Surrogate Modeling of Aerodynamic Simulations for Multiple Operating Conditions Using Machine Learning

TL;DR

This paper introduces a novel local decomposition surrogate modeling approach that efficiently predicts turbulent aerodynamic fields across multiple flow regimes, significantly reducing computational costs and improving accuracy.

Contribution

The paper develops a Local Decomposition Method combining machine learning and physical criteria to automatically identify flow regimes and build local reduced-order models from limited high-fidelity simulations.

Findings

Significant accuracy improvement over classical surrogate models.

Effective automatic decomposition of input space into flow regimes.

Successful application to turbulent flow around an airfoil.

Abstract

This article presents an original methodology for the prediction of steady turbulent aerodynamic fields. Due to the important computational cost of high-fidelity aerodynamic simulations, a surrogate model is employed to cope with the significant variations of several inflow conditions. Specifically, the Local Decomposition Method presented in this paper has been derived to capture nonlinear behaviors resulting from the presence of continuous and discontinuous signals. A combination of unsupervised and supervised learning algorithms is coupled with a physical criterion. It decomposes automatically the input parameter space, from a limited number of high-fidelity simulations, into subspaces. These latter correspond to different flow regimes. A measure of entropy identifies the subspace with the expected strongest non-linear behavior allowing to perform an active resampling on this low-dimensional structure. Local reduced-order models are built on each subspace using Proper Orthogonal Decomposition coupled with a multivariate interpolation tool. The methodology is assessed on the turbulent two-dimensional flow around the RAE2822 transonic airfoil. It exhibits a significant improvement in term of prediction accuracy for the Local Decomposition Method compared with the classical method of surrogate modeling for cases with different flow regimes.