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

Autor:	Clément Scherding, Georgios Rigas, Denis Sipp, Peter J. Schmid, Taraneh Sayadi
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	FOS: Computer and information sciences Fluid Flow and Transfer Processes Computer Science - Machine Learning Modeling and Simulation Fluid Dynamics (physics.flu-dyn) Computational Mechanics FOS: Physical sciences Physics - Fluid Dynamics Machine Learning (cs.LG)
Popis:	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. 28 pages, 19 figures, 3 tables
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c139472664307c3cf119ca3e7164780f http://arxiv.org/abs/2210.04269 Zobrazit plný text záznamu