High-dimensional aerodynamic data modeling using a machine learning method based on a convolutional neural network

Advances in Aerodynamics

Research

Wen-Zheng Wang¹  Zhong-Hua Han²  Chen-Zhou Xu²  Bo-Wen Zan²  Ming-Qi Liu² 

[1] Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province, School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, 611731, Chengdu, China;Institute of Aerodynamic and Multidisciplinary Design Optimization, National Key Laboratory of Science and Technology On Aerodynamic Design and Research, School of Aeronautics, Northwestern Polytechnical University, 710072, Xi’an, China;

关键词: Aerodynamic data modeling;    High-dimensional problem;    Machine learning;    Convolutional neural network;    Computational fluid dynamics;   

DOI  :  10.1186/s42774-022-00128-8

 received in 2022-07-12, accepted in 2022-10-13,  发布年份 2022

来源: Springer

【 摘 要 】

Modeling high-dimensional aerodynamic data presents a significant challenge in aero-loads prediction, aerodynamic shape optimization, flight control, and simulation. This article develops a machine learning approach based on a convolutional neural network (CNN) to address this problem. A CNN can implicitly distill features underlying the data. The number of parameters to be trained can be significantly reduced because of its local connectivity and parameter-sharing properties, which is favorable for solving high-dimensional problems in which the training cost can be prohibitive. A hypersonic wing similar to the Sanger aerospace plane carrier wing is employed as the test case to demonstrate the CNN-based modeling method. First, the wing is parameterized by the free-form deformation method, and 109 variables incorporating flight status and aerodynamic shape variables are defined as model input. Second, more than 7000 sample points generated by the Latin hypercube sampling method are evaluated by performing computational fluid dynamics simulations using a Reynolds-averaged Navier–Stokes flow solver to obtain an aerodynamic database, and a CNN model is built based on the observed data. Finally, the well-trained CNN model considering both flight status and shape variables is applied to aerodynamic shape optimization to demonstrate its capability to achieve fast optimization at multiple flight statuses.

【 授权许可】

CC BY   
© The Author(s) 2022

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