Multi-scale data-driven modeling of radiative transport through thermal protection systems

The NASA Kentucky EPSCoR Program’s mission is to enhance research and intellectual capacity of the state's universities and colleges through strategic investments in NASA-priority research areas and to increase researcher competitiveness for non-EPSCoR NASA funding. With this motivation, and entry, descent, and landing (EDL) technologies being a key NASA priority, we propose to partner with three NASA centers to fundamentally transform the current state-of-the-art approach in modeling the degradation and failure of thermal protection system (TPS) materials used on space capsules. Specifically, we aim to improve the understanding of the penetration of flowfield (shock-layer and wake flow) radiative emissions into the TPS material, its coupling with other thermochemical processes, and the potential degradation and failure of the TPS material. These advancements will benefit the New Frontiers Dragonfly mission, and missions under development such as the Mars Sample Return (MSR), Venus Entry, and potential Discovery class missions to the gas and ice giants. It will also aid the analysis of MEDLI2 data from the Mars 2020 mission. The proposed effort that blends modeling tools with rigorous experimental validation is aimed at reducing the uncertainties associated with the effect of flowfield radiation on TPS materials. Advancements will be made to reveal insights on radiative physics at the microscale by explicitly accounting for the microstructure of the TPS material and simulating the transport of photons through the material microstructure. The modeling will be validated through novel spectral measurements, where effective transmission and scattering functions for thin TPS small samples will be obtained. Investigation of radiative transfer (transport) through the entire TPS material will be performed through the expansion of the current state-of-the-art material response solver developed at the University of Kentucky, the only U.S. University with such capability. The macroscale modeling through the entire TPS material will be performed in conjunction with a custom variant of Knowledge Distillation, a machine learning technique, to reduce the errors and uncertainties associated with the underlying assumptions in the macroscale approach required for radiative transport modeling. The machine learning technique will be trained against the microscale data, which will contain all of the relevant physics at the highest fidelity. Modeling efforts at the macroscale will be compared against innovative effective radiative conductivity measurements that will precisely validate the energy transport mechanism in each mode of heating. Finally, custom-designed experiments will be performed in the Hypersonic Materials Environmental Test System (HYMETS) arc-jet facility at NASA Langley Research Center (LaRC) to demonstrate the reliability of the models and tools developed in this research effort. The modeling approach, coupled with the unique experiments is the first-of-its-kind collaborative effort, with the goal of creating an entirely new modeling paradigm for the design of TPS materials.

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