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Flow field reconstruction method based on array neural network

Part of: International Symposium on Smart Aircraft 2019 Collection Smart Aircraft

Published online by Cambridge University Press:  02 October 2020

W. Yuqi Open the ORCID record for W. Yuqi [Opens in a new window] ,
Y. Wu ,
L. Shan ,
Z. Jian ,
R. Huiying ,
Y. Tiechui  and
K. Menghai
W. Yuqi
Affiliation:
Renmin University of ChinaBeijing 100872 China
Y. Wu*
Affiliation:
Computer Network Information Center Chinese Academy of Sciences Beijing 100190 China
L. Shan
Affiliation:
Computer Network Information Center Chinese Academy of Sciences Beijing 100190 China
Z. Jian
Affiliation:
Computer Network Information Center Chinese Academy of Sciences Beijing 100190 China
R. Huiying
Affiliation:
Computer Network Information Center Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of SciencesBeijing 100049 China
Y. Tiechui
Affiliation:
Computer Network Information Center Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of SciencesBeijing 100049 China
K. Menghai
Affiliation:
China Unicom Software Research InstituteBeijing 100176 China
*
yuanwu@sccas.cn
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Abstract

Multi-dimensional aerodynamic database technology is widely used, but its model often has the curse of dimensionality. In order to solve this problem, we need projection to reduce the dimension. In addition, due to the lack of traditional method, we have improved the traditional flow field reconstruction method based on artificial neural networks, and we proposed an array neural network method.

In this paper, a set of flow field data for the target problem of the fixed Mach number is obtained by the existing CFD method. Then we arrange all the sampled flow field data into a matrix and use proper orthogonal decomposition (POD) to reduce the dimension, whose size is determined by the first few modals of energy. Therefore, significantly reduced data are obtained. Then we use an arrayed neural network to map the flow field data of simplified target problem and the flow field characteristics. Finally, the unknown flow field data can be effectively predicted through the flow field characteristic and the trained array neural network.

At the end of this paper, the effectiveness of the method is verified by airfoil flow fields. The calculation results show that the array neural network can reconstruct the flow field of the target problem more accurately than the traditional method, and its convergence speed is significantly faster. In addition, for the case of high angle flow field, the array neural network also performs well. There are no obvious jumps, and huge errors are found in results. In general, the proposed method is better than the traditional method.

Keywords

neural networkflow field reconstructionproper orthogonal decompositionmachine learning
Type
Research Article
Information
The Aeronautical Journal , Volume 125 , Issue 1283 , January 2021 , pp. 223 - 243
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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