Application of Convolutional Neural Network to Predict Airfoil Lift Coefficient

Authors: Yao Zhang, Woong-Je Sung, Dimitri Mavris
Published: 2017-12-29 | Updated: 2018-01-16

Source: https://arxiv.org/abs/1712.10082

PDF: https://arxiv.org/pdf/1712.10082

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機械学習アルゴリズム モデル設計 機械学習フレームワーク

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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.