Increasing computational power is expanding the potential of utilizing high fidelity simulations for engineering design and development. Especially in reacting turbulent flow simulations of high-speed vehicles and their combustors. A transition from Reynolds Averaged Navier-Stokes (RANS) simulations, conducted on coarse meshes, to Large Eddy Simulations (LES) – where a majority of the turbulent energy scales are resolved – has the possibility of capturing a larger range of physical processes that influence design outcomes. Highly resolved reacting flow simulations increases our understanding of propulsion devices, such as solid fuel ramjets and scramjets, particularly at the edges of the operation limits of the flight-vehicle. Limited experimental diagnostics of these devices make the use of high-fidelity simulations a fundamental tool to visualize the reacting flow-field at these flight conditions. However, solving the complete set of reacting, compressible Navier-Stokes equations on the finer meshes required for LES are still encumbered by an increased number of partial differential equations and numerical stiffness. Both of which are associated with a higher number of molecular species to be tracked along with their chemical reaction source terms, leaving open a rich field of research into reduced order models as a means of increasing the computational efficiency of LES simulations while still maintaining a high level of accuracy. Particularly in the area of solid fuel combustion, reduced order models, including the use of neural network approaches, are needed for – a) combustion chemistry reduction, b) solid fuel decomposition and motion, and c) in-situ physics adaptation. The development of high-fidelity, reduced order models, have the potential to increase the viability of employing highly resolved simulations during the design process and accurately capturing the rich physics within the multiphase flow-field typical of high-speed propulsion devices.

References

Pace, H., Schlussel, E., Young, G., and Massa, L., “Contribution of Unsteadiness to Solid Fuel Burning Characteristics in a Scramjet Combustor,” J. Propul. Power, Vol. 40, No. 6, (2024).

Bojko, B.T., Geipel, C.M., Fisher, B.T., and Kessler, D.A., “Numerical sensitivity analysis of HTPB counterflow

combustion using neural networks,” Combust. Flame, 271 (2024).

Fureby, C., Nilsson, T., Peterson, D.M., Ombrello, T.M., and Eklund, D., “Large-Eddy Simulation of Supersonic Combustion in a Mach 2 Cavity-Based Model Scramjet Combustor,” AIAA SciTech Forum, Orlando, FL (2024).

Yao, W., Liu, H., Zhang, Z., Zhang, X., Yue, L., Zhang, X., and Li, J., “Effects of Thermal/Chemical Nonequilibrium on a High-Mach Ethylene-Fueled Scramjet,” J. Propul. Power, Vol. 39, No. 4, (2023).

key words

Combustion; Propulsion; Reduced-order Models; Computational Fluid Dynamics; Multi-phase Flows

Citizenship:  Open to U.S. citizens and permanent residents

Level:  Open to Postdoctoral applicants

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