A gradient aerodynamic optimization method based on deep learning

Abstract

Gradient-based aerodynamic optimization methods involve complex gradient calculations with high computational costs. To address these problems, this paper proposes a rapid deep learning-based method for predicting the aerodynamic force gradients and establishes an aerodynamic optimization framework. A deep neural network is used to determine the latent mapping relationship between the near-wall flow field information and the aerodynamic force gradients, and the resulting gradient prediction model is integrated into an aerodynamic optimization platform. Combining grid deformation techniques with gradient-based optimization enables the aerodynamic optimization of airfoils. The deep neural network model can rapidly predict gradients that are basically consistent with those from the traditional discrete adjoint method, and decouples the gradient computation from the flow field solution process. The proposed method is validated through the aerodynamic optimization of the airfoil. Under various optimization objectives and initial conditions, the deep neural network model significantly improves the aerodynamic performance of the airfoil and substantially enhances the efficiency of optimization compared with traditional methods.