A maximum confidence enhancement (MCE)-based sequential sampling approach is developed for reliability-based design optimization (RBDO) using surrogate models. The developed approach employs the ordinary Kriging method for surrogate model development and defines a cumulative confidence level (CCL) measure to quantify the accuracy of reliability estimation when Monte Carlo simulation is used based on the developed surrogate model. To improve the computational efficiency, an MCE-based sequential sampling scheme is developed to successively select sample points for surrogate model updating based on the defined CCL measure, in which a sample point that produces the largest CCL improvement will be selected. To integrate the MCE-based sequential sampling approach with RBDO, a new sensitivity analysis approach is developed, enabling smooth design sensitivity information to be accurately estimated based upon the constructed surrogate model without incurring any extra computational costs, thus greatly enhancing the efficiency and robustness of the design process. Two case studies are used to demonstrate the efficacy of the developed approach.
A Maximum Confidence Enhancement Based Sequential Sampling Scheme for Simulation-Based Design
Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received January 29, 2013; final manuscript received November 4, 2013; published online December 11, 2013. Assoc. Editor: David Gorsich.
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Wang, Z., and Wang, P. (December 11, 2013). "A Maximum Confidence Enhancement Based Sequential Sampling Scheme for Simulation-Based Design." ASME. J. Mech. Des. February 2014; 136(2): 021006. https://doi.org/10.1115/1.4026033
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