NDLI: Nonparametric approach for uncertainty-based multidisciplinary design optimization considering limited data

Content Provider	Springer Nature Link
Author	Cho, Su gil Jang, Junyong Kim, Shinyu Park, Sanghyun Lee, Tae Hee Lee, Minuk Choi, Jong Su Kim, Hyung Woo Hong, Sup
Copyright Year	2016
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.
Starting Page	1671
Ending Page	1688
Page Count	18
File Format	PDF
ISSN	1615147X
Journal	Structural and Multidisciplinary Optimization
Volume Number	54
Issue Number	6
e-ISSN	16151488
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2016-07-13
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Akaike information criterion Kolmogorov–Smirnov test Limited data Nonparametric approach Reliability-based design optimization Uncertainty-based multidisciplinary design optimization Theoretical and Applied Mechanics Computational Mathematics and Numerical Analysis Engineering Design
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Computer Graphics and Computer-Aided Design Control and Systems Engineering Computer Science Applications Software

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