Interval analysis for geometric uncertainty and robust aerodynamic optimization design

被引：0
|
作者：
Song X. [1 ,2 ]
Zheng G. [1 ,2 ]
Yang G. [1 ,2 ]
Jiang Q. [1 ,2 ]
机构：
[1] Key Laboratory for Mechanics in Fluid Solid Coupling Systems, Institute of Mechanics, Chinese Academy of Sciences, Beijing
[2] School of Engineering Science, University of Chinese Academy of Sciences, Beijing
来源：
Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2019年 / 45卷 / 11期
基金：
中国国家自然科学基金;
关键词：
Adaptive multi-objective particle swarm optimization algorithm; Aerodynamic optimization design; Directly manipulated free-form deformation (DFFD); Geometric uncertainty; Kriging model; Nonlinear interval analysis; Robust optimization design;
D O I：
10.13700/j.bh.1001-5965.2019.0077
中图分类号：
学科分类号：
摘要：
Uncertainties will make aircraft deviate from the designed aerodynamic performance, resulting in the decrease in aerodynamic performance and even destruction. Due to the problem that the probability distribution of geometric uncertainty cannot be given in engineering and nonlinear aerodynamic problem in transonic flows, the non-probabilistic parametric modeling of geometric uncertainty is studied, and the fast nonlinear interval analysis method is established in combination with Kriging model and optimization method. The effects of geometric uncertainties on a symmetric airfoil are analyzed using the above method, and the quantitative variation range of aerodynamic performance is obtained. Based on interval uncertainty analysis, a robust optimization design process is established. The single-objective interval uncertainty optimization problem was transformed into deterministic multi-objective optimization problem based on the order relation and possibility degree model of interval number, and the optimization problem was solved by adaptive multi-objective particle swarm optimization which is based on Pareto entropy. The robust optimization design is implemented for the supercritical airfoil with the drag objective as well as lift, moment and area constraints under geometric uncertainties. The results compared with deterministic optimization design show that deterministic design is prone to failure under the influence of uncertainties, while the robust design is more secure and reliable. © 2019, Editorial Board of JBUAA. All right reserved.
引用
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页码：2217 / 2227
页数：10
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