A NUMERICAL STUDY AIMED AT FINDING OPTIMAL ARTIFICIAL NEURAL NETWORK MODEL COVERING EXPERIMENTALLY OBTAINED HEAT TRANSFER CHARACTERISTICS OF HYDRONIC UNDERFLOOR RADIANT HEATING SYSTEMS RUNNING VARIOUS NANOFLUIDS

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dc.authoridAcikgoz, Ozgen/0000-0002-0095-829X
dc.authoridDalkilic, Ahmet Selim/0000-0002-5743-3937
dc.authoridKarakoyun, Yakup/0000-0003-1868-452X
dc.contributor.authorColak, Andac Batur
dc.contributor.authorKarakoyun, Yakup
dc.contributor.authorAcikgoz, Ozgen
dc.contributor.authorYumurtaci, Zehra
dc.contributor.authorDalkilic, Ahmet Selim
dc.date.accessioned2024-11-07T13:32:40Z
dc.date.available2024-11-07T13:32:40Z
dc.date.issued2022
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractIn this paper, three unique artificial neural network models have been developed for three different working fluid cases to predict the radiative, convective, and total heat transfer coefficients over the floor surface of radiant floor heating system in a real-size room. Pure water, multiwall carbon nanotube with 0.7 vol.% and 0.07 vol.% contents, and aluminium oxide with 1.26 vol.% content are the operating fluids having inlet temperatures ranging from 30 degrees C to 60 degrees C, while the mass flow rates are 0.056, 0.09, and 0.125 kg/s. The performances of multilayer perceptron networks with the Levenberg-Marquardt, Bayesian regularization, and scaled conjugate gradient as training algorithms and different neuron numbers have been developed and the Levenberg-Marquardt algorithm, having the highest prediction performance with 99% accuracy, is selected as a result of detailed computational numerical analyses. This study can be considered as a pioneer artificial neural network one on the floor heating systems having nanofluids.
dc.description.sponsorshipYildiz Technical University Scientific Research Projects Coordination Department [FBA-2019-374]
dc.description.sponsorshipThis study has been financially supported by Yildiz Technical University Scientific Research Projects Coordination Department, Project No. FBA-2019-374.
dc.identifier.endpage71
dc.identifier.issn1064-2285
dc.identifier.issn2162-6561
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85126670982
dc.identifier.scopusqualityQ3
dc.identifier.startpage51
dc.identifier.urihttps://hdl.handle.net/11480/15546
dc.identifier.volume53
dc.identifier.wosWOS:000765025000001
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherBegell House Inc
dc.relation.ispartofHeat Transfer Research
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectradiant floor heating
dc.subjectANN
dc.subjectLevenberg-Marquardt
dc.subjectthermal characteristics
dc.titleA NUMERICAL STUDY AIMED AT FINDING OPTIMAL ARTIFICIAL NEURAL NETWORK MODEL COVERING EXPERIMENTALLY OBTAINED HEAT TRANSFER CHARACTERISTICS OF HYDRONIC UNDERFLOOR RADIANT HEATING SYSTEMS RUNNING VARIOUS NANOFLUIDS
dc.typeArticle

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