Data-driven Augmentation of a Turbulence Model in Three-dimensional Separated Flows

TL;DR

This paper introduces a data-driven turbulence model enhancement for 3D flows by adding a correction term derived from high-fidelity data, improving accuracy in 3D separated flows while maintaining 2D performance.

Contribution

The study develops a 3D correction term for a 2D-trained turbulence model using field inversion and symbolic regression, enabling accurate 3D flow predictions without losing 2D accuracy.

Findings

The enhanced model performs equally well in 2D flows as the original.

The model shows improved accuracy in complex 3D flows like the JAXA high-lift configuration.

The approach effectively bridges the gap between 2D training and 3D application.

Abstract

Classic turbulence models often struggle to accurately predict complex flows. Although data-driven techniques have addressed these shortcomings, most existing research has concentrated on two-dimensional (2D) cases. This study bridges this gap by enhancing a data-driven turbulence model, the SST-CND (shear stress transport-conditioned) model, which was originally trained on 2D separated flows, in 3D scenarios. An additional correction term, \b{eta}_3D, is introduced to account for 3D effects. The distribution of this term is determined through a 3D field inversion process using high-fidelity data obtained from the flow around a cube. An algebraic expression for \b{eta}_3D is then derived through symbolic regression and formulated to degrade to zero in 2D cases. The performance of the resulting SST-CND3D model is evaluated across a range of flows. In 2D flows, the SST-CND3D model performs identically to its 2D-trained predecessor. However, the model exhibits superior performance in 3D flows, such as the flow around the complex JAXA standard model high-lift configuration. These findings indicate that a sequential approach, constructing a 3D correction term that vanishes in 2D on top of a 2D-trained model, constitutes a promising method for developing data-driven turbulence models that perform accurately in 3D while preserving their effectiveness in 2D.