Flow field prediction of supercritical airfoils via variational autoencoder based deep learning framework

Abstract

Effective access to obtain the complex flow fields around an airfoil is crucial in improving the quality of supercritical wings. In this study, a systematic method based on generative deep learning is developed to extract features for depicting the flow fields and predict the steady flow fields around supercritical airfoils. To begin with, a variational autoencoder (VAE) network is designed to extract representative features of the flow fields. Specifically, the principal component analysis technique is adopted to realize feature reduction, aiming to obtain the optimal dimension of features in VAE. Afterward, the extracted features are incorporated into the dataset, followed by the mapping from the airfoil shapes to features via a multilayer perception (MLP) model. Eventually, a composite network is adopted to connect the MLP and the decoder of VAE for predicting the flow fields given the airfoil. The proposed VAE network achieves compression of high-dimensional flow field data into ten representative features. The statistical results indicate the accurate and generalized performance of the proposed method in reconstructing and predicting flow fields around a supercritical airfoil. Especially, our method obtains accurate prediction results over the shock area, indicating its superiority in conducting turbulent flow under high Reynolds number.