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

TL;DR

This paper introduces a novel conditional VAE-WGAN-gp model for airfoil shape generation, combining feature extraction and high-quality shape synthesis, outperforming individual VAE and WGAN-gp models.

Contribution

The paper proposes a new combined VAE-WGAN-gp model for airfoil generation, enhancing feature extraction and shape quality over existing models.

Findings

VAE-WGAN-gp outperforms individual VAE and WGAN-gp in accuracy and smoothness.

The model effectively generates airfoils meeting lift coefficient requirements.

Latent space analysis shows improved feature extraction capabilities.

Abstract

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.