Application of Convolutional Neural Network to Predict Airfoil Lift Coefficient

Yao Zhang; Woong-Je Sung; Dimitri Mavris

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arXiv · 1712.10082

Application of Convolutional Neural Network to Predict Airfoil Lift Coefficient

Yao Zhang·Woong-Je Sung·Dimitri Mavris

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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.

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BibTeX RIS

Yao Zhang, Woong-Je Sung, Dimitri Mavris. 2018-01-16. Application of Convolutional Neural Network to Predict Airfoil Lift Coefficient. https://arxiv.org/abs/1712.10082

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