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. 2022 Jan 6;7(2):2275–2285. doi: 10.1021/acsomega.1c05904

Formulation of 7-Component Surrogate Mixtures for Military Jet Fuel and Testing in Diesel Engine

Dianne Jeanne Luning Prak †,*, Gretchen R Simms ‡, Terrence Dickerson ‡, Andy McDaniel ‡, Jim S Cowart §
PMCID: PMC8772332  PMID: 35071916

Abstract

graphic file with name ao1c05904_0008.jpg

In this work, military jet fuel JP-5 surrogates were formulated and tested in comparison to a nominal JP-5 fuel. Combustion experiments were conducted in an advanced engine technology (AET) ignition quality tester (IQT) and a Yanmar L100W Tier 4 diesel engine due to the potential use of jet fuel in diesel engines in military situations. The surrogate development process began with determining the fuel chemical composition based on analyses of 256 JP-5 fuel samples. The physical and chemical properties of density, viscosity, flash point, surface tension, speed of sound, and distillation behavior guided the selection of the surrogate components and their composition. JP-5 differs from other aviation fuels in its properties, but most importantly in flash point, which is higher for safety purposes. Surrogates were prepared from n-dodecane, n-butylbenzene, 1-methylnaphthalene, tetralin, trans-decalin, iso-cetane, and n-butylcyclohexane as representatives of seven of the nine major chemical categories found in jet fuel. The mass fraction of each compound in the surrogates that fell within the range for that chemical class was found in real JP-5 fuels. After optimizing the surrogates for physical and chemical properties, six surrogates were selected for combustion testing in the Yanmar diesel engine, one of which was specifically selected for a low-derived cetane number (DCN). This surrogate performed poorly in the Yanmar engine. Four of the remaining five surrogates performed similarly to the baseline JP-5 in the diesel engine in terms of values and variability of ignition delay, rate of heat release, peak pressure, and the crank angle at which 50% of the fuel is burned. Of the six surrogates tested, the best one in terms of physical properties, chemical properties, and combustion behavior was the one that contained 0.2421, 0.1503, 0.0500, 0.0141, 0.0121, 0.2532, and 0.2782 mass percentages of n-dodecane, n-butylbenzene, 1-methylnaphthalene, tetralin, trans-decalin, iso-cetane, and n-butylcyclohexane, respectively.

1. Introduction

The use of jet fuel in military diesel engines enables operational fuel flexibility and can streamline the purchasing process. Fuel scientists and engineers often make and test surrogate mixtures with a limited number of components to understand the combustion process of real complex fuels containing hundreds of components.1−31 The combustion process can be broken into the physical processes of injection, spray formation, and evaporation and the chemical reaction kinetics. Researchers have probed the effect of various physical properties on spray behavior and have developed jet fuel surrogates using physical properties.32 Huber et al.5 used density, speed of sound, viscosity, thermal conductivity, and advanced distillation curve data to design surrogates for rocket propellants. These properties with the addition of cetane number (CN) were used to formulate surrogates for Jet A.7 Won et al.33 used hydrogen-to-carbon (H/C) molar ratio, derived cetane number (DCN), average molecular weight, and threshold sooting index to formulate surrogates for jet fuel. CN and DCN are diesel engine combustion metrics that capture ignition delay behavior that incorporates both physical and chemical properties of fuels.

A large number of studies have formulated and tested surrogates for commercial aviation jet fuel, Jet A, kerosene, and military jet fuel JP-8,3−9,11,12,14,15,17−19,22,28,31 but fewer studies have focused on Navy jet fuel, JP-5.1,3,13,29 Edwards and Maurice3 reported on studies that had developed different surrogates for the various jet fuels JP-4, JP-5, JP-8, and RP-1. For JP-5, the 11- and 12-component surrogates contained combinations of six linear alkanes, two monocyclohexanes, one dicyclohexane, two monoaromatic, one polyaromatic, and one cycloaromatic compounds.3 Wood et al.2 showed that surrogates for JP-4 were not good representatives for JP-5. Allen et al.13 reported that Jet A surrogates were not able to capture the complete combustion behavior of JP-5. In an “Overview of the National Jet Fuels Combustion program” in 2017, Colket et al.34 summarized some differences between JP-8, Jet A, and JP-5 including their flash points, viscosities, aromatic contents, and distillation curves. For example, the minimum value of flash point for Jet A is 38 °C, which is much lower than JP-5’s minimum requirement of 60 °C.35,36 Given that surrogates for other jet fuels do not always capture the behavior of JP-5, this study sought to formulate a surrogate mixture for JP-5.

In surrogate development, the selection of components for the surrogate is related in some way to the chemical composition of the fuel. In some cases, fuel components are divided into classes and representatives are chosen from those classes to be included in the surrogate. Divisions have included paraffins, monocycloparaffins, dicycloparaffins, alkylbenzenes, indans and tetralins, indenes and dihydronaphthalenes, and naphthalenes.2 The categories of compounds can be further subdivided by carbon number, and modern two-dimensional gas chromatography (GC × GC) with flame ionization or mass spectrometry greatly facilitates compound identification.23,37−40 Greater detail on fuel composition in recent years has led to many successful predictions of fuel properties such as density, volatility, viscosity, flash point, freezing point, and distillation profile.38,41 It can also be used to more accurately estimate the molar mass of fuel. Based on compositional information, cost, and commercial availability, a list of prospective compounds for the surrogate is created. Then, specific compounds or hydrocarbon blends are chosen for the surrogate by matching selected physical, chemical, and/or combustion metrics of the surrogate to that of the fuel of interest. Previously used metrics have included DCN, octane number, flash point, total sooting index, lower heating value, thermal conductivity, H/C molar ratio, volatility measurements through a distillation curve, density, speed of sound, and viscosity.2,3,5−9,15,16,23,42−48 In their formulation of Jet A and JP-8 surrogates, Dryer et al.18 used either specific compounds, such as 1,3,5-trimethylbenzene, or commercially available hydrocarbon blends like Solvesso Aromatic 100 to prepare surrogates whose TSI, H/C ratio, and DCN matched that of the fuel. In the current study, the surrogates were formulated based on the average GC × GC chemical composition of multiple samples of JP-5 produced over several years, and the combustion of the surrogate was compared to that of a specific production lot of JP-5. The metrics used for surrogate development and selection included density, viscosity, speed of sound, surface tension, flash point, distillation curve information, and DCN. Density, viscosity, and vapor pressure have been shown to be important in direct injection spray and ignition behaviors.32 Vapor pressure information can be captured in the flash point and distillation curve information. As mentioned earlier, flash point is a key difference between JP-5 and other aviation fuels, so its inclusion limited the selection of components in the surrogate mixture.

One factor to consider when choosing compounds for a surrogate is the possibility of chemical kinetic modeling.18,19,21,26,28,49−55 For some compounds, there are detailed mechanisms. Westbrook et al.50 used over 8000 reactions with more than 2000 chemical species to model the combustion of dodecane. Dagaut and Hadj-Ali56 used two kinetic models in their simulation of the oxidation of 2,2,4,4,6,8,8-heptamethylnonane (iso-cetane) in a jet-stirred reactor. One model contained 4969 reactions with 174 chemical species, while the other had 1653 reactions with 199 chemical species. Mao et al.57 recently developed a surrogate for RP-3 that contained 27.44% n-dodecane, 28.81% isododecane, 26.12% decalin, and 17.63% n-butylbenzene. They modeled the combustion process using 11 898 reactions that included 3065 chemical species. For modeling purposes, there is a balance between including more components to more accurately represent the fuel and using fewer components whose reaction kinetics need to be included in the model.

Most of the combustion and modeling work with jet fuel surrogates have been in aviation-related hardware and test rigs,2,4,9−13,16−18,24,49,58 but there are some studies that have focused on jet fuel combustion in diesel engine applications.20,22,23,29,59 The use of military jet fuel in military diesel engine enables fuel flexibility in operational situations. Yu et al.59 developed a Jet A surrogate and used a soot model for diesel engines. Kang et al.22 measured the carbon monoxide emission, apparent heat release rates, and ignition delay of a Jet A surrogate using (1) a constant volume combustion chamber and (2) a cooperative fuel research (CRF) engine. In the author’s laboratory, a CFR engine was used to measure burn duration, thermal efficiency, maximum rate of heat release (ROHR), and ignition delay for a catalytic hydrothermal conversion jet fuel and its surrogates.23 In the current study, the combustion experiments were conducted in an advanced engine technology (AET) ignition quality tester (IQT) to obtain the derived cetane number (DCN) and in a Yanmar L100W Tier 4 diesel engine.

2. Materials and Methods

2.1. Methods

The chemical composition of the JP-5 jet fuel was determined using GC × GC techniques reported in previous work.39 Other properties measured in this work were flash point, distillation curve, surface tension, density, speed of sound, and viscosity using methods reported previously.40,60 In summary, the instruments used were a Stanhope-Seta Setaflash Series 8 closed-cup flash point tester (Model 82000-0) operating in a temperature ramping mode, a Grabner MINIDIS ADXpert automatic distillation apparatus, a Kruss Axisymmetric drop shape analyzer (DS100), an Anton Parr Density and Sound analyzer (DSA 5000), and an Anton Parr Stabinger viscometer (SVM 3000).

DCN was quantified with an advanced engine technology ignition quality tester (IQT) rig-operated according to the ASTM D6890 protocol.61 The method has been described in previous studies.23,29 A Tier 4 (U.S. EPA Emissions Level) single cylinder Yanmar L100W diesel engine was instrumented for combustion analysis and operated on a small set of surrogate mixtures and the baseline JP-5. This engine has been described in previous work and run with genset at 3600 nominal revolutions per minute (RPM).29 The engine was operated under a nominal “light-load” condition of 1.5 kW and a “high-load” condition of 4 kW (approximately 1/4–3/4 of the maximum engine load). These engine metrics were measured using a Kistler 6052C in-cylinder pressure sensor and a Kistler piezo-resistive fuel line pressure sensor for the start of injection (nominally one degree before top center). Engine position was measured with a dual channel BEI shaft encoder on the crankshaft and an ETAS wide-band air-fuel ratio meter in the engine exhaust. The heat release analysis methodology followed a conventional first law of thermodynamic analysis.

2.2. Materials

The compositional analysis provided mass percentages of various categories of compounds subdivided in the number of carbon atoms. The surrogate mixtures were prepared from compounds that fall within these categories and number of carbon atoms. The surrogate mixtures were prepared from n-undecane (99.5% pure, Sigma-Aldrich), n-dodecane (99.6% pure, Alfa Aesar), n-tridecane (99.4% pure, Acros), n-propylcyclohexane (99.7% pure, TCI), n-butylcyclohexane (99.1% pure, TCI), n-hexylcyclohexane (98.8% pure, TCI), 1,2,3,4-tetrahydronaphthalene (tetralin, 98.6% pure, Sigma-Aldrich), 1-methylnaphthalene (99.3% pure, Acros Organics), n-propylbenzene (99.8% pure, TCI), n-butylbenzene (99.1% pure, Acros Organics), n-hexylbenzene (Alfa Aesar, 99.0% pure), trans-decahydronaphthalene (trans-decalin, TCI, 99.5% pure), cis-decahydronaphthalene (cis-decalin, TCI, 98.2% pure), 2,2,4,4,6,8,8-heptamethylnonane (iso-cetane, 99.7% pure, Acros Organics), and 2,2,4,6,6-pentamethylheptane (isododecane, 99.2% pure, TCI). The JP-5 was supplied by Naval Air Warfare Center-Aircraft Division’s (NAWCAD) Fuels Group.

3. Results and Discussion

3.1. Chemical Composition of JP-5 and Surrogate Compounds to Represent Component Classes in Fuel

The formulation of the surrogate mixtures was based on the chemical compositions of 256 JP-5 jet fuel samples. The steps of the formulation process are shown in Figure 1. The average mass percentages of n-paraffins, iso-paraffins, monocycloparaffins, dicycloparaffins, tricycloparaffins, aromatics, diaromatic compounds, triaromatic compounds, and cycloaromatic compounds in these fuel samples were 17.31, 23.69, 27.28, 11.62, 1.83, 9.97, 1.08, 0.0011, and 6.54, respectively. This set was reduced to seven categories by eliminating the triaromatics and tricycloparaffins due to their small concentrations and the high cost of tricycloparaffins. Using the average compositions and the range of values in each category, 7-component surrogate mixtures were prepared using one or more chemicals to represent each of the categories. The specific compounds were chosen (1) to be among the categories and carbon numbers found in the fuels, (2) to enable the flash point of the mixture to be above 60 °C, and (3) to be commercially available at reasonable costs for preparing large quantities for combustion. As described below, most surrogates contained n-dodecane (n-paraffin), iso-cetane (iso-paraffin), n-butylcyclohexane (monocycloparaffin), trans-decalin (dicycloparaffin), n-butylbenzene (aromatic compound), tetralin (cycloaromatic), and 1-methylnaphthalene (diaromatic compound). Other compounds were also examined, and those mixtures are given in the Supporting Information. The composition of the specific JP-5 used in the combustion experiments is given in Table 1.

Figure 1.

Figure 1

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Flowchart of surrogate development.

Table 1. Mass Percentage of Compounds Found in JP-5 by Category and Number of Carbons Using GC × GC/FID.

carbon number n-paraffin average mass % iso-paraffin average mass % monocycloparaffin average mass % dicycloparaffin, average mass % tricycloalkanes, average mass % aromatic compounds, average mass % cycloaromatics, average mass % diaromatic compounds, average mass %
C7           0.01    
C8 0.03 0.02 0.06     0.08    
C9 0.32 0.12 0.73 0.20   1.55 0.05  
C10 3.25 2.06 3.66 1.67   3.03 0.61 0.08
C11 4.91 5.12 5.46 2.99 0.01 2.42 2.07 0.32
C12 4.34 4.99 5.62 2.98 0.09 1.91 1.79 0.51
C13 3.47 5.03 4.95 2.93 0.16 1.26 1.11 0.13
C14 2.19 4.08 3.22 1.28 0.08 0.77 0.29  
C15 0.70 2.50 1.40 0.21   0.11    
C16 0.07 0.86 0.10          
C17 0.02 0.03            
C18 0.01              
total 19.31 24.81 25.20 12.26 0.34 11.13 5.92 1.04
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3.2. Development of JP-5 Surrogate Mixtures and Selection of Surrogates for Combustion Testing

Surrogate mixtures were prepared using three approaches, as summarized in Figure 1. In the first method, a mixture was prepared with six of the seven components (n-dodecane, n-butylbenzene, 1-methylnaphthalene, tetralin, trans-decalin, iso-cetane, and n-butylcyclohexane) in the same relative proportions of each chemical category found for the average of 256 JP-5 jet fuel samples, which is given in Section 3.1 (mixtures designated A–G) (Table 2). Then, submixtures were made by adding the 7th component at its average (“base case”), minimum, and maximum concentration found in the 256 JP-5 jet fuel samples (mixtures designated with number and letter, like A1) (Table 2). The properties of these mixtures are given in Table 3. In some cases, additional base-case mixtures were prepared with an alternate 7th component that was in the same category of components (see Supporting Information, Table S1). While some of these alternate base-case mixtures had densities closer to that of JP-5 than that of the base-case mixtures, the other parameters were further away (see Supporting Information, Table S2). The remaining discussion only includes surrogate formulations that contain the seven components, as listed in Table 2.

Table 2. Mass Fraction of Components in Surrogate Mixtures Containing n-Dodecane, Butylbenzene, 1-Methylnaphthalene, Tetralin, trans-Decalin, iso-Cetane, and Butylcyclohexane.

sample name n-paraffin aromatic diaromatic cycloaromatic dicyclo-paraffin iso-paraffin monocycloparaffin estimated molar mass (g/mol)
n-dodecane n-butyl-benzene 1-methyl-naphthalene tetralin trans-decalin iso-cetane n-butyl-cyclohexane
A 0.0000 0.1247 0.0137 0.0810 0.1446 0.2955 0.3404 156
A1 0.0364 0.1202 0.0132 0.0781 0.1394 0.2848 0.3280 156
A2 0.1780 0.1025 0.0113 0.0666 0.1189 0.2429 0.2798 158
A3 0.2000 0.0997 0.0110 0.0648 0.1157 0.2364 0.2723 159
A4 0.3335 0.0831 0.0091 0.0540 0.0964 0.1970 0.2269 160
B 0.1977 0.0000 0.0126 0.0743 0.1326 0.2708 0.3119 162
B1 0.1898 0.0400 0.0121 0.0713 0.1273 0.2600 0.2994 160
B2 0.1671 0.1548 0.0107 0.0628 0.1121 0.2289 0.2636 157
C 0.1795 0.1038 0.0000 0.0674 0.1203 0.2459 0.2832 158
C1 0.1700 0.0983 0.0529 0.0638 0.1140 0.2329 0.2682 157
D 0.1901 0.1099 0.0122 0.0000 0.1274 0.2605 0.3000 160
D1 0.1886 0.1090 0.0121 0.0079 0.1264 0.2584 0.2976 160
D2 0.1636 0.0946 0.0105 0.1391 0.1097 0.2242 0.2582 156
E 0.2015 0.1165 0.0132 0.0757 0.0000 0.2754 0.3177 161
E1 0.1995 0.1154 0.0131 0.0750 0.0097 0.2727 0.3146 161
E2 0.1528 0.0884 0.0100 0.0574 0.2417 0.2089 0.2409 155
F 0.2344 0.1355 0.0150 0.0881 0.1572 0.0000 0.3698 144
F1 0.1946 0.1125 0.0124 0.0731 0.1305 0.1701 0.3069 154
F2 0.1177 0.0681 0.0075 0.0442 0.0790 0.4978 0.1857 176
G 0.2464 0.1424 0.0158 0.0926 0.1653 0.3375 0.0000 166
G1 0.2077 0.1200 0.0133 0.0781 0.1393 0.2844 0.1572 162
G2 0.1637 0.0946 0.0105 0.0615 0.1098 0.2242 0.3358 157
H1 0.0301 0.1501 0.0500 0.1200 0.0100 0.4798 0.1601 170
H2 0.3300 0.0400 0.0000 0.0025 0.1775 0.1700 0.2800 160
H3 0.1200 0.0900 0.0400 0.1100 0.0101 0.4300 0.2000 170
H4 0.1599 0.0800 0.0102 0.0701 0.0100 0.3499 0.3199 166
H5 0.1900 0.0600 0.0300 0.1000 0.1800 0.1600 0.2799 153
I1 0.2050 0.1500 0.0086 0.0417 0.0336 0.2571 0.3039 160
I2 0.3303 0.1121 0.0101 0.0671 0.1200 0.1770 0.1834 158
I3 0.1002 0.1500 0.0100 0.0023 0.1192 0.2881 0.3302 159
I4 0.2421 0.1503 0.0500 0.0141 0.0121 0.2532 0.2782 162
I5 0.2001 0.1601 0.0113 0.0041 0.0881 0.2481 0.2881 160
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Table 3. Properties of 7-Component Surrogate Mixtures and JP-5.

            Distillation curve temperatures (°C)
  
fuel or surrogate mixture density at 15 °C (g/mL) viscosity at -20 °C (mm2/s) flash point (°C) speed of sound at 20 °C (m/s) surface tension (mN/m) (°C) T10 T50 T90 DCN
jet fuel spec36 0.788–0.845 <7 >60     205 max report report naa
JP-5 0.80549 5.00 63.5 1318.7 25.9 at 21.2 191.9 205.7 233.2 44.5
A 0.82956 5.10 59.0 1338.6 26.7 at 21.4 nmb nmb nmb nmb
A1 0.82642 5.07 59.0 1336.3 26.5 at 21.6 189.95 197.35 223.3 42.7
A2 0.81454 4.96 61.0 1328.9 26.4 at 21.4 190.35 197.65 222.6 40.9
A3 0.81274 4.96 62.0 1327.8 26.3 at 26.6 193.25 200.6 221.3 48.8
A4 0.80193 4.91 64.0 1321.5 25.9 at 21.5 nmb nmb nmb nmb
B 0.80964 5.70 62.0 1328.3 26.1 at 21.1 nmb nmb nmb nmb
B1 0.81153 5.37 62.0 1328.5 26.2 at 21.8 191.05 198.85 224.9 38.5
B2 0.81717 4.71 60.0 1329.4 26.8 at 21.3 nmb nmb nmb nmb
C 0.81265 4.96 60.0 1327.3 26.2 at 20.6 nmb nmb nmb nmb
C1 0.82175 5.03 62.0 1335.0 26.7 at 21.3 nmb nmb nmb nmb
D 0.80514 4.98 59.5 1320.1 26.1 at 21.3 nmb nmb nmb nmb
D1 0.80625 4.98 60.1 1321.1 26.1 at 21.5 189.25 196.5 225.8 41.1
D2 0.82510 4.99 61.5 1338.9 26.7 at 21.3 nmb nmb nmb nmb
E 0.80704 4.92 62.2 1320.7 25.9 at 20.9 nmb nmb nmb nmb
E1 0.80769 4.92 62.2 1321.3 26.0 at 21.5 190.15 199.35 226.1 41.7
E2 0.82254 5.16 59.5 1337.8 26.7 at 21.9 nmb nmb nmb nmb
F 0.82345 3.93 58.0 1342.5 27.3 at 21.0 nmb nmb nmb nmb
F1 0.81724 4.61 60.0 1333.0 26.4 at 21.0 nmb nmb nmb nmb
F2 0.80542 6.84 66.0 1314.7 25.6 at 21.2 nmb nmb nmb nmb
G 0.81924 5.73 68.0 1330.0 nmb nmb nmb nmb nmb
G1 0.81665 5.27 63.0 1329.3 26.5 at 21.3 nmb nmb nmb nmb
G2 0.81368 4.91 60.0 1328.9 26.2 at 21.3 188.1 194.3 220.4 40.0
H1 0.82944 6.28 66.0 1332.2 25.7 at 21.3 196.4 210.3 238.0 26.7
H2 0.79676 5.01 61.0 1320.2 25.9 at 20.9 191.1 197.8 216.6 48.5
H3 0.81826 6.16 66.5 1326.5 26.0 at 20.8 197.6 210.8 236.3 36.6
H4 0.80561 5.48 63.0 1318.5 25.9 at 21.2 192.0 202.0 230.1 nmb
H5 0.82533 4.92 61.3 1342.4 26.9 at 21.5 189.3 194.8 215.6 nmb
I1 0.80561 4.73 61.0 1318.9 26.1 at 21.2 189.4 196.7 224.5 41.0
I2 0.80759 4.80 64.0 1325.9 26.3 at 21.1 192.8 199.8 217.9 45.1
I3 0.81130 4.89 59.0 1322.2 26.0 at 21.2 186.5 193.5 226.2 nmb
I4 0.80562 4.83 63.0 1318.9 26.0 at 20.7 191.1 200.1 226.6 44.2
I5 0.80532 4.70 61.0 1318.7 26.0 at 20.8 188.6 195.4 223.7 41.3
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a

na, no reporting requirement for DCN; Cetane Index is a report-only requirement.

b

nm, not measured.

In the second method, surrogates were made to roughly span a range of possible DCN values. Branched and aromatic compounds lower a fuel’s cetane number. A low cetane surrogate was prepared using the highest concentrations of aromatic and branched compounds found in the fuels’ survey. Linear and cyclic compounds increase a fuel’s cetane number. A high cetane surrogate was prepared from the highest concentration of linear and cyclic compounds found in the fuels’ survey. Three more surrogates were prepared for DCN values between the high and low cetane surrogates. Their composition was based on pure component values. The average DCN values62 for n-dodecane, n-butylbenzene, tetralin, trans-decalin, iso-cetane, and n-butylcyclohexane are 73.5, 12.5, 13.8, 31.9, 14.5, and 47.8, respectively. The DCN of 1-methylnaphthalene is not reported, but its cetane number is 0 by definition. These five surrogate mixtures prepared in the second method are designated “H” (Table 2) with their properties in Table 3.

The surrogates prepared in the first two methods represent the mass percent range of each category of compounds from the 256 JP-5 jet fuel samples. All of these surrogates have densities that fall within the military specification (Table 3). These surrogates have viscosities below the maximum limit based on military specifications. Some of the surrogates have flash points that are below the 60 °C military cutoff value, and these surrogates contain higher concentrations of the low flash point components: n-butylcyclohexane (51 °C flash point), trans-decalin (55.7 °C flash point), and n-butylbenzene (56 °C flash point).40,63 For those mixtures, the sums of the mass fractions of these three components are greater than 53.7%. In comparison with the specific JP-5 studied herein, the speed of sound of the surrogates differs by less than 24 m/s and the surface tensions differ by less than 1.5 mN/m. The estimated molar masses of the surrogate mixtures range from 153 to 176 g/mol (Table 2), which are within 13 g/mol of the 164 g/mol estimate for JP-5 based on the chemical analysis of the specific JP-5 in Table 1.

Finally, the data from the first two sets of surrogates were used to create linear correlations of density, speed of sound, viscosity, and flash point. When modeling flash point using the Liaw-type model64 with the Antoine equation and coefficients for vapor pressure, the predictions were low by 1 K. Since the flash point range was small, this model was not used. To weight these properties evenly, the units for density, viscosity, and speed of sound were converted to g/mL, km/s, and 10–1 mm2/s, respectively. The correlations were used to predict the density (Psurrogate1), viscosity (Psurrogate), and speed of sound (Psurrogate3) of a surrogate based on the amount of each component in the surrogate. The amount of each compound was constrained within its minimum and maximum for the category based on the fuel survey. Surrogates were then designed by minimizing the difference between the sum of three predicted physical properties of the surrogate and their target values for the jet fuel (eq 1)

3.2. 1

The GRC nonlinear solver in Excel was used to optimize the surrogate composition. The composition and properties of these “I” mixtures are given in Tables 2 and 3, respectively. All of the surrogates prepared had viscosities and densities that fall within that specification. Some of the surrogates had flash points that are below a minimum value of 60 °C. The DCN and distillation curve data were measured for a subset of surrogates (Table 3).

Six surrogate fuel mixtures were selected for engine testing, A3, E1, H2, H3, I2, and I4. The A3 surrogate had a composition that matched the average of the referenced JP-5 samples, and E1 had properties in the first round that were close to JP-5 and a DCN at the military specification limit for diesel fuel, F-76, of 42.65 Surrogate H2 was the surrogate with a high concentration of high DCN compounds, and surrogate H3 was the surrogate with the highest amounts of low DCN components. Note that H1 had an even lower DCN than H3, but it would not have combusted because the DCN is too low, so it was not used. Surrogates I2 and I4 had DCN values closest to the JP-5. The best surrogate in terms of properties was I4. The percentage differences between the surrogate property value and that of JP-5 were calculated by

3.2. 2

The percentage differences between JP-5 and surrogate I4 were of 0.016% for density, 3.4% for viscosity, 0.79% for flash point, 0.015% for speed of sound, 0.39% for surface tension, 0.42% for T10, 2.7% for T50 and 2.9% for T90, and 1.5% for average molar mass. The DCN matched within the error of the measurements, being 0.3 lower than the JP-5 (Tables 3 and 4).

Table 4. Percentage Difference (eq 2) between the Property for JP-5 and the Surrogate Mixtureb.

              distillation temperature
  
surrogate density viscosity flash point speed of sound surface tension DCN T10 T50 T90 average molar mass
A3 0.90 0.80 2.4 0.69 1.5 9.7 0.70 2.5 5.1 3.1
E1 0.27 1.6 2.1 0.20 0.39 6.3 0.91 3.1 3.0 1.8
H2 1.1 0.20 4.0 0.11 0.00 9.0 0.42 3.8 7.1 3.1
H3 1.6 23 4.7 0.59 0.39 18 2.97 2.5 1.3 3.1
I2 0.26 4.0 0.79 0.55 1.5 1.4 0.47 2.9 6.6 3.7
I4 0.016 3.4 0.79 0.015 0.39 0.67 0.42 2.7 2.8 1.2
previous worka 1.929 4.829 4.529 0.940 1.540 0.8629 0.3629 3.929 4.529 3.1
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a

The mole fractions of the components in this surrogate mixture were 0.25, 0.40, 0.15, and 0.20 for n-dodecane, n-butylcyclohexane, n-butylbenzene, and iso-cetane, respectively.

b

A previously formulated surrogate using four compounds is included for comparison.

3.3. Combustion Testing

The combustion behavior of the JP-5 and six surrogate mixtures in the Yanmar Tier IV diesel engine was quantified using the following engine metrics: ignition delay, maximum rate of heat release, and peak pressure location, and crank angle location of when 50% of the fuel was burned. The ignition delay (from the start of injection to 10% of the fuel burned) engine results at both engine loads are shown in Figure 2. In general, the trends are as expected with higher DCN fuels leading to more reactive shorter IGDs. Also, high-load IGDs are shorter due to the hotter thermal state of the engine (faster overall chemical kinetics). The reference JP-5 used in this study has a nominal DCN of 44.5. The final two developed surrogate candidates, I2 and I4, are the two surrogates that bound the JP-5 “x” data points with slightly lower and higher DCN values. Considering the 95% confidence interval results, the four surrogates whose DCN values range from 44.2 to 48.8 have IGDs that agree with the baseline JP-5 at both high and light engine loads. The large error bars for the lowest cetane fuel are caused by erratic combustion, which is not consistent with the behavior of the JP-5. In general, the IGD behavior of the five surrogates with DCN > 40 is very similar (statistically) to the baseline JP-5.

Figure 2.

Figure 2

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Ignition delay in Yanmar Tier 4 engine for JP-5 (designed by an ×) and six surrogate mixtures. Surrogates on the order of DCN: H3 (DCN = 36.6), E1 (DCN = 41.7), I4 (DCN = 44.2), I2 (DCN = 45.1), H2 (DCN = 48.5), and A3 (DCN = 48.8).

The maximum rate of heat release (ROHR) results from the Yanmar engine testing are shown in Figure 3. The general trend is that the maximum ROHR falls off for both low DCN and high DCN fuels. In the case of low DCN fuels, combustion occurs very late (after engine top dead center) with relatively large cylinder volumes leading to lower overall combustion pressures. While for high DCN fuels, IGD is short leading to very little fuel–air premixing, followed by longer lower-intensity combustion events. From the statistical perspective (confidence interval, CI 95%, based on the cycle-to-cycle engine variations), five of the surrogates (with higher DCNs) have the same maxROHRs as the reference JP-5. The variabilities in their measurements are similar also.

Figure 3.

Figure 3

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Maximum rate of heat release in Yanmar Tier 4 engine for JP-5 (designed by an ×) and six surrogate mixtures. Surrogates on the order of DCN: H3 (DCN = 36.6), E1 (DCN = 41.7), I4 (DCN = 44.2), I2 (DCN = 45.1), H2 (DCN = 48.5), and A3 (DCN = 48.8).

The peak pressure location (PPLOC) experimental results from the Yanmar engine testing are shown in Figure 4. The very low DCN results show peak pressure results close to the piston top center (0°). This effect is due to the very late combustion with very low in-cylinder pressures that are not detectable by the MATLAB peak detection logic. There is a lot of variability in these data due to incomplete combustion events. In moving from DCN 41.7 to higher cetane levels, a modest advancing occurs due to the shorter IGDs and earlier overall combustion. At DCN 41.7, the variability in peak pressure location is much greater than that of JP-5, suggesting that the behavior of this surrogate is different. As with IGD, the surrogates with DCN values of 44.2–48.8 have peak pressure locations that agree with the JP-5 at both high and light engine loads and the combustion variations are similar.

Figure 4.

Figure 4

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Peak pressure location in Yanmar Tier 4 engine for JP-5 (designed by an ×) and six surrogate mixtures. Surrogates on the order of DCN: H3 (DCN = 36.6), E1 (DCN = 41.7), I4 (DCN = 44.2), I2 (DCN = 45.1), H2 (DCN = 48.5), and A3 (DCN = 48.8).

The engine crankshaft location where 50% of the fuel has burned is shown next in Figure 5. This combustion metric is also referred to as CAD50 or a crank angle degree of 50% fuel burned. It is seen that the CAD50 for the lowest DCN fuel is very late from 35 to 40 crank angle degrees after engine top center. This result supports the very low max ROHR results for the lowest DCN fuel in Figure 3. It also further explains the location of the peak pressure in Figure 4 for the lowest DCN. As DCN increases above 40, it is evident that this mid-burn CAD50 combustion phasing is more conventional. Increasing DCN through the 40s range shows the CAD50 advancing from 18 degATC to nominally 13 degATC with little sensitivity to engine load. As with the other combustion properties, the surrogates with DCN values of 44.2–48.8 have CAD50s that agree with the JP-5 at both high and light engine loads and the combustion variations are similar.

Figure 5.

Figure 5

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Location of 50% of the fuel burned (CAD50) in Yanmar Tier 4 engine for JP-5 (designed by an ×) and six surrogate mixtures. Surrogates on the order of DCN: H3 (DCN = 36.6), E1 (DCN = 41.7), I4 (DCN = 44.2), I2 (DCN = 45.1), H2 (DCN = 48.5), and A3 (DCN = 48.8). The upper half of the confidence interval bars is not shown for the lowest DCN fuel.

3.4. Discussion

JP-5 surrogates were formulated in this experimental work in the context of a diesel engine, which has been done in only a few cases for jet fuel.20,22,23,29,59 Recent past surrogates prepared in the author’s laboratory29,40 for JP-5 had four components and were tested in a diesel engine. The surrogate development process in the current study differs from that of past work. For the past work, the density, viscosity, speed of sound, surface tension, and flash point (physical properties only) were measured for more than 70 surrogate samples containing butylbenzene, butylcyclohexane, dodecane, and iso-cetane and then compared to one specific JP-5 sample to determine which mixtures would be the best surrogates.40 The current study additionally used the chemical compositions of 256 jet fuel samples to set the average and limits to the concentration of each type of chemical class used in the surrogate. Three more chemical classes were included in the current surrogate to better fit the properties. The properties of fewer surrogates were measured in the present study, and preliminary measurements led to rough correlations that could be used to formulate mixtures that were closest to the JP-5 fuel sample and were within the bounds of real JP-5. It is important to develop a surrogate specifically for JP-5 because surrogates for other jet fuels do not always capture the behavior of JP-5.

When comparing how close the properties were to the fuel, the percentage differences between the property value for the fuel and that of the surrogate mixture were calculated. For the previously developed 4-component surrogate that best matched the combustion of the baseline JP-5, the percentage differences in density, viscosity, flash point, and speed of sound were higher than for surrogates A3, E1, H2, I2, and I4 (Table 4). For surface tension, the % difference for the 4-component surrogate was the same or higher than the current surrogates. There was no consistent pattern for the volatility. The DCN for the 4-component surrogate matched that of its JP-5 within the error of the measurements, being only 0.4 higher than the JP-5 sample.29 It is important to note that the percentage composition of the linear, branched, aromatic, and cyclic compounds in the “best” 4-component JP-5 surrogate in those studies did fall within the bounds of the category of components from the current surrogate study when grouping together all aromatic and cyclic compounds and not including cycloaromatic compounds like tetralin.

The JP-5 surrogates developed herein have components with flash points greater than 50 °C. When the mass fraction of components with flash points less than 60 °C exceeded 53.7%, the flash point of the surrogate fell below the 60 °C military minimum requirement. Jet fuel surrogates, such as those for Jet A, that do not have this flash point (FP) requirement have been prepared from components with flash points below the compounds used herein. Jet A, JP-8, and kerosene surrogates3,4,10,11,15,19,24,66−69 have contained benzene (−11 °C FP), iso-octane (−12 °C FP), methylcyclohexane (−4 °C FP), toluene (4 °C FP), meta-xylene (27 °C), propylbenzene (30 °C FP), ortho-xylene (32 °C FP), 1,2,4-trimethylbenzene (44 °C FP), and 1,3,5-trimethylbenzene (50 °C FP).70 Two JP-8 surrogates9,34 contained three components with flash points below 60 °C, butylbenzene, butylcyclohexane, and n-decane (51 °C FP).70 The optimal surrogate developed in the current study has a flash point of 63 °C.

Diesel combustion experiments on five 4-component JP-5 surrogates29 showed that DCN is an important metric for predicting the performance of a diesel engine. The ensuing discussion highlighted the importance of measuring DCN and how difficult it is to predict DCN from mixture composition because studies have shown both linear and nonlinear relationships between DCN and composition.7,71−73 This difficulty in predicting DCN is further supported by Figure 6, which shows that DCN is not well predicted when using either a simple mole fraction or mass fraction weighting of the DCN of the individual components. More advanced prediction is beyond the scope of this paper, and many researchers are seeking ways to predict the DCN of individual compounds and mixtures. Researchers have sought various approaches to predicting CN or DCN from physical properties and chemical composition. CN and DCN correlations have been developed based on compound category,74−76 functional group classification within molecules (e.g., aromatic bond, double bond, quaternary carbon, etc.),77−791H NMR to get carbon types,80−8213C NMR,83 and physical properties.84 ASTM methods (ASTM D976 or D4737) estimate CN using density and distillation curve temperature information and call the estimate a cetane index.85,86 Machine learning and neural networks have also been used to predict the CN of diesel and gasoline from IR spectroscopy data,87,88 the CN of hydrocarbons with and without oxygenates using molecular descriptors (e.g., polar surface area, average bond enthalpy, etc.),89,90 major chemical categories,91 functional group classification within molecules (e.g., aromatic bond, double bond, quaternary carbon, etc.),92,93 simplified molecular-input line-entry system (SMILES) strings,94 and physical properties (e.g., viscosity, aniline point, molecular weight, refractive index, hydrogen content, distillation curve data).95 Surrogate development will be enhanced by a correct method to predict DCN, and engine combustion can be used to determine if DCN represents the actual combustion behavior.

Figure 6.

Figure 6

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Prediction of DCN using a mole fraction and mass fraction weighting of the individual component DCN values.

4. Conclusions

In this study, six JP-5 surrogates composed of seven pure fuel components were formulated and tested in comparison to a nominal JP-5 fuel. The physical properties of five of the surrogates were very similar to that of the JP-5 and were within the military specifications. A key metric here was flash point, which is required to be above 60 °C, and makes this surrogate differ from those previously formulated for JP-8 and Jet A. The similarity in density, viscosity, and surface tension helps ensure comparable spray patterns in the engine. The similarity in distillation temperature helps ensure comparable volatilization behavior of the fuel. Surrogate I4 developed last had the closest physical properties to those of JP-5 and its DCN values within 1 DCN of JP-5. In the combustion experiments, several of the surrogates were able to match the combustion IGD, peak pressure, max heat release, and CAD50 of JP-5 in terms of the engine’s cycle by cycle average values and variability. The lowest DCN surrogate designed and tested contained a higher amount of lower cetane compounds (Mix H3) leading to erratic combustion behavior, similar to that seen in previous low cetane JP-5 surrogates.29 Of the six surrogates tested, the best one in terms of physical properties, chemical properties, and combustion behavior was the one that contained 0.2421, 0.1503, 0.0500, 0.0141, 0.0121, 0.2532, and 0.2782 mass percentages of n-dodecane, n-butylbenzene, 1-methylnaphthalene, tetralin, trans-decalin, iso-cetane, and n-butylcyclohexane, respectively. These are good components to have in a surrogate mixture because chemical kinetic models have been established or are currently being researched for these compounds. Lawrence Livermore Laboratory has developed kinetic models for dodecane, iso-cetane, and butylbenzene96 and is working to developing kinetics models for butylcyclohexane, tetralin, 1-methylnaphthalene, and trans-decalin.97

Acknowledgments

This work was supported by the Naval Air Systems Command (NAVAIR).

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.1c05904.

  • Composition and properties of 7-component mixtures prepared with other components (PDF)

The authors declare no competing financial interest.

Supplementary Material

ao1c05904_si_001.pdf (77.3KB, pdf)

References

  1. Wood C. P.The Development and Appliaiton of Surrogate Blends in Simulating the Combuation Performance of Distillate Aviation Fuels. M.S. Thesis, University of California, Irvine, California, 1989. [Google Scholar]
  2. Wood C. P.; McDonell V. G.; Smith R. A.; Samuelsen G. S. Development and application of a surrogate distillate fuel. J. Propul. Power 1989, 5, 399–405. 10.2514/3.23168. [DOI] [Google Scholar]
  3. Edwards T.; Maurice L. Q. Surrogate mixtures to represent complex aviation and rocket fuels. J. Propul. Power 2001, 17, 461–466. 10.2514/2.5765. [DOI] [Google Scholar]
  4. Dean A. J.; Penyazkov O. G.; Sevruk K. L.; Varatharajan B. Autoignition and surrogate fuels at elevated temperatures and pressure. Proc. Combust. Inst. 2007, 31, 2481–2488. 10.1016/j.proci.2006.07.162. [DOI] [Google Scholar]
  5. Huber M. L.; Lemmon E. W.; Ott L. S.; Bruno T. J. Preliminary surrogate mixture models for the thermophysical properties of rocket propellants RP-1 and RP-2. Energy Fuels 2009, 23, 3083–3088. 10.1021/ef900216z. [DOI] [Google Scholar]
  6. Bruno T. J.; Smith B. L. Evaluation of the physicochemical authenticity of aviation kerosene surrogate mixtures. Part 1: Analysis of volatility with the Advanced Distillation Curve. Energy Fuels 2010, 24, 4266–4276. 10.1021/ef100496j. [DOI] [Google Scholar]
  7. Huber M. L.; Lemmon E. W.; Bruno T. J. Surrogate mixture models for the thermophysical properties of aviation fuel Jet-A. Energy Fuels 2010, 24, 3565–3571. 10.1021/ef100208c. [DOI] [Google Scholar]
  8. Bruno T. J.; Huber M. L. Evaluation of the physicochemical authenticity of aviation kerosene surrogate mixtures. Part 2: Analysis and prediction of thermophysical properties. Energy Fuels 2010, 24, 4277–4284. 10.1021/ef1004978. [DOI] [Google Scholar]
  9. Dooley S.; Won S. H.; Chaos M.; Heyne J.; Ju Y.; Dryer F. L.; Kumar K.; Sung C.-J.; Wang H.; Oehlschlaeger M. A.; Santoro R. J.; Litzinger T. A. A jet fuel surrogate formulated by real fuel properties. Combust. Flame 2010, 157, 2333–2339. 10.1016/j.combustflame.2010.07.001. [DOI] [Google Scholar]
  10. Dooley S.; Won S. H.; Heyne J.; Farouk T. I.; Ju Y.; Dryer F. L.; Kumar K.; Hui X.; Sung C.-J.; Wang H.; Oehlschlaeger M. A.; Iyer V.; Iyer S.; Litzinger T. A.; Santoro R. J.; Malewicki T.; Brezinsky K. The experimental evaluation of a methodology for surrogate fuel formulation to emulate gas phase combustion kinetic phenomena. Combust. Flame 2012, 159, 1444–1466. 10.1016/j.combustflame.2011.11.002. [DOI] [Google Scholar]
  11. Echavarria C. A.; Jaramillo I. C.; Sarofim A. F.; Lighty J. S. Burnout of soot particles in a two-stage burner with a JP-8 surrogate fuel. Combust. Flame 2012, 159, 2441–2448. 10.1016/j.combustflame.2012.03.011. [DOI] [Google Scholar]
  12. Dooley S.; Won S. H.; Jahangirian S.; Ju Y.; Dryer F. L.; Wang H.; Oehlschlaeger M. A. The combustion kinetics of a synthetic paraffinic jet aviation fuel and a fundamentally formulated, experimentally validated surrogate fuel. Combust. Flame 2012, 159, 3014–3020. 10.1016/j.combustflame.2012.04.010. [DOI] [Google Scholar]
  13. Allen C.; Valco D.; Toulson E.; Yoo J. H.; Lee T. JP-5 and HRJ-5 Autoignition Characteristics and Surrogate Modeling. Energy Fuels 2013, 27, 7790–7799. 10.1021/ef401629d. [DOI] [Google Scholar]
  14. Allen C.; Valco D.; Toulson E.; Edwards T.; Lee T. Ignition behavior and surrogate modeling of JP-8 and of camelina and tallow hydrotreated renewable jet fuels at low temperatures. Combust. Flame 2013, 160, 232–239. 10.1016/j.combustflame.2012.10.008. [DOI] [Google Scholar]
  15. Kim D.; Martz J.; Violi A. A surrogate for emulating the physical and chemical properties of conventional jet fuel. Combust. Flame 2014, 161, 1489–1498. 10.1016/j.combustflame.2013.12.015. [DOI] [Google Scholar]
  16. Dooley S.; Heyne J.; Won S. H.; Dievart P.; Ju Y.; Dryer F. L. Importance of a cycloalkane functionality in the oxidation of a real fuel. Energy Fuels 2014, 28, 7649–7661. 10.1021/ef5008962. [DOI] [Google Scholar]
  17. Iyer V. R.; Iyer S. S.; Linevsky M. J.; Litzinger T. A.; Santoro R. J.; Dooley S.; Dryer F. L.; Mordaunt C. J. Simulating the sooting propensity of JP-8 with surrogate fuels from hydrocarbon fluids. J. Propul. Power 2014, 30, 1410–1418. 10.2514/1.B35139. [DOI] [Google Scholar]
  18. Dryer F. L.; Jahangirian S.; Dooley S.; Won S. H.; Heyne J.; Iyer V. R.; Litzinger T. A.; Santoro R. J. Emulating the combustion behavior of real jet aviation fuels by surrogate mixtures of hydrocarbon fluid blends: Implications for science and engineering. Energy Fuels 2014, 28, 3474–3485. 10.1021/ef500284x. [DOI] [Google Scholar]
  19. Zeng W.; Liang S.; Li H. X.; Ma H. A. Chemical kinetic simulation of kerosene combustion in an individual flame tube. J. Adv. Res. 2014, 5, 357–366. 10.1016/j.jare.2013.06.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Luning Prak D. J.; Jones M. H.; Trulove P.; McDaniel A. M.; Dickerson T.; Cowart J. S. Physical and chemical analysis of alcohol-to-jet (ATJ) fuel and development of surrogate fuel mixtures. Energy Fuels 2015, 29, 3760–3769. 10.1021/acs.energyfuels.5b00668. [DOI] [Google Scholar]
  21. Ahmedi A.; Ahmed S. S.; Kalghatgi G. T. Simulating combustion in a PCI (premixed compression ignition) engine using DI-SRM and 3 components surrogate model. Combust. Flame 2015, 162, 3728–3739. 10.1016/j.combustflame.2015.07.011. [DOI] [Google Scholar]
  22. Kang D.; Kalaskar V.; Kim D.; Martz J.; Violi A.; Boehman A. Experimental study of autoignition characteristics of Jet-A surrogates and their validation in a motored engine and a constant-volume combustion chamber. Fuel 2016, 184, 565–580. 10.1016/j.fuel.2016.07.009. [DOI] [Google Scholar]
  23. Luning Prak D. J.; Romanczyk M.; Wehde K. E.; Ye S.; McLaughlin M.; Luning Prak P. J.; Foley M. P.; Kenttamaa H. I.; Trulove P. C.; Kilaz G.; Xu L.; Cowart J. Analysis of catalytic hydrothermal conversion jet fuel and surrogate mixture formulation: Components, properties, and combustion. Energy Fuels 2017, 31, 13802–13814. 10.1021/acs.energyfuels.7b02960. [DOI] [Google Scholar]
  24. Das D. D.; McEnally C. S.; Kwan T. A.; Zimmerman J. B.; Cannella W. J.; Mueller C. J.; Pfefferle L. D. Sooting tendencies of diesel fuels, jet fuels, and their surrogates in diffusion flames. Fuel 2017, 197, 445–458. 10.1016/j.fuel.2017.01.099. [DOI] [Google Scholar]
  25. Wu Z.; Mao Y.; Raza M.; Zhu J.; Feng Y.; Wang S.; Qian Y.; Yu L.; Lu X. Surrogate fuels for RP-3 kerosene formulated by emulating molecular structures, functional groups, physical and chemical properties. Combust. Flame 2019, 208, 388–401. 10.1016/j.combustflame.2019.07.024. [DOI] [Google Scholar]
  26. Fang X.; Huang Z.; Qiao X.; Ju D.; Bai X. Skeletal mechanism development for a 3-component jet fuel surrogate using semi-global sub-mechanism construction and mechanism reduction. Fuel 2018, 229, 53–59. 10.1016/j.fuel.2018.04.159. [DOI] [Google Scholar]
  27. Yu J.; Gou X. Comprehensive Surrogate for Emulating Physical and Kinetic Properties of Jet Fuels. J. Propul. Power 2018, 34, 679–689. 10.2514/1.B36766. [DOI] [Google Scholar]
  28. Wang B. Y.; Liu Y. X.; Weng J. J.; Pan G. F.; Tian Z. Y. An experimental and modeling study on the low temperature oxidation of surrogate for JP-8 part II: Comparison between neat 1,3,5-trimethylbenzene and its mixture with n-decane. Combust. Flame 2018, 192, 517–529. 10.1016/j.combustflame.2018.01.001. [DOI] [Google Scholar]
  29. Cowart J.; Foley M. P.; Luning Prak D. The development and testing of Navy jet fuel (JP-5) surrogates. Fuel 2019, 249, 80–88. 10.1016/j.fuel.2019.03.096. [DOI] [Google Scholar]
  30. Jin Z.-H.; Chen J.-T.; Li W.; Song S.-B.; Yang J.-Z.; Tian Z.-Y. Pyrolysis of 2-methylfuran/jet fuel surrogate blends: An experimental and kinetic modeling study. Combust. Flame 2021, 232, 111509 10.1016/j.combustflame.2021.111509. [DOI] [Google Scholar]
  31. Farouk T. I.; Won S. H.; Dryer F. L. Sub-millimeter sized multi-component jet fuel surrogate droplet combustion: Physicochemical preferential vaporization effects. Proc. Combust. Inst. 2021, 38, 3313–3323. 10.1016/j.proci.2020.06.200. [DOI] [Google Scholar]
  32. Kim D.; Martz J.; Violi A. Effects of fuel physical properties on direct injection spray and ignition behavior. Fuel 2016, 180, 481–496. 10.1016/j.fuel.2016.03.085. [DOI] [Google Scholar]
  33. Won S. H.; Haas F. M.; Dooley S.; Edwards T.; Dryer F. L. Reconstruction of chemical structure of real fuel by surrogate formulation based upon combustion property targets. Combust. Flame 2017, 183, 39–49. 10.1016/j.combustflame.2017.04.032. [DOI] [Google Scholar]
  34. Colket M.; Heyne J.; Rumizen M.; Gupta M.; Edwards T.; Roquemore W. M.; Andac G.; Boehm R.; Lovett J.; Williams R.; Condevaux J.; Turner D.; Rizk N.; Tishkoff J.; Li C.; Moder J.; Friend D.; Sankaran V. Overview of the National Jet Fuels Combustion Program. AIAA J. 2017, 55, 1087–1104. 10.2514/1.J055361. [DOI] [Google Scholar]
  35. ASTM D1655-18 Standard Specification for Aviation Turbine Fuels; ASTM International, West Conshohocken, PA, 2018. [Google Scholar]
  36. Detail Specification Turbine Fuel, Aviation, Grades JP-4 and JP-5, MILDTL-5624W; United States Department of Defense, Washington, DC, 2016.
  37. Vozka P.; Mo H.; Šimáček P.; Kilaz G. Middle distillates hydrogen content via GC× GC-FID. Talanta 2018, 186, 140–146. 10.1016/j.talanta.2018.04.059. [DOI] [PubMed] [Google Scholar]
  38. Vozka P.; Modereger B. A.; Park A. C.; Zhang W. T. J.; Trice R. W.; Kenttämaa H. I.; Kilaz G. Jet fuel density via GC × GC-FID. Fuel 2019, 235, 1052–1060. 10.1016/j.fuel.2018.08.110. [DOI] [Google Scholar]
  39. Metz A. E.; Loegel T. N.; Wrzesinski P. J.; Simms G. R.; Peretich M. E.; McDaniel A. M.; Kamin R. A.; Baniszewski D. J.. GC×GC Hydrocarbon Method Alignment Across Multiple DOD Facilities, Proceedings of the 16th International Symposium on Stability, Handling, and Use of Liquid Fuels, Long Beach CA, IASH, 2019.
  40. Luning Prak D. J.; Fries J. M.; Gober R. T.; Vozka P.; Kilaz G.; Johnson T. R.; Graft S. L.; Trulove P. C.; Cowart J. S. Densities, viscosities, speeds of sound, bulk moduli, surface tensions, and flash points of quaternary mixtures of n-dodecane (1), n-butylcyclohexane (2), n-butylbenzene (3), and 2,2,4,4,6,8,8-heptamethylnonane (4) at 0.1 MPa as potential surrogate mixtures for military jet fuel, JP-5. J. Chem. Eng. Data 2019, 64, 1725–1745. 10.1021/acs.jced.8b01233. [DOI] [Google Scholar]
  41. Vozka P.; Šimáček P.; Kilaz G. Impact of HEFA feedstocks on fuel composition and properties in blends with Jet A. Energy Fuels 2018, 32, 11595–11606. 10.1021/acs.energyfuels.8b02787. [DOI] [Google Scholar]
  42. Honnet S.; Seshadri K.; Niemann U.; Peters N. A surrogate fuel for kerosene. Proc. Combust. Inst. 2009, 32, 485–492. 10.1016/j.proci.2008.06.218. [DOI] [Google Scholar]
  43. Kim D.; Violi A. Hydrocarbons for the next generation of jet fuel surrogates. Fuel 2018, 228, 438–444. 10.1016/j.fuel.2018.04.112. [DOI] [Google Scholar]
  44. Yu W.; Yang W.; Tay K.; Zhao F. An optimization method for formulating model-based jet fuel surrogate by emulating physical, gas phase chemical properties and threshold sooting index (TSI) of real jet fuel under engine relevant conditions. Combust. Flame 2018, 193, 192–217. 10.1016/j.combustflame.2018.03.024. [DOI] [Google Scholar]
  45. Luning Prak D. J.; Cowart J. S.; Hamilton L. J.; Hoang D. T.; Brown E. K.; Trulove P. C. Development of a surrogate mixture for algal-based hydrotreated renewable diesel. Energy Fuels 2013, 27, 954–961. 10.1021/ef301879g. [DOI] [Google Scholar]
  46. Luning Prak D. J.; Luning Prak P. J.; Trulove P. C.; Cowart J. S. Formulation of surrogate fuel mixtures based on physical and chemical analysis of hydrodepolymerized cellulosic diesel fuel. Energy Fuels 2016, 30, 7331–7341. 10.1021/acs.energyfuels.6b01114. [DOI] [Google Scholar]
  47. Pitz W. J.; Mueller C. J. Recent progress in the development of diesel surrogate fuels. Prog. Energy Combust. Sci. 2011, 37, 330–350. 10.1016/j.pecs.2010.06.004. [DOI] [Google Scholar]
  48. Mueller C. J.; Cannella W. J.; Bruno T. J.; Bunting B.; Dettman H. D.; Franz J. A.; Huber M. L.; Natarajan M.; Pitz W. J.; Ratcliff M. A.; Wright K. Methodology for formulating diesel surrogate fuels with accurate compositional, ignition-quality, and volatility characteristics. Energy Fuels 2012, 26, 3284–3303. 10.1021/ef300303e. [DOI] [Google Scholar]
  49. Dagaut P.; Karsenty F.; Dayma G.; Diévart P.; Hadj-Ali K.; Mzé-Ahmed A.; Braun-Unkhoff M.; Herzler J.; Kathrotia T.; Kick T.; Naumann C.; Riedel U.; Thomas L. Experimental and detailed kinetic model for the oxidation of a Gas to Liquid (GtL) jet fuel. Combust. Flame 2014, 161, 835–847. 10.1016/j.combustflame.2013.08.015. [DOI] [Google Scholar]
  50. Westbrook C. K.; Pitz W. J.; Herbinet O.; Curran H. J.; Silke E. J. A comprehensive detailed chemical kinetic reaction mechanism for combustion of n-alkane hydrocarbons from n-octane to n-hexadecane. Combust. Flame 2009, 156, 181–199. 10.1016/j.combustflame.2008.07.014. [DOI] [Google Scholar]
  51. Wang F.; Zheng Z.; He Z. Reduced polycyclic aromatic hydrocarbon formation chemical kinetic model of diesel surrogate fuel for homogeneous charge compression ignition combustion. Energy Fuels 2012, 26, 1612–1620. 10.1021/ef201937k. [DOI] [Google Scholar]
  52. Xiao G.; Zhang Y.; Lang J. Kinetic modeling study of the ignition process of homogeneous charge compression ignition engine fueled with three-component diesel surrogate. Ind. Eng. Chem. Res. 2013, 52, 3732–3741. 10.1021/ie303406k. [DOI] [Google Scholar]
  53. Luo J.; Yao M.; Luiu H. A reduced chemical kinetic mechanism for low temperature diesel combustion and soot emissions. Combust. Sci. Technol. 2014, 186, 1975–1990. 10.1080/00102202.2014.945024. [DOI] [Google Scholar]
  54. Chang Y.; Jia M.; Li Y.; Liu Y.; Xie M.; Wang H.; Reitz R. D. Development of a skeletal mechanism for diesel surrogate fuel by using a decoupling methodology. Combust. Flame 2015, 162, 3785–3802. 10.1016/j.combustflame.2015.07.016. [DOI] [Google Scholar]
  55. Narayanaswamy K.; Pitsch H.; Pepiot P. A component library framework for deriving kinetic mechanisms for multi-component fuel surrogates: Application for jet fuel surrogates. Combust. Flame 2016, 165, 288–309. 10.1016/j.combustflame.2015.12.013. [DOI] [Google Scholar]
  56. Dagaut P.; Hadj-Ali K. Chemical Kinetic Study of the Oxidation of Isocetane (2,2,4,4,6,8,8-Heptamethylnonane) in a Jet-stirred Reactor: Experimental and Modeling. Energy Fuels 2009, 23, 2389–2395. 10.1021/ef900089n. [DOI] [Google Scholar]
  57. Mao Y.; Yu L.; Qian Y.; Wang S.; Wu Z.; Raza M.; Zhu L.; Hu X.; Lu X. Development and validation of a detailed kinetic model for RP-3 aviation fuel based on a surrogate formulated by emulating macroscopic properties and microscopic structure. Combust. Flame 2021, 229, 111401 10.1016/j.combustflame.2021.111401. [DOI] [Google Scholar]
  58. Wu Y.; Modica V.; Yu X.; Grisch F. Experimental investigation of laminar flame speed measurement for kerosene fuels: Jet A-1, surrogate Fuel, and its pure components. Energy Fuels 2018, 32, 2332–2343. 10.1021/acs.energyfuels.7b02731. [DOI] [Google Scholar]
  59. Yu W.; Tay K.; Zhao F.; Yang W.; Li H.; Xu H. Development of a new jet fuel surrogate and its associated reaction mechanism coupled with a multistep soot model for diesel engine combustion. Appl. Energy 2018, 228, 42–56. 10.1016/j.apenergy.2018.06.076. [DOI] [Google Scholar]
  60. Luning Prak D.; Cooke J.; Dickerson T.; McDaniel A.; Cowart J. Cetane number, derived cetane number, and cetane index: When correlations fail to predict combustibility. Fuel 2021, 289, 119963 10.1016/j.fuel.2020.119963. [DOI] [Google Scholar]
  61. ASTM D6890-18 Standard Test Method for Determination of Ignition Delay and Derived Cetane Number (DCN) of Diesel Fuel Oils by Combustion in a Constant Volume Chamber; ASTM International, West Conshohocken, PA, 2018. [Google Scholar]
  62. Yanowitz J.; Ratcliff M. A.; McCormick R. L.; Taylor J. D.; Murphy M. J.. Compendium of Experimental Cetane Numbers. National Renewable Energy Lab, Technical Report NREL/TP-5400-67585, National Renewable Energy Laboratory, OSTI.GOV, 2017.
  63. Luning Prak D. J.; Cowart J. S.; Trulove P. C. Density and viscosity from 293.15 to 373.15 K, speed of sound and bulk modulus from 293.15 to 343.15 K, surface tension, and flash point of binary mixtures of bicyclohexyl and 1,2,3,4-tetrahydronaphthalene or trans-decahydronaphthalene at 0.1 MPa. J. Chem. Eng. Data 2016, 61, 650–661. 10.1021/acs.jced.5b00790. [DOI] [Google Scholar]
  64. Liaw H.-J.; Lee Y.-H.; Tang C.-L.; Hsu H.-H.; Liu J.-H. A mathematical model for predicting the flash point of binary solutions. J. Loss Prev. Process Ind. 2002, 15, 429–438. 10.1016/S0950-4230(02)00068-2. [DOI] [Google Scholar]
  65. Naval Distillate, Military Specification MIL-PRF-16884N; United States Department of Defense, Washington,DC, 2014.
  66. Humer S.; Seiser R.; Seshardi K. Experimental investigation of combustion of jet fuels and surrogates in nonpremixed flows. J. Propul. Power 2011, 27, 847–855. 10.2514/1.46916. [DOI] [Google Scholar]
  67. Seshadri K.; Frassoldati A.; Cuoci A.; Faravelli T.; Niemann U.; Weydert P.; Ranzi E. Experimental and kinetic modeling study of combustion of JP-8, its surrogates and components in laminar premixed flows. Combust. Theory Modell. 2011, 15, 569–583. 10.1080/13647830.2011.552635. [DOI] [Google Scholar]
  68. Yu J.; Wang Z.; Zhuo X.; Wang W.; Gou X. Surrogate definition and chemical kinetic modeling for two different jet aviation fuels. Energy Fuels 2016, 30, 1375–1382. 10.1021/acs.energyfuels.5b02414. [DOI] [Google Scholar]
  69. Chen X.; Khani E.; Chen C. P. A unified jet fuel surrogate for droplet evaporation and ignition. Fuel 2016, 182, 284–291. 10.1016/j.fuel.2016.05.114. [DOI] [Google Scholar]
  70. Rumble J. R.CRC Handbook of Chemistry and Physics; 102th ed. (internet edition), CRC Press/Taylor & Francis, Boca Raton, FL, 2021. [Google Scholar]
  71. Baumgardner M. E.; Sarathy S. M.; Marchese A. J. Autoignition Characterization of Primary Reference Fuels and n-Heptane/n-Butanol Mixtures in a Constant Volume Combustion Device and Homogeneous Charge Compression Ignition Engine. Energy Fuels 2013, 27, 7778–7789. 10.1021/ef4015982. [DOI] [Google Scholar]
  72. Haas F. M.; Ramcharan A.; Dryer F. L. Relative Reactivities of the Isomeric Butanols and Ethanol in an Ignition Quality Tester. Energy Fuels 2011, 25, 3909–3916. 10.1021/ef2008024. [DOI] [Google Scholar]
  73. Kuszewski H.; Jaworski A.; Ustrzycki A.; Lejda K.; Balawender K.; Woś P. Use of the constant volume combustion chamber to examine the properties of autoignition and derived cetane number of mixtures of diesel fuel and ethanol. Fuel 2017, 200, 564–575. 10.1016/j.fuel.2017.04.021. [DOI] [Google Scholar]
  74. Chakravarthy R.; Acharya C.; Savalia A.; Naik G. N.; Das A. K.; Saravanan C.; Verma A.; Gudasi K. B. Property prediction of diesel fuel based on the composition analysis data by two-dimensional gas chromatography. Energy Fuels 2018, 32, 3760–3774. 10.1021/acs.energyfuels.7b03822. [DOI] [Google Scholar]
  75. Creton B.; Dartiguelongue C.; de Bruin T.; Toulhoat H. Prediction of the cetane number of diesel compounds using the quantitative structure property relationships. Energy Fuels 2010, 24, 5396–5403. 10.1021/ef1008456. [DOI] [Google Scholar]
  76. Ghosh P.; Jaffe S. B. Detailed Composition-Based Model for Predicting the Cetane Number of Diesel Fuels. Ind. Eng. Chem. Res. 2006, 45, 346–351. 10.1021/ie0508132. [DOI] [Google Scholar]
  77. Dahmen M.; Marquardt W. A Novel Group Contribution Method for the Prediction of the Derived Cetane Number of Oxygenated Hydrocarbons. Energy Fuels 2015, 29, 5781–5801. 10.1021/acs.energyfuels.5b01032. [DOI] [Google Scholar]
  78. Li R.; Herreros J. M.; Tsolakis A.; Yang W. Novel Functional Group Contribution Method for Surrogate Formulation with Accurate Fuel Compositions. Energy Fuels 2020, 34, 2989–3012. 10.1021/acs.energyfuels.9b04270. [DOI] [Google Scholar]
  79. Ogawa H.; Nishimoto H.; Morita A.; Shibata G. Predicted diesel ignitability index based on the molecular structures of hydrocarbons. Int. J. Engine Res. 2016, 17, 766–775. 10.1177/1468087415612612. [DOI] [Google Scholar]
  80. Gulder O. L.; Glavincevski B. Prediction of cetane number of diesel fuels from carbon type structural composition determined by proton NMR spectroscopy. Ind. Eng. Chem. Prod. Res. Dev. 1986, 25, 153–156. 10.1021/i300022a005. [DOI] [Google Scholar]
  81. Kapur G. S.; Ecker A.; Meusinger R. Establishing Quantitative Structure–Property Relationships (QSPR) of Diesel Samples by Proton-NMR & Multiple Linear Regression (MLR) Analysis. Energy Fuels 2001, 15, 943–948. 10.1021/ef010021u. [DOI] [Google Scholar]
  82. Souza C. R.; Silva A. H.; Nagata N.; Ribas J. L. T.; Simonelli F.; Barison A. Cetane Number Assessment in Diesel Fuel by 1H or Hydrogen Nuclear Magnetic Resonance-Based Multivariate Calibration. Energy Fuels 2014, 28, 4958–4962. 10.1021/ef501000a. [DOI] [Google Scholar]
  83. Heredia-Langner A.; Cort J. R.; Grubel K.; O’Hagan M. J.; Jarman K. H.; Linehan J. C.; Albrecht K. O.; Polikarpov E.; King D. L.; Smurthwaite T. D.; Bays J. T. Methodology for the Development of Empirical Models Relating 13C NMR Spectral Features to Fuel Properties. Energy Fuels 2020, 34, 12556–12572. 10.1021/acs.energyfuels.0c00883. [DOI] [Google Scholar]
  84. Ladommatos N.; Goacher J. Equations for predicting the cetane number of diesel fuels from their physical properties. Fuel 1995, 74, 1083–1093. 10.1016/0016-2361(95)00040-C. [DOI] [Google Scholar]
  85. ASTM D4737-10(2016) Standard Test Method for Calculated Cetane Index by Four Variable Equation; ASTM International, West Conshohocken, PA, 2016. [Google Scholar]
  86. ASTM D976-06(2016) Standard Test Method for Calculated Cetane Index of Distillate Fuels; ASTM International, West Conshohocken, PA, 2016. [Google Scholar]
  87. Al Ibrahim E.; Farooq A. Prediction of the Derived Cetane Number and Carbon/Hydrogen Ratio from Infrared Spectroscopic Data. Energy Fuels 2021, 35, 8141–8152. 10.1021/acs.energyfuels.0c03899. [DOI] [Google Scholar]
  88. Al-kaf H. A. G.; Chia K. S.; Alduais N. A. M. A comparison between single layer and multilayer artificial neural networks in predicting diesel fuel properties using near infrared spectrum. Pet. Sci. Technol. 2018, 36, 411–418. 10.1080/10916466.2018.1425717. [DOI] [Google Scholar]
  89. Baghban A.; Adelizadeh M. On the determination of cetane number of hydrocarbons and oxygenates using Adaptive Neuro Fuzzy Inference System optimized with evolutionary algorithms. Fuel 2018, 230, 344–354. 10.1016/j.fuel.2018.05.032. [DOI] [Google Scholar]
  90. Guo Z.; Lim K. H.; Chen M.; Thio B. J. R.; Loo B. L. W. Predicting cetane numbers of hydrocarbons and oxygenates from highly accessible descriptors by using artificial neural networks. Fuel 2017, 207, 344–351. 10.1016/j.fuel.2017.06.104. [DOI] [Google Scholar]
  91. Yang H.; Ring Z.; Briker Y.; McLean N.; Friesen W.; Fairbridge C. Neural network prediction of cetane number and density of diesel fuel from its chemical composition determined by LC and GC–MS. Fuel 2002, 81, 65–74. 10.1016/S0016-2361(01)00121-1. [DOI] [Google Scholar]
  92. Kubic W. L.; Jenkins R. W.; Moore C. M.; Semelsberger T. A.; Sutton A. D. Artificial Neural Network Based Group Contribution Method for Estimating Cetane and Octane Numbers of Hydrocarbons and Oxygenated Organic Compounds. Ind. Eng. Chem. Res. 2017, 56, 12236–12245. 10.1021/acs.iecr.7b02753. [DOI] [Google Scholar]
  93. Saldana D. A.; Starck L.; Mougin P.; Rousseau B.; Pidol L.; Jeuland N.; Creton B. Flash Point and Cetane Number Predictions for Fuel Compounds Using Quantitative Structure Property Relationship (QSPR) Methods. Energy Fuels 2011, 25, 3900–3908. 10.1021/ef200795j. [DOI] [Google Scholar]
  94. Schweidtmann A. M.; Rittig J. G.; König A.; Grohe M.; Mitsos A.; Dahmen M. Graph Neural Networks for Prediction of Fuel Ignition Quality. Energy Fuels 2020, 34, 11395–11407. 10.1021/acs.energyfuels.0c01533. [DOI] [Google Scholar]
  95. Stratiev D.; Marinov I.; Dinkov R.; Shishkova I.; Velkov I.; Sharafutdinov I.; Nenov S.; Tsvetkov T.; Sotirov S.; Mitkova M.; Rudnev N. Opportunity to Improve Diesel-Fuel Cetane-Number Prediction from Easily Available Physical Properties and Application of the Least-Squares Method and Artificial Neural Networks. Energy Fuels 2015, 29, 1520–1533. 10.1021/ef502638c. [DOI] [Google Scholar]
  96. Lawrence Livermore Laboratories. Combustion Mechanisms. https://combustion.llnl.gov/mechanisms (accessed July 29, 2021).
  97. Lawrence Livermore Laboratory. Combustion. https://combustion.llnl.gov (accessed July 29, 2021).

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