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

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 Spalart-Allmaras (SA) model, we build that maps features eddy viscosity. The then used replace SA mutually couple with CFD solver. We suitable data-driven mainly inputs, outputs and loss function of model. A feature selection method importance also implemented. results show can effectively remove redundant features. new input has better accuracy stability in mutual coupling force coefficient obtained solution match sample well. shows strong generalization different inflow condition (angle attack, Mach number, number airfoil).