Volume 39, Number 6, December 2021
|Page(s)||1240 - 1248|
|Published online||21 March 2022|
Time-varying reliability analysis of compressor blisk based on particle swash optimization extreme Kriging model
School of Astronautics, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
2 School of Astronautics, Northwestern Polytechnical University, Xi'an 710072, China
3 Department of Aeronautics and Astronautics, Fudan University, Shanghai 200433, China
4 School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China
In order to effectively realize the dynamic time-varying reliability analysis of the radial deformation of the aero-engine low pressure compressor, based on Kriging model combined with particle swarm optimization (PSO) and extreme value idea, a particle swarm optimization extremum Kriging model (PSOEKM) method was proposed. Firstly, the analysis principle of PSOEKM method is expounded. Secondly, the modeling idea of PSOEKM method is discussed. Then, the implementation approach of time-varying reliability analysis based on PSOEKM is explored. Finally, taking the aero-engine low pressure compressor blisk as an example, the dynamic reliability analysis is carried out by using PSOEKM. The analysis results show that the reliability is 99.76% when the allowable value of radial deformation of the compressor blisk is 1.594×10-3 m. Compared with the traditional Kriging model and the extreme response surface model, the PSOEKM method has high analysis accuracy and calculation efficiency. The method presented in this paper provides a new research idea for time-varying reliability analysis of complex structures.
为了有效实现航空发动机低压压气机叶盘径向变形的动态时变可靠性分析，基于Kriging模型，结合粒子群算法（particle swarm optimization，PSO）与极值思想，提出了粒子群极值Kriging模型（particle swarm optimization extremum Kriging model，PSOEKM）方法。阐述了PSOEKM方法的分析原理；论述了PSOEKM方法的建模思想；探究了基于PSOEKM方法的时变可靠性分析实现途径；以航空发动机低压压气机叶盘为案例，运用PSOEKM实现其动态可靠性分析。分析结果表明：当压气机叶盘径向变形许用值为1.594×10-3 m时，可靠度为99.76%。通过方法对比显示：PSOEKM方法具有较高的分析精度与计算效率。所提出的PSOEKM方法为复杂结构时变可靠性分析提供了一种新的研究思路。
Key words: particle swarm optimization / Kriging / reliability analysis / aeroengine / compressor blisk
关键字 : 粒子群算法 / Kriging / 可靠性分析 / 航空发动机 / 压气机叶盘
© 2021 Journal of Northwestern Polytechnical University. All rights reserved.
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