Generating minimal Pareto sets in multi-objective topology optimisation: an application to the wing box structural layout

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dc.contributor.authorCrescenti, Fabio
dc.contributor.authorKipouros, Timoleon
dc.contributor.authorMunk, David J.
dc.contributor.authorSavill, Mark A.
dc.date.accessioned2020-10-28T13:37:37Z
dc.date.available2020-10-28T13:37:37Z
dc.date.issued2020-10-26
dc.description.abstractMulti-objective topology optimisation problems are often tackled by compromising the cost functions according to the designer’s knowledge. Such an approach however has clear limitations and usually requires information which especially at the preliminary design stage could be unavailable. This paper proposes an alternative multi-objective approach for the generation of minimal Pareto sets in combination with density-based topology optimisation. Optimised solutions are generated integrating a recently revised method for a posteriori articulation of preferences with the Method of Moving Asymptotes. The methodology is first tested on an academic two-dimensional structure and eventually employed to optimise a full-scale aerospace structure with the support of the commercial software Altair OptiStructⓇ. For the academic benchmark, the optimised layouts with respect to static and dynamic objectives are visualised on the Pareto frontier and reported with the corresponding density distribution. Results show a progressive and consistent transition between the two extreme single-objective layouts and confirm that the minimum number of evaluations was required to fill the smart Pareto front. The multi-objective strategy is then coupled with Altair OptiStruct and used to optimise a full-scale wing box, with the clear purpose to fill a gap in multi-objective topology optimisation applied to the wing primary structure. The proposed methodology proved that it can generate efficiently non-dominated optimised configurations, at a computational cost that is mainly driven by the model complexity. This strategy is particularly indicated for the preliminary design phase, as it releases the designer from the burden to assign preferences. Furthermore, the ease of integration into a commercial design tool makes it available for industrial applications.en_UK
dc.identifier.citationCrescenti F, Kipouros T, Munk DJ, Savill MA. (2021) Generating minimal Pareto sets in multi-objective topology optimisation: an application to the wing box structural layout. Structural and Multidisciplinary Optimization, Volume 63, Issue 3, March 2021, pp. 1119-1134en_UK
dc.identifier.issn1615-147X
dc.identifier.urihttps://doi.org/10.1007/s00158-020-02745-7
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/15923
dc.language.isoenen_UK
dc.publisherSpringeren_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectMulti-objectiveen_UK
dc.subjectTopology optimisationen_UK
dc.subjectSIMP methoden_UK
dc.subjectSmart Normal Constraint methoden_UK
dc.subjectWing designen_UK
dc.titleGenerating minimal Pareto sets in multi-objective topology optimisation: an application to the wing box structural layouten_UK
dc.typeArticleen_UK

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