Flowfield prediction of airfoil off-design conditions based on a modified variational autoencoder

TL;DR

This paper introduces a modified variational autoencoder framework that leverages cruise flowfields to accurately predict off-design airfoil flowfields, enhancing robustness and efficiency for industrial aerodynamic optimization.

Contribution

A novel deep learning model utilizing cruise flowfields as priors, with physical loss functions, improves off-design flowfield prediction accuracy and generalization over traditional methods.

Findings

Reduces prediction error by 30% compared to traditional models

Further decreases error by 4% with physical-based loss functions

Achieves a better balance of accuracy and computational cost for industrial use

Abstract

Airfoil aerodynamic optimization based on single-point design may lead to poor off-design behaviors. Multipoint optimization that considers the off-design flow conditions is usually applied to improve the robustness and expand the flight envelope. Many deep learning models have been utilized for the rapid prediction or reconstruction of flowfields. However, the flowfield reconstruction accuracy may be insufficient for cruise efficiency optimization, and the model generalization ability is also questionable when facing airfoils different from the airfoils with which the model has been trained. Because a computational fluid dynamic evaluation of the cruise condition is usually necessary and affordable in industrial design, a novel deep learning framework is proposed to utilize the cruise flowfield as a prior reference for the off-design condition prediction. A prior variational autoencoder is developed to extract features from the cruise flowfield and to generate new flowfields under other free stream conditions. Physical-based loss functions based on aerodynamic force and conservation of mass are derived to minimize the prediction error of the flowfield reconstruction. The results demonstrate that the proposed model can reduce the prediction error on test airfoils by 30% compared to traditional models. The physical-based loss function can further reduce the prediction error by 4%. The proposed model illustrates a better balance of the time cost and the fidelity requirements of evaluation for cruise and off-design conditions, which makes the model more feasible for industrial applications.