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In this paper, we present a novel model-agnostic machine learning techniqueto extract a reduced thermochemical model for reacting hypersonic flowssimulation. A first simulation gathers all relevant thermodynamic states andthe corresponding gas properties via a given model. The states are embedded ina low-dimensional space and clustered to identify regions with different levelsof thermochemical (non)-equilibrium. Then, a surrogate surface from the reducedcluster-space to the output space is generated using radial-basis-functionnetworks. The method is validated and benchmarked on a simulation of ahypersonic flat-plate boundary layer with finite-rate chemistry. The gasproperties of the reactive air mixture are initially modeled using theopen-source Mutation++ library. Substituting Mutation++ with the light-weight,machine-learned alternative improves the performance of the solver by 50% whilemaintaining overall accuracy.

Data-driven framework for input/output lookup tables reduction: Application to hypersonic flows in chemical non-equilibrium