Article Cite This: Energy Fuels XXXX, XXX, XXX-XXX
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Simulated Kinetics and Chemical and Physical Properties of a FourComponent Diesel Surrogate Fuel Qiaoling Wang and C. P. Chen* University of Michigan−Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China ABSTRACT: Real diesel fuels are mixtures composed of hundreds to thousands of components; thus, developing surrogate fuels composed of a few representative hydrocarbon components is essential for multidimensional computational fluid dynamics spray combustion simulation purposes. Surrogates that can characterize the thermophysical properties and evaporation processes of real fuel are the “physical” surrogates. Surrogates that are able to mimic fuel chemical-kinetics-related properties are viewed as the “chemical” surrogate models. For spray combustion modeling, fuels experience thermophysical (heating and evaporation) and chemical kinetics (ignition and combustion) processes. To model the multiphase spray combustion process, a “unified” diesel surrogate, which can emulate both the physical and chemical (kinetics) properties of the real diesel fuel, is proposed in this study. A group of hydrocarbon species was selected, using an inversed batch distillation methodology, to match the experimental distillation curve of standard diesel blends. For the chemical kinetics target, a detailed reaction mechanism of 352 species with 13 264 reactions was used for gas-phase ignition delay time predictions and a reduced reaction mechanism of 200 species with 6907 reactions was used for laminar flame speed simulations. On the basis of the hydrocarbon class concentrations of typical diesel fuels of normal/isoalkanes, cycloalkanes and aromatics, this study identified the four-componet surrogate fuel for diesel fuel as 1,2,4-trimethylbenzene (C9H12), trans-decalin (C10H18), heptamethylnonane (iC16H34), and n-hexadecane (C16H34) with mole fraction 0.262:0.065:0.365:0.308. Important thermophysical and chemical targets, including molecular weight, lower heating value, cetane number, hydrogen/carbon mass ratio, density, kinematic viscosity, surface tension, and specific heat, are also predicted using this surrogate. In addition, chemical kinetics characteristics, including ignition delay times as well as laminar flame speeds, are used to validate the proposed surrogate fuel.
1. INTRODUCTION During the last few decades, diesel engines have become important energy-supplying sources for various industrial sectors, including transportation and manufacturing. Although diesel engines have better combustion and thermal efficiencies when compared to gasoline engines, issues related to emissions and cold starts require further improvement. Engine performance depends highly upon the fuel type and the subsequent fuel atomization, fuel droplet breakup, vaporization, mixing, and combustion processes. Multidimensional computational fluid dynamics (CFD) methods have been routinely used to gain insight of complex phenomena associated with spray combustion involving real transportation fuels. Real diesel fuels are mixtures composed of hundreds to thousands of components; thus, it is not possible to perform real fuel spray combustion simulations, even with high-performance computing. Therefore, in developing surrogate fuels composed of only a few representative hydrocarbon components, it is essential that they mimic the properties of real fuel. Component selection for surrogate fuels is closely related to the targeted applications. Surrogates that can characterize the thermophysical properties and evaporation processes of real fuel are called “physical” surrogates,1 and surrogates that are capable of mimicking fuel chemical kinetics-related phenomena (for example, gas-phase ignition delay times, laminar flame speed, etc.) are considered as “chemical” surrogate models.2 In the previous research, much emphasis has been put on gasphase chemical kinetics. Therefore, the suggested surrogates usually focus on a single-phase gas combustion process. © XXXX American Chemical Society
However, some of the chemical surrogates are not able to match physical properties, including fuel volatility or distillation curve, and, thus, are not suitable for characterizing the complex process of droplet heating and evaporation. In many cases, surrogate development and validation studies predict the distillation curve, evaporation process, and chemical kinetics separately. For spray combustion modeling, fuels experience thermophysical (heating and evaporation) and chemical kinetics (ignition and combustion) processes. To model the multiphase spray combustion process, a “unified” diesel surrogate that can emulate the physical, chemical, and combustion (kinetics) properties of the real diesel fuel is highly desirable. Research activities on developing surrogate diesel fuels have been active for the past few decades. Recent proposed multicomponent-based surrogates are summarized in Table 1. Table 1a summarizes the surrogates based on physical and chemical property predictions, while Table 1b summarizes surrogates focused on chemical kinetics properties of diesel predictions. Luo et al.3 uses a three-component surrogate to simulate ignition delay and combustion phenomenon in a simplified engine. This surrogate, however, shows high discrepancy in physical properties, such as density and C/H mass ratio. Liang et al.4 uses two different surrogates for engine Received: July 6, 2017 Revised: November 8, 2017 Published: November 9, 2017 A
DOI: 10.1021/acs.energyfuels.7b01940 Energy Fuels XXXX, XXX, XXX−XXX
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Energy & Fuels Table 1. (a) Surrogates Focused More on Diesel Physical Properties5−8 and (b) Surrogates Focused More on Diesel Combustion Properties3,4,9,10 (a) Surrogates Focused More on Diesel Physical Properties5−8 fuel 15 a
fuel 26
fuel 47
fuel 58
mol %, vol %, and wt %
species
mol %
species
mol %
species
mol %
species
mol %
n-alkanes
nC18H38 nC16H34 nC14H30 nC12H26 nC10H22
11.5 13.4 23.7 22.2 14.2
nC18H38 nC16H34
20.2 2.7
nC16H34
27.8
nC20H42 nC18H38
0.8 10.8
nC22H46 nC20H42 nC16H34
6.3 6.44 10.04
iC16H34 C10H20 C10H18
29.2 5.1 5.5
iC16H34 C10H18
36.3 14.8
18.54 4.23
14.4 15.4 7.5
C11H10
21.1
7.3 19.1 11.0 6.0 14.7 16.4 13.9
iC16H34 C8H16
C11H10 C10H12 C9H12
iC18H38 C10H20 C15H30 C14H24 C15H24 C10H12 C11H10
C11H10 C10H12 C9H12 C8H10 C18H36 846 (288 K) 42.6 46.6 7.02
11.5 13.17 10.73 4.18 14.86
isoalkanes cyclohexane
aromatics
C7H8
olefins density at 293 K (kg/m3) LHV (MJ/kg) DCN C/H mass ratio
769.35 44.10 82.79 5.81
15.8
species
817 818 853 43.15 43.3 42.5 47.7 46.2 42.6 6.55 6.30 6.96 (b) Surrogates Focused More on Diesel Combustion Properties3,4,9,10 fuel 69
a
a
fuel 37
fuel 710
fuel 83
fuel 94
wt %
fuel 104
mol %, vol %, and wt %
species
mol %
species
wt %
species
vol %
species
vol %
species
vol %
n-alkanes
nC10H22
50.39
nC14H30 nC7H16
47.6 12.9
nC7H16
76.0
51.0 33.5
iC8H18 C7H14 C7H8
10.56 12.86 26.19
27.3 21.9 30.2
nC14H30 nC10H22
isoalkanes cyclohexane aromatics olefin density at 293 K (kg/m3) LHV (MJ/kg) DCN C/H mass ratio chemical properties
nC14H30 nC10H22 iC16H34
C6H12 C11H16
7.0 32.5
C11H10
20.5
C11H10
15.5
43.43 52.17 6.83 ignition delay and premixed laminar flames
low-temperature combustion and emission processes
C7H8 19.0 C6H12 5 722 44 47.3 5.784 ignition delay, combustion, and emission
817.7 43.2 46.2 6.6 diesel engine combustion
796.9 43.5 72 6.4 diesel engine combustion
Mole fraction, volume fraction, and mass fraction.
Table 2. Physical and Chemical Properties of CFA6 and U.S. #2 Diesel Fuel12,22 diesel fuel
DCN
LHV (MJ/kg)
C/H mass ratio
density, ρ(T) (kg/m3)
kinematic viscosity, μ(T), at 313 K (m2/s)
surface tension at 293 K (mN/m)
CFA U.S. #2
43.7 46
42.9 42.98
6.68 6.53
848 (293 K) 843 (288 K)
2.30 × 10−6 2.35 × 10−6
25.723
spray combustion simulations. Although the surrogate (fuel 10 in Table 1b) used in that study matches overall engine combustion properties, it yields a derived cetane number (DCN) of 72 that is not consistent with a typical real diesel DCN. From Table 1, some surrogates (e.g., fuels 2, 3 and 4) can match the chemical (DCN, hydrocarbon composition, and carbon bond type sooting tendency) and physical characteristics; however, the validation process has not been extended to include simulations of ignition delay over a range of temperature and pressure conditions or laminar flame speed predictions. The objective and new contribution of the present study is to propose a four-component surrogate fuel that can simultaneously mimic the distillation curve, physical and chemical characteristics, as well as kinetics properties of real
diesel fuel. In the following, the methodologies used and the validation studies are described.
2. METHODOLOGIES FOR SURROGATE DIESEL FUEL The current methodology began by selecting components within the major hydrocarbon groups and using the inversed batch distillation methodology11 to determine the composition that matched the diesel distillation curve profile. To this end, a 2007 #2 ultralow-sulfur diesel (ULSD) Certification Fuel Batch A (CFA, introduced by Mueller et al.6) and a typical U.S. #2 diesel fuel (described by Singh et al.12) are used as targets because these fuels have been extensively studied. These two diesels are referred to as the “ultralow-sulfur 2007 emissions certification number 2 diesel fuels”. There are nonsignificant differences in physical properties as a result of different measurement techniques used, as seen in Table 2. B
DOI: 10.1021/acs.energyfuels.7b01940 Energy Fuels XXXX, XXX, XXX−XXX
Article
Energy & Fuels This study first formulates the diesel surrogates based on the distillation curve (fuel volatility) property matching, because it is one of the important properties for surrogates;5,6,8 specifically, the fuel volatility will directly influence the liquid fuel penetration, evaporation process, and, thus, the whole spray combustion process.1,13 The concentration of components was adjusted to match the physical and chemical properties of U.S. #2 diesel. To consider the chemical kinetics properties of diesel surrogates, components whose chemical reaction mechanism is available in the literature were selected.6,7,14 Boiling points of U.S. #2 diesel components vary from 463 to 623 K.12 Because of the heavy components in diesel fuel, the boiling point of diesel is higher than those of gasoline and kerosene. This makes it hard to select the proper components because the chemical kinetics of heavy components are rare compared to that of light components. Table 3 shows the typical hydrocarbon classes of diesel fuel. For n-
the baseline surrogate, named Joint Institute diesel surrogate (JI-D), is listed in Table 4. In addition to this baseline surrogate, two additional surrogates [first adjusted Joint Institute diesel surrogate (JI-D_ad1) and second adjusted Joint Institute diesel surrogate (JI-D_ad2)] were further proposed by manually adjusting the compositions to better match other physical properties. Because nC16H34 and iC16H34 have lower density and C/H mass ratio compared to real diesel, their concentrations were slightly reduced to better match the real diesel fuel properties. These two properties significantly affect the liquid jet penetration13 and the fuel combustion process.21 At the same time, the simulated distillation curves were rechecked to match the ASTM D86 distillation curve. The two additional surrogates were identified after these adjustments.
3. SURROGATE PHYSICAL AND CHEMICAL PROPERTIES The simulated distillation curves using the three surrogates are shown in Figure 1. All distillation curves match reasonably well
Table 3. Hydrocarbon Class Concentration of CFA6 and U.S. #2 Diesel17,24 mass fraction
n-/isoalkanes
cycloalkanes
aromatics
others
CFA U.S #2
0.254 0.25−0.50
0.435 0.20−0.40
0.308 0.15−0.40
0.003 _
alkane, the heaviest component with available chemical kinetics is nhexadecane. It is interesting to note that these four components were among the surrogate palette compounds recently suggested by Mueller et al.7 To determine the composition, the inversed batch distillation methodology of Abianeh et al.11 was used. The inversed batch distillation problem can be simply stated as, given an experimental distillation curve that is a sequence of temperatures containing the initial and final boiling points of a mixture of unknown composition, given the fraction of moles collected for each temperature interval as well as the fraction of moles of the residue, and given a set of molecular species that will be used to define the surrogate mixture, find the composition of the surrogate mixture that will satisfy the fraction of distillate cuts at the prescribed interval temperatures. The solution methodology developed by Abianeh et al.11 requires the same number of chemical species in the surrogate mixture as the points of the distillation curve that will be matched precisely. The choice of exact points to match on the distillation curve conforms a square system of equations, where the number of equations is equal to the number of unknowns. Other points of the distillation curve are satisfied within a prescribed small error tolerance. The system of equations was derived on the basis of the mass balance for each component at each distillate cut, subject to the molar fraction and bubble point constraints at the end temperature of the cut. The solution of the set of equations determines the surrogate composition. After the mole fractions of each species are obtained, the simulated distillation curve can be generated using the direct (forward) batch distillation method attributed to Rayleigh,15 with the phase equilibrium described by Raoult’s law.16 It should be noted that any experimentally obtained distillation curve, such as the advanced distillation curve (see ref 6 and the cited references therein), the boiling curve according to ASTM 2887 (see ref 8 and the cited references therein), etc., can be used. In this study, we chose the standard test ASTM D86 distillation curve of diesel fuel because it was used extensively in the literature.17−20 Using the inversed batch distillation methodology, the resulting composition of
Figure 1. Predicted distillation curves of surrogates JI-D, JI-D_ad1, and JI-D_ad2, with experimental data from CFA6 (measured by the ASTM D86 method) and U.S. #212 used for comparison.
with the experimental data,6,12 measured with the ASTM D86 standard, from 10 to 80% volume fractions. The wide range of the boiling temperature of the components in diesel fuel makes it hard to match the whole distillation curve. Toward the end of distillation, surrogates deviate from real diesel fuel as a result of the lack of a heavy component in the current proposed surrogates. Some important physical and chemical properties of the three proposed surrogates are compared to CFA and U.S. #2 diesel fuel in Figure 2. For each single component, its thermophysical properties are estimated using correlations from the Design Institute for Physical Properties (DIPPR) equation library.25 Following the practice previously used in the literature14,26 to calculate mixture properties, this study uses a volume-fraction-averaged model for the density and cetane number and a mass-fraction-averaged model for the lower
Table 4. Composition of JI-D, JI-D_ad1, and JI-D_ad2 JI-D
JI-D_ad1
JI-D_ad2
composition
mass fraction
mole fraction
mass fraction
mole fraction
mass fraction
mole fraction
nC16H34 iC16H34 C10H18 C9H12 MW
0.398 0.440 0.003 0.159 209.24
0.348 0.385 0.005 0.262
0.362 0.428 0.047 0.163 204.99
0.308 0.365 0.065 0.262
0.319 0.455 0.061 0.165 203.49
0.269 0.384 0.085 0.262
C
DOI: 10.1021/acs.energyfuels.7b01940 Energy Fuels XXXX, XXX, XXX−XXX
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Energy & Fuels
Figure 2. Predicted physical and chemical properties: (a) LHV, (b) ignition qualities quantified by DCN, (c) carbon and hydrogen mass fraction, (d) surrogate density profile compared to experimental data,31 (e) surrogate kinematic viscosity profile plotted against the data of No. 2 diesel,32 (f) surrogate surface tension compared to experimental surface tension data,23 and (g) surrogate specific heat plotted against V0a (V0a is the fourcomponent surrogate of Mueller et al.7) and mean specific heat value (from 273 to 373 K) of ESSO diesel. The error bars in panels a and b indicate the minimum and maximum values of the measured LHV and DCN, respectively. D
DOI: 10.1021/acs.energyfuels.7b01940 Energy Fuels XXXX, XXX, XXX−XXX
Article
Energy & Fuels
4. CHEMICAL KINETIC SIMULATIONS To investigate the chemical kinetics properties of the surrogate JI-D_ad1, the detailed multicomponent chemical kinetics of 352 and 13 264 reactions (POLIMI_PRF_PAH_RFUELS_LT_1412) from The CRECK Modeling Group34 were used to predict the chemical ignition delay times. For ignition delay predictions, chemical kinetics covering both low and high temperatures should be used, because ignition characteristics in the low-to-intermediate temperature range determine diesel engine performance. The detailed kinetics scheme34 contains all components of the JI-D surrogate and is suitable for ignition delay predictions with its wide temperature range. This detailed mechanism can be used for prediction of real transportation fuels, such as gasoline, jet fuels, and diesel, and involves n-alkanes (up to C16 n-alkanes), isoalkanes, methylcyclohexane, aromatics, decalin, and tetralin. The gasphase ignition delay predictions of the surrogate were performed using the CANTERA35 constant volume homogeneous reactor model. The ignition delay time is defined as the time when the OH mass fraction rising profile reaches the maximum slope. In Figure 3, ignition delay times of JI-D_ad1
heating value (LHV). Mixture kinematic viscosity is estimated using the Refutas index method,27 while surface tension is predicted with the parachor correlation.28 The LHV is one of the important properties for the surrogate, because it will influence the heat release in the combustion process. Viscosity and surface tension are also included here because they have certain effects on the spray and atomization processes.29 For the LHV, all three proposed surrogates give good predictions. The LHV for CFA and U.S. #2 are 42.9 and 42.98 MJ/kg, while JI-D_ad1 and JI-D_ad2 give 43.36 and 43.34 MJ/kg, respectively. For LHV, the reproducibility uncertainty of ASTM D4809 was 0.2−0.45 MJ/kg. The overall deviation of the predicted LHV versus experimental data is about 0.84− 1.1%. For DCN, CFA diesel and U.S. #2 vary and give values of 43.7 and 46. Uncertainty of DCN by the ASTM D6890, shown in Figure 2b, is 0.3.6 DCN of each component within the JI-D surrogate refers to National Renewable Energy Laboratory (NREL) data.30 The DCN of JI-D_ad1 is 46.8, which is close to that of U.S. #2 and has a deviation of 1.7%. The DCN of JID_ad2 is 43.3, which is close to that of CFA and gives a deviation of 0.92%. The C/H mass ratio of JI-D_ad1 and JID_ad2 gives a deviation of 7−9% when compared to the experimental data. The temperature-dependent properties of density, kinematic viscosity, and surface tension are plotted in panels d−f of Figure 2 for all three surrogates. U.S. #2 diesel has a density of 843 kg/m3 at a temperature of 288 K, while adjusted JI-D_ad1 and JI-D_ad2 predict 803 and 805 kg/m3. The JI-D_ad1 surrogate captures the trend when compared to the density profile data,31 and the overall deviation is about 4.7−5.7%. The kinematic viscosity of a diesel fuel influences the atomization and breakup processes, the resulting fuel droplet sizes, and the in-cylinder fuel spray penetration. Estimated kinematic viscosity is plotted against the data of No. 2 diesel32 and shows a deviation up to 19.8%. Tolerance of viscosity by ASTM D455 is within 0.02 mm2/s.32 As pointed out in the recent study by Kanaveli et al.,33 the mixing formula for kinematic viscosity is quite complex and contains extra empirical parameters. These parameters need to be specifically tuned to specific mixtures. Several mixing rules described by Kanaveli et al.33 were tested, and the Refutas index method26 was found to perform the best. This deviation is the largest among all physical property predictions. More complex rules and formulas correlating the viscosity of mixtures with the viscosity of pure components can be further developed; however, it is out of the scope of the current study. The surface tension of the current JI-D_ad1 surrogate is 26.2 mN/ m compared to 25.7 mN/m of diesel at 293 K. Experimental surface tension data23 are shown with the predicted results in Figure 2f. The temperature-dependent specific heat of the three surrogates is plotted in Figure 2g. Experimental data of specific heat as a function of the temperature for No. 2 diesel are not available in the literature. As a reference, the four-component diesel surrogate (V0a of Mueller et al.7) and the mean specific heat value (from 273 to 373 K) of ESSO diesel (Esso Marketing Technical Bulletin, ExxonMobil, 2001) were used for comparisons. The overall deviation is less than 8.3% compared to the mean experimental data. On the basis of these comparisons, the JI-D_ad1 surrogate showed the best overall performance and was chosen for further (combustion characteristics) study.
Figure 3. Comparisons of predicted gas-phase ignition delay times of surrogate JI-D_ad1 versus U.S. #2 diesel experimental data of Belarus et al.,36 Haylett et al.,37 Kukkadapu and Sung.38 Under the hightemperature condition, prediction of ignition delay of JI-D_ad1 show a favorable match with experimental data of Belarus et al.36 and Haylett et al.37
are compared to those of the U.S. #2 diesel under a constant fuel/air equivalence ratio (ϕ) and various pressure conditions. Because no experimental U.S. #2 ignition delay data are available for both high- and low-temperature regions, comparisons are made separately. In a high-temperature region, simulated JI-D_ad1 ignition delay times are compared to U.S. #2 data36,37 under ϕ = 0.5 and pressure (P) = 6.0 atm conditions. In the high-temperature region, temperatures vary from 1050 to 1360 K. In this region, the predicted results are not smooth as a result of scattered pressure conditions (around 6 atm) used in the experimental conditions of Haylett et al.;37 the predicted ignition delay times of the surrogates and experimental data match well. In the low-temperature region, comparisons are made between JI-D_ad1 and experimental data of U.S. #238 under ϕ = 0.5 and P = 10.0 to 20.0 bar conditions. Under ϕ = 0.5 and P = 10.0 bar conditions, JID_ad1 slightly underestimates the delay times, while for ϕ = 0.5 and P = 20.0 bar conditions, JI-D_ad1 slightly overestimates the delay times. From Figure 3, the negative temperature E
DOI: 10.1021/acs.energyfuels.7b01940 Energy Fuels XXXX, XXX, XXX−XXX
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Energy & Fuels coefficient (NTC) behaviors are well-captured under ϕ = 0.5 and P = 10.0, 20.0, and 40.0 bar conditions. Predicted ignition delays of JI-D_ad1 under different conditions for the low-temperature region are also plotted against other experimental data38 in Figure 4. At a higher
Figure 5. Comparison of predicted laminar flame speeds of surrogate JI-D_ad1 (detailed kinetics and reduced kinetics are both used for validation) versus ultralow-sulfur-grade diesel data from Chong et al.39
laminar flame speed at ϕ = 1.4 reaches around 23.9%. Except for the surrogate component choice, this may be caused by chemical kinetics as well as the flame speed simulation model. Chong et al.39 used the jet-wall stagnation flame technique that has significant multidimensional configuration effects. However, simulation in the study used a one-dimensional adiabatic freely propagating model; therefore, one explanation for the discrepancy is due to the simplified model (also see discussion in ref 21). The laminar flame speed predictions by the JI-D_ad1 surrogate capture the overall trend.
Figure 4. Comparisons of predicted ignition delay times of surrogate JI-D_ad1 under low-temperature conditions versus U.S. #2 diesel experimental data of Kukkadapu and Sung.38
pressure condition, predicted ignition delay times of JI-D_ad1 match better with experimental data compared to low-pressure predictions. Under ϕ = 0.69/P = 10.0 bar and ϕ = 1.02/P = 15.0 bar conditions, JI-D_ad1 underestimates the ignition delay. Some discrepancies between the measured ignition delay times and the predictions can also be observed at the NTC region. The kinetic mechanisms34 were validated for high and low temperatures. One reason for the deviation may be caused by the intermediate temperature range chemical kinetics. From these comparisons, the overall trend of ignition delay times was captured reasonably well by the JI-D_ad1 surrogate. The laminar flame speed is another important target for kinetics property validation for diesel surrogate fuels and alternative fuels.38 The laminar flame speed property of surrogate JI-D_ad1 was estimated using an adiabatic freely propagating one-dimensional flame model in CANTERA.35 For this case, the reduced chemical kinetics of 200 species and 6907 reactions34 (POLIMI_PRF_PAH_RFUELS_HT_1412) for high-temperature combustion was used. The reduced hightemperature scheme34 also contained the four components of JI-D_ad1 and was suitable for laminar flame speed calculation because high-temperature reaction dominates the process. Validation studies using the detailed kinetics mechanism (as described in the ignition delay time calculations) were performed with no significant differences (when compared to the results using the reduced kinetics for flame speed simulations). Therefore, the reduced high-temperature kinetics mechanism was used to save computational time. Experimental data from Chong et al.39 were used for comparison. Chong et al.39 used the jet-wall stagnation flame technique to measure the laminar flame speed. In Figure 5, the predicted laminar flame speeds of JI-D_ad1 are shown under the atmospheric pressure and temperature of the 470 K condition. In the fuel-lean region, the laminar flame speed trend is well-captured versus equivalence ratio. The maximum laminar flame speed of the surrogate is 82.6 cm/s at ϕ = 1.1, while experimental data give 86.6 cm/s at ϕ = 1.1. The deviation of the maximum laminar flame speed is 4.6%. At the higher equivalence ratio, the accuracy of the prediction deteriorates. The deviation of the
5. CONCLUSION To achieve the overarching goal of implementing computationally effective models that can predict the evaporation and combustion processes using a multidimensional CFD code, it is essential to develop a surrogate fuel to represent the complex real fuel. Using the inversed batch distillation methodology, a “unified” diesel surrogate (JI-D_ad1) that can emulate the physical, chemical, and kinetics properties of the real diesel fuel was developed in this study. Important thermophysical and chemical properties predicted results using the current surrogate, including distillation curves, LHV, cetane number, hydrogen/carbon ratio, density, kinematic viscosity, surface tension, as well as specific heat, are compared to available diesel experimental data. In addition, the ignition delay times and laminar flame speed properties of JI-D_ad1 are validated with available experimental data using the CANTERA simulation package. The current validation study has pointed to the direction that the JI-D_ad1 surrogate is suitable for further applications in multidimensional two-way coupled spray combustion simulations.
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AUTHOR INFORMATION
Corresponding Author
*Telephone: +86-21-34204243. Fax: +86-21-34206525. E-mail:
[email protected]. ORCID
C. P. Chen: 0000-0001-6800-2035 Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS The authors acknowledge the financial support from the subsection of the third round “985 Project” through the F
DOI: 10.1021/acs.energyfuels.7b01940 Energy Fuels XXXX, XXX, XXX−XXX
Article
Energy & Fuels
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University of Michigan−Shanghai Jiao Tong University Joint Institute.
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NOMENCLATURE CFD = computational fluid dynamics DIPPR = Design Institute for Physical Properties LHV = lower heating value DCN = derived cetane number MW = molecular weight ULSD = ultralow-sulfur diesel CFA = 2007 #2 ULSD Certification Fuel Batch A JI-D = Joint Institute diesel surrogate JI-D_ad1 = first adjusted Joint Institute diesel surrogate JI-D_ad2 = second adjusted Joint Institute diesel surrogate ϕ = fuel/air equivalence ratio P = pressure ρ = density μ = kinematic viscosity NTC = negative temperature coefficient
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DOI: 10.1021/acs.energyfuels.7b01940 Energy Fuels XXXX, XXX, XXX−XXX
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DOI: 10.1021/acs.energyfuels.7b01940 Energy Fuels XXXX, XXX, XXX−XXX
