DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Lee, Bong Jae | - |
dc.contributor.advisor | 이봉재 | - |
dc.contributor.advisor | Lee, Ikjin | - |
dc.contributor.advisor | 이익진 | - |
dc.contributor.author | Song, Joonho | - |
dc.contributor.author | 송준호 | - |
dc.date.accessioned | 2017-03-29T02:30:30Z | - |
dc.date.available | 2017-03-29T02:30:30Z | - |
dc.date.issued | 2016 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=663291&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/221277 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 기계공학과, 2016.8 ,[v, 40 p. :] | - |
dc.description.abstract | Recently, nanostructure is being introduced to solar thermal absorber for its outstanding performance. However, nanostructure has a fabrication uncertainty causing inaccurate design and thus unexpected performance. In this study, robust design optimization (RDO) of nanostructure for solar thermal absorber is sought to resolve the uncertainty issue with two new RDO methodologies: Hessian-eigenvector dimension reduction (HeDR) method and Basis screening (BS) Kriging. Dimension reduction (DR) method is widely used for estimating statistical moments of a performance model with N-dimensional random variables as the input parameters for its computational efficiency. DR method reduces an N-dimensional integration into N decomposed 1-D numerical integrations. The decomposition deletes the effect of cross-terms and causes a falling off in the computation accuracy. However, the error can be minimized with HeDR method setting sample point pattern transformed into the directions the eigenvector of Hessian matrix indicate. Metamodeling is widely used for simulation-based design optimization for computationally heavy engineering models. The dynamic Kriging method is one of the most popular metamodeling methods due to its accuracy. BS Kriging method, an improved dynamic Kriging method seeks a promising basis function set in a more efficient way with 3 additional technical strategies: 1. Limitation of the maximum order of basis functions, 2. Estimation of the importance of basis terms, 3. Determination of the basis function using cross-validation error. Both methods will be numerically verified and utilized to obtain the robust optimal design of nanostructure for solar thermal absorption. RDO of 1-D and 2-D pattern nanostructure will be conducted with HeDR method and BS Kriging method, respectively. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Robust design optimization | - |
dc.subject | Hessian-eigenvector dimension reduction method | - |
dc.subject | Basis screening Kriging method | - |
dc.subject | Nanostructure | - |
dc.subject | Solar thermal absorber | - |
dc.subject | 강건최적설계 | - |
dc.subject | 헤시안-아이겐벡터 차원감소법 | - |
dc.subject | 베이시스 스크리닝 크리깅 | - |
dc.subject | 나노구조 | - |
dc.subject | 태양열 흡수체 | - |
dc.title | Robust design optimization of nanostructure for solar thermal absorption | - |
dc.title.alternative | 태양열 흡수체를 위한 나노구조의 강건최적설계 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :기계공학과, | - |
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