2023/11/09 by Kazuo Yonekura, Yonekura, Kazuo, Yuki Tomori +3
Computer Science · Engineering · Physics and Astronomy · #Aerodynamics and Acoustics in Jet Flows #Computational Engineering #FOS: Computer and information sciences #Finance #Generative Adversarial Networks and Image Synthesis #Model Reduction and Neural Networks #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2311.05445
openalex publication_date 2023/11/09 · openalex created_date 2023/11/11 · openalex updated_date 2026/07/28
A machine learning method was applied to solve an inverse airfoil design problem. A conditional VAE-WGAN-gp model, which couples the conditional variational autoencoder (VAE) and Wasserstein generative adversarial network with 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 model couples the VAE and GAN models, which enables feature extraction in the GAN models. In airfoil generation tasks, to generate airfoil shapes that satisfy lift coefficient requirements, it is known that VAE outperforms WGAN-gp with respect to the accuracy of the reproduction of the lift coefficient, whereas GAN outperforms VAE with respect to the smoothness and variations of generated shapes. In this study, VAE-WGAN-gp demonstrated a good performance in all three aspects. Latent distribution was also studied to compare the feature extraction ability of the proposed method.