Machine learning approach to predict the heat transfer coefficients pertaining to a radiant cooling system coupled with mixed and forced convection

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dc.authoridColak, Andac Batur/0000-0001-9297-8134
dc.authoridKarakoyun, Yakup/0000-0003-1868-452X
dc.authoridDalkilic, Ahmet Selim/0000-0002-5743-3937
dc.authoridCamci, Muhammet/0000-0003-4283-1307
dc.authoridAcikgoz, Ozgen/0000-0002-0095-829X
dc.contributor.authorAcikgoz, Ozgen
dc.contributor.authorColak, Andac Batur
dc.contributor.authorCamci, Muhammet
dc.contributor.authorKarakoyun, Yakup
dc.contributor.authorDalkilic, Ahmet Selim
dc.date.accessioned2024-11-07T13:32:41Z
dc.date.available2024-11-07T13:32:41Z
dc.date.issued2022
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractMixed convection phenomenon over radiant cooled surfaces with displacement ventilation in living environments is becoming a popular issue due to the airborne viruses and energy economy. Artificial neural networks are one of the machine learning methods that are widely evaluated as an engineering tool. In the current study, heat transfer coefficients for a radiant wall cooling system coupled with mixed and forced convection have been predicted by a machine learning approach. This approach should be noted as a first experimental investigation couple with an artificial neural network analysis in the open sources in which mixed convection systems in real sized living environments is examined. Experimentally obtained heat transfer coefficients have been used in the development of the feed forward back propagation multi-layer perceptron network structure. So as to analyze the impact of the input factors on the prediction performance, two neural network structures with dissimilar input parameters such as various temperatures, velocities, and heat transfer rates have been developed. By means of feed forward back propagation multi-layer perceptron neural network algorithms, convection, radiation, and total heat transfer coefficients have been predicted using the experimentally acquired dataset including 35 data points belonging to the mixed and forced convection conditions. Training, validation, and test data groups include 70%, 15%, and 15% of the dataset, in turn. Training algorithm has been computed via LevenbergMarquardt one with 10 neurons in the hidden layer. The findings obtained from the computational solution have been evaluated as a result of the contrast with the target data with in the +/- 5% deviation band for all heat transfer coefficients. The performance factors have been computed and the estimation precision of the numerical models has been thoroughly examined.
dc.description.sponsorshipYildiz Technical Uni-versity Scientific Research Projects Coordination Department [FBA-2019-3743]
dc.description.sponsorshipThis study has been financially supported by Yildiz Technical Uni-versity Scientific Research Projects Coordination Department, Project Number: FBA-2019-3743. The authors would like to acknowledge that this paper is submitted in partial fulfilment of the requirements for PhD degree at Yildiz Technical University.
dc.identifier.doi10.1016/j.ijthermalsci.2022.107624
dc.identifier.issn1290-0729
dc.identifier.issn1778-4166
dc.identifier.scopus2-s2.0-85127656779
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.ijthermalsci.2022.107624
dc.identifier.urihttps://hdl.handle.net/11480/15554
dc.identifier.volume178
dc.identifier.wosWOS:000805485100002
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier France-Editions Scientifiques Medicales Elsevier
dc.relation.ispartofInternational Journal of Thermal Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectMachine learning
dc.subjectLevenberg-marquardt
dc.subjectMixed convection
dc.subjectForced convection
dc.titleMachine learning approach to predict the heat transfer coefficients pertaining to a radiant cooling system coupled with mixed and forced convection
dc.typeArticle

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