dc.contributor.author | Rajan, A. | |
dc.contributor.author | Ooi, Melanie | |
dc.contributor.author | Kuang, Y.C. | |
dc.contributor.author | Demidenko, S. | |
dc.date.accessioned | 2017-06-06T01:24:43Z | |
dc.date.available | 2017-06-06T01:24:43Z | |
dc.date.issued | 2016-10-17 | |
dc.identifier.issn | 2051-3305 | |
dc.identifier.uri | https://hdl.handle.net/10652/3784 | |
dc.description.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 | en_NZ |
dc.language.iso | en | en_NZ |
dc.publisher | Institution of Engineering and Technology (IET) | en_NZ |
dc.relation.uri | http://digital-library.theiet.org/content/journals/10.1049/joe.2016.0264 | en_NZ |
dc.rights | This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/ | en_NZ |
dc.subject | systems design | en_NZ |
dc.subject | univariate dimension reduction method (DRM) | en_NZ |
dc.subject | reliability-based robust design optimisation (RBRDO). | en_NZ |
dc.subject | vehicle design | en_NZ |
dc.title | Efficient analytical moments for the robustness analysis in design optimisation | en_NZ |
dc.type | Journal Article | en_NZ |
dc.date.updated | 2017-05-10T05:40:07Z | |
dc.rights.holder | Institution of Engineering and Technology (IET) | en_NZ |
dc.identifier.doi | doi: 10.1049/joe.2016.0264 | en_NZ |
dc.subject.marsden | 120404 Engineering Systems Design | en_NZ |
dc.identifier.bibliographicCitation | Rajan, A., Ooi, M. P-L., Kuang, Y. C., & Demidenko, S. (2016). Efficient Analytical Moments for the Robustness Analysis in Design Optimisation. IET Journal of Engineering, 1(1), pp.17. doi: 10.1049/joe.2016.0264 | en_NZ |
unitec.publication.spage | 17 | en_NZ |
unitec.publication.lpage | 8 | en_NZ |
unitec.publication.volume | 1 | en_NZ |
unitec.publication.issue | 1 | en_NZ |
unitec.publication.title | Journal of Engineering (IET) | en_NZ |
unitec.peerreviewed | yes | en_NZ |
dc.contributor.affiliation | Unitec Institute of Technology | en_NZ |
unitec.identifier.roms | 60032 | en_NZ |
unitec.publication.place | London | en_NZ |
unitec.institution.studyarea | Construction + Engineering | |