Efficient analytical moments for the robustness analysis in design optimisation. Issue 11 (15th November 2016)

• Record Type: Journal Article
• Authors: Rajan, Arvind; Ooi, Melanie Po‐Leen; Kuang, Ye Chow; Demidenko, Serge N.
• Is Part Of: Journal of engineering. Volume 2016:Issue 11(2016)
• Page Start: 423
• Page End: 430
• Publication Date: 2016-11-15
• Subjects: statistical analysis -- optimisation -- iterative methods -- sensitivity analysis -- design engineering; RBRDO problems -- computational cost scalability -- iterative optimisation procedure -- performance function -- computational efhciency scalability -- reliability‐based robust design optimisation -- advanced design optimisation paradigm -- statistical moments -- PMI approach -- performance moment integration approach -- DRM -- univariate dimension reduction method -- system design analysis -- system uncertainties -- robustness analysis -- analytical moments; Engineering -- Periodicals; Engineering; Electronic journals; Periodicals; 620.005
• DOI: 10.1049/joe.2016.0264
• Languages: English
• ISSNs: 2051-3305

Abstract

Abstract : 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.