Data augmented turbulence modeling for three-dimensional separation flows

TL;DR

This study combines field inversion and machine learning to improve turbulence modeling for three-dimensional separation flows, specifically around an axisymmetric hill, enhancing physical understanding and model accuracy.

Contribution

It introduces a novel approach integrating field inversion with neural networks to augment turbulence models for complex 3D separation flows.

Findings

Non-equilibrium turbulence effects dominate upstream boundary layers.

Turbulence anisotropy has limited impact on mean flow.

Proposed methods effectively address sample imbalance and reduce training costs.

Abstract

Field inversion and machine learning are implemented in this study to describe three-dimensional separation flow around an axisymmetric hill and augment the Spart-Allmaras model. The discrete adjoint method is used to solve the field inversion problem, and an artificial neural network is used as the machine learning model. A validation process for field inversion is proposed to adjust the hyperparameters and obtain a physically acceptable solution. The field inversion result shows that the non-equilibrium turbulence effects in the boundary layer upstream of the mean separation line and in the separating shear layer dominate the flow structure in the 3-D separating flow, which agrees with prior physical knowledge. However, the effect of turbulence anisotropy on the mean flow appears to be limited. Two approaches are proposed and implemented in the machine learning stage to overcome the problem of sample imbalance while reducing the computational cost during training. The results are all satisfactory, which proves the effectiveness of the proposed approaches.