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A machine learning method was applied to solve an inverse airfoil designproblem. A conditional VAE-WGAN-gp model, which couples the conditionalvariational autoencoder (VAE) and Wasserstein generative adversarial networkwith gradient penalty (WGAN-gp), is proposed for an airfoil generation method,and then it is compared with the WGAN-gp and VAE models. The VAEGAN modelcouples the VAE and GAN models, which enables feature extraction in the GANmodels. In airfoil generation tasks, to generate airfoil shapes that satisfylift coefficient requirements, it is known that VAE outperforms WGAN-gp withrespect to the accuracy of the reproduction of the lift coefficient, whereasGAN outperforms VAE with respect to the smoothness and variations of generatedshapes. In this study, VAE-WGAN-gp demonstrated a good performance in all threeaspects. Latent distribution was also studied to compare the feature extractionability of the proposed method.

Airfoil generation and feature extraction using the conditional VAE-WGAN-gp