Article pubs.acs.org/EF
Surrogate Definition and Chemical Kinetic Modeling for Two Different Jet Aviation Fuels Jin Yu, Zijun Wang, Xiaofang Zhuo, Wei Wang, and Xiaolong Gou* College of Power Engineering, Chongqing University, Chongqing 400044, China S Supporting Information *
ABSTRACT: For emulation of the chemical kinetic combustion phenomena and physical properties of S-8 POSF 4734 and JetA POSF 4658, two surrogate fuels were formulated by directly matching their molecular structure and functional groups. The same functional groups, CH3, CH2, CH, C, and phenyl, were chosen to formulate the S-8 and Jet-A surrogates with n-dodecane/ 2,5-dimethylhexane (0.581/0.419 mole fraction) and n-dodecane/2,5-dimethylhexane/toluene (0.509/0.219/0.272 mole fraction), respectively. The numerical results using the surrogate fuels were compared with the experimental data and the results predicted by other surrogate fuel formulation methods. The results show that the present method can formulate surrogate mixtures of both jet fuels and Fischer−Tropsch real fuels and reproduce the combustion characteristics in homogeneous ignition and the flow reactor oxidation process. The idea presented here could be extended to other real fuels with the appropriate choice of surrogate fuel components.
1. INTRODUCTION With the development of a sustained economy, the world’s consumption of fuels is expected to constantly increase over the long term. The transportation sector, including aviation, an essential part of our modern society, represents the largest section of petroleum-based fuels consumption. Future global energy and environmental issues have imposed challenges on the operating conditions of turbojet engines, which are laregely for military usage. For saving energy and improving combustion efficiency, it is necessary to develop detailed chemical kinetic models of jet fuels, which play an important role in the computational design and optimization of internal combustion engines and propulsion systems by enabling quantitative simulations of the combustors. As in other sectors, with the increasing depletion of fossil energy, synthetic Fischer− Tropsch (F-T) fuels have become a new energy source, which can be an alternative to jet fuels. Synthetic F-T fuels that derive from coal, natural gas, and/or biomass are important sources of energy security from depleting crude oil supplies. An even more restricted limit for jet fuel composition is provided by an important option to alternative jet fuel in F-T fuel. However, jet and F-T fuels are complex mixtures that contain several hundred components, and it is not a practical solution to obtain their combustion characteristics by directly using a mixture model of all the constituents. Alternatively, simplified “surrogate fuels” with only a few components are regarded as one of the most important ways for representing combustion and physical properties of real fuels.1 Because fewer fuel components are involved, surrogates provide a cleaner and more reproducible basis for developing and testing the performance of practical combustors and for including detailed chemical kinetics in the analyses. Different guidelines have been developed to determine the relevant components of surrogates for different real fuels,2−7 and several surrogates have been proposed for jet fuels8−16 and F-T fuels.17−21 In the literature, the primary guideline for © 2016 American Chemical Society
choosing surrogate components is to consider a practical fuel as a mixture of a number of specific classes of hydrocarbon molecules, and different methodologies of surrogate fuel formulation have been proposed for several real fuels. However, until now, the guideline of choosing surrogate components and the formulation of a surrogate fuel are still highly empirical and an engineering art. Moreover, once the surrogate composition is chosen, compact and reliable kinetic models of multicomponent surrogates still need to be developed. Several comprehensive chemical models have been proposed for jet fuels and F-T fuel surrogates;9,10,17−19,22 however, their methodologies still have many disadvantages with respect to accuracy and simplicity. In this work, a methodology of surrogate fuel formulation by directly using molecular structure and functional groups for both oxygenated and hydrocarbon fuels is proposed and applied to Jet-A POSF 4658 and one type of F-T fuel, namely Syntroleum S-8 (the “S” referring to synthetic). Two new surrogate models were built to accurately predict the molecular weight and hydrogen/carbon molar ratio of the jet fuels and FT surrogate fuels. Thereafter, the performance of proposed surrogates and their reaction mechanisms were evaluated by comparing with other surrogate models and experiments for jet fuels and F-T fuels in a shock tube and variable pressure flow reactor.
2. SURROGATE FORMULATION METHODOLOGY 2.1. Selection of Surrogate Target Properties. In previous work, surrogate fuels were used to mimic the behavior of a real fuel in various combustion devices, and the definition and complexity of the surrogate fuel formulation depended on the intended applications. There are different targets due to Received: October 14, 2015 Revised: January 9, 2016 Published: January 11, 2016 1375
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Figure 1. (a) Molecular class compositions of Jet-A POSF 4658 as identified by GC−MS relative signal area percentage analysis.23 (b) Molecular class compositions of a typical Fischer−Tropsch fuel (S-8 POSF 4734) as identified by GC−MS relative signal area percentage analysis.24
average molecular formula, liquid density, molecular weight, and threshold sooting index. From the above discussion, it is concluded that most of the surrogate fuel property targets, such as MW, H/C, DCN, TSI, and distillation curves, are macroscopic and phenomenological quantities. Although these phenomenological methodologies for surrogate fuel model construction are intuitive to understand and simple to operate, they still have many disadvantages in accuracy and in extension to other fuels. In this work, a methodology of surrogate fuel formulation by directly using molecular structure and functional groups is proposed and applied to jet fuels and F-T surrogate fuels. The theoretical foundation of this new methodology is the Group Contributions method25,26 that the thermochemical, physical, and combustion properties of a fuel are the summed results of the fuel molecular structures and functional groups. The goal of a surrogate fuel mixture is to match certain physical and combustion properties of a target fuel by using a small number of pure compounds. It is reasonable to infer that if the molecular structure of a target fuel is known and one can match the functional groups of surrogate fuel mixture with the target fuel, the chemical, physical, and combustion properties will likely to be matched simultaneously without knowing the global combustion properties. The explicit composition data for Jet-A POSF 4658 and S-8 identified by gas chromatograph−mass spectrometer (GC− MS) relative signal area percentage analysis were adopted from Widegren et al.23 and Huber et al.,24 respectively (Figure 1). Considering the molecular structure of Jet-A POSF 4658 and S8, we split the Jet-A fuel and S-8 molecular structures into five parts: CH3, CH2, CH, C, and phenyl. In this study, these five functional groups have been proposed as matching targets to formulate surrogate mixtures. CH3, CH2, CH, C, and phenyl are the most common functional groups in the Group Contributions method.25,26 The methyl and methylene are the most common structures in CH hydrocarbon fuels. The metric of CH2 × [CH 2 + CH3]
different applications during the chemistry formulating process of surrogate fuels. Three different types of targets, property, development, and application, have been proposed to quantitatively compare the performance of a surrogate fuel with real diesel.2 For diesel and gasoline fuels, Liang et al.3 created a diesel surrogate that matched the cetane number, C/ H ratio, low heating value, and 50 vol % distillation temperature. Mueller et al.4 utilized the state-of-the-art techniques of 13C and 1H nuclear magnetic resonance (NMR) spectroscopy and the advanced distillation curve to characterize diesel fuel composition and volatility, respectively. Anand et al.5 proposed a surrogate model using nine basic fuels for studying low-emission, high-efficiency advanced diesel engines. The surrogate compositions of the fuels were obtained through the simulation results using KIVA-ERC-CHEMKIN. Pera et al.6 proposed a methodology matching the H/C ratio, O/C ratio, molecular weight, research octane number (RON), and the motor octane number (MON) to define proportions of compounds in gasoline surrogates dedicated to autoignition modeling in engines. Mehl et al.7 proposed an alternate method for the formulation of gasoline surrogate mixtures based on limited composition information and chemical kinetic modeling calculations. For jet fuels, Kim et al.8 built a JP-8 surrogate model by matching eight target properties: cetane number, lower heating value, H/C ratio, molecular weight and temperature-dependent density, viscosity, surface tension, and distillation characteristics. Dooley et al.9,10 proposed a systematic methodology to create a surrogate fuel mixture of real jet fuel by matching four combustion property targets: the average fuel molecular weight (MW), hydrogen/carbon molar ratio (H/C), derived cetane number (DCN), and threshold sooting index (TSI). The resulting surrogate fuel mixtures mimicked well the global targets of ignition, flame speeds, extinction limits, and speciation in flow reactor, except for some discrepancies in the low temperature oxidation region. Similarly, Naik et al.17 proposed a methodology matching cetane number (CN), the H/C ratio, lower heating value, ASTM D-86 T50 distillation point, and density to define proportions of compounds in F-T fuel surrogates. Narayanaswamy et al.20 proposed a systematic methodology to create a surrogate fuel mixture of real jet fuel by matching five combustion property targets: H/C ratio,
3
globally correlates the low temperature alkylperoxy radical reactivity for large paraffinic fuels,27 whereupon the numbers of methyl and methylene are proposed as matching targets. In choosing functional groups to represent molecular structure, the most widely applicable groups have been considered at the 1376
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dodecane, 2,5-dimethylhexane, and toluene, have been selected for Jet-A fuel. n-Dodecane is widely used as a surrogate component for jet fuels,8,10,17,18,20 and it can effectively reflect the role of fuel cracking in aliphatic hydrocarbon combustion.29 Therefore, ndodecane is selected as a surrogate fuel component to represent the paraffin class of the S-8 and Jet-A fuels. In previous research work, iso-octane (2,2,4-trimethylpentane) has been used as a surrogate component to represent the branched alkanes class for jet and F-T fuels.8−11,15−20 Recently, monomethylated alkanes and dimethylated alkanes have attracted more and more attention and been used in more research studies.30−36 A research study by Bruno et al.37 indicated that dimethylated alkanes represent an important fraction of the synthetic paraffinic fuel composition. Huber et al.24 have also proposed 2,6-dimethyloctane as a surrogate component for synthetic aviation fuel S-8. However, prior fundamental combustion studies on dimethylalkanes are limited, and the detailed chemical kinetic mechanism of dimethylated alkanes is rare. Fortunately, in recent years, Sarathy et al.36 developed a comprehensive chemical kinetic model of 2,5-dimethylhexane for both the low and high temperatures. Thus, considering molecular class compositions of Jet-A and S-8 fuels as well as the new principle of choosing surrogate components, which surrogate components should provide various kinds of functional groups abundantly, 2,5dimethylhexane is selected as a surrogate fuel component to represent the branched alkanes and provide CH functional groups. S-8 and Jet-A fuels have a very low percentage of cycloalkanes (Figure 1); moreover, Dryer et al.38 inferred that the special provision of cycloalkanes impart very limited extra capability for the surrogate to emulate real fuel prevaporized gas-phase combustion responses. Dooley et al.39 also indicated that the cycloalkane functionality imparts no distinctive influence on the low-temperature alkyl-peroxy-radical-governed global reactivity of complex liquid transportation fuel types, nor do the extinction limit observations indicate any significant effects on global behaviors at high temperature. However, the cycloalkane functionality does slightly influence the hot-ignition transition. Therefore, in this paper, no component has been chosen to represent the cyclo-alkanes class. S-8 fuel contains no aromatic fraction and large percentages of mono and dimethylated fractions, whereas Jet-A fuels have a high percentage of aromatics. Therefore, toluene, which is the simplest substituted aromatic that can be modeled reliably, has been chosen to represent the aromatic class for Jet-A fuels in this paper. The structural matching targets of Jet-A and S-8 surrogate fuels are given in Table 1. 2.3. Surrogate Fuel Formulation. Once surrogate compounds with the basic functional groups have been selected, the mole fraction of each component fuel in the surrogate fuel mixtures will be unambiguously defined with the precise constraints of the number of elements, the number of functional groups, and the weighting functions. To evaluate the matching result of different selected compounds, we defined normalized target difference terms as
cost and convenience of matching molecular structure groups. We want to choose the minimum number of groups to represent all the molecular structures of jet fuels. Thus, the combination of CH3, CH2, CH, C, and phenyl is the optimal strategy. The amounts of those five parts in Jet-A POSF 4658 and S-8 POSF 4734 fuels are given in Table 1. From Table 1, Table 1. Structural Matching Targets for Jet-A and S-8 and Their Surrogate Fuel Components fuel
CH3
CH2
CH
C
phenyl group
n-dodecane 2,5-dimethylhexane toluene S-8 POSF 473424 Jet-A POSF 465823
2 4 1 2.87 2.56
10 2 0 7.14 6.11
0 2 0 0.91 0.50
0 0 0 0 0.01
0 0 1 0 0.31
we can see that there is no C functional group in S-8 POSF 4734 fuels, and the amount of C functional group in Jet-A POSF 4658 is very small. Thus, in this study, the C functional group is ignored when we are choosing matching targets. 2.2. Surrogate Candidate Components Selection. A natural procedure to select suitable components of the surrogate mixture for a particular fuel is to identify one representative hydrocarbon for each of the major hydrocarbon classes found in the real fuel.20 The most obvious and useful approach is to consider a practical fuel as a mixture of a number of specific classes of hydrocarbon molecules, including nparaffins, iso-paraffins, olefins, cyclo-alkanes, aromatics, and oxygenates for jet fuels.28 The major hydrocarbon classes present in a typical Jet-A fuel are depicted in Figure 1(a).23 In contrast, the S-8 fuels are mainly comprised of normal, branched, and cyclic alkanes24 and are depicted in Figure 1(b). To date, a lot of surrogate mixtures have been chosen for jet and synthetic fuels. For synthetic fuels, Naik et al.17 combined the models of n-decane, n-dodecane, and iso-octane to obtain a blended F-T surrogate fuel mixture. Dooley et al.18 adopted ndodecane and iso-octane as the basic components for nonaromatic synthetic fuels. Narayanaswamy et al.20 suggested using the mixture of n-dodecane, methylcyclohexane, and isooctane to represent the normal alkanes, cyclic alkanes, and branched alkanes in F-T fuels, respectively. For the jet fuels, the first generation Jet-A POSF 4658 surrogate9 was comprised of a mixture of n-decane, iso-octane, and toluene. n-Dodecane, methylcyclohexane, and m-xylene have been chosen as the components of the JP-8 surrogate in ref 20. Kim et al.8 proposed two surrogates for a conventional jet fuel. The first surrogate, UM1, is a mixture of n-dodecane/iso-cetane/ methylcyclohexane/toluene, whereas the second, UM2, is a mixture of n-dodecane/iso-cetane/decalin/toluene. The chief considerations of what individual components should be used for the formulation of surrogate fuels are some practical issues outlined by Violi et al.16 and Kim et al.8 All of the criteria proposed by Violi et al. and Kim et al. were satisfied by the surrogate components considered in our work. Moreover, a new principle of choosing surrogate components that can provide the bases of the representative functional groups and comparable molecular sizes has been proposed for choosing individual components. In this paper, based on those guidelines, two components of the surrogate mixture, ndodecane and 2,5-dimethylhexane, have been selected for S-8 fuel, and three components of the surrogate mixture, n-
Fi = 100
FGNi ,calc − FGNi ,targ FGNi ,targ
(1)
where Fi is a normalized difference of the ith molecular structure target, FGNi,targ is the ith functional group number of 1377
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Dooley18
present
composition (% mole) H/C MW
n-dodecane 2,5-dimethylhexane 2.19 146.54
n-dodecane iso-octane 2.19 143.4
58.1% 41.9%
S-8 POSF 473412 51.9% 48.1%
normal, branched, and cyclic alkanes 2.17 163 ± 15
Table 3. Mixture Mole Fraction and Some Matching Targets for Jet-A Surrogate Formulations Matrix Jet surrogate fuels type
Dooley9
present
composition (% mole)
H/C MW
n-dodecane 2,5-dimethylhexane toluene 1.98 136.52
50.9% 21.9% 27.2%
n-decane iso-octane toluene 2.02 126.3
Nf
∑ njfgni ,j j=1
(2)
where nj is the molar quantity of the jth surrogate fuel component, and fgni,j is the ith functional group number of the jth surrogate fuel component. The normalized target difference Fi needs to satisfy the condition
Fi ≤ εi
(3)
where εi is the threshold value of the ith normalized difference. Normally, there are a lot of reasonable formulations satisfying eq 3. For the optimum formulation of the surrogate mixture to be found, an objective function, which can be minimized in the regression model, is defined as Ng
S=
∑ WF i i i=1
46.7% 33.0% 24.3%
normal, branched, and cyclic alkanes, aromatics, napthalenes
1.96 142 ± 20
3. REACTION SCHEME FOR THE JET FUEL AND F-T FUEL SURROGATES Pepiot40 proposed a component library approach to construct chemical models for surrogate fuels. It is a modular approach that relies on a library of skeletal mechanisms, or a component library, to efficiently build kinetic schemes for mixtures. Naik et al.17 assembled the detailed reaction mechanism for the F-T fuel surrogates in a stepwise fashion: First, assemble a single mechanism containing all surrogate components using the individual component mechanisms from the literature. Second, add submechanisms for NOx and polycyclic aromatic hydrocarbons (PAH) for soot predictions. Third, establish selfconsistency in the mechanism and then improve the master mechanism to achieve more accurate predictions. Dooley et al.9 also developed a reaction mechanism for the jet fuel surrogates by combining three individual kinetic models. Despite rapid advancements in computing power, it is generally formidable to integrate such detailed reaction mechanisms into large-scale computational simulations in terms of CPU time and memory requirements. Because of these computational demands, reduction of large mechanisms is necessary to facilitate practical simulations using realistic chemistry with modern computational tools. Moreover, model reduction techniques are essential, not only to reduce computational costs but also to reduce the numerical stiffness associated with such a large number of conservation equations for broad classes of independent fuel chemistries and reaction pathways.41 In this study, the path flux analysis (PFA) kinetic model reduction scheme42 has been utilized to reduce the detailed 2,5dimethylhexane mechanism, which contain 883 species and 4204 reactions.36 A procedure written by our group was applied to produce reduced models under the Senkin model. Finally, we obtained a reduced 2,5-dimethylhexane mechanism containing 211 species and 1185 reactions. Luo et al.43 developed a skeletal mechanism for n-dodecane with 105 species and 420 reactions. The reduction starts from the most recent detailed mechanism for n-alkanes consisting of 2755 species and 11,173 reactions as developed by the Lawrence Livermore National Laboratory.44 A reduced chemical kinetic mechanism for S-8 POSF 4734 fuel surrogate has been assembled from the two individually reduced kinetic models: the reduced 2,5-dimethylhexane model
the target fuel, and the FGNi,calc is the ith functional group number of the surrogate mixture. FGNi,calc can be calculated as FGNi, calc =
Jet-A POSF 465810
(4)
where S is the objective function to be minimized, and Wi is a weighting factor that satisfies the condition ∑i N=g 1Wi = 1. The weighting factors can exactly reflect the importance of each functional group. For simplicity, in this paper, we assumed that all of the functional groups and molecular structure have the same importance and all the weighting factors have the same value. Initially, in this methodology, the mole weight of each surrogate fuel component (nj) can be obtained according to eq 2. Then, the mole weight of each surrogate fuel component can be converted to mole fraction. In this study, a simple program has been written to calculate the optimized mole weight of surrogate fuel components. The mixture mole fraction and some matching targets for S-8 and Jet-A surrogate formulations matrix are shown in Tables 2 and 3, respectively. As such, choosing different targets with this new methodology can meet not only the traditional matching targets: MW and H/C ration, but also some new targets, such as CH 2 × [CH 2 + CH3], different types of chemical bonds, CH 3
chemical-bond energy, different types of functional groups, and their transport and thermochemical properties. 1378
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Energy & Fuels and the reduced n-dodecane model of Luo et al.43 Then, add the toluene kinetic mechanism45 to the S-8 surrogate mechanism to get the Jet-A POSF 4658 surrogate mechanism. Finally, the ternary components surrogate mechanism, which contains 373 species and 1763 reactions, can be applied to both S-8 and Jet-A surrogate fuel. The mechanism is provided in the Supporting Information. The cross reactions involving high molecular weight fuel radicals are not considered in this kinetic model. When there is a conflict or duplication of nomenclature, chemistry or thermodynamic parameters between the three individual component submodels, chemical reaction rate constant, and thermodynamic parameters, the following preferential order is used: 2,5-dimethylhexane, n-dodecane, and toluene. All three individual component submodels share the same base C0−C5 mechanism, which was developed by NUI Galway and includes their recent C0−C2 Aramco Mech 1.3.46 The validation of the model against the parent mechanisms have been provided in the Supporting Information.
Figure 3. Flow reactor oxidation data with representative uncertainty bars for conditions of 12.5 atm, 0.3% carbon, φ = 1.0, and t = 1.8 s. Symbols are experimental measurements,18 and lines are model computations.
4. PERFORMANCE OF THE PROPOSED SURROGATES 4.1. Fischer−Tropsch Fuel Surrogate. Ignition delay times of stoichiometric mixtures of S-8 POSF 4734 and the surrogate fuel are obtained using a heated shock tube at Rensselaer Polytechnic Institute.18 The numerical data using the present surrogate were conducted with CHEMKIN PRO.47 Reflected shock ignition delay times compared with the data of Dooley et al.18 at conditions of φ = 1.0 in air at 20 atm for S-8 POSF 4734 and its surrogate are shown in Figure 2. Compared with different models, the present kinetic model can predict the ignition well in the temperature range of 700−900 K.
measurements. The comparisons of CO mole fraction and heat release (ΔT) between Dooley et al.8 and the current kinetic model for flow reactor oxidation, at conditions of 12.5 atm, 0.3% carbon, φ = 1.0, and t = 1.8 s, are shown in Figure 4. In
Figure 4. Comparisons of CO and ΔT between Dooley and the new kinetic model for flow reactor oxidation, at conditions of 12.5 atm, 0.3% carbon, φ = 1.0, and t = 1.8 s. Symbols are experimental measurements;18 dashed lines are model computations for Dooley et al.,18 and real lines are model computations for the present model. Figure 2. Reflected shock ignition delay times at conditions of equivalence ratio = 1.0 in air at 20 atm for S-8 POSF 4734 fuel. Symbols are experimental measurements;18 dashed lines are model computations for Dooley et al,18 and real lines are model computations for the present model.
the temperature range of 800−1050 K, the concentration of CO simulated by the present model is closer to the experimental data, and in the temperature range of 650−750 K, the prediction precision of the CO mole fraction and heat release (ΔT) for the present model are lower than those of the Dooley’s model. 4.2. Jet Fuel Surrogate. Ignition delay times of stoichiometric mixtures of Jet-A POSF 4734 and the surrogate fuel in air are obtained using a heated shock tube at Rensselaer Polytechnic Institute.9 Reflected shock ignition delay times compared with the data of Dooley et al.,9 at conditions of φ = 1.0 in air at 20 atm for Jet-A POSF 4734, and its surrogate are shown in Figure 5.
The flow reactor S-8 POSF 4734 oxidation experiments were performed at 12.5 atm, 0.3 mol % carbon, residence time of 1.8 s, and a temperature range of 511−1023 K.8 Measurements and calculations of oxygen, carbon dioxide, carbon monoxide, and water mole fractions are shown in Figure 3. The kinetic model reproduces the qualitative features of the S-8 POSF 4734 oxidation very well, although the oxygen mole fractions in the range of 850−1050 K are characteristically lower than the 1379
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of O2 and the formation of CO, CO2 and H2O. At the temperature range of 900−1000 K, the concentration of CO simulated by the present kinetic is closer to the experimental measurement, although the prediction precisions of O2 and H2O mole fraction are lower than those of Dooley’s model in the temperature range of 650−750 K.
5. PHYSICAL PROPERTY EMULATION The surrogate formulation strategy tested in this study is designed to produce surrogate fuels to emulate a specific range of real fuel gas phase combustion kinetic phenomena. Though air-breathing energy conversion processes utilizing liquid fuels depend on gas phase reactions for chemical heat release, most also depend on multiphase combustion phenomena that are affected by vaporization and mixing phenomena associated with fuel physical properties. Ultimately, a successful numerical combustion model must include these multiphase aspects.10 Huber et al.48 have measured the liquid density and viscosity of Jet-A POSF 4658, both of which are important properties in the liquid fuel vaporization process of gas turbines. The liquid density and viscosity behaviors of the present and Dooley’s9 surrogate fuels are calculated by the SUPERTRAPP code49 and compared to the measured values of Jet-A POSF 4658 in Figures 7 and 8, respectively. In this study, molecular weight,
Figure 5. Reflected shock ignition delay times at conditions of equivalence ratio = 1.0 in air at 20 atm for POSF 4658 fuel. Symbols are experimental measurements;9 dashed lines are model computations for Dooley et al,9 and real lines are model computations for the present model.
The model of Dooley et al.9 qualitatively reproduces the complex temperature dependence of ignition delay observed by experiment. However, quantitatively, the computational ignition delays are longer than the experiment values at most temperature regions. Comparing the ignition performance of the two different surrogate models, there is no visible difference at the temperature range of 900−1200 K, but at low temperature (900−650 K), the ignition delay time of the new surrogate model is significantly less than that of Dooleys. The present surrogate model can reproduce the ignition delay well at the temperature range of 650−750 K. The flow reactor Jet-A POSF 4734 oxidation experiments were performed at 12.5 atm, 0.3 mol % carbon, residence time of 1.8 s, and a temperature range of 511−1023 K. 9 Measurements and calculations of oxygen, carbon dioxide, carbon monoxide, and water mole fraction are shown in Figure 6. As shown in Figure 6, the present and Dooley’s surrogate models are both able to capture the overall trends of the decay
Figure 7. Measured density for Jet-A POSF 4658 at 0.083 MPa (symbols48) and calculated densities at 0.083 MPa for Dooley’s first generation surrogate9 and present surrogate.
density, and viscosity are not considered as direct matching targets; however, once the functional groups are matched, some physical properties including density and viscosity can be matched automatically. The present surrogate is of higher density and viscosity than Dooley’s first generation surrogate. The density of the present surrogate is estimated to be 4−5% lower than the measurements of Huber et al. for Jet-A POSF 4658. Similarly, the viscosity of the present surrogate is estimated to be 20−50% lower than the measurements of Huber et al. for Jet-A POSF 4658. The liquid density and viscosity behaviors of the current and Dooley’s18 S-8 surrogate fuels are calculated and compared to the measured values of S-8 fuel in Figure 9. The density of the present surrogate is faintly higher than that of Dooley’s S-8 surrogate and estimated to be 2−5% lower than the measurement of Bruno et al.50
Figure 6. Flow reactor oxidation data with representative uncertainty bars at conditions of 12.5 atm, 0.3% carbon, φ = 1.0, and t = 1.8 s for POSF 4658. Symbols are experimental measurements;9 dashed lines are model computations for Dooley et al,9 and real lines are model computations for the present model. 1380
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ASSOCIATED CONTENT
S Supporting Information *
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.energyfuels.5b02414. Chemical kinetic mechanism for present surrogate fuels (TXT) Thermodynamic parameters for present surrogate fuels (TXT)
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AUTHOR INFORMATION
Corresponding Author
*Tel.: +86-23-6510-3080. Fax: +86-23-6510-2473. E-mail:
[email protected]. Notes
Figure 8. Measured viscosity for Jet-A POSF 4658 at 0.083 MPa (symbols48) and calculated densities at 0.083 MPa for Dooley’s first generation surrogate9 and present surrogate.
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS This work was supported by National Natural Science Foundation of China (No. 51276206 and 91441112).
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REFERENCES
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Figure 9. Measured density for S-8 at 0.51 MPa (symbols50) and calculated densities at 0.51 MPa for Dooleỳs and new surrogate fuel.
6. CONCLUSIONS A model-based surrogate formulation methodology by directly using molecular structure and functional groups to define the surrogates for two types jet fuels, POSF-4658 and S-8, is proposed. The same functional groups, CH3, CH2, CH, C, and phenyl, were used to formulate the S-8 and Jet-A surrogates with ndodecane/2,5-dimethylhexane and n-dodecane/2,5-dimethylhexane/toluene, respectively. The comparisons with experimental data and other models show that the surrogate fuel mixtures formulated by the present method can reproduce the combustion characteristics well in a homogeneous ignition and flow reactor combustion process over a wide temperature range. Although the physical properties are not considered as direct matching targets, once the functional groups are matched, some of the basic physical properties, including density and viscosity, can be matched well automatically. The ideas presented here could be extended to other real fuels with the appropriate choice of surrogate fuel components. 1381
DOI: 10.1021/acs.energyfuels.5b02414 Energy Fuels 2016, 30, 1375−1382
Article
Energy & Fuels
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