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Contribution to Book
Robust Design Optimization by Adaptive-Sparse Polynomial Dimensional Decomposition
Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Xuchun Ren, Georgia Southern University
  • Sharif Rahman, University of Iowa
Document Type
Conference Proceeding
Publication Date
8-21-2016
DOI
10.1115/DETC2016-59691
ISBN
978-0-7918-5008-4
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.

Citation Information
Xuchun Ren and Sharif Rahman. "Robust Design Optimization by Adaptive-Sparse Polynomial Dimensional Decomposition" Charlotte, NCProceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (2016) p. 1 - 10
Available at: http://works.bepress.com/xuchun_ren/31/