High Reynolds number airfoil turbulence modeling method based on machine learning technique

TL;DR

This paper introduces a deep neural network-based turbulence model for high Reynolds number airfoil flows, improving accuracy and stability over traditional models by using feature selection and data-driven techniques.

Contribution

A novel neural network turbulence model that replaces the Spalart-Allmaras model, with enhanced accuracy, stability, and generalization for high Reynolds number airfoil simulations.

Findings

Model achieves better accuracy and stability in CFD simulations.

Feature selection effectively reduces redundant inputs.

Strong generalization across different flow conditions.

Abstract

In this paper, a turbulence model based on deep neural network is developed for turbulent flow around airfoil at high Reynolds numbers. According to the data got from the Spalart-Allmaras (SA) turbulence model, we build a neural network model that maps flow features to eddy viscosity. The model is then used to replace the SA turbulence model to mutually couple with the CFD solver. We build this suitable data-driven turbulence model mainly from the inputs, outputs features and loss function of the model. A feature selection method based on feature importance is also implemented. The results show that this feature selection method can effectively remove redundant features. The model based on the new input features has better accuracy and stability in mutual coupling with the CFD solver. The force coefficient obtained from solution can match the sample data well. The developed model also shows strong generalization at different inflow condition (angle of attack, Mach number, Reynolds number and airfoil).