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

TL;DR

This paper introduces a machine learning-based method to reduce thermochemical models for hypersonic flow simulations, significantly improving computational efficiency while maintaining accuracy.

Contribution

A novel, model-agnostic machine learning approach for creating reduced thermochemical models applicable to hypersonic flows with non-equilibrium chemistry.

Findings

Achieved 50% performance improvement in hypersonic flow simulation.

Successfully validated the method on a hypersonic boundary layer case.

Maintained accuracy comparable to detailed models.

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.