An aerodynamic surrogate model of launch vehicle based on relevance vector machine

• Published in: Journal of physics. Conference series Vol. 2181; no. 1; pp. 12021 - 12026
• Main Authors: Peng, Bo, Ma, Cheng, Wang, Guodong, Hu, Fengyan, Mei, Ke, Yang, Jian
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.01.2022
• Subjects: Aerodynamic coefficients; Design optimization; Machine learning; Multidisciplinary design optimization; Physics; Training
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/2181/1/012021

Abstract In the process of launch vehicle multidisciplinary design optimization, aerodynamic calculation takes a long time, which affects the overall design cycle. In order to solve the above problems, based on the idea of machine learning, this paper constructs the surrogate model of relevance vector machine and calculates the aerodynamic coefficients of launch vehicles quickly. Firstly, the aerodynamic model of launch vehicle is established, and the orthogonal design method is used to generate test sample points. Then, the aerodynamic coefficients of the sample points are calculated by using Fluent software, and the training data of the surrogate model are obtained. On this basis, the relevance vector machine model is trained with training data, generating correlation vector machine agent model. Finally, the calculation accuracy of the surrogate model is evaluated by simulation, and the feasibility and validity of the method are verified.