Artificial neural network approach for investigating the impact of convector design parameters on the heat transfer and total weight of panel radiators

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dc.authoridAydin, Devrim/0000-0002-5292-7567
dc.authoridCalisir, Tamer/0000-0002-0721-0444
dc.authoridDalkilic, Ahmet Selim/0000-0002-5743-3937
dc.authoridColak, Andac Batur/0000-0001-9297-8134
dc.contributor.authorCalisir, Tamer
dc.contributor.authorColak, Andac Batur
dc.contributor.authorAydin, Devrim
dc.contributor.authorDalkilic, Ahmet Selim
dc.contributor.authorBaskaya, Senol
dc.date.accessioned2024-11-07T13:34:32Z
dc.date.available2024-11-07T13:34:32Z
dc.date.issued2023
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractThe difficulty of the production stages of panel radiators used for heating purposes reveals the importance of determining the heat transfer performance and panel radiator weight values, which are determined depending on the design parameters. In the present work, an artificial neural network model is proposed for predicting the heat transfer and weight values of a panel radiator as outputs depending on the design parameters of convectors. In the multilayer network model developed with 78 numerically obtained data sets, 8 different design parameters were defined as input parameters and heat transfer and in the output layer panel weight values were obtained. The design parameters of the convectors, in other words, input parameters of network model were chosen as the height of convector, thickness of convector sheet, the trapezoidal height of convector, convector base length, opposing convector distance, tip width of convector, convector vertical location and distance between convectors. For the proposed neural network model, the mean squared errors obtained for the heat transfer and panel radiator weight are -1.25E-04 and -7.54E-05 respectively. In addition, an R-value of 0.99999 has been obtained, and the average deviation value has been calculated as 0.001%. The obtained results show that, depending on the design parameters, the proposed artificial neural network model can predict the rate of heat transfer and weight of the panel radiator with high accuracy. This investigation is supposed to fill a significant gap since it is the pioneer one in open sources on machine learning modeling of panel radiators. Thus, it can possibly make a crucial contribution to the related manufacturing industry.
dc.description.sponsorshipMinistry of Science, Industry and Technology of Turkey [0641.STZ.2014]
dc.description.sponsorshipMinistry of Science, Industry and Technology of Turkey (Grant No. 0641.STZ.2014) for financial support, and DemirDokum A.S. are thankfully acknowledged.
dc.identifier.doi10.1016/j.ijthermalsci.2022.107845
dc.identifier.issn1290-0729
dc.identifier.issn1778-4166
dc.identifier.scopus2-s2.0-85136324029
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.ijthermalsci.2022.107845
dc.identifier.urihttps://hdl.handle.net/11480/16036
dc.identifier.volume183
dc.identifier.wosWOS:000893097800004
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/openAccess
dc.snmzKA_20241106
dc.subjectPanel radiator
dc.subjectHeat transfer
dc.subjectConvector
dc.subjectArtificial neural network (ANN)
dc.subjectComputational fluid dynamics (CFD)
dc.titleArtificial neural network approach for investigating the impact of convector design parameters on the heat transfer and total weight of panel radiators
dc.typeArticle

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