(443d) Prediction of Physical Properties and Phase Behavior for Future Jet Fuel Selection Via Monte Carlo Simulations

Our fuel sources will change over time as we transition to different raw fuel sources, strive to reduce emissions, and develop renewable fuel blends. These changes will drive the automobile and aviation industries to evaluate their fuel sources and fuel blends. The industries will adapt to these changes and environmental concerns, and the aviation industry shall develop new fuel alternatives and fuel blends. If these new fuel combinations can reduce the overall energy consumption or the emissions even by a few percentage points, the overall sum of these savings will be quite large.

Liquid fuels used in internal combustion and jet engines contain hundreds of species, and therefore computational modeling efforts of combustion dynamics have focused on fuel “surrogates” that mimic the physical and combustion properties of the fuel of interest. These surrogates are composed primarily of linear and branched alkanes containing 8 to 17 carbon atoms, as well as cyclic molecules, such as toluene and decalin.

This work validates the use of the current molecular models (force fields), such as the Transferable Potentials for Phase Equilibria (TraPPE) for reproducing physical properties (liquid density, surface tension, and heat of vaporization), and vapor-liquid phase diagrams of typical jet fuel surrogates (e.g., JP-8, Jet-A). Properties are predicted from Monte Carlo simulations using the GOMC¹ simulation package, with workflows created using the Molecular Simulation Design Framework (MoSDeF)^2-5, which automatically generates this output with minimal command input. The combination of accurate molecular models, high-performance simulation engines, and automated workflows are expected to enable the use of Monte Carlo simulations for testing future jet fuel combinations while minimizing the overall cost of research by narrowing down the required experimental testing to a few candidates.

¹ Nejahi, Y, Barhaghi, MS, Mick, J, Jackman, B, Rushaidat, K, Li, YZ, et al. GOMC: GPU Optimized Monte Carlo for the simulation of phase equilibria and physical properties of complex fluids. Softwarex, 2019; 9: 20-7.

² Klein, C.; Sallai, J.; Jones, T. J.; Iacovella, C. R.; McCabe, C.; Cummings, P. T.

“In Foundations of Molecular Modeling and Simulation: Select Papers from FOMMS

2015”; Springer: Singapore, 2016.

³ Klein, C.; Summers, A. Z.; Thompson, M. W.; Gilmer, J. B.; McCabe, C.; Cummings, P. T.; Sallai, J.; Iacovella, C. R. “Formalizing atom-typing and the dissemination of force fields with foyer”. Computational Materials Science 2019, 167, 215-227.

⁴ Cummings, P. T.; Gilmer, J. B. Open-Source Molecular Modeling Software in Chemical Engineering. Current Opinion in Chemical Engineering 2019, 23, 99-105.

⁵ Jankowski, E.; Ellyson, N.; Fothergill, J. W.; Henry, M. M.; Leibowitz, M. H.; Miller, E. D.; Alberts, M. e. a. Perspective on Coarse-Graining, Cognitive Load, and Materials Simulation. Computational Materials Science 109129, 171, 2020.