Probability Analysis and Reliability Based Design Optimization Methods for Low Cycle Fatigue Life of Turbine Shaft

被引：0
|
作者：
Lu Y.-X. [1 ]
Lyu Z.-Z. [1 ]
Feng K.-X. [1 ]
He L.-L. [1 ]
机构：
[1] Institute of Aeronautics, Northwestern Polytechnical University, Xi'an
来源：
Tuijin Jishu/Journal of Propulsion Technology | 2022年 / 43卷 / 02期
关键词：
Low cycle fatigue; Optimal design; Parameterized platform; Reliability analysis; Structure analysis; Turbine shaft;
D O I：
10.13675/j.cnki.tjjs.210510
中图分类号：
学科分类号：
摘要：
In order to ensure reliability and increase low cycle fatigue life (LCFL) of turbine shaft under various random uncertainties, the reliability analysis and reliability based design optimization (RBDO) were studied. A parameter platform is established for structure analysis, reliability analysis and RBDO of the LCFL of turbine shaft. Based on this platform, calling structure finite element software and reliability analysis can be automatically realized at different design parameters visited by RBDO iteration. For improving efficiency of reliability analysis greatly, an advanced Monte Carlo simulation combined with adaptive Kriging model (A-MCS-AK) is proposed by strategy of multi-training-point at one updating and candidate sample pool reduction. For two RBDO models of respectively maximizing average expectation of LCFL and minimizing failure probability of LCFL of the turbine shaft, a quasi-sequential decoupling method is presented by combining cooperatively adaptive surrogate. The analysis results of the turbine shaft show that the parameterized platform completes the automatic and orderly transmission of data, and the proposed A-MCS-AK is more efficient than traditional adaptive Kriging combined with MCS (AK-MCS) for reliability analysis. The solutions of two RBDO models of the turbine shaft LCFL show that cooperatively adaptive surrogate strategy can improve the efficiency of solving RBDO under acceptable precision, and the average LCFL and the reliability can be improved simultaneously. © 2022, Editorial Department of Journal of Propulsion Technology. All right reserved.
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页码：8 / 20
页数：12
相关论文
共 33 条
  • [1] (2008)
  • [2] (2014)
  • [3] Sacks J, Schiller S B, Welch W J., Design for Computer Experiment, Technometrics, 31, 1, pp. 41-47, (1989)
  • [4] (2019)
  • [5] (2020)
  • [6] Nikolaidis E, Burdisso R., Reliability-Based Design Optimization Using Probabilistic Sufficiency Factory, Computers & Structures, 28, 6, pp. 781-788, (1988)
  • [7] Chen Huitao, Fan Junkai, Jing Shuangxi, Et al., Probabilistic Design Optimization of Wind Turbine Gear Transmission System Based on Dynamic Reliability, Journal of Mechanical Science and Technology, 33, 2, pp. 579-589, (2019)
  • [8] Li Huaxia, Cho Hyunkyoo, Sugiyama Hiroyuki, Reliability-Based Design Optimization of Wind Turbine Drive Train with Integrated Multibody Gear Dynamics Simulation Considering Wind Load Uncertainty, Structural and Multidis-Ciplinary Optimization, 56, pp. 183-201, (2017)
  • [9] Kuschel N, Rackwitz R., Optimal Design under Time-Variant Reliability Constraints, Structural Safety, 22, 2, pp. 113-127, (2000)
  • [10] Royset J O, Kiureghian A D, Polak E., Reliability-Based Optimal Structural Design by the Decoupling Approach, Reliability Engineering System Safety, 73, 3, pp. 213-221, (2001)
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