Surrogate-based design optimisation tool for dual-phase fluid driving jet pump apparatus

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dc.contributor.authorMifsud, Darren
dc.contributor.authorVerdin, Patrick
dc.date.accessioned2019-12-05T16:12:16Z
dc.date.available2019-12-05T16:12:16Z
dc.date.issued2019-11-22
dc.description.abstractA comparative study of four well established surrogate models used to predict the non-linear entrainment performance of a dual-phase fluid driving jet pump (JP) apparatus is performed. A JP design flow configuration comprising a dual-phase (air and water) flow driving a secondary gas-air flow, for which no one has ever provided a unique set of design solutions, is described. For the construction of the global approximations (GA), the response surface methodology (RSM), Kriging and the radial basis function artificial neural network (RBFANN), were primarily used. The stacked/ensemble models methodology was integrated in this study, to improve the predictive model results, thus providing accurate GA that facilitate the multi-variable non-linear response design optimisation. An error analysis of all four models along with a multiple model accuracy analysis of each case study were performed. The RSM, Kriging, RBFANN and stacked models formed part of the surrogate-based optimisation, having the entrainment ratio as the main objective function. Optimisation problems were solved by the interior-point algorithm and the genetic algorithm and incurred a hybrid formulation of both algorithms. A total of 60 optimisation problems were formulated and solved with all three approximation models. Results showed that the hybrid formulation having the level-2 ensemble Kriging model performed best, predicting the experimental performance results for all JP models within an error margin of less than 10 % in 90 % of the cases.en_UK
dc.identifier.citationMifsud D, Verdin PG. (2021) Surrogate-based design optimisation tool for dual-phase fluid driving jet pump apparatus. Archives of Computational Methods in Engineering, Volume 28, Issue 1, pp. 53-89en_UK
dc.identifier.issn1134-3060
dc.identifier.urihttps://doi.org/10.1007/s11831-019-09373-5
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/14802
dc.language.isoenen_UK
dc.publisherSpringeren_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectDual-phase jet pumpen_UK
dc.subjectSurrogate modellingen_UK
dc.subjectGlobal approximationsen_UK
dc.subjectGlobal optimisationen_UK
dc.subjectEnsemble modellingen_UK
dc.subjectGenetic algorithmen_UK
dc.subjectGaussian processen_UK
dc.subjectRadial basis functionen_UK
dc.titleSurrogate-based design optimisation tool for dual-phase fluid driving jet pump apparatusen_UK
dc.typeArticleen_UK

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