Application of Convolutional Neural Network to Predict Airfoil Lift Coefficient

TL;DR

This paper explores the use of convolutional neural networks for predicting airfoil lift coefficients across various shapes and flow conditions, demonstrating competitive accuracy and minimal geometric constraints.

Contribution

It develops a CNN architecture tailored for aerodynamic meta-modeling, capable of handling variable flow conditions and geometries, and compares its performance with an MLP.

Findings

CNN achieves prediction accuracy comparable to MLP.

CNN requires minimal geometric constraints.

The approach is effective across multiple flow conditions.

Abstract

The adaptability of the convolutional neural network (CNN) technique for aerodynamic meta-modeling tasks is probed in this work. The primary objective is to develop suitable CNN architecture for variable flow conditions and object geometry, in addition to identifying a sufficient data preparation process. Multiple CNN structures were trained to learn the lift coefficients of the airfoils with a variety of shapes in multiple flow Mach numbers, Reynolds numbers, and diverse angles of attack. This is conducted to illustrate the concept of the technique. A multi-layered perceptron (MLP) is also used for the training sets. The MLP results are compared with that of the CNN results. The newly proposed meta-modeling concept has been found to be comparable with the MLP in learning capability; and more importantly, our CNN model exhibits a competitive prediction accuracy with minimal constraints in a geometric representation.