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A Machine Learning Approach to Predict Near-optimal Meshes for Turbulent Compressible Flow Simulations

Sergi Sanchez-Gamero, Oubay Hassan Orcid Logo, Rubén Sevilla Orcid Logo

International Journal of Computational Fluid Dynamics, Volume: 38, Issue: 2-3, Pages: 221 - 245

Swansea University Authors: Sergi Sanchez-Gamero, Oubay Hassan Orcid Logo, Rubén Sevilla Orcid Logo

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DOI (Published version): 10.1080/10618562.2024.2306941

Abstract

This work presents a methodology to predict a near-optimal spacing function, which defines the element sizes, suitable to perform steady RANS turbulent viscous flow simulations. The strategy aims at utilising existing high fidelity simulations to compute a target spacing function and train an artifi...

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Published in: International Journal of Computational Fluid Dynamics
ISSN: 1061-8562 1029-0257
Published: Informa UK Limited 2024
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa65507
Abstract: This work presents a methodology to predict a near-optimal spacing function, which defines the element sizes, suitable to perform steady RANS turbulent viscous flow simulations. The strategy aims at utilising existing high fidelity simulations to compute a target spacing function and train an artificial neural network (ANN) to predict the spacing function for new simulations, either unseen operating conditions or unseen geometric configurations. Several challenges induced by the use of highly stretched elements are addressed. The final goal is to substantially reduce the time and human expertise that is nowadays required to produce suitable meshes for simulations. Numerical examples involving turbulent compressible flows in two dimensions are used to demonstrate the ability of the trained ANN to predict a suitable spacing function. The influence of the NN architecture and the size of the training dataset are discussed. Finally, the suitability of the predicted meshes to perform simulations is investigated.
Keywords: Mesh generation; spacing function; machine learning; artificial neural network; turbulent compressible viscous flow
College: Faculty of Science and Engineering
Funders: The financial support of the Engineering and Physical Sciences Research Council (Grant Number: EP/T009071/1) is gratefully acknowledged.
Issue: 2-3
Start Page: 221
End Page: 245

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