Efficient analytical moments for the robustness analysis in design optimisation

The Journal of Engineering

Arvind Rajan¹  Ye Chow Kuang¹  Serge N. Demidenko²  Melanie Po-Leen Ooi³ 

[1] Advanced Engineering Platform and Department of Electrical and Computer Systems Engineering, School of Engineering, Monash University, Bandar Sunway 47500, Malaysia;School of Engineering and Advanced Technology, Massey University, Private Bag 102904, Auckland 0745, New Zealand;School of Engineering and Physical Sciences, Heriot-Watt University Malaysia, Jalan Venna P5/2, Precinct 5, Putrajaya 62200, Malaysia

关键词: computational cost scalability;    PMI approach;    RBRDO problems;    reliability-based robust design optimisation;    DRM;    system uncertainties;    robustness analysis;    computational efhciency scalability;    univariate dimension reduction method;    performance function;    iterative optimisation procedure;    system design analysis;    performance moment integration approach;    analytical moments;    advanced design optimisation paradigm;    statistical moments;   

DOI  :  10.1049/joe.2016.0264

学科分类：工程和技术（综合）

来源: IET

【 摘 要 】

System uncertainties play a vital role in the robustness (or sensitivity) analysis of system designs. In an iterative procedure such as design optimisation, the robustness analysis that is simultaneously accurate and computationally efficient is essential. Accordingly, the current state-of-the-art techniques such as univariate dimension reduction method (DRM) and performance moment integration (PMI) approach have been developed. They are commonly used to express the sensitivity while utilising the statistical moments of a performance function in an advanced design optimisation paradigm known as the reliability-based robust design optimisation (RBRDO). However, the accuracy and computational efficiency scalability for increasing the problem dimension (i.e. the number of input variables) have not been tested. This study examines the scalability of the above-mentioned pioneering techniques. Additionally, it also introduces a novel analytical method that symbolically calculates the sensitivity of the performance function prior to the iterative optimisation procedure. As a result, it shows a better computational cost scalability when tested on performance functions with increased dimensionality. Most importantly, when applied to real-world RBRDO problems such as the vehicle side impact crashworthiness, the proposed technique is three times faster than the mainstream method while yielding a high quality and safe vehicle design.

【 授权许可】

CC BY