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.

Robust Design Optimization for Crashworthiness of Vehicle Side Impact

2017 ◽  
Vol 3 (3) ◽  
Author(s):  
Souvik Chakraborty ◽  
Tanmoy Chatterjee ◽  
Rajib Chowdhury ◽  
Sondipon Adhikari
Keyword(s):  
Design Optimization ◽  
New Product Development ◽  
Robust Design ◽  
Automobile Industry ◽  
Human Error ◽  
Differential Evolution Algorithm ◽  
Side Impact ◽  
Robust Design Optimization ◽  
Parameter Uncertainties ◽  
Design Engineers

Optimization for crashworthiness is of vast importance in automobile industry. Recent advancement in computational prowess has enabled researchers and design engineers to address vehicle crashworthiness, resulting in reduction of cost and time for new product development. However, a deterministic optimum design often resides at the boundary of failure domain, leaving little or no room for modeling imperfections, parameter uncertainties, and/or human error. In this study, an operational model-based robust design optimization (RDO) scheme has been developed for designing crashworthiness of vehicle against side impact. Within this framework, differential evolution algorithm (DEA) has been coupled with polynomial correlated function expansion (PCFE). An adaptive framework for determining the optimum basis order in PCFE has also been presented. It is argued that the coupled DEA–PCFE is more efficient and accurate, as compared to conventional techniques. For RDO of vehicle against side impact, minimization of the weight and lower rib deflection of the vehicle are considered to be the primary design objectives. Case studies by providing various emphases on the two objectives have also been performed. For all the cases, DEA–PCFE is found to yield highly accurate results.

Sign in / Sign up

Export Citation Format

Share Document