Prediction of heat transfer characteristics in a microchannel with vortex generators by machine learning

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dc.authoridDalkılıç, Ahmet Selim/0000-0002-5743-3937
dc.authoridÇolak, Andaç Batur/0000-0001-9297-8134
dc.authoridKAYACI, NURULLAH/0000-0002-8843-8191
dc.authoridOKBAZ, ABDULKERIM/0000-0002-8866-6047
dc.authorwosidDalkılıç, Ahmet Selim/G-2274-2011
dc.authorwosidÇolak, Andaç Batur/AAV-3639-2020
dc.authorwosidOKBAZ, Abdulkerim/HZH-8886-2023
dc.authorwosidKAYACI, NURULLAH/GRS-4033-2022
dc.authorwosidOkbaz, Abdulkerim/AAC-9682-2021
dc.contributor.authorGönül, Alişan
dc.contributor.authorÇolak, Andaç Batur
dc.contributor.authorKayacı, Nurullah
dc.contributor.authorOkbaz, Abdülkerim
dc.contributor.authorDalkılıç, Ahmet Selim
dc.date.accessioned2023-05-06T17:20:46Z
dc.date.available2023-05-06T17:20:46Z
dc.date.issued2023
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Makine Mühendisliği Bölümü
dc.description.abstractBecause of the prompt improvements in Micro-Electro-Mechanical Systems, thermal management necessities have altered paying attention to the compactness and high energy consumption of actual electronic devices in industry. In this study, 625 data sets obtained numerically according to the change of five different geometric parameters and Reynolds numbers for delta winglet type vortex generator pairs placed in a microchannel were utilized. Four dissimilar artificial neural network models were established to predict the heat transfer characteristics in a microchannel with innovatively oriented vortex generators in the literature. Friction factor, Nusselt number, and performance evaluation criteria were considered to explore the heat transfer characteristics. Different neuron numbers were determined in the hidden layer of each of the models in which the Levethenberg-Marquardt training algorithm was benefited as the training algorithm. The predicted values were checked against the target data and empirical correlations. The coefficient of determination values calculated for each machine learning model were found to be above 0.99. According to obtained results, the designed artificial neural networks can provide high prediction performance for each data set and have higher prediction accuracy compared to empirical correlations. All data predicted by machine learning models were collected within the range of +/- 3% deviation bands, whereas the majority of the estimated data by empirical correlations dispersed within & PLUSMN;20% ones. For that reason, a full evaluation of the estimation performance of artificial neural networks versus empirical correlations data is enabled to fill a gap in the literature as one of the uncommon works.
dc.identifier.doi10.1515/kern-2022-0075
dc.identifier.endpage99
dc.identifier.issn0932-3902
dc.identifier.issn2195-8580
dc.identifier.issue1en_US
dc.identifier.startpage80
dc.identifier.urihttps://doi.org/10.1515/kern-2022-0075
dc.identifier.urihttps://hdl.handle.net/20.500.11776/11923
dc.identifier.volume88
dc.identifier.wosWOS:000907641800001
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.institutionauthorKayacı, Nurullah
dc.language.isoen
dc.publisherWalter De Gruyter Gmbh
dc.relation.ispartofKerntechnik
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectartificial neural network
dc.subjectheat transfer enhancement
dc.subjectLevenberg-Marquardt
dc.subjectmachine learning
dc.subjectmicrochannel
dc.subjectvortex generator
dc.subjectArtificial Neural-Network
dc.subjectThermal-Conductivity
dc.subjectSingle-Phase
dc.subjectRectangular Microchannel
dc.subjectForced-Convection
dc.subjectLiquid Flow
dc.subjectNanofluid
dc.subjectChannel
dc.subjectModel
dc.subjectSink
dc.titlePrediction of heat transfer characteristics in a microchannel with vortex generators by machine learning
dc.typeArticle

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