Machine Learning-augmented Predictive Modeling of Turbulent Separated Flows over Airfoils

TL;DR

This paper presents a machine learning-enhanced modeling approach for turbulent separated flows over airfoils, improving predictive accuracy by integrating experimental data and inverse modeling with neural networks.

Contribution

It introduces a novel methodology combining inverse modeling and neural networks to augment turbulence models using limited experimental data, enhancing flow prediction accuracy.

Findings

Improved lift predictions for unseen geometries and conditions.

Neural network-augmented models accurately predict surface pressures.

Method maintains performance across different simulation solvers.

Abstract

A modeling paradigm is developed to augment predictive models of turbulence by effectively utilizing limited data generated from physical experiments. The key components of our approach involve inverse modeling to infer the spatial distribution of model discrepancies, and, machine learning to reconstruct discrepancy information from a large number of inverse problems into corrective model forms. We apply the methodology to turbulent flows over airfoils involving flow separation. Model augmentations are developed for the Spalart Allmaras (SA) model using adjoint-based full field inference on experimentally measured lift coefficient data. When these model forms are reconstructed using neural networks (NN) and embedded within a standard solver, we show that much improved predictions in lift can be obtained for geometries and flow conditions that were not used to train the model. The NN-augmented SA model also predicts surface pressures extremely well. Portability of this approach is demonstrated by confirming that predictive improvements are preserved when the augmentation is embedded in a different commercial finite-element solver. The broader vision is that by incorporating data that can reveal the form of the innate model discrepancy, the applicability of data-driven turbulence models can be extended to more general flows.