Stochastic design optimization accounting for structural and distributional design variables. Issue 8 (13th November 2018)

• Record Type: Journal Article
• Authors: Ren, Xuchun; Rahman, Sharif
• Is Part Of: Engineering computations. Volume 35:Issue 8(2018)
• Page Start: 2654
• Page End: 2695
• Publication Date: 2018-11-13
• Subjects: Augmented polynomial dimensional decomposition -- Distributional design variables -- Stochastic design optimization -- Structural design variables; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-10-2017-0409
• Languages: English
• ISSNs: 0264-4401

Abstract

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.