基于元模型的高效重要度抽样与单回路估算方法相结合,用于参数全局可靠性灵敏度分析

IF 3 3区 工程技术 Q2 ENGINEERING, MECHANICAL Probabilistic Engineering Mechanics Pub Date : 2024-03-12 DOI:10.1016/j.probengmech.2024.103597
Wanying Yun , Fengyuan Li , Xiangming Chen , Zhe Wang
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引用次数: 0

摘要

为了有效估计不确定分布参数对故障概率不确定性的主效应和总效应,我们通过等概率变换引入辅助变量,构建了单环估计公式。这种方法规避了原有的嵌套三环过程。为了生成推导出的单环估计公式中使用的样本,可以直接采用蒙特卡罗模拟。为减少蒙特卡罗模拟中的样本数量,可将重要的抽样技术集成到所提出的单环估计公式中。此外,为了提高识别所有使用样本的状态(故障或安全)的效率,可以引入自适应克里金模型。随后,将自适应克里金模型与蒙特卡罗模拟相结合,以及将自适应克里金模型与重要性抽样技术相结合,整合到推导出的单环公式中,从而同时有效地估计不确定分布参数的主效应和总效应。三个案例研究的结果验证了所提方法的准确性和高效性。
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Efficient metamodel-based importance sampling coupled with single-loop estimation method for parameter global reliability sensitivity analysis

To efficiently estimate the main effects and total effects of uncertain distribution parameters on the uncertainty of failure probability, we construct single-loop estimation formulas by introducing auxiliary variables through the equal probability transformation. This approach circumvents the original nested triple-loop process. For generating samples used in the derived single-loop estimation formulas, direct Monte Carlo simulation can be employed. To reduce the number of samples in Monte Carlo simulation, the important sampling technique can be integrated into the proposed single-loop estimation formulas. Additionally, to enhance the efficiency of identifying the states (failure or safety) of all used samples, an adaptive Kriging model can be introduced. Subsequently, the adaptive Kriging model coupled with Monte Carlo simulation, and the adaptive Kriging model coupled with the importance sampling technique, are integrated into the derived single-loop formulas to concurrently and efficiently estimate the main effects and total effects of uncertain distribution parameters. The results of three case studies validate the accuracy and efficiency of the proposed method.

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来源期刊
Probabilistic Engineering Mechanics
Probabilistic Engineering Mechanics 工程技术-工程:机械
CiteScore
3.80
自引率
15.40%
发文量
98
审稿时长
13.5 months
期刊介绍: This journal provides a forum for scholarly work dealing primarily with probabilistic and statistical approaches to contemporary solid/structural and fluid mechanics problems encountered in diverse technical disciplines such as aerospace, civil, marine, mechanical, and nuclear engineering. The journal aims to maintain a healthy balance between general solution techniques and problem-specific results, encouraging a fruitful exchange of ideas among disparate engineering specialities.
期刊最新文献
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