A machine learning approach to predict near-optimal meshes for turbulent compressible flow simulations

TL;DR

This paper introduces a machine learning method that predicts near-optimal mesh spacing functions for turbulent compressible flow simulations, aiming to reduce manual effort and improve simulation efficiency.

Contribution

It develops an ANN-based approach trained on high-fidelity data to automatically generate effective meshes for complex turbulent flow simulations.

Findings

ANN accurately predicts suitable mesh spacing functions.

Predicted meshes lead to successful flow simulations.

Method reduces manual mesh generation effort.

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.