Aerodynamic Coefficient Prediction of Airfoils with Convolutional Neural Network

Abstract

A general and flexible approximation model based on convolutional neural network (ConvNet) technique as well as a signed distance function (SDF) is proposed to predict aerodynamic coefficients of the airfoils in this paper. Traditional surrogate-based prediction methods are blamed for its limited dimensions of design variables and powerless for strong nonlinear engineering problems. Considering that ConvNets have been proven to be suitable for nonlinear and high-dimensional practical tasks in complex image identification and speech recognition, a two-layer ConvNet framework rather than conventional Kriging surrogate model is built to predict aerodynamic coefficients for large-scale nonlinear problems. In order to build the bridge between geometry information and the ConvNet, a new geometry representation method based on SDF is also applied. Furthermore, numerical studies are presented for wind turbine airfoils at a high angle of attack. Compared to ordinary Kriging model, the ConvNet-based method exhibits competitive prediction accuracy within the certain error margin. Moreover, the influence of the ConvNet’s nonlinear activation functions on the predictive effect is studied in both training and validation datasets.