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Robust Design Optimization by Adaptive-Sparse Polynomial Dimensional Decomposition
Conference proceeding

Robust Design Optimization by Adaptive-Sparse Polynomial Dimensional Decomposition

Xuchun Ren and Sharif Rahman
Volume 1B: 36th Computers and Information in Engineering Conference, Vol.1
ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Charlotte, North Carolina, USA, Aug. 21 - 24, 2016
08/21/2016
DOI: 10.1115/DETC2016-59691

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Abstract

This work proposes a new methodology for robust design optimization (RDO) of complex engineering systems. The method, capable of solving large-scale RDO problems, involves (1) an adaptive-sparse polynomial dimensional decomposition (AS-PDD) for stochastic moment analysis of a high-dimensional stochastic response, (2) a novel integration of score functions and AS-PDD for design sensitivity analysis, and (3) a multi-point design process, facilitating standard gradient-based optimization algorithms. Closed-form formulae are developed for first two moments and their design sensitivities. The method allow that both the stochastic moments and their design sensitivities can be concurrently determined from a single stochastic simulation or analysis. Precisely for this reason, the multi-point framework of the proposed method affords the ability of solving industrial-scale problems with large design spaces. The robust shape optimization of a three-hole bracket was accomplished, demonstrating the efficiency of the new method to solve industry-scale RDO problems.

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