Journal article
Stochastic design optimization accounting for structural and distributional design variables
Engineering computations, Vol.35(8), pp.2654-2695
11/05/2018
DOI: 10.1108/EC-10-2017-0409
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.
Details
- Title: Subtitle
- Stochastic design optimization accounting for structural and distributional design variables
- Creators
- Xuchun Ren - Georgia Southern UniversitySharif Rahman - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Engineering computations, Vol.35(8), pp.2654-2695
- DOI
- 10.1108/EC-10-2017-0409
- ISSN
- 0264-4401
- eISSN
- 1758-7077
- Language
- English
- Date published
- 11/05/2018
- Academic Unit
- Mechanical Engineering
- Record Identifier
- 9984196524402771
Metrics
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