Reliability analysis of the truck frame FE model considering the statistically distributed design variables
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kwon, Sung Hun | - |
dc.contributor.author | Yoo, Hong Hee | - |
dc.date.accessioned | 2022-12-20T16:08:59Z | - |
dc.date.available | 2022-12-20T16:08:59Z | - |
dc.date.created | 2022-09-16 | - |
dc.date.issued | 2010-08 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/174323 | - |
dc.description.abstract | Reliability analysis using the Pearson System considering the variance of tolerance for the design variables of the truck frame structure is performed in this paper. First, the fatigue life of the structure is estimated by three steps. Multibody dynamic model of 9-DOF vehicle to calculate constraint forces applied on the frame is simulated. Then the quasi-static analysis of the truck frame to calculate the stress coefficient of structure is performed. Finally using the stress histories based on the linear combination of constraint forces and stress coefficients, fatigue analysis to calculate the fatigue life is done. It is assumed that the design variables related to the multibody model such as mass, spring and damping coefficients and the dimension of the section in FE model are statistically distributed. By employing the Pearson System, the reliability analysis of the structure based on the statistical moment is performed. Then the variance of the probability density function considering the tolerance size of the design variables is estimated to analyze the effect of the design variable on the variance of fatigue failure probability. From these procedures, most sensitive design variables are known and the proper tolerance size for each design variables could be determined compared to the required reliability of structure. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | International Federation for the Promotion of Mechanism and Machine Science (IFToMM) | - |
dc.title | Reliability analysis of the truck frame FE model considering the statistically distributed design variables | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Yoo, Hong Hee | - |
dc.identifier.scopusid | 2-s2.0-84912115471 | - |
dc.identifier.bibliographicCitation | 5th Asian Conference on Multibody Dynamics 2010, ACMD 2010, v.1, pp.184 - 190 | - |
dc.relation.isPartOf | 5th Asian Conference on Multibody Dynamics 2010, ACMD 2010 | - |
dc.citation.title | 5th Asian Conference on Multibody Dynamics 2010, ACMD 2010 | - |
dc.citation.volume | 1 | - |
dc.citation.startPage | 184 | - |
dc.citation.endPage | 190 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Computer simulation | - |
dc.subject.keywordPlus | Fatigue of materials | - |
dc.subject.keywordPlus | Finite element method | - |
dc.subject.keywordPlus | Probability density function | - |
dc.subject.keywordPlus | Reliability analysis | - |
dc.subject.keywordPlus | Trucks | - |
dc.subject.keywordPlus | Constraint forces | - |
dc.subject.keywordPlus | Damping coefficients | - |
dc.subject.keywordPlus | Distributed design | - |
dc.subject.keywordPlus | Linear combinations | - |
dc.subject.keywordPlus | Multi-body dynamic modeling | - |
dc.subject.keywordPlus | Quasi static analysis | - |
dc.subject.keywordPlus | Statistical moments | - |
dc.subject.keywordPlus | Stress coefficients | - |
dc.subject.keywordPlus | Structural design | - |
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.