Stochastic design optimization accounting for structural and distributional design variables
ISSN: 0264-4401
Article publication date: 13 November 2018
Issue publication date: 27 November 2018
Abstract
Purpose
This paper aims to present a new method, named as augmented polynomial dimensional decomposition (PDD) method, for robust design optimization (RDO) and reliability-based design optimization (RBDO) subject to mixed design variables comprising both distributional and structural design variables.
Design/methodology/approach
The method involves a new augmented PDD of a high-dimensional stochastic response for statistical moments and reliability analyses; an integration of the augmented PDD, score functions, and finite-difference approximation for calculating the sensitivities of the first two moments and the failure probability with respect to distributional and structural design variables; and standard gradient-based optimization algorithms.
Findings
New closed-form formulae are presented for the design sensitivities of moments that are simultaneously determined along with the moments. A finite-difference approximation integrated with the embedded Monte Carlo simulation of the augmented PDD is put forward for design sensitivities of the failure probability.
Originality/value
In conjunction with the multi-point, single-step design process, the new method provides an efficient means to solve a general stochastic design problem entailing mixed design variables with a large design space. Numerical results, including a three-hole bracket design, indicate that the proposed methods provide accurate and computationally efficient sensitivity estimates and optimal solutions for RDO and RBDO problems.
Keywords
Citation
Ren, X. and Rahman, S. (2018), "Stochastic design optimization accounting for structural and distributional design variables", Engineering Computations, Vol. 35 No. 8, pp. 2654-2695. https://doi.org/10.1108/EC-10-2017-0409
Publisher
:Emerald Publishing Limited
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