Title: Adaptive reduction of random variables using global sensitivity in reliability-based optimisation
Authors: Nam Ho Kim, Haoyu Wang, Nestor V. Queipo
Addresses: Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL 32611, USA. ' Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL 32611, USA. ' Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL 32611, USA; Applied Computing Institute, Faculty of Engineering, University of Zulia, Venezuela
Abstract: This paper presents an efficient shape optimisation technique based on Stochastic Response Surfaces (SRS) and adaptive reduction of random variables using global sensitivity information. The SRS is a polynomial chaos expansion that uses Hermite polynomial bases and provides a closed form solution of the model output from a significantly lower number of model simulations than those required by conventional methods such as the Monte Carlo simulations and Latin Hypercube sampling. Random variables are adaptively fixed before constructing the SRS if their corresponding Global Sensitivity Indices (GSI) calculated using the low-order SRS are below a certain threshold. It has been shown that the GSI can be calculated analytically because the SRS employs the Hermite polynomials as bases. Using SRS and adaptive reduction of random variables, reliability-based optimisation problems are solved with a significant reduction in computational cost. The efficiency and convergence of the proposed approach is demonstrated using a benchmark case and an industrial Reliability-Based Design Optimisation (RBDO) problem.
Keywords: reliability based optimisation; reliability analysis; uncertainty; global sensitivity; sensitivity analysis; stochastic response surfaces; polynomial chaos; design optimisation; random variables; shape optimisation; simulation; benchmarking.
International Journal of Reliability and Safety, 2006 Vol.1 No.1/2, pp.102 - 119
Available online: 19 Aug 2006 *Full-text access for editors Access for subscribers Purchase this article Comment on this article