000904446 001__ 904446
000904446 005__ 20220224125203.0
000904446 0247_ $$2doi$$a10.1016/j.ymssp.2021.107630
000904446 0247_ $$2ISSN$$a0888-3270
000904446 0247_ $$2ISSN$$a1096-1216
000904446 0247_ $$2WOS$$aWOS:000634833100006
000904446 037__ $$aFZJ-2021-06016
000904446 082__ $$a004
000904446 1001_ $$0P:(DE-HGF)0$$aCheng, Kai$$b0
000904446 245__ $$aResampling method for reliability-based design optimization based on thermodynamic integration and parallel tempering
000904446 260__ $$aAmsterdam [u.a.]$$bElsevier$$c2021
000904446 3367_ $$2DRIVER$$aarticle
000904446 3367_ $$2DataCite$$aOutput Types/Journal article
000904446 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1642777678_11563
000904446 3367_ $$2BibTeX$$aARTICLE
000904446 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000904446 3367_ $$00$$2EndNote$$aJournal Article
000904446 500__ $$aEin Postprint steht leider nicht zur Verfügung
000904446 520__ $$aIn this paper, a fully decoupled simulation method is proposed for reliability-based design optimization (RBDO) based on thermodynamic integration and parallel tempering (TIPT). We show that the failure probability function and its gradient can be obtained simultaneously with once generalized reliability analysis, and thus the RBDO problem is converted to the traditional optimization problem efficiently. Firstly, the design parameters are deemed as uniformly distributed random variables, and an auxiliary probability density function (PDF) of random design variables is constructed to cover its whole parameter space. Then, based on thermodynamic integration, the estimation of failure probability is converted to a series of simple integration problems with smooth integrand, and they are estimated by running multiple Markov chains using the so-called parallel tempering method. Finally, importance sampling (IS) is used to estimate the failure probability function and its gradient, and the IS samples are obtained by resampling from the existing Markov chains without extra computation. The proposed method is tested with severa benchmarks, and the results show that it provides robust solution for problems with various nonlinear constraints compared to other popular methods, include double-loop Monte Carlo simulation (MCS), Quantile MCS, sequential optimization and reliability assessment, performance measure approach and reliability index approach.
000904446 536__ $$0G:(DE-HGF)POF4-2173$$a2173 - Agro-biogeosystems: controls, feedbacks and impact (POF4-217)$$cPOF4-217$$fPOF IV$$x0
000904446 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
000904446 7001_ $$0P:(DE-HGF)0$$aLu, Zhenzhou$$b1
000904446 7001_ $$0P:(DE-Juel1)185940$$aXiao, Sinan$$b2
000904446 7001_ $$0P:(DE-HGF)0$$aZhang, Xiaobo$$b3
000904446 7001_ $$0P:(DE-HGF)0$$aOladyshkin, Sergey$$b4
000904446 7001_ $$0P:(DE-HGF)0$$aNowak, Wolfgang$$b5
000904446 773__ $$0PERI:(DE-600)1471003-1$$a10.1016/j.ymssp.2021.107630$$gVol. 156, p. 107630 -$$p107630 -$$tMechanical systems and signal processing$$v156$$x0888-3270$$y2021
000904446 909CO $$ooai:juser.fz-juelich.de:904446$$pVDB
000904446 9131_ $$0G:(DE-HGF)POF4-217$$1G:(DE-HGF)POF4-210$$2G:(DE-HGF)POF4-200$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-2173$$aDE-HGF$$bForschungsbereich Erde und Umwelt$$lErde im Wandel – Unsere Zukunft nachhaltig gestalten$$vFür eine nachhaltige Bio-Ökonomie – von Ressourcen zu Produkten$$x0
000904446 9141_ $$y2021
000904446 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2021-01-27
000904446 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2021-01-27
000904446 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2021-01-27
000904446 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2021-01-27
000904446 915__ $$0StatID:(DE-HGF)1160$$2StatID$$aDBCoverage$$bCurrent Contents - Engineering, Computing and Technology$$d2021-01-27
000904446 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2021-01-27
000904446 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2021-01-27
000904446 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bMECH SYST SIGNAL PR : 2019$$d2021-01-27
000904446 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2021-01-27
000904446 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2021-01-27
000904446 915__ $$0StatID:(DE-HGF)9905$$2StatID$$aIF >= 5$$bMECH SYST SIGNAL PR : 2019$$d2021-01-27
000904446 9201_ $$0I:(DE-Juel1)IBG-3-20101118$$kIBG-3$$lAgrosphäre$$x0
000904446 980__ $$ajournal
000904446 980__ $$aVDB
000904446 980__ $$aI:(DE-Juel1)IBG-3-20101118
000904446 980__ $$aUNRESTRICTED