The single-loop Kriging model combined with Bayes’ formula for time-dependent failure probability based global sensitivity

Structures ◽  
2021 ◽  
Vol 32 ◽  
pp. 987-996
Author(s):  
Jingyu Lei ◽  
Zhenzhou Lu ◽  
Liangli He
Keyword(s):  
Failure Probability ◽  
Kriging Model ◽  
Time Dependent ◽  
Single Loop ◽  
Global Sensitivity ◽  
Bayes Formula ◽  
Dependent Failure

Advanced time-dependent reliability analysis based on adaptive sampling region with Kriging model

2020 ◽  
Vol 234 (4) ◽  
pp. 588-600
Author(s):  
Yan Shi ◽  
Zhenzhou Lu ◽  
Ruyang He
Keyword(s):  
Reliability Analysis ◽  
Failure Probability ◽  
Surrogate Model ◽  
Adaptive Sampling ◽  
Computational Cost ◽  
Kriging Model ◽  
Time Dependent ◽  
Computational Time ◽  
Optimization Strategy ◽  
Dependent Failure

Aiming at accurately and efficiently estimating the time-dependent failure probability, a novel time-dependent reliability analysis method based on active learning Kriging model is proposed. Although active surrogate model methods have been used to estimate the time-dependent failure probability, efficiently estimating the time-dependent failure probability by a fewer computational time remains an issue because screening all the candidate samples iteratively by the active surrogate model is time-consuming. This article is intended to address this issue by establishing an optimization strategy to search the new training samples for updating the surrogate model. The optimization strategy is performed in the adaptive sampling region which is first proposed. The adaptive sampling region is adjustable by the current surrogate model in order to provide a proper candidate samples region of the input variables. The proposed method employs the optimization strategy to select the optimal sample to be the new training sample point in each iteration, and it does not need to predict the values of all the candidate samples at every time instant in each iterative step. Several examples are introduced to illustrate the accuracy and efficiency of the proposed method for estimating the time-dependent failure probability by simultaneously considering the computational cost and precision.

A nested single-loop Kriging model-based method for time-dependent failure credibility

2020 ◽  
Vol 62 (6) ◽  
pp. 2881-2900
Author(s):  
Ning Wei ◽  
Zhenzhou Lu ◽  
Kaixuan Feng ◽  
Yingshi Hu
Keyword(s):  
Kriging Model ◽  
Time Dependent ◽  
Single Loop ◽  
Model Based ◽  
Dependent Failure

Effect of the geometrical size on time dependent failure probability of the solid oxide fuel cell

2019 ◽  
Vol 44 (21) ◽  
pp. 11033-11046 ◽  
Author(s):  
Yu-Cai Zhang ◽  
Min-Jie Lu ◽  
Wenchun Jiang ◽  
Shan-Tung Tu ◽  
Xian-Cheng Zhang
Keyword(s):  
Fuel Cell ◽  
Solid Oxide Fuel Cell ◽  
Failure Probability ◽  
Solid Oxide ◽  
Time Dependent ◽  
Oxide Fuel ◽  
Geometrical Size ◽  
Dependent Failure

scholarly journals Global Sensitivity Analysis of Fuzzy Distribution Parameter on Failure Probability and Its Single-Loop Estimation

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Lei Cheng ◽  
Zhenzhou Lu ◽  
Luyi Li
Keyword(s):  
Sensitivity Analysis ◽  
Failure Probability ◽  
Global Sensitivity Analysis ◽  
Computational Cost ◽  
Single Loop ◽  
Global Sensitivity ◽  
Sensitivity Indices ◽  
Distribution Parameters ◽  
Input Variables ◽  
Sampling Probability

An extending Borgonovo’s global sensitivity analysis is proposed to measure the influence of fuzzy distribution parameters on fuzzy failure probability by averaging the shift between the membership functions (MFs) of unconditional and conditional failure probability. The presented global sensitivity indices can reasonably reflect the influence of fuzzy-valued distribution parameters on the character of the failure probability, whereas solving the MFs of unconditional and conditional failure probability is time-consuming due to the involved multiple-loop sampling and optimization operators. To overcome the large computational cost, a single-loop simulation (SLS) is introduced to estimate the global sensitivity indices. By establishing a sampling probability density, only a set of samples of input variables are essential to evaluate the MFs of unconditional and conditional failure probability in the presented SLS method. Significance of the global sensitivity indices can be verified and demonstrated through several numerical and engineering examples.

scholarly journals Resonance risk and global sensitivity analysis of a straight–curved combination pipe based on active learning Kriging model

2019 ◽  
Vol 11 (3) ◽  
pp. 168781401983835
Author(s):  
Yuzhen Zhao ◽  
Yongshou Liu ◽  
Qing Guo ◽  
Tao Han ◽  
Baohui Li
Keyword(s):  
Sensitivity Analysis ◽  
Active Learning ◽  
Coordinate System ◽  
Failure Probability ◽  
Global Sensitivity Analysis ◽  
Dynamic Stiffness ◽  
Kriging Model ◽  
Global Sensitivity ◽  
Pipes Conveying Fluid ◽  
Conveying Fluid

The resonance failure of straight–curved combination pipes conveying fluid which are widely used in engineering is becoming a serious issue. But there are only few studies available on the resonance failure of combination pipes. The resonance failure probability and global sensitivity analysis of straight–curved combination pipes conveying fluid are studied by the active learning Kriging method proposed in this article. Based on the Euler–Bernoulli beam theory, the dynamic stiffness matrices of straight and curved pipes are derived in the local coordinate system, respectively. Then the dynamic stiffness matrix and characteristic equation of a straight–curved combination pipe conveying fluid are assembled under a global coordinate system. The natural frequency is calculated based on the characteristic equation. A resonance failure performance function is established based on the resonance failure mechanism and relative criterions. The active learning Kriging model based on expected risk function is introduced for calculating the resonance failure probability and moment-independent global sensitivity analysis index. The importance rankings of input variables are obtained with different velocities. According to the results, it is shown that the method proposed in this article provides a lot of guidance for resonance reliability analysis and anti-resonance design in combination pipes conveying fluid.

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