%A LIU Shenshen, Chen Jiangtao, GUI Yewei, TANG Wei, WANG Anling, HAN Qinghua %T Knowledge discovery for vehicle aerodynamic configuration design using data mining %0 Journal Article %D 2021 %J Acta Aeronautica et Astronautica Sinica %R 10.7527/S1000-6893.2020.24708 %P 524708-524708 %V 42 %N 4 %U {https://hkxb.buaa.edu.cn/CN/abstract/article_18222.shtml} %8 %X To gain a deeper understanding of the relationship between multiple objectives and multiple design parameters in the optimization process of vehicle aerodynamic configuration design and improve the scientificity and efficiency of the optimization model, we study the knowledge discovery of aircraft aerodynamic configuration design based on data mining methods. Four machine learning methods including analysis of variance, decision tree, isometric mapping, and self-organizing map are applied to data mining for aerodynamic design space of a hypersonic glide vehicle configuration optimization problem. Trade-offs between four objective functions (lift-to-drag ratio, lateral/side stability and volumetric efficiency) and influences of the design variables on the objective functions obtained quantitatively and qualitatively by the four methods are presented and discussed. Meanwhile, the design rules for variable values to generate better results are also analyzed. The features of the four data mining techniques are discussed respectively and the design knowledge obtained which can be applied to hypersonic glide vehicle configuration design is summarized.