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
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.
Ren, Xuchun, Sharif Rahman.
"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: 1-10 Charlotte, NC: American Society for Mechanical Engineers.
doi: 10.1115/DETC2016-59691 source: https://doi.org/10.1115/DETC2016-59691 isbn: 978-0-7918-5008-4