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dc.contributor.authorRajan, A.
dc.contributor.authorOoi, Melanie
dc.contributor.authorKuang, Y.C.
dc.contributor.authorDemidenko, S.
dc.date.accessioned2017-06-06T01:24:43Z
dc.date.available2017-06-06T01:24:43Z
dc.date.issued2016-10-17
dc.identifier.issn2051-3305
dc.identifier.urihttps://hdl.handle.net/10652/3784
dc.description.abstractSystem 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 designen_NZ
dc.language.isoenen_NZ
dc.publisherInstitution of Engineering and Technology (IET)en_NZ
dc.relation.urihttp://digital-library.theiet.org/content/journals/10.1049/joe.2016.0264en_NZ
dc.rightsThis 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.subjectsystems designen_NZ
dc.subjectunivariate dimension reduction method (DRM)en_NZ
dc.subjectreliability-based robust design optimisation (RBRDO).en_NZ
dc.subjectvehicle designen_NZ
dc.titleEfficient analytical moments for the robustness analysis in design optimisationen_NZ
dc.typeJournal Articleen_NZ
dc.date.updated2017-05-10T05:40:07Z
dc.rights.holderInstitution of Engineering and Technology (IET)en_NZ
dc.identifier.doidoi: 10.1049/joe.2016.0264en_NZ
dc.subject.marsden120404 Engineering Systems Designen_NZ
dc.identifier.bibliographicCitationRajan, 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.2016.0264en_NZ
unitec.publication.spage17en_NZ
unitec.publication.lpage8en_NZ
unitec.publication.volume1en_NZ
unitec.publication.issue1en_NZ
unitec.publication.titleJournal of Engineering (IET)en_NZ
unitec.peerreviewedyesen_NZ
dc.contributor.affiliationUnitec Institute of Technologyen_NZ
unitec.identifier.roms60032en_NZ
unitec.publication.placeLondonen_NZ
unitec.institution.studyareaConstruction + Engineering


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