A Multivariable Method for Calculating Failure Probability of Aeroengine Rotor Disk

被引:1
作者
Li, Guo [1 ]
Liu, Junbo [1 ]
Yang, Liu [2 ]
Zhou, Huimin [1 ]
Ding, Shuiting [1 ]
机构
[1] Beihang Univ, Sch Energy & Power Engn, Aircraft & Engine Integrated Syst Safety Beijing K, Beijing 100191, Peoples R China
[2] Hubei Univ Technol, Sch Mech Engn, Wuhan 430068, Peoples R China
关键词
probabilistic damage tolerance analysis; airworthiness; multivariable; numerical integration method; calculation efficiency; STRUCTURAL RELIABILITY; COMPUTATIONAL METHODS; RISK-ASSESSMENT; PREDICTION;
D O I
10.3390/aerospace10030296
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
The probabilistic damage tolerance analysis of aeroengine rotor disks is essential for determining if the disk is safe. To calculate the probability of failure, the numerical integration method is efficient if the integral formula of the probability density function is known. However, obtaining an accurate integral formula for aeroengine disks is generally complicated due to their complex failure mechanism. This article proposes a multivariable numerical integral method for calculating the probability of failure. Three random variables (initial defect length a, life scatter factor S, and stress scatter factor B) are considered. A compressor disk model is evaluated. The convergence, efficiency, and accuracy of the proposed method are compared with the Monte Carlo simulation and importance sampling method. The results show that the integral-based method is 100 times more efficient under the same convergence and accuracy conditions.
引用
收藏
页数:23
相关论文
共 32 条
  • [31] An efficient Kriging-based subset simulation method for hybrid reliability analysis under random and interval variables with small failure probability
    Xiao, Mi
    Zhang, Jinhao
    Gao, Liang
    Lee, Soobum
    Eshghi, Amin Toghi
    [J]. STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2019, 59 (06) : 2077 - 2092
  • [32] A new efficient simulation method based on Bayes' theorem and importance sampling Markov chain simulation to estimate the failure-probability-based global sensitivity measure
    Wang, Yanping
    Xiao, Sinan
    Lu, Zhenzhou
    [J]. AEROSPACE SCIENCE AND TECHNOLOGY, 2018, 79 : 364 - 372