Data-driven turbulence modeling in separated flows considering physical mechanism analysis

TL;DR

This paper presents a data-driven turbulence modeling approach using field inversion and machine learning to improve the accuracy of separated flow simulations, incorporating physical mechanisms and demonstrating good generalization.

Contribution

It introduces a novel combination of field inversion and neural networks to modify turbulence models considering physical turbulence mechanisms, enhancing predictive accuracy and generalization.

Findings

Model corrections align with physical turbulence mechanisms.

High accuracy in reproducing observed data.

Demonstrated generalization in similar flow conditions.

Abstract

Accurate simulation of turbulent flow with separation is an important but challenging problem. In this paper, a data-driven Reynolds-averaged turbulence modeling approach, field inversion and machine learning is implemented to modify the Spalart-Allmaras model separately on three cases, namely, the S809 airfoil, a periodic hill and the GLC305 airfoil with ice shape 944. Field inversion based on a discrete adjoint method is used to quantify the model-form uncertainty with limited experimental data. An artificial neural network is trained to predict the model corrections with local flow features to extract generalized modeling knowledge. Physical knowledge of the nonequilibrium turbulence in the separating shear layer is considered when setting the prior model uncertainty. The results show that the model corrections from the field inversion demonstrate strong consistency with the underlying physical mechanism of nonequilibrium turbulence. The quantity of interest from the observation data can be reproduced with relatively high accuracy by the augmented model. In addition, the validation in similar flow conditions shows a certain extent of generalization ability.