Numerical investigation and ANN modeling of performance for hexagonal boron Nitride-water nanofluid PVT collectors

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dc.authoridCERCI, KAMIL NEYFEL/0000-0002-3126-707X
dc.authoridGuzelel, Yunus Emre/0000-0002-7122-0241
dc.authoridOLMUS, UMUTCAN/0000-0002-5799-1840
dc.contributor.authorBuyukalaca, Orhan
dc.contributor.authorKilic, Haci Mehmet
dc.contributor.authorOlmus, Umutcan
dc.contributor.authorGuzelel, Yunus Emre
dc.contributor.authorCerci, Kamil Neyfel
dc.date.accessioned2025-03-17T12:25:55Z
dc.date.available2025-03-17T12:25:55Z
dc.date.issued2023
dc.departmentTarsus Üniversitesi
dc.description.abstractIn this study, performance of hexagonal boron nitride (hBN)/water nanofluid used as a coolant in a PVT collector for the first time in the open literature was numerically analyzed based on various input parameters. Numerical analyzes were carried out by varying the flow rate between 14.5 and 43.4 l/h, solar radiation intensity between 200 and 1000 W/m2, hBN nanoparticle volumetric ratio between 0 and 0.22% and nanoparticle diameter between 20 and 80 nm. The results revealed that the thermal efficiency increases up to 0.18 volumetric ratio and then decreases, while the electrical efficiency continuously increases as the volumetric ratio increases. Additionally, an increase in the volumetric ratio leads to an improvement in all exergy parameters. The utilization of 20 nm diameter hBN nanoparticles results in an increase of 0.7%, 3.01%, 2.71%, and 1.80% in electrical, thermal, overall, and exergy efficiency, respectively, in comparison to pure water. In addition to the numerical analysis conducted with hBN/water nanofluid, simulations were also performed for graphene/water nanofluid, which is commonly studied for PVT collectors in the literature, and it was shown that the former exhibits better performance than the latter, albeit to a minimal extent. Finally, two different sets of ANN models were developed to predict five performance parameters of the PVT collector using hBN/water nanofluid. In the first set, each model predicted only one of the five performance parameters, while in the second set, a single ANN model predicted all output parameters. Different numbers of neurons and training functions were tested in the ANN models, and the Feed Forward Backpropagation algorithm was used as the training algorithm for all the models. Additionally, Logsig and Purelin transfer functions were used for the hidden and output layers, respectively. The proposed models were able to successfully reproduce the performance parameters.
dc.description.sponsorshipScientific Research Project Office of ukurova University [FBA-2023-15407]
dc.description.sponsorshipAcknowledgements The research was supported by the Scientific Research Project Office of Cukurova University under contract no: FBA-2023-15407.
dc.identifier.doi10.1016/j.tsep.2023.101997
dc.identifier.issn2451-9049
dc.identifier.scopus2-s2.0-85164214841
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.tsep.2023.101997
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1934
dc.identifier.volume43
dc.identifier.wosWOS:001037512900001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofThermal Science and Engineering Progress
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250316
dc.subjectPhotovoltaic thermal (PVT)
dc.subjecthBN
dc.subjectwater nanofluid
dc.subjectNumerical simulation
dc.subjectArtificial neural network
dc.subjectEnergy and exergy analysis
dc.subjectCFD simulation
dc.titleNumerical investigation and ANN modeling of performance for hexagonal boron Nitride-water nanofluid PVT collectors
dc.typeArticle

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