A surrogate for emulating the physical and chemical properties of jet fuel Doohyun Kim, Jason Martz, Angela Violi

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1 : Distribution Statement A. Approved for public release. Paper # 070IC-0269 Topic: Laminar Flames 8 th U. S. National Combustion Meeting Organized by the Western States Section of the Combustion Institute and hosted by the University of Utah May 19-22, 2013 A surrogate for emulating the physical and chemical properties of jet fuel Doohyun Kim, Jason Martz, Angela Violi Mechanical Engineering, University of Michigan, Ann Arbor MI To address the need for more accurate jet fuel surrogates, two four-component surrogates, UM1 and UM2, were formulated to emulate both the physical and chemical properties of a representative real jet fuel, Jet-A POSF The surrogate target properties include cetane number, lower heating value, hydrogen to carbon ratio, molecular weight and temperature dependent density, viscosity, surface tension and distillation characteristics. Candidate species were constrained to include only those available within existing chemical mechanisms, while the surrogate composition was defined to minimize the difference between the surrogate and target fuel properties. With the exception of surface tension, the average absolute difference between the surrogate and Jet-A properties was less than 5% for the UM2 surrogate. Finally, the volume fractions of the major hydrocarbon classes were reasonably captured. Properties of the newly developed surrogates and existing surrogate formulations obtained from the literature were compared to real JP-8 properties to evaluate the merits and demerits of each. Simulations of non-reacting jet fuel sprays within a constant volume bomb were performed with the newly developed and existing surrogate formulations. In turn, the simulation predictions were compared to experimental liquid and vapor spray penetration data. Introduction The U.S. Army s single fuel policy mandates the use of jet fuel for the simplification of supply chain logistics [1]. As a result, the Army s diesel engines must be capable of operating with JP-8, which is derived from aviation kerosene, Jet-A. The resulting difference between the fuels is the additive package within JP-8, which includes a corrosion inhibitor/lubricity enhancer, an icing inhibitor and a static dissipater [2]. It has been reported that the gas-phase ignition characteristics of JP-8 and Jet-A are very similar [3]. Gas turbines are the main consumer of Jet-A and JP-8, and combustion within these devices is only marginally affected by the autoignition characteristics of fuel, which is measured by the cetane number (CN). Due to the lack of CN regulation for JP-8, batch to batch variations in JP-8 can range from [4]. The mean CN variation becomes even more drastic with non-petroleum-derived jet fuels, where the CN of coal-derived jet fuel (SASOL IPK) is on the order of ~ 31 [5], while the CN of a natural-gas-derived jet fuel (S8) is ~ 60 [6]. It is well known that during the ignition delay period in a diesel engine, complex fuel dependent physical and chemical phenomena prepare the stratified fuel-air charge for combustion [7]. The variations in JP-8 CN can present significant challenges for compression ignition engines, often manifesting themselves in ignition behaviors ranging from instantaneous ignition to no ignition at all. In computational fluid dynamics (CFD), a number of phenomenological and physical sub-models are used to determine the atomization, evaporation and gaseous fuel-air mixture preparation. Chemical kinetic models are also used to describe the autoignition of the mixture that has been physically prepared for combustion. Real fuels are often composed of thousands of molecules, and the use of such a large number of species is beyond the current reach of engine level CFD applications. Therefore, surrogate fuels can be formulated to represent the physical and chemical properties of the real fuel. The current work reports the development of a conventional jet fuel surrogate intended for the use in the CFD simulation of diesel combustion. The surrogate emulates both the chemical and physical properties of JP-8; starting with the properties known to affect mixture preparation and ignition within diesel engines, eight target properties were chosen for the formulation of new JP-8 surrogates, which were developed with only those species readily available in existing kinetic mechanisms. The resulting properties of the newly developed jet fuel surrogates are compared to the properties of real jet fuel and existing surrogates. Additional surrogate performance assessment is performed with liquid length and vapor spray penetration data from non-reacting constant-volume spray experiments.

2 Surrogate Formulation Methodology Table 1 shows the eight target properties chosen for the development of the JP-8 surrogate. Three of the property targets are chemical in nature, including CN, Lower Heating Value (LHV) and Hydrogen-Carbon ratio (H/C). Molecular Weight (MW) is chosen as a fourth property as it can be used to calculate the molecular diffusivity of the fuel species. Also, three thermo physical property targets widely used in the spray breakup sub-models of CFD simulation are selected, including liquid density, viscosity and surface tension. These sub-models include the KH-RT spray breakup model and the dynamic drop drag model [8]. Finally, the distillation curve is used to represent the volatility of fuel, and emulating the volatility, which is critical for predicting evaporation process. The candidate species for the surrogate formulation are selected based upon the following considerations: 1. The relevance to the hydrocarbon (HC) molecules in real jet fuels in terms of HC class and molecular size. 2. The existence of chemical mechanism(s) that are capable of predicting the ignition process of the neat component. 3. Previous use in other jet fuel surrogate studies or use that has been suggested in the literature [9-13]. Based upon these criteria, seven pure hydrocarbons were considered as candidate species. These species include n- decane and n-dodecane for n-alkane representation, iso-octane and iso-cetane for iso-alkane representation, methylcyclohexane (MCH) and decalin for cycloalkane representation, and toluene as an aromatic representative. The properties of the target fuel used in this work, Jet-A POSF-4658, are summarized in Table 1. POSF-4658 is a composite of a number of different Jet-A batches, so its properties can be considered as representatives of nominal Jet-A. Moreover, a wide range of experimental data is readily available for POSF This is critical for the complete specification of property targets and for the validation of various characteristics of the surrogate mixture. Available experimental data include ignition delay measurements from shock tubes [3] [14] and rapid compression machines [15], chemical composition [16], along with a number of chemical properties [11] and physical properties [13] [17]. Table 1: Eight surrogate target properties for POSF Target properties POSF-4658 CN 47.1 (DCN) [11] LHV MJ/kg [18] H/C [11] MW 142 [11] Density Temperature dependent [17] Viscosity Temperature dependent [17] Surface Tension Temperature dependent [19] Distillation Curve Temperature dependent [13] An optimizer was developed and utilized to formulate the surrogate composition for POSF The surrogate optimizer iteratively determines the optimum surrogate mixture composition that minimizes the objective function, which is defined as the overall deviation from the target properties. The surrogate properties were estimated using the following models. The surrogate CN was estimated with the linear volume fraction of the pure component CN s, as suggested by Murphy et al. [20]. The density of the mixture was estimated with the volume fraction average. The viscosity of the mixture was estimated using the Grunberg-Nissan equation [21] with the regression term G ij set to zero, which is applicable to hydrocarbon mixtures [22]. The Parachor correlation [23] was used to estimate the mixture surface tension while neglecting the vapor mixture molar density, which is very small compared to liquid mixture molar density. The distillation curve of the mixture was estimated by the mixture bubble point calculation using Raoult s law. The procedure of the mixture distillation was discretized with a constant volume of liquid mixture leaving the liquid phase, similar to the approach of Huber et al. [24]. The distillation curve model was validated against the experimental data of 75/25 and 50/50 mixtures of n-decane and n-tetradecane in [24]. The iterative optimization procedure employed a genetic algorithm to find the global minimums, while the MATLAB function fmincon was used to search for local minimums. The optimizer started with the genetic algorithm and then transitioned to fmincon with the best composition from genetic algorithm as the starting point to ensure that the global minimum in the search space was found. 2

3 Formulation Results and Discussion Initially, attempts were made to formulate a surrogate with six molecules (n-dodecane/n-decane/iso-cetane/isooctane/mch/toluene) focused on four temperature independent properties (CN, LHV, H/C, MW) as well as the liquid density at room temperature. Although the optimization process was initialized with six components, the concentrations of n-decane and iso-octane in the optimized mixture were zero or negligibly small, resulting in a four-component surrogate (UM1). The composition of UM1 is shown in Table 2. This result indicates that all the properties of n- dodecane and iso-cetane are favored over lighter components such as n-decane and iso-octane. As shown in Table 3, UM1 successfully emulated all targeted temperature independent properties of POSF-4658 within 1.2% relative error. However, as shown in Figure 1, the temperature dependent physical properties of UM1 shows notable discrepancy compared to those of POSF Although the liquid density was matched with a relatively small error of ~ 3.3%, liquid viscosity and surface tension errors were ~ 21% and ~ 9%, respectively. Also, the surrogate was estimated to be significantly more volatile than the target jet fuel, as indicated by the lower distillation temperatures. Most notably, boiling of the UM1 starts ~ 50K lower than the target fuel. This is primarily caused by the inclusion of highly volatile species such as MCH and toluene, which constitute more than 45% of UM1 on a mole fraction basis. In order to improve the physical property match of UM1, decalin was added as a cycloalkane representative. This decision was based upon decalin s higher density, viscosity and lower vapor pressure compared to MCH. Such physical property variations were in turn expected to increase the mixture density and viscosity, as well as the distillation temperatures. Additional optimization with all seven candidate components (n-dodecane/n-decane/iso-cetane/isooctane/mch/decalin/toluene) generated another four component surrogate, UM2. As shown in Table 3, CN and LHV are very well matched, within 1% error. The MW and H/C of mixture UM2 are still reasonably matched but when compared to UM1, the error is increased from 1% to 4.6% for MW and from 0.5% to -3.9% for H/C. Since decalin has a higher MW and a lower H/C than MCH, such a change in the MW and H/C match were as expected. Comparing to UM1, all temperature dependent physical properties except for surface tension were significantly improved for UM2. As shown in the plot titles in Figure 1, the average absolute error for density and viscosity decreased from 3.4% to 0.6% and from 21.2% to 3.6%, respectively. Also, the distillation characteristics that were trend wise poorly emulated by UM1 improved notably for UM2. The distillation curve of the UM2 at the early stages of the distillation process better matches that of the real fuel when compared to UM1, due to the reduced volatility of the mixture as noted above. Also, the large curvature in the distillation curve of UM1 has been significantly reduced. This results from the better distribution of vapor pressures among the surrogate components. Although the surface tension match is not as good as UM1, the property emulation of UM2 is thought to be better than UM1 mainly because of the improved density and distillation curve matches. A previous CFD simulation study by Ra et al. [25] showed that the spray breakup/evaporation process and resulting cylinder pressure development during diesel combustion has the highest sensitivity to density and vapor pressure characteristics. Figure 2 shows the HC class composition of the UM surrogates as well as average jet fuel composition suggested in [16]. The overall distribution of the major HC classes (n-alkane, iso-alkane, cycloalkane and aromatics) of the UM surrogates is similar to that of real jet fuel. Table 2: Composition of the UM surrogates in mole fraction. UM1 UM2 Mole fraction Mole fraction n-dodecane iso-cetane MCH decalin toluene Table 3: The estimated temperature independent target properties of the UM surrogates vs. POSF-4658 POSF-4658 UM1 UM2 Property Target Estimated Error (%) Estimated Error (%) CN LHV (MJ/kg) H/C MW

4 Volume fraction UNCLASSIFIED Figure 1: Estimated target physical properties of the UM surrogates vs. POSF Solid dots are measurements of POSF-4658 while lines are surrogate properties for (a) liquid density (b) liquid viscosity (c) surface tension and (d) distillation curve. 100% 80% 60% 40% 20% aromatic cyclo-alkane iso-alkane n-alkane 0% World Average UM1 UM2 Figure 2: The HC class composition in volume fraction of the UM surrogates and of the average jet fuel suggested in [16]. 4

5 Comparison to Existing Jet Fuel Surrogates The newly developed UM surrogates are compared with existing surrogates to assess the property emulation capability as well as the ability to properly simulate JP-8 cold flow spray characteristics. The temperature independent properties of the UM and select jet fuel surrogates [10-12, 26-29] are shown in Table 4. The UM1, UM2 and MURI2 are shown to match all temperature independent targets within 5%, while the UCSD surrogate matches within 9%. The Aachen surrogate, Violi #3 and UTAH HEX12 reasonably match MW, LHV and H/C, but the predicted CN of those surrogates is significantly higher than the real fuel. The surrogates physical properties, predicted with the same models used above in the surrogate optimization are plotted in Figure 3. Among the tested surrogates, the UM2, UTAH HEX12 and DrexelS5 are shown to emulate all of the target physical properties, except for surface tension, with reasonable error. However, as shown in Table 4, the temperature independent property emulation of the UTAH HEX12 and DrexelS5 shows a significant discrepancy with the real fuel. The MURI2 also matches all of the properties in Table 4 with less than 5% error, however there is a large discrepancy shown for the temperature dependent physical properties in Figure 3. For all of the surrogate evaluated above, the UM2 surrogate most successfully emulates both the chemical and the physical properties of Jet-A with reasonable errors. Fuel spray simulations were performed with multi-component surrogates using the CFD software CONVERGE [8]. The simulation results were compared to non-reacting Jet-A constant volume spray bomb experiments from Sandia National Laboratories [30]. Prior to conducting these simulations, model tuning, grid sensitivity and validation studies were performed with n-dodecane and were compared against experimental data in [31]. As summarized in Table 6, identical injector, injection pressure and ambient conditions were employed for both the Jet-A blend and n-dodecane experiments. The finalized modeling parameters used in the following simulations are summarized in Table 5. The spray simulations results for both UM surrogates as well as four other surrogates are shown in Figure 4. The MURI1 and MURI2 surrogates are selected since their fundamental gas phase combustion kinetic phenomena have been experimentally validated against POSF The DrexelS5 and UTAH HEX12 surrogates are also selected for their good physical property emulations. As shown in Figure 4 (a), the calculated liquid penetration length is observed to have a strong correlation to the surrogate physical properties. Among the six surrogates tested, the density, viscosity, distillation temperatures as well as the predicted liquid penetration are shown to have exactly same order: S5>HEX12>UM2>UM1>MURI2>MURI1. Such an observation is expected as higher density, higher viscosity and higher distillation temperatures all contribute to longer liquid breakup time. Although the surface tension emulation of the MURI1 and MURI2 surrogates is significantly better than that of the other tested surrogates, the liquid penetration length is under predicted by over 20%. This observation infers that the effects of surface tension are less important to liquid length penetration than density, viscosity and volatility. The vapor penetration length of the leading edge of the jet is shown in Figure 4 (b). The results are nearly identical for all surrogates, indicating that the liquid fuel property variations within the tested surrogates have marginal affect on the vapor penetration. Table 4: Estimated/reported temperature independent properties of the UM surrogates and currently available jet fuel surrogates. CN LHV H/C MW POSF MJ/kg kg/kmol value error (%) value error (%) value error (%) value error (%) UM UM Aachen [26] USCD [27] MURI1 [11] MURI2 [12] DrexelS5 [28] Violi#3 [10] UTAH HEX12 [29]

6 Figure 3: Estimated physical properties of the UM surrogates and available jet fuel surrogates. Solid dots are measurements of POSF while lines are surrogate properties for (a) liquid density (b) liquid viscosity (c) surface tension and (d) distillation curve. Table 5: CONVERGE sub-models and parameters used for the spray simulations. More details are available in [8]. Turbulence modeling RANS with the RNG k-ε Spray submodels Breakup KH-RT breakup KH time constant (B 1 ) = 30 Drop drag Dynamic drop drag Evaporation Frossling correlation Collision/coalescence NTC, Post outcome Numerical parameters Minimum grid size 0.25mm Automatic Mesh Refinement Number of injected parcels

7 Table 6: Conditions for the constant volume, non-reacting spray experiments in Sandia National Laboratories. Fuel Jet-A blend [30], n-dodecane [31] Chamber Sandia Constant Volume Chamber, Cubic, 108mm Ambient composition N 2 :CO 2 :H 2 O=0.8971:0.0652: in mole fraction Ambient temperature 900K Ambient density 22.8 kg/m 3 Injection pressure 1500 bar Nozzle diameter 90 µm Figure 4: Liquid penetration length (a) and vapor penetration length (b) of the surrogates predicted by CFD spray simulation vs. experimental Jet-A data [30]. Time-averaged liquid length at quasi steady state is plotted for the experimental data in (a). Expected uncertainty for the experimental vapor penetration measurement in (b) is up to 2mm as noted in [30]. Summary and Conclusion Two jet fuel surrogates were formulated for the CFD simulation of diesel combustion. Eight target properties, including both chemical and physical ones, were considered in the optimization. UM1, composed of n-dodecane/isocetane/mch/toluene, successfully emulated the temperature independent target properties of a representative real jet fuel, POSF In order to improve the physical property match, decalin was included as an additional cycloalkane component in the surrogate UM2, which was a mixture of n-dodecane/iso-cetane/decalin/toluene. Properties as well as the spray characteristics of the UM surrogates were compared to existing surrogates. The results imply that UM2 has potential to successfully emulate the various chemical and physical properties important to the mixture formation and ignition processes in diesel engines. Finally, the surface tension variation of the surrogate is shown to have a relatively minor affect on the spray liquid length, while the liquid fuel property variations within the tested surrogates have marginal affect on the vapor penetration. Acknowledgements Funding for this work was provided by the Automotive Research Center (ARC), A U.S. Army Center of Excellence in the modeling and simulation of ground vehicles at the University of Michigan, with funding from government contract DoD-DoA W56HZV through the U.S. Army Tank Automotive Research, Development, and Engineering Center. The authors wish to thank Dr. Daniel Lee of Convergent Science for his technical assistance and for the provision of the CONVERGE CFD code used in this work. The authors also gratefully acknowledge the assistance of Dr. Peter Kelly Senecal of Convergent Science and Dr. Sibendu Som of Argonne National Laboratory for their technical assistance with the CFD simulations. 7

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