Aerodynamic coefficient prediction of airfoils based on deep learning

To prevent the dependence of prediction methods on design parameters and the exponential increase of algorithm complexity with increasing prediction accuracy, an aerodynamic coefficient prediction method of airfoils based on deep learning is proposed. First, the fundamental theory, network structure and training method of Convolutional Neural Networks (CNN) are introduced. Then, according to the characteristics of airfoil image processing, the structure of CNN model is designed and the parameters are trained by 6000 random samples. Finally, the normal force coefficients of 561 airfoils are predicted and compared with those prediction of some other parameterization methods. The simulation results show that the proposed graphical prediction method has high prediction accuracy.