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

• URL: http://arxiv.org/abs/2210.04269v1
• Date: Sun, 9 Oct 2022 14:03:26 GMT
• Title: Data-driven framework for input/output lookup tables reduction -- with application to hypersonic flows in chemical non-equilibrium
• Abstract summary: We present a novel machine learning technique to extract a reduced thermochemical model for reacting hypersonic flows simulation. The method is validated and benchmarked on a simulation of a hypersonic flat-plate boundary layer with finite-rate chemistry. Substituting Mutation++ with the light-weight, machine-learned alternative improves the performance of the solver by 50% while maintaining overall accuracy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we present a novel model-agnostic machine learning technique to extract a reduced thermochemical model for reacting hypersonic flows simulation. A first simulation gathers all relevant thermodynamic states and the corresponding gas properties via a given model. The states are embedded in a low-dimensional space and clustered to identify regions with different levels of thermochemical (non)-equilibrium. Then, a surrogate surface from the reduced cluster-space to the output space is generated using radial-basis-function networks. The method is validated and benchmarked on a simulation of a hypersonic flat-plate boundary layer with finite-rate chemistry. The gas properties of the reactive air mixture are initially modeled using the open-source Mutation++ library. Substituting Mutation++ with the light-weight, machine-learned alternative improves the performance of the solver by 50% while maintaining overall accuracy.

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