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

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dc.authoridOKBAZ, ABDULKERIM/0000-0002-8866-6047
dc.authoridGonul, Alisan/0000-0002-6106-2251
dc.authoridDalkilic, Ahmet Selim/0000-0002-5743-3937
dc.authoridKAYACI, NURULLAH/0000-0002-8843-8191
dc.authoridColak, Andac Batur/0000-0001-9297-8134
dc.contributor.authorGonul, Alisan
dc.contributor.authorColak, Andac Batur
dc.contributor.authorKayaci, Nurullah
dc.contributor.authorOkbaz, Abdulkerim
dc.contributor.authorDalkilic, Ahmet Selim
dc.date.accessioned2024-12-24T19:30:07Z
dc.date.available2024-12-24T19:30:07Z
dc.date.issued2023
dc.departmentSiirt Üniversitesi
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.issue1
dc.identifier.scopus2-s2.0-85146184679
dc.identifier.scopusqualityQ3
dc.identifier.startpage80
dc.identifier.urihttps://doi.org/10.1515/kern-2022-0075
dc.identifier.urihttps://hdl.handle.net/20.500.12604/7399
dc.identifier.volume88
dc.identifier.wosWOS:000907641800001
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWalter De Gruyter Gmbh
dc.relation.ispartofKerntechnik
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectartificial neural network
dc.subjectheat transfer enhancement
dc.subjectLevenberg-Marquardt
dc.subjectmachine learning
dc.subjectmicrochannel
dc.subjectvortex generator
dc.titlePrediction of heat transfer characteristics in a microchannel with vortex generators by machine learning
dc.typeArticle

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