Inverse airfoil design method for generating varieties of smooth airfoils using conditional WGAN-gp

Abstract

Machine learning models are recently adopted to generate airfoil shapes. A typical task is to obtain airfoil shapes that satisfy the required lift coefficient. These inverse design problems can be solved by generative adversarial networks (GAN). However, the shapes obtained from ordinal GAN models are not smooth; hence, flow analysis cannot be conducted. Therefore, Bézier curves or smoothing methods are required. This study employed conditional Wasserstein GAN with gradient penalty (cWGAN-gp) to generate smooth airfoil shapes without any smoothing method. In the proposed method, the cWGAN-gp model outputs a shape that indicates the specified lift coefficient. Then, the results obtained from the proposed model are compared with those of ordinal GANs and variational autoencoders; in addition, the proposed method outputs the smoothest shape owing to the earth mover's distance used in cWGAN-gp. By adopting the proposed method, no additional smoothing method is required to conduct flow analysis.