Physical interpretation of neural network-based nonlinear eddy viscosity models

TL;DR

This paper interprets neural network-based turbulence models using the ensemble Kalman method, revealing how learned models improve flow predictions by adjusting eddy viscosity and capturing flow features.

Contribution

It introduces an ensemble Kalman approach for physically interpreting neural network turbulence models, demonstrating its effectiveness on airfoil and duct flow cases.

Findings

Optimal linear eddy viscosity model improves lift prediction.

Nonlinear model captures secondary flows accurately.

Method adjusts nonlinearity to represent flow separation and secondary flow.

Abstract

Neural network-based turbulence modeling has gained significant success in improving turbulence predictions by incorporating high--fidelity data. However, the interpretability of the learned model is often not fully analyzed, which has been one of the main criticism of neural network-based turbulence modeling. Therefore, it is increasingly demanding to provide physical interpretation of the trained model, which is of significant interest for guiding the development of interpretable and unified turbulence models. The present work aims to interpret the predictive improvement of turbulence flows based on the behavior of the learned model, represented with tensor basis neural networks. The ensemble Kalman method is used for model learning from sparse observation data due to its ease of implementation and high training efficiency. Two cases, i.e., flow over the S809 airfoil and flow in a square duct, are used to demonstrate the physical interpretation of the ensemble-based turbulence modeling. For the flow over the S809 airfoil, our results show that the ensemble Kalman method learns an optimal linear eddy viscosity model, which improves the prediction of the aerodynamic lift by reducing the eddy viscosity in the upstream boundary layer and promoting the early onset of flow separation. For the square duct case, the method provides a nonlinear eddy viscosity model, which predicts well secondary flows by capturing the imbalance of the Reynolds normal stresses. The flexibility of the ensemble-based method is highlighted to capture characteristics of the flow separation and secondary flow by adjusting the nonlinearity of the turbulence model.