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

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dc.contributor.authorGönül, Alişan
dc.contributor.authorÇolak, Andaç Batur
dc.contributor.authorKayaci, Nurullah
dc.contributor.authorOkbaz, Abdulkerim
dc.contributor.authorDalkilic, Ahmet Selim
dc.date.accessioned2023-02-03T08:28:17Z
dc.date.available2023-02-03T08:28:17Z
dc.date.issued2022en_US
dc.departmentRektörlük, Bilişim Teknolojileri Uygulama ve Araştırma Merkezien_US
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 ±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.en_US
dc.identifier.doi10.1515/kern-2022-0075en_US
dc.identifier.scopus2-s2.0-85146184679en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11467/6190
dc.identifier.urihttps://doi.org/10.1515/kern-2022-0075
dc.identifier.wosWOS:000907641800001en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWalter de Gruyteren_US
dc.relation.ispartofKerntechniken_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectartificial neural network; heat transfer enhancement; Levenberg–Marquardt; machine learning; microchannel; vortex generatoren_US
dc.titlePrediction of heat transfer characteristics in a microchannel with vortex generators by machine learningen_US
dc.typeArticleen_US

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