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Numerical and ensemble machine learning-based investigation of the energy and exergy yields of a concentrating photovoltaic thermal device equipped with a perforated twisted tube turbulator

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dc.citedby6
dc.contributor.authorWang G.en_US
dc.contributor.authorHai T.en_US
dc.contributor.authorPaw J.K.S.en_US
dc.contributor.authorPasupuleti J.en_US
dc.contributor.authorAbdalla A.N.en_US
dc.contributor.authorid58839436200en_US
dc.contributor.authorid36350315600en_US
dc.contributor.authorid58168727000en_US
dc.contributor.authorid11340187300en_US
dc.contributor.authorid25646071000en_US
dc.date.accessioned2024-10-14T03:17:35Z
dc.date.available2024-10-14T03:17:35Z
dc.date.issued2023
dc.description.abstractThe current research was carried out with the aim of numerically investigating the effect of employing a perforated twisted tube turbulator on the energy and exergy yields of a concentrating photovoltaic thermal (PVT) device. The simulations were performed in 4 different Reynolds numbers (Re) (i.e. 500, 1000, 1500 and 2000) and considering 4 different twist distance (TD) (i.e. L/25, L/50, L/70, and L/100) for the non-perforated turbulator and three different perforated turbulators (with 1, 2, and 3 holes) with TD = L/100. Among the examined cases, the best and worst performance belonged to the PVT device with perforated turbulator and without a turbulator, respectively. For the PVT device with non-perforated turbulator, the lowest PV panel temperature, the highest water outlet temperature, and the highest energy and exergy efficiencies occurred at the highest Re (i.e. 2000) and the lowest TD (i.e. L/100). Also, it was revealed that among the examined perforated turbulators, the best performance belongs to the turbulator with 3 holes in each pitch. In this case, the temperature of the PV panel, the overall energy efficiency and the overall exergy efficiency of the PVT device are respectively 3 �C lower, 7.43% higher and 3.21% higher than the case without turbulator. As another novelty, a new ensemble machine learning model, namely boosted regression tree (BRT) was developed to simulation of the overall energy and exergy efficiencies based on the Reynolds number and volume fraction. The outcomes revealed the promising accuracy for both targets in terms various statistical metrics. � 2023 Elsevier Ltden_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.enganabound.2023.06.033
dc.identifier.epage765
dc.identifier.scopus2-s2.0-85164670953
dc.identifier.spage754
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85164670953&doi=10.1016%2fj.enganabound.2023.06.033&partnerID=40&md5=cf6b204d2331549b0d0bbc6c04a0ee6a
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/33989
dc.identifier.volume155
dc.pagecount11
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleEngineering Analysis with Boundary Elements
dc.subjectBoosted regression tree
dc.subjectEnergy
dc.subjectExergy
dc.subjectPhotovoltaic thermal collector
dc.subjectSolar energy
dc.subjectTurbulator
dc.subjectEnergy efficiency
dc.subjectMachine learning
dc.subjectPhotovoltaic effects
dc.subjectReynolds number
dc.subjectSolar energy
dc.subjectSolar panels
dc.subjectSolar power generation
dc.subjectBoosted regression trees
dc.subjectConcentrating photovoltaic
dc.subjectEnergy
dc.subjectEnergy and exergy
dc.subjectPhotovoltaic thermal collector
dc.subjectPhotovoltaic thermals
dc.subjectReynold number
dc.subjectThermal collectors
dc.subjectThermal devices
dc.subjectTurbulators
dc.subjectExergy
dc.titleNumerical and ensemble machine learning-based investigation of the energy and exergy yields of a concentrating photovoltaic thermal device equipped with a perforated twisted tube turbulatoren_US
dc.typeArticleen_US
dspace.entity.typePublication
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