Knowledge discovery for vehicle aerodynamic configuration design using data mining

被引:0
作者
Liu S. [1 ,2 ]
Chen J. [1 ,2 ]
Gui Y. [2 ]
Tang W. [3 ]
Wang A. [2 ]
Han Q. [2 ]
机构
[1] State Key Laboratory of Aerodynamics, Mianyang
[2] Computational Aerodynamics Institute, China Aerodynamics Research and Development Center, Mianyang
[3] State Key Laboratory of Environment-friendly Energy Materials, Southwest University of Science and Technology, Mianyang
来源
Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica | 2021年 / 42卷 / 04期
基金
中国国家自然科学基金;
关键词
Aerodynamic configuration optimization design; Analysis of variance; Data mining; Decision tree; Isometric mapping; Knowledge discovery; Self-organizing map;
D O I
10.7527/S1000-6893.2020.24708
中图分类号
学科分类号
摘要
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. © 2021, Beihang University Aerospace Knowledge Press. All right reserved.
引用
收藏
相关论文
共 30 条
[1]  
GUI Y W, TANG W, DU Y X., Thermal safety issues of near-space hypersonic vehicles, pp. 180-185, (2019)
[2]  
TANG W, FENG Y, YANG X F, Et al., Practices of aerodynamic configuration design for non-ballistic trajectory vehicles, Physics of Gases, 2, 1, pp. 1-12, (2017)
[3]  
GUI Y W, LIU L, DAI G Y, Et al., Research status on hypersonic vehicle fluid-thermal-structural coupling and software development, Acta Aeronautica et Astronautic Sinica, 38, 7, (2017)
[4]  
GUI Y W., Combined thermal phenomena of hypersonic vehicle, Scientia Physica, Mechanica & Astronomica, 49, 11, (2019)
[5]  
GUI Y W, LIU L, WEI D., Combined thermal phenomena issues of long endurance hypersonic vehicles, Acta Aerodynamica Sinica, 38, 4, pp. 641-650, (2020)
[6]  
DAI G Y, ZENG L, LIU S S, Et al., Prediction of flight trajectory considering fluid-thermal-structure coupling effect, Acta Aeronautica et Astronautica Sinica, 39, 12, (2018)
[7]  
WANG W, MO R, ZHANG Y., Applied research on simulation data of blade optimization designing based on data mining, Computer Engineering and Applications, 49, 12, pp. 11-15, (2013)
[8]  
SIMPSON T W, TOROPOV V, BALABANOV V, Et al., Design and analysis of computer experiments in multidisciplinary design optimization: a review of how far we have come or not: AIAA-2008-5802, (2008)
[9]  
GUO Z D, SONG L M, LI J, Et al., Meta model-based global design optimization and exploration method, Journal of Propulsion Technology, 36, 2, pp. 207-216, (2015)
[10]  
JEONG S, SHIMOYAMA K., Review of data mining for multi-disciplinary design optimization, Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 225, pp. 465-479, (2011)