Efficient estimation of the functional reliability of a passive system by means of an improved Line Sampling method

Wang, Baosheng; Wang, Dongqing; Jiang, Jin; Zhang, Jianmin

doi:10.1016/j.anucene.2012.12.015

Cited by 4 publications

(5 citation statements)

References 25 publications

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“…(5) Calculate the failure probability of the ith sample P fi , then the failure probability estimator b P f and its variance estimator r 2 ð b P f Þ of N samples in failure margin are obtained in Eqs. (13), (14).…”

Section: Improved Line Sampling For Truncated Random Variablesmentioning

confidence: 98%

“…13 The underlying idea of line sampling is to employ lines instead of the random points to probe the failure domain of the reliability system. 14,15 In aerospace engineering, reliability problem is a typically highly reliable, nonlinear problem. How to use the truncated probability information in aerospace mechanism to run line sampling to obtain reliability is an important research issue.…”

Section: Introductionmentioning

confidence: 99%

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ABCLS method for high-reliability aerospace mechanism with truncated random uncertainties

Peng

Zhang

Zhu

2015

Chinese Journal of Aeronautics

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The random variables are always truncated in aerospace engineering and the truncated distribution is more feasible and effective for the random variables due to the limited samples available. For high-reliability aerospace mechanism with truncated random variables, a method based on artificial bee colony (ABC) algorithm and line sampling (LS) is proposed. The artificial bee colony-based line sampling (ABCLS) method presents a multi-constrained optimization model to solve the potential non-convergence problem when calculating design point (is also as most probable point, MPP) of performance function with truncated variables; by implementing ABC algorithm to search for MPP in the standard normal space, the optimization efficiency and global searching ability are increased with this method dramatically. When calculating the reliability of aerospace mechanism with too small failure probability, the Monte Carlo simulation method needs too large sample size. The ABCLS method could overcome this drawback. For reliability problems with implicit functions, this paper combines the ABCLS with Kriging response surface method, therefore could alleviate computational burden of calculating the reliability of complex aerospace mechanism. A numerical example and an engineering example are carried out to verify this method and prove the applicability.

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Section: Improved Line Sampling For Truncated Random Variablesmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

ABCLS method for high-reliability aerospace mechanism with truncated random uncertainties

Peng

Zhang

Zhu

2015

Chinese Journal of Aeronautics

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“…One-dimensional problems are solved along an "important direction" that optimally points towards the failure domain, in place of the high-dimensional problem [65]. The approach overcomes standard MCS in a wide range of engineering applications [22,39,51,65,[68][69][70][71] and allows ideally reducing to zero the variance of the failure probability estimator if the "important direction" is perpendicular to the almost linear boundaries of the failure domain [64].…”

Section: Advanced Monte Carlo Simulation Approachmentioning

confidence: 99%

Reliability Assessment of Passive Safety Systems for Nuclear Energy Applications: State-of-the-Art and Open Issues

et al. 2021

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Passive systems are fundamental for the safe development of Nuclear Power Plant (NPP) technology. The accurate assessment of their reliability is crucial for their use in the nuclear industry. In this paper, we present a review of the approaches and procedures for the reliability assessment of passive systems. We complete the work by discussing the pending open issues, in particular with respect to the need of novel sensitivity analysis methods, the role of empirical modelling and the integration of passive safety systems assessment in the (static/dynamic) Probabilistic Safety Assessment (PSA) framework.

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“…On the other side, advanced Monte Carlo Simulation methods can be adopted to realize robust estimations by means of a limited amount of random samples [62][63][64] . This class of methods includes Stratified Sampling [65,66] , Subset Simulation (SS) [67][68][69][70][71][72][73][74] , the Response Conditioning Method (RCM) [75,76] for structural reliability analysis (also developed from SS and employing approximate solutions), Line Sampling (LS) [77][78][79][80][81][82][83][84] and splitting methods [85][86][87][88][89] . However, the advanced MCS methods that is adopted more frequently is perhaps Importance Sampling (IS), where the original PDF of the uncertain inputs is replaced by a proper Importance Sampling Density (ISD): this favors the MC samples to lie close to the failure region, thus artificially increasing the frequency of the (rare) failure event [90][91][92] .…”

Section: Introductionmentioning

confidence: 99%

“…• in Ref. [100] , the focus of the comparisons between AM-SIS and other MC schemes (in particular, standard MCS, LHS [65] , IS [90][91][92][93][94] and LS [77][78][79][80][81][82][83][84] ) is exclusively put on the efficiency of stage (2) of the algorithm, i.e., of random importance sampling for failure probability estimation. In this work, instead, comparisons are made also to other methods that combine SS and metamodels and that are very close to stage (1) of our approach.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Metamodel-Based Subset Importance Sampling approach for the assessment of the functional failure probability of a thermal-hydraulic passive system

Pedroni

Zio

2017

Applied Mathematical Modelling

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An Adaptive Metamodel-Based Subset Importance Sampling (AM-SIS) approach, previously developed by the authors, is here employed to assess the (small) functional failure probability of a thermal-hydraulic (T-H) nuclear passive safety system. The approach relies on an iterative Importance Sampling (IS) scheme that efficiently couples the powerful characteristics of Subset Simulation (SS) and fast-running Artificial Neural Networks (ANNs). In particular, SS and ANNs are intelligently employed to build and progressively refine a fully nonparametric estimator of the ideal, zero-variance Importance Sampling Density (ISD), in order to: (i) increase the robustness of the failure probability estimates and (ii) decrease the number of expensive T-H simulations needed (together with the related computational burden). The performance of the approach is thoroughly compared to that of other efficient Monte Carlo (MC) techniques on a case study involving an advanced nuclear reactor cooled by helium.

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Efficient estimation of the functional reliability of a passive system by means of an improved Line Sampling method

Cited by 4 publications

References 25 publications

ABCLS method for high-reliability aerospace mechanism with truncated random uncertainties

ABCLS method for high-reliability aerospace mechanism with truncated random uncertainties

Reliability Assessment of Passive Safety Systems for Nuclear Energy Applications: State-of-the-Art and Open Issues

An Adaptive Metamodel-Based Subset Importance Sampling approach for the assessment of the functional failure probability of a thermal-hydraulic passive system

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