Reliability-based design optimization by adaptive-sparse polynomial dimensional decomposition

2015 ◽  
Vol 53 (3) ◽  
pp. 425-452 ◽  
Author(s):  
Xuchun Ren ◽  
Vaibhav Yadav ◽  
Sharif Rahman
Keyword(s):  
Design Optimization ◽  
Reliability Based Design Optimization ◽  
Reliability Based Design ◽  
Sparse Polynomial ◽  
Dimensional Decomposition ◽  
Polynomial Dimensional Decomposition

Robust Design Optimization by Adaptive-Sparse Polynomial Dimensional Decomposition

2016 ◽  
Author(s):  
Xuchun Ren ◽  
Sharif Rahman
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
Large Scale ◽  
Design Sensitivity ◽  
Robust Design Optimization ◽  
Score Functions ◽  
Design Sensitivities ◽  
Sparse Polynomial ◽  
Dimensional Decomposition ◽  
Polynomial Dimensional Decomposition

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.

Sign in / Sign up

Export Citation Format

Share Document