Flow Field Reconstruction of 2D Hypersonic Inlets Based on a Variational Autoencoder

Abstract

The inlet is one of the most important components of a hypersonic vehicle. The design and optimization of the hypersonic inlet is of great significance to the research and development of hypersonic vehicles. In recent years, artificial intelligence techniques have been used to improve the efficiency of aerodynamic optimization. Deep generative models, such as variational autoencoder (VAE) and generative adversarial network (GAN), have been used in a variety of flow problems in the last two years, making fast reconstruction and prediction of the full flow field possible. In this study, a hybrid multilayer perceptron (MLP) combined with a VAE network is used to reconstruct and predict the flow field of a two-dimensional multiwedge hypersonic inlet. The obtained results show that the VAE network can reconstruct the overall flow structure of the hypersonic flow field with high accuracy. The reconstruction accuracy of complex flow structures, such as shockwaves, boundary layers, and separation bubbles, is satisfactory. The flow field prediction model based on the MLP-VAE hybrid model has a strong generalization and generation ability, achieving relatively accurate flow field prediction for inlets with geometric configurations outside the training set.