Publisher : The Korean Society of Mechanical Engineers
DOI : 10.3795/KSME-A.2016.40.6.587
Title & Authors
An Efficient Constraint Boundary Sampling Method for Sequential RBDO Using Kriging Surrogate Model Kim, Jihoon; Jang, Junyong; Kim, Shinyu; Lee, Tae Hee; Cho, Su-gil; Kim, Hyung Woo; Hong, Sup;
Reliability-based design optimization (RBDO) requires a high computational cost owing to its reliability analysis. A surrogate model is introduced to reduce the computational cost in RBDO. The accuracy of the reliability depends on the accuracy of the surrogate model of constraint boundaries in the surrogated-model-based RBDO. In earlier researches, constraint boundary sampling (CBS) was proposed to approximate accurately the boundaries of constraints by locating sample points on the boundaries of constraints. However, because CBS uses sample points on all constraint boundaries, it creates superfluous sample points. In this paper, efficient constraint boundary sampling (ECBS) is proposed to enhance the efficiency of CBS. ECBS uses the statistical information of a kriging surrogate model to locate sample points on or near the RBDO solution. The efficiency of ECBS is verified by mathematical examples.
Jin, R., Chen, W. and Sudjianto, A., 2002, "On Sequential Sampling for Global Metamodeling for in Engineering Design," Proceedings ASME 2002 Design Engineering Technical Conferences and Computer and Information in Engineering Conference, Vol. 2, pp. 1-10.
Crombecq, K., Laermans, E. and Dhaene, T., 2011, "Efficient Space-filling and Non-collapsing Sequential Design Strategies for Simulation-based Modeling," European Journal of Operation Research, Vol. 214, No. 3, pp. 683-696.
Lee, T. H. and Jung, J. J., 2008, "A Sampling Technique Enhancing Accuracy and Efficiency of Metamodel-based RBDO: Constraint Boundary Sampling," Computer and Structures, Vol. 86, No. 13, pp. 1463-1476.
Zhenzhong, C., Siping, p., Xiaoke, L., Haobo, Q., Huadi, X., Liang, G. and Peigen, L., 2014, "An Important Boundary Sampling Method for Reliability- based Design Optimization Using Kriging Model," Structural and Multidisciplinary Optimization, Vol. 52, No. 1, pp. 55-70.
Jang, J., Cho, S. and Lee, T. H., 2009, "Weight Function-Based Sequential Maximin Distance Design to Enhance Accuracy and Robustness of Surrogate Model," Trans. Korean Soc. Mech. Eng. A, Vol. 39, No. 4, pp. 369-374.
Cho, S. K., Byun, H. and Lee, T. H., 2009, "Selection Method of Global Model and Correlation Coefficients for Kriging Metamodel," Trans. Korean Soc. Mech. Eng. A, Vol. 33, No. 8, pp. 813-818.
Kushner, H. J., 1962, "Stochastic Model of an Unknown Function," Journal of Mathematical Analysis and Application, Vol. 5, pp. 150-167.
Park, J., 1994, "Optimal Latin-hypercube Designs for Computer Experiments," Journal of Statistical planning and inference, Vol. 37, No. 1, pp. 95-111.
Tu, J. and Choi, K. K., 1999, "A New Study on Reliability Based Design Optimization," Journal of Mechanical Design, Vol. 121, No. 4, pp. 557-564.
Soren, N. L., Hans, B. N. and Jacob, S., "DACE-A MATLAB Kriging Toolbox," Technical Report IMM- TR-2002-12, pp. 1-26.