Solid Mechanics and Vehicle Conceptual Design

An efficient surrogate method for analyzing parameter global reliability sensitivity

  • Wanying YUN ,
  • Zhenzhou LYU
Expand
  • 1.Innovation Center NPU Chongqing,Northwestern Polytechnical University,Chongqing 400000,China
    2.School of Aeronautics,Northwestern Polytechnical University,Xi’an 710072,China
    3.Research & Development Institute of Northwestern Polytechnical University in Shenzhen,Shenzhen 518063,China

Received date: 2022-06-22

  Revised date: 2022-07-14

  Accepted date: 2022-07-28

  Online published: 2022-08-17

Supported by

National Natural Science Foundation of China(12002237);Natural Science Foundation of Chongqing(CSTB2022NSCQ-MSX0861);Guangdong Basic and Applied Basic Research Foundation(2022A1515011515);Fundamental Research Funds for the Central University(D5000211035)

Abstract

To give an efficient analysis of parameter reliability global sensitivity, this paper proposes a method by integrating the single-loop importance sampling technique and the adaptive Kriging model. First, a single-loop importance sampling algorithm for estimating the parameter reliability global sensitivity is constructed based on the Bayes theorem, Metropolis-Hastings algorithm and Edgeworth expansion, which unifies the analyses of reliability and parameter reliability global sensitivity. Based on the proposed single-loop importance sampling algorithm, the estimation of the parameter reliability global sensitivity is converted into the identification of states (failure or safety) of all unconditional importance sampling samples, so that each parameter reliability global sensitivity can be evaluated by repeatedly using the unconditional importance sampling samples. Secondly, the Kriging model surrogating the performance function is adaptively constructed to approximate the optimal importance sampling Probability Density Function (PDF) and then generate the corresponding importance samples. Finally, the Kriging model used to construct the approximately optimal importance sampling PDF is continuously updated among the candidate sampling pool of the generated importance samples until the states of all importance samples are accurately identified by the Kriging model. Based on the accurately identified states of all importance samples, parameter global reliability sensitivity of each uncertain distribution parameter is assessed.Results of a numerical example and a missile wing structure verify the efficiency and accuracy of the proposed method.

Cite this article

Wanying YUN , Zhenzhou LYU . An efficient surrogate method for analyzing parameter global reliability sensitivity[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023 , 44(12) : 227670 -227670 . DOI: 10.7527/S1000-6893.2022.27670

References

1 SALTELLI A. Sensitivity analysis for importance assessment[J]. Risk Analysis200222(3): 579-590.
2 POHYA A A, WICKE K, KILIAN T. Introducing variance-based global sensitivity analysis for uncertainty enabled operational and economic aircraft technology assessment[J]. Aerospace Science and Technology2022122: 107441.
3 YUN W Y, LU Z Z, JIANG X. An efficient sampling approach for variance-based sensitivity analysis based on the law of total variance in the successive intervals without overlapping[J]. Mechanical Systems and Signal Processing2018106: 495-510.
4 BORGONOVO E. A new uncertainty importance measure[J]. Reliability Engineering & System Safety200792(6): 771-784.
5 ZHANG F, XU X Y, CHENG L, et al. Global moment-independent sensitivity analysis of single-stage thermoelectric refrigeration system[J]. International Journal of Energy Research201943(15): 9055-9064.
6 PAPAIOANNOU I, STRAUB D. Variance-based reliability sensitivity analysis and the FORM α-factors[J]. Reliability Engineering & System Safety2021210: 107496.
7 张磊刚, 吕震宙, 陈军. 基于失效概率的矩独立重要性测度的高效算法[J]. 航空学报201435(8): 2199-2206.
  ZHANG L G, LYU Z Z, CHEN J. An efficient method for failure probability-based moment-independent importance measure[J]. Acta Aeronautica et Astronautica Sinica201435(8): 2199-2206 (in Chinese).
8 WANG P, LU Z Z, TANG Z C. An application of the Kriging method in global sensitivity analysis with parameter uncertainty[J]. Applied Mathematical Modelling201337(9): 6543-6555.
9 YUN W Y, LU Z Z, HE P F, et al. Parameter global reliability sensitivity analysis with meta-models: A probability estimation-driven approach[J]. Aerospace Science and Technology2020106: 106040.
10 LIU P L, DER KIUREGHIAN A. Multivariate distribution models with prescribed marginals and covariances[J]. Probabilistic Engineering Mechanics19861(2): 105-112.
11 DOBRIC J, SCHMID F. A goodness of fit test for copulas based on Rosenblatt’s transformation[J]. Computational Statistics & Data Analysis200751(9): 4633-4642.
12 DENG J. Probabilistic characterization of soil properties based on the maximum entropy method from fractional moments: Model development, case study, and application[J]. Reliability Engineering & System Safety2022219: 108218.
13 张洪铭, 顾晓辉, 邸忆. 基于树形马氏链模型的可靠性分析方法[J]. 航空学报201940(5): 222643.
  ZHANG H M, GU X H, DI Y. Reliability analysis method based on Tree Markov Chain model[J]. Acta Aeronautica et Astronautica Sinica201940(5): 222643 (in Chinese).
14 PERNINGE M. Stochastic optimal power flow by multi-variate Edgeworth expansions[J]. Electric Power Systems Research2014109: 90-100.
15 METROPOLIS N, ROSENBLUTH A W, ROSENBLUTH M N, et al. Equation of state calculations by fast computing machines[J]. The Journal of Chemical Physics195321(6): 1087-1092.
16 HASTINGS W K. Monte Carlo sampling methods using Markov chains and their applications[J]. Biometrika197057(1): 97-109.
17 DUBOURG V, SUDRET B, DEHEEGER F. Metamodel-based importance sampling for structural reliability analysis[J]. Probabilistic Engineering Mechanics201333: 47-57.
18 KLEIJNEN J P C. Regression and Kriging metamodels with their experimental designs in simulation: A review[J]. European Journal of Operational Research2017256(1): 1-16.
19 LI Y H, SHI J J, YIN Z F, et al. An improved high-dimensional kriging surrogate modeling method through principal component dimension reduction[J]. Mathematics20219(16): 1985.
20 AFSHARI S S, ENAYATOLLAHI F, XU X, et al. Machine learning-based methods in structural reliability analysis: A review[J]. Reliability Engineering & System Safety2022219: 108223.
21 RIDLEY G, FORGET B. A simple method for rejection sampling efficiency improvement on SIMT architectures[J]. Statistics and Computing202131(3): 30.
22 SIVULA T, MAGNUSSON M, VEHTARI A. Unbiased estimator for the variance of the leave-one-out cross-validation estimator for a Bayesian normal model with fixed variance[J/OL]. Communications in Statistics-Theory and Methods (2022-02-03)[2022-06-22]. .
23 ECHARD B, GAYTON N, LEMAIRE M. AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation[J]. Structural Safety201133(2): 145-154.
24 SOBOL IM. On quasi-Monte Carlo integrations[J]. Mathematics and Computers in Simulation199847: 103-112.
Outlines

/