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dc.contributor.authorDaniels, SJen
dc.contributor.authorRahat, AAMen
dc.contributor.authorEverson, RMen
dc.contributor.authorTabor, GRen
dc.contributor.authorFieldsend, JEen
dc.date.accessioned2018-10-29T09:21:57Z
dc.date.available2018-10-29T09:21:57Z
dc.date.issued2018-08-21en
dc.identifier.isbn9783319992587en
dc.identifier.issn0302-9743en
dc.identifier.urihttp://hdl.handle.net/10026.1/12661
dc.description.abstract

© 2018, Springer Nature Switzerland AG. In many product design and development applications, Computational Fluid Dynamics (CFD) has become a useful tool for analysis. This is particularly because of the accuracy of CFD simulations in predicting the important flow attributes for a given design. On occasions when design optimisation is applied to real-world engineering problems using CFD, the implementation may not be available for examination. As such, in both the CFD and optimisation communities, there is a need for a set of computationally expensive benchmark test problems for design optimisation using CFD. In this paper, we present a suite of three computationally expensive real-world problems observed in different fields of engineering. We have developed Python software capable of automatically constructing geometries from a given decision vector, running appropriate simulations using the CFD code OpenFOAM, and returning the computed objective values. Thus, users may easily evaluate a decision vector and perform optimisation of these design problems using their optimisation methods without developing custom CFD code. For comparison, we provide the objective values for the base geometries and typical computation times for the test cases presented here.

en
dc.format.extent296 - 307en
dc.language.isoenen
dc.titleA suite of computationally expensive shape optimisation problems using computational fluid dynamicsen
dc.typeConference Contribution
plymouth.volume11102 LNCSen
plymouth.journalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en
dc.identifier.doi10.1007/978-3-319-99259-4_24en
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA11 Computer Science and Informatics
dcterms.dateAccepted2018-05-14en
dc.rights.embargodate2019-10-19en
dc.identifier.eissn1611-3349en
dc.rights.embargoperiodNot knownen
rioxxterms.versionofrecord10.1007/978-3-319-99259-4_24en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2018-08-21en
rioxxterms.typeConference Paper/Proceeding/Abstracten


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