Flow optimization in a microchannel with vortex generators using genetic algorithm

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dc.authorscopusid56462583800
dc.authorscopusid55256278300
dc.authorscopusid55645106500
dc.authorscopusid57330326600
dc.contributor.authorGönül, A.
dc.contributor.authorOkbaz, A.
dc.contributor.authorKayacı, Nurullah
dc.contributor.authorSelim Dalkılıç, A.
dc.date.accessioned2022-05-11T14:26:52Z
dc.date.available2022-05-11T14:26:52Z
dc.date.issued2022
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Makine Mühendisliği Bölümü
dc.description.abstractIn this study, delta winglet-type vortex generators, widely used in conventional macro channels and proven to be effective, are used in microchannels to increase their heat transfer capacities. The effects of vortex generators on heat transfer and pressure loss characteristics are studied numerically for different angles of attack, vortex generator arrangement type, the transverse and longitudinal distance between vortex generators, vortex generator length and height, and different Reynolds numbers. The thermal and hydraulic characteristics are presented as the Nusselt number, the friction factor, and the performance evaluation criteria number (PEC) that takes into account the heat transfer enhancement and the corresponding increase in pressure loss. The variation of Nu/Nu0, f/f0, and PEC are found to be in the range of 1.03–1.87, 1.04–1.8, and 0.92–1.62, respectively. A multi-objective optimization study are performed with the response surface methodology analysis to see how different parameters affect heat transfer and pressure loss and to determine the most optimum design. Besides, local sensitivity analysis study is carried out through the RSM, and water inlet velocity for heat transfer enhancement is found to be the most effective parameter. Among the geometric parameters, vortex generator height is determined as the most effective factor. Finally, practical Nusselt number and friction factor correlations taking many parameters into account are proposed to be able to compare the results of other researchers, and for engineers designing microchannel cooling systems. © 2021 Elsevier Ltd
dc.description.sponsorshipWO.007–16N; International Association for the Study of Pain, IASP; Horizon 2020 Framework Programme, H2020; H2020 Marie Sk?odowska-Curie Actions, MSCA: 706475
dc.description.sponsorshipThis project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 706475 and was supported by the 2016 Early Research Career Grant of the International Association for the Study of Pain (IASP) of Dimitri Van Ryckeghem. Finally, the manuscript was initiated at the Expert Meeting “Cognitive biases” in Belgium, supported by the International Research Community grant “Pain, Action and INterference (WO.007–16N)”.
dc.identifier.doi10.1016/j.applthermaleng.2021.117738
dc.identifier.issn1359-4311
dc.identifier.scopus2-s2.0-85118854784
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.applthermaleng.2021.117738
dc.identifier.urihttps://hdl.handle.net/20.500.11776/6629
dc.identifier.volume201
dc.indekslendigikaynakScopus
dc.institutionauthorKayacı, Nurullah
dc.language.isoen
dc.publisherElsevier Ltd
dc.relation.ispartofApplied Thermal Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectGenetic algorithm
dc.subjectHeat transfer enhancement
dc.subjectMicrochannel
dc.subjectMulti-objective optimization
dc.subjectVortex generator
dc.subjectAngle of attack
dc.subjectCooling systems
dc.subjectDelta wing aircraft
dc.subjectFriction
dc.subjectGenetic algorithms
dc.subjectHeat transfer coefficients
dc.subjectHeat transfer performance
dc.subjectMultiobjective optimization
dc.subjectNanofluidics
dc.subjectNusselt number
dc.subjectReynolds number
dc.subjectSensitivity analysis
dc.subjectVortex flow
dc.subjectVorticity
dc.subjectDelta winglets
dc.subjectFlow optimization
dc.subjectHeat Transfer enhancement
dc.subjectHeat transfer loss
dc.subjectLoss characteristics
dc.subjectMulti-objectives optimization
dc.subjectPerformance evaluation criteria
dc.subjectPressure loss
dc.subjectTransfer capacities
dc.subjectVortex generators
dc.subjectMicrochannels
dc.titleFlow optimization in a microchannel with vortex generators using genetic algorithm
dc.typeArticle

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