NDLI: A Maximum Confidence Enhancement Based Sequential Sampling Scheme for Simulation-Based Design

Content Provider	The American Society of Mechanical Engineers (ASME) Digital Collection
Author	Wang, Zequn Wang, Pingfeng
Copyright Year	2013
Abstract	This paper presents a maximum confidence enhancement based sequential sampling approach for simulation-based design under uncertainty. In the proposed approach, the ordinary Kriging method is adopted to construct surrogate models for all constraints and thus Monte Carlo simulation (MCS) is able to be used to estimate reliability and its sensitivity with respect to design variables. A cumulative confidence level is defined to quantify the accuracy of reliability estimation using MCS based on the Kriging models. To improve the efficiency of proposed approach, a maximum confidence enhancement based sequential sampling scheme is developed to update the Kriging models based on the maximum improvement of the defined cumulative confidence level, in which a sample that produces the largest improvement of the cumulative confidence level is selected to update the surrogate models. Moreover, a new design sensitivity estimation approach based upon constructed Kriging models is developed to estimate the reliability sensitivity information with respect to design variables without incurring any extra function evaluations. This enables to compute smooth sensitivity values and thus greatly enhances the efficiency and robustness of the design optimization process. Two case studies are used to demonstrate the proposed methodology.
Sponsorship	Design Engineering Division Computers and Information in Engineering Division
File Format	PDF
ISBN	9780791855898
DOI	10.1115/DETC2013-12608
Volume Number	Volume 3B: 39th Design Automation Conference
Conference Proceedings	ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
Language	English
Publisher Date	2013-08-04
Publisher Place	Portland, Oregon, USA
Access Restriction	Subscribed
Subject Keyword	Design Robustness Simulation Reliability Design under uncertainty Optimization
Content Type	Text
Resource Type	Article

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