Nonparametric approach for uncertainty-based multidisciplinary design optimization considering limited data
DC Field | Value | Language |
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dc.contributor.author | Cho, Su-gil | - |
dc.contributor.author | Jang, Junyong | - |
dc.contributor.author | Kim, Shinyu | - |
dc.contributor.author | Park, Sanghyun | - |
dc.contributor.author | Lee, Tae Hee | - |
dc.contributor.author | Lee, Minuk | - |
dc.contributor.author | Choi, Jong-Su | - |
dc.contributor.author | Kim, Hyung-Woo | - |
dc.contributor.author | Hong, Sup | - |
dc.date.accessioned | 2021-08-03T04:31:58Z | - |
dc.date.available | 2021-08-03T04:31:58Z | - |
dc.date.issued | 2016-12 | - |
dc.identifier.issn | 1615-147X | - |
dc.identifier.issn | 1615-1488 | - |
dc.identifier.uri | https://www.kriso.re.kr/sciwatch/handle/2021.sw.kriso/643 | - |
dc.description.abstract | Uncertainty-based multidisciplinary design optimization (UMDO) has been widely acknowledged as an advanced methodology to address competing objectives and reliable constraints of complex systems by coupling relationship of disciplines involved in the system. UMDO process consists of three parts. Two parts are to define the system with uncertainty and to formulate the design optimization problem. The third part is to quantitatively analyze the uncertainty of the system output considering the uncertainty propagation in the multidiscipline analysis. One of the major issues in the UMDO research is that the uncertainty propagation makes uncertainty analysis difficult in the complex system. The conventional methods are based on the parametric approach could possibly cause the error when the parametric approach has ill-estimated distribution because data is often insufficient or limited. Therefore, it is required to develop a nonparametric approach to directly use data. In this work, the nonparametric approach for uncertainty-based multidisciplinary design optimization considering limited data is proposed. To handle limited data, three processes are also adopted. To verify the performance of the proposed method, mathematical and engineering examples are illustrated. | - |
dc.format.extent | 18 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | SPRINGER | - |
dc.title | Nonparametric approach for uncertainty-based multidisciplinary design optimization considering limited data | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1007/s00158-016-1540-0 | - |
dc.identifier.scopusid | 2-s2.0-84978087176 | - |
dc.identifier.wosid | 000391422800020 | - |
dc.identifier.bibliographicCitation | STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, v.54, no.6, pp 1671 - 1688 | - |
dc.citation.title | STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION | - |
dc.citation.volume | 54 | - |
dc.citation.number | 6 | - |
dc.citation.startPage | 1671 | - |
dc.citation.endPage | 1688 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Mechanics | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Engineering, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Mechanics | - |
dc.subject.keywordPlus | ROBUST OPTIMIZATION | - |
dc.subject.keywordPlus | SYSTEMS | - |
dc.subject.keywordPlus | TOLERANCES | - |
dc.subject.keywordPlus | MOTION | - |
dc.subject.keywordAuthor | Akaike information criterion | - |
dc.subject.keywordAuthor | Kolmogorov-Smirnov test | - |
dc.subject.keywordAuthor | Limited data | - |
dc.subject.keywordAuthor | Nonparametric approach | - |
dc.subject.keywordAuthor | Reliability-based design optimization | - |
dc.subject.keywordAuthor | Uncertainty-based multidisciplinary design optimization | - |
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