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

Advances in aerodynamics 2022年第1期4卷 845-875页

作者： Bo-Wen Zan Zhong-Hua Han Chen-Zhou Xu Ming-Qi Liu Wen-Zheng Wang Institute of aerodynamic and Multidisciplinary Design Optimization National Key Laboratory of Science and Technology On Aerodynamic Design and ResearchSchool of AeronauticsNorthwestern Polytechnical UniversityXi’an710072China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province School of Aeronautics and AstronauticsUniversity of Electronic Science and Technology of ChinaChengdu611731China

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.

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

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