Data-driven framework for input/output lookup tables reduction: Application to hypersonic flows in chemical non-equilibrium
This work addresses efficiency bottlenecks in computational fluid dynamics for aerospace engineering, offering a domain-specific incremental improvement.
The paper tackles the computational cost of simulating hypersonic flows in chemical non-equilibrium by developing a data-driven framework to reduce input/output lookup tables, resulting in a 50% performance improvement in solver speed while maintaining accuracy.
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.