Monte Carlo simulation-compatible stochastic field for application to expansion-based stochastic finite element method
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Noh, Hyuk-Chun | - |
dc.contributor.author | Park, Taehyo | - |
dc.date.accessioned | 2022-12-21T09:48:24Z | - |
dc.date.available | 2022-12-21T09:48:24Z | - |
dc.date.created | 2022-09-16 | - |
dc.date.issued | 2006-12 | - |
dc.identifier.issn | 0045-7949 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/180711 | - |
dc.description.abstract | In order to endow the expansion-based stochastic formulation with the capability of representing the characteristic behavior of stochastic systems, i.e., the non-linear dependence of the response variability on the coefficient of variation of the stochastic field, a Monte Carlo simulation-compatible stochastic field is suggested. Through a theoretical comparison of displacement vectors in the Monte Carlo method and an expansion-based scheme, it is found that the stochastic field adopted in the expansion-based scheme is not compatible with that appearing in the Monte Carlo simulation. The Monte Carlo simulation-compatible stochastic field is established by means of enforcing the compatibility between the stochastic fields in the expansion-based scheme and the Monte Carlo simulation. Employing the stochastic field suggested in this study, the response variability is reproduced with high precision even for uncertain fields with a moderately large coefficient of variation. Furthermore, the formulation proposed here can be used as an indirect Monte Carlo scheme by directly substituting the numerically simulated random fields into the covariance formula. This yields a pronounced reduction in the computation cost while resulting in virtually the same response variability as the Monte Carlo technique. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.title | Monte Carlo simulation-compatible stochastic field for application to expansion-based stochastic finite element method | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Park, Taehyo | - |
dc.identifier.doi | 10.1016/j.compstruc.2006.07.001 | - |
dc.identifier.scopusid | 2-s2.0-33751426835 | - |
dc.identifier.wosid | 000243267400023 | - |
dc.identifier.bibliographicCitation | COMPUTERS & STRUCTURES, v.84, no.31-32, pp.2363 - 2372 | - |
dc.relation.isPartOf | COMPUTERS & STRUCTURES | - |
dc.citation.title | COMPUTERS & STRUCTURES | - |
dc.citation.volume | 84 | - |
dc.citation.number | 31-32 | - |
dc.citation.startPage | 2363 | - |
dc.citation.endPage | 2372 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Engineering, Civil | - |
dc.subject.keywordPlus | PLATE STRUCTURES | - |
dc.subject.keywordPlus | UPPER-BOUNDS | - |
dc.subject.keywordPlus | VARIABILITY | - |
dc.subject.keywordPlus | FORMULATION | - |
dc.subject.keywordPlus | BEAMS | - |
dc.subject.keywordAuthor | Monte Carlo simulation | - |
dc.subject.keywordAuthor | expansion-based stochastic method | - |
dc.subject.keywordAuthor | compatible stochastic field | - |
dc.subject.keywordAuthor | weighted integral stochastic finite element method | - |
dc.subject.keywordAuthor | coefficient of variation | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S0045794906002938?via%3Dihub | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.