both OH and O2 under lean conditions.35,54,55 In the present model, the OH concentration is calculated on the basis of the concept of chemical equilibrium and the equilibrium system, including species of H2, O2, N2, H2O, CO, CO2, and radicals of H, O, N, and OH. The detailed description of the model by Neoh et al. can be found in ref 25. Step 9: C2H2 Oxidation. ω9

C2H 2 + O2 → 2CO + H 2 :

where α is the van der Waals enhancement factor, kB is the Boltzmann constant, ρs is the mass density of soot, T is the ambient temperature, dp is the mean diameter of soot nuclei, and ms is the soot mass concentration. However, under high-pressure diesel engine conditions, it is unreasonable to treat soot coagulation as a free-molecular process because the gas mean free path and the particle size are at comparable levels. Therefore, in the present model, the collision frequency is described with the expression suggested by Pratsinis51 and Kazakov and Foster,52 which is valid for both the free-molecular regime and near-continuum regime k5 =

1 H2: 2 ⎛ −2.0 × 104 ⎞ ω10 = 1.0 × 1010 exp⎜ ⎟[precursor][OH] T ⎠ ⎝ ω10

(19)

where k10 = 1.0 × 10 exp(−2.0 × 10 /T) with the unit of mol−1 cm3 s−1. The rate coefficient is an order of magnitude higher than that in the model by Tao et al.21 Acetylene and soot precursor are both the gas-phase products involved in preparticle chemistry. As suggested by Tao et al.21 and Bi et al.,27 oxidation processes of acetylene and soot precursors have been considered, which are described in steps 9 and 10, respectively. 3.2. Oxidation Effect on the Soot Number Density. In both of the models by Fusco et al. and Tao et al., it was assumed that oxidation did not affect the particle number density, and the soot molar concentration and mass concentration are given by eqs 20 and 21, respectively

(13)

8kBT (1 + 1.257k n) μ

(14)

where μ is the gas viscosity, kn is the Knudsen number, and the near-continuum slip correction factor is adopted following the suggestion by Pratsinis,51 which is equal to 1.257. Step 6: Surface Growth. ω6

Csoot + C2H 2 → Csoot + H 2 :

ω6 = k6[C2H 2]A soot1/2 (15)

where k6 = 1.05 × 104 exp(−3.1 × 103/T) with the unit of cm−1 s−1. The rate coefficient is kept the same as that in the model by Tao et al.21 In eq 15, Asoot is the total surface area of soot particles. As suggested by Leung et al.,24 (Asoot)1/2 is adopted instead of Asoot, which is proven to achieve a better description of soot growth in laminar diffusion flames. Step 7: O2-Related Oxidation. ω7

Csoot + O2 → Csoot + 2CO:

ω7 = k 7A soot

(18)

4

⎯ CO + precursor + OH ⎯→

and the collision frequency in the near-continuum regime can be given by k nc =

ω9 = k 9[C2H 2][O2 ]

where k9 = 6.0 × 10 exp(−2.52 × 10 /T) with the unit of mol−1 cm3 s−1. The rate coefficient is kept the same as that in the model by Tao et al.21 Step 10: Soot Precursor Oxidation. 12

10

k fmk nc k fm + k nc

ω8 = k 8A soot (17)

6kBTd p ρs

1 H2: 2

4

dNs = ω4 − ω5 dt

(20)

dms = ω4 MWinc + (ω6 − ω7 − ω8)MWc dt

(21)

where Ns and ms represent the soot molar concentration and soot mass concentration, respectively, MWinc is the molecular weight of incipient soot particles, and MWc is the molecular weight of the carbon atom. As shown in eqs 20 and 21, only soot inception and coagulation were taken into account for the calculation of the soot number and the oxidation of soot affected only the size of particles, while the soot number remained the same. Nevertheless, Kazakov et al.52 pointed out that this assumption could result in a numerical problem when encountering a strong

(16)

where k7 is the O2-related soot surface oxidation rate constant, which is calculated on the basis of the Nagle/StricklandConstable (NSC) oxidation model.53 Briefly, the NSC model E


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competition against the combined effects of soot inception and surface growth, which indicates a extremely strong oxidation effect. In this case, the total soot mass will decrease and all incipient soot particles are assumed to burn out first. Thereby, the change of the soot molar concentration is only related to the coagulation effect, which is given by

oxidation effect. This assumption may lead to an unrealistic particle size, which is smaller than the incipient soot particle size or even in the carbon atom level. On the other hand, situations in which soot particles are completely burnt out cannot be excluded in the real engine case. In the present model, the oxidation effect on the soot number density is considered through four situations, as shown in Figure 4. The soot number density will be updated according

dNs = ω5 dt

(25)

(3) RG−O < 0, dms/dt ≥ 0, and ds ≥ 1.28 nm. This indicates that surface oxidation dominates over surface growth. However, the total soot mass will increase because of soot inception. In this situation, a critical diameter is defined to be equal to the diameter of incipient soot particles, which is 1.28 nm. If the mean diameter of soot particles ds is larger than or equal to this critical diameter, only the molar concentration of incipient soot will decrease because of the oxidation effect, which is given by ⎛ ⎞ dNs R = ⎜ω4 + G−O ⎟ − ω5 dt MWinc ⎠ ⎝

(4) RG−O < 0, dms/dt ≥ 0, and ds < 1.28 nm. The first two conditions are the same as those described in situation 3. However, ds is smaller than the critical diameter, which indicates that the oxidation effect is stronger than that in situation 3 but still weaker than that in situation 2. In this case, it is assumed that molar concentrations of both incipient and mature soot particles will decrease because of the oxidation effect, which is given by

Figure 4. Schematic of the oxidation effect on the soot number density.

to the combined effect of particle inception, coagulation, and oxidation. Before being updated, the soot number density is the sum of two kinds of soot particles. One is mature soot particles with a mean diameter from the last time step, and the other is incipient soot particles. These two kinds of particles are represented with the white and black spherical symbols in Figure 4. Because of the complex soot evolution process, the molecular weight of soot is renewed at each computational step based on the total soot mass and updated soot number density. The mean particle diameter and molecular weight can be given by eqs 22 and 23, respectively ⎛ 6m ⎞1/3 s ⎟⎟ ds = ⎜⎜ ⎝ πNsNAρs ⎠

(22)

ms Ns

(23)

MW =

⎛R ⎛ dNs t⎞ R R t⎞R = ω4 − ⎜ G−O ⎟ G−O + ⎜1 + G−O ⎟ G−O − ω5 dt ms ⎠ MW ⎝ ms ⎠ MWinc ⎝ (27)

3.3. Numerical Implementation. In this study, the KIVA-3 V Release 2 code is used as the simulation tool to solve the mass, momentum, and energy conservation equations for main species whose concentrations are affected by the present soot model. Several physics and chemistry sub-models are implemented in the code. The renormalization group (RNG) k−ε model56 is used to simulate the low Mach-number turbulent effect on transportation and flow field. The “blob” model57 is used for simulating the fuel parcel injection dynamics, while the Kelvin−Helmholtz and Rayleigh−Taylor models58,59 are used for simulation of spray atomization and droplet breakup. For the combustion process, the “Shell” ignition model60 is used for simulating the autoignition process related to low-temperature reactions. For different fuels, the “Shell” model specifies different values for the kinetic parameter of the crucial reaction, leading to the production of the branching agent. To reproduce the ABE ignition delay measurements, the pre-exponential coefficient (Af4) of the reaction rate in this reaction is modified or proposed in the present study, which is equal to 5.85 × 106. Once the value is chosen, it is kept the same under different operation conditions. As the temperature increases, the one-step reaction model is used instead of the “Shell” model. The switching temperature used in this study is 1150 K. To reduce the computational cost for the axial symmetrical geometry and conditions, a 60° sector mesh is used to simulate a single spray of the injector.

where NA is Avogadro’s constant. The competition between the soot particle surface growth and oxidation can be given by R G−O = (ω6 − ω7 − ω8)MWc

(26)

(24)

On the basis of the evaluation of the right-hand side of eqs 21, 22, and 24, the four regimes of soot growth and oxidation are as follow: (1) RG−O ≥ 0. It is indicated that surface oxidation loses the competition against surface growth. On the basis of eq 21, it can be seen that the total soot mass will increase. In this situation, the effect of surface oxidation on the soot number can be neglected, and the soot molar concentration is given by eq 20, which is the same as that in the model by Tao et al. (2) RG−O < 0, and dms/dt < 0. Surface oxidation wins the F


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Figure 5. Comparison of measured and predicted chamber pressure and heat release rate under 1000 K initial temperature with 21, 16, and 11% oxygen concentrations.

4. RESULTS AND DISCUSSION In the present study, the KIVA-3 V Release 2 code coupled with the ABE phenomenological soot model was validated on the basis of the measured chamber pressure, apparent heat release rate (AHRR), and time-related soot mass under 1000 K initial temperature with oxygen concentrations of 21, 16, and 11%. The predicted soot number density, mass concentrations of soot-relevant species, and rates of soot formation and oxidation were presented to explain the soot behavior. In addition, two-dimensional (2D) distributions of soot-relevant species were also presented to give a further understanding of the soot formation and oxidation process in the diesel-like spray flame. 4.1. Combustion Characteristics. Figure 5 presents the comparison of measured and predicted chamber pressure and AHRR for ABE at 1000 K ambient temperature with different oxygen concentrations. The predictions of the combustion process were in good agreement with experiments, which demonstrated the feasibility of using KIVA 3 V Release 2 code for the simulation in a constant volume chamber. Because the pressure is directly proportional to the temperature in a constant volume chamber, the chamber pressure represents the pressure rise relative to the initial pressure in the present study.

As shown in Figure 5, ignition timing retards slightly when the oxygen concentration decreases from 21 to 11%. Similar results can be observed in previous studies,13,61 which include diesel, biodiesel, and ABE−diesel blending. On the basis of the theory of chemical kinetics, a lower oxygen concentration reduces the chance of effective collision between oxygen and fuel molecules, which results in a lower chemical reaction rate. Figure 5 also shows that AHRR curves decay slower after the first peak with lower oxygen concentrations, which confirms the impact of ambient oxygen on the chemical reaction rate. It is interesting to see that a sharp increase of AHRR is observed at 1000 K with lower oxygen concentrations of 16 and 11%. Similar results have been reported by Nan et al.13 As mentioned before, the ignition delay becomes longer as the oxygen concentration decreases. With a longer ignition delay, there is more time for air−fuel mixing. As a result, a large amount of premixed charge is formed before achieving selfignition and a stronger premixed dominate combustion mode can be observed. Furthermore, because the ABE mixture has a high oxygen ratio, an efficient combustion can be achieved, even at low ambient oxygen concentrations. 4.2. Effect of the Oxygen Concentration on Soot Behavior. Figure 6 presents the predicted and measured timerelated soot mass traces at 1000 K with 21−11% oxygen G


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the “Shell” ignition model and one-step reaction model oversimplify the chemical reaction kinetics, and phenomenological soot models describe the complex process of soot formation and oxidation in terms of several global steps. Despite these existing discrepancies, it is computationally efficient when such simple sub-models are adopted in practical engine simulation. Tao et al.62 also reported that the prediction capacity of this kind of MSP soot model was comparable to simplified chemical kinetics models, which contained thousands of reactions and a large amount of intermediate species. The evolution process of soot mass can be divided into two parts, as shown in Figure 6. In the first period, the soot mass starts to increase rapidly and reaches its peak value because of the dominating effect of soot formation at the early stage. Then, the soot oxidation effect gains strength, and total soot mass decays quickly. Similar trends can be observed in the previous studies.18,19,21−23,27 The threshold of soot mass is delayed gradually when the oxygen concentration decreases from 21 to 11%, which has the same trend as that of ignition delay. Because the temperature is the most essential parameter to determine the pre-flame reaction rate, a longer ignition delay indicates lower formation rates of soot and relevant species at the early stage of combustion. As shown in Figure 7, the thresholds of soot number density and relevant species are also retarded with decreasing oxygen concentrations. Lower peak

concentrations. As mentioned above, all of the simulations are conducted using the same rate coefficients of the soot model. A correction factor of 1.2 is needed for predictions of all cases to match the magnitude of the measurements. As shown in Figure 6, the predicted results match reasonably well with the measurements in trend and in quantity to some extent. The discrepancies can be a result of several sources. For example,

Figure 6. Comparison of predicted and measured soot mass behaviors under 1000 K initial temperature with 21, 16, and 11% oxygen concentrations.

Figure 7. Predicted soot number density and mass traces of soot relevant species from 21 to 11% oxygen concentrations at 1000 K initial temperature. H


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Figure 8. Predicted soot formation and oxidation rates from 21 to 11% oxygen concentrations at 1000 K initial temperature.

values of soot relevant species, such as acetylene and precursors, can be observed when the oxygen concentration decreases from 21 to 11%. Another important observation is that the peak value of the total soot mass initially increases with decreasing ambient oxygen concentrations from 21 to 16% and then decreases to the lowest level at 11% oxygen concentration. This observation is coincident with the experimental results reported by Idicheria et al.63 and Bi et al.26 To understand this non-monotonic soot behavior, the soot formation and oxidation rates are presented in Figure 8. All of the rates in Figure 8 are normalized results, which are divided by the inception rate under 1000 K with 21% oxygen concentration. The soot inception and surface growth reactions are the two constructive pathways related to total soot mass in the present model, while the O2- and OH-related oxidation reactions are the destructive pathways. As shown in Figure 8, both formation and oxidation rates continue to drop because of the lower mean combustion temperature when ambient oxygen decreases from 21 to 11%. At 16% oxygen concentration, a sharper decrease of soot oxidation rates than that of formation rates can be observed. Therefore, the higher yield of soot at 16% ambient oxygen should be mainly caused by the suppression of the oxidation mechanism. As ambient oxygen is further diluted to 11%, the reaction rates and mass concentrations of relevant species decrease to a extremely low level. However, the peak value of total soot mass turns to

decrease. Therefore, it is evident that soot formation rates take the place of oxidation rates in determining the soot evolution at high-diluted ambient oxygen conditions. On the basis of panels a and b of Figure 8, the rapid increase of the soot inception rate comes out later than the surface growth rate, regardless of the oxygen concentration. In the present model, the soot formation process is separated into soot inception and surface growth reactions with different activation energies, with which both high- and low-temperature formation regimes are covered. The same approach has been employed in the models by Fusco et al. and Tao et al., and the activation energy of surface growth is much lower than that of the inception reaction. At the early stage of combustion, only a few soot particles and precursor species are formed because of the low temperature. It is indicated that the early stage growth of soot mass is mainly related to the surface growth reaction, while the soot inception reaction becomes the dominant effect as the temperature increases. 4.3. Spatial Distributions of Soot and Relevant Species. Figure 9 presents the spatial distributions of the local temperature, equivalence ratio, and mass fractions of soot, acetylene, soot precursors, and OH radicals at 1000 K with 21, 16, and 11% ambient oxygen. The spatial distributions shown in Figure 9 are instances at the time when the soot mass concentration reaches its peak value after fuel injection. It can be seen that the equivalence ratio, acetylene, precursors, soot I


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although the OH radicals also decrease significantly at such high-dilution ambient oxygen conditions, the resulting lowest soot mass concentration indicates that the suppressed soot formation mechanism dominates the soot behavior instead of the soot oxidation mechanism. On the basis of the distributions of C2H2, soot precursors, and soot particles shown in parts c, d, and e of Figure 9, C2H2 has a wider distribution than that of soot precursors and soot particles. However, the distribution of soot particles almost duplicates the distributions of precursor species. This observation reveals that soot precursors are the species that determine the locations where soot particles are formed, while the total yield of soot mass depends upon both the effects of acetylene and soot precursor species.

5. CONCLUSION The ABE mixture is a competitive alternative fuel or fuel additive, because it cannot only improve the thermal efficiency but also reduce soot emission. In the present study, experiments are conducted in an optical constant volume chamber to explore combustion and soot emission characteristics of ABE under 1000 K initial ambient temperature with oxygen concentrations of 21, 16, and 11%. In parallel, an ABE phenomenological soot model is proposed and implemented into KIVA-3 V Release 2 code. Simulations are also conducted under the experimental conditions. A comparison of high timeresolved quantitative soot measurements and the predictions confirms the capability of using this phenomenological soot model to reproduce the ABE soot behavior qualitatively. In addition, predicted traces and spatial distributions of sootrelevant species are also presented to bring more insights into the soot formation and oxidation processes. The main conclusions are summarized as follows: (1) The phenomenological ABE soot model is fundamentally consistent with the physics and chemistry of soot formation and oxidation processes in spray combustion engines. The trends of predicted soot behavior under various oxygen concentrations are in agreement with measurements. Furthermore, it is computationally efficient and suitable to be implemented into multi-dimensional CFD tools for engine system analysis. In comparison to other semiempirical soot models, the oxidation effect on the soot number density is considered in the present model, which can simulate the burnout of the soot particle. (2) As ambient oxygen decreases from 21 to 11%, ignition delay is prolonged because of the suppressed pre-flame chemical reaction rate, which leads to more time for air and fuel mixing. As a result, spatial distributions of local temperature and equivalence ratio become more homogeneous and the area of local high-temperature and equivalence ratio regions start to shrink. (3) The total yield of soot particles initially increases with decreasing oxygen concentrations from 21 to 16% and then decreases to the lowest level at 11% ambient oxygen. On the basis of the analysis of soot formation and oxidation rates, this non-monotonic soot behavior with various oxygen concentrations reveals the transition of the dominant effect from the soot oxidation mechanism to the formation mechanism. (4) Acetylene dominates the soot formation at the early stage of combustion via the surface growth reaction, while the soot inception reaction becomes the dominant effect as the temperature increases. Although acetylene has a wider distribution than soot precursors, the results indicate that soot precursors are the species to determine the locations where soot particles are formed.

Figure 9. Predicted spatial distributions of the chamber temperature, equivalence ratio, and mass fractions of acetylene, soot precursor, soot nuclei, and OH radical from 21 to 11% oxygen concentrations.

particles, and OH radicals are mainly detected within the high-temperature regions. These distributions illustrate the essential role that the temperature played in soot formation and oxidation processes. According to Figure 9a, the local peak temperature decays at lower ambient oxygen operation conditions, especially at 11% ambient oxygen. Furthermore, the area of the high-temperature region shrinks obviously with a decrease of ambient oxygen, and a more homogeneous distribution of the temperature can be observed. Because the ignition delay retards at low ambient oxygen conditions, a longer flame lift-off length can be observed. According to previous studies, flame lift-off allows fuel and entrained air to premix upstream of the spray jet, which affects the combustion and soot formation processes downstream. Therefore, in general, a longer lift-off length enhances oxidation species entrainment, hence leading to a lower yield of soot. At 16% ambient oxygen, however, the total yield of soot is higher than that of the other two cases. On the basis of parts d and f of Figure 9, the precursor species and OH radicals at 16% ambient oxygen are both lower than those at 21% ambient oxygen. Nevertheless, OH radicals, the representative reactants during the soot oxidation process, decrease faster than precursor species, which are involved in the soot inception reaction. In other words, soot oxidation rates decrease faster than formation rates at 16% ambient oxygen, as mentioned before. When ambient oxygen falls to 11%, the prolonged ignition delay further reduces the area of the fuel-rich region and the distribution of the equivalence ratio becomes more homogeneous, as shown in Figure 9b. Kitamura et al.64 reported that soot did not form in the regions where the equivalence ratio was lower than 2, which is also observed in the present study. In combination with the effect of a continuous drop of the combustion temperature, the regions with high concentrations of precursor species and C2H2 shrink obviously, which lead to significant suppression of the soot formation rate. Therefore, J


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AUTHOR INFORMATION

Corresponding Author

*Telephone: 1-217-3335879. Fax: 1-217-2446534. E-mail: cfl[email protected]. Notes

Disclaimer: Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This material is based on work supported by the National Science Foundation under Grant CBET-1236786 and the National Basic Research Program of China (973 Program) under the Project 2011CB707201. This work was also supported by the China Scholarship Council (201206130033) and the Hunan Provincial Innovation Foundation for Postgraduate under the Project CX2013B149.

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NOMENCLATURE ABE = acetone−butanol−ethanol FILE = forward-illumination light-extinction I = reflected light intensities with the presence of the soot cloud I0 = reflected light intensities without the presence of the soot cloud Kext = extinction coefficient for the soot cloud L = thickness of the soot cloud λ = wavelength of monocolor light Ka = dimensionless absorption constant ρs = mass density of the soot particle Cv = soot volume fraction ω = reaction rate Ns = soot number density kB = Boltzmann constant kfm = collision frequency in the free-molecular regime knc = collision frequency in the near-continuum regime kn = Knudsen number NA = Avogadro’s constant MWinc = molecular weight of incipient soot particles MWc = molecular weight of carbon atom i = index of the computational cell n = index of the time step Δt = time interval

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Computational Investigation of Oxygen Concentration Effects on a

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