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Predication of entropy generation rate in a concentrating photovoltaic thermal system with twisted tube turbulator using Boosted regression tree algorithm

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dc.citedby5
dc.contributor.authorWang G.en_US
dc.contributor.authorPaw J.K.S.en_US
dc.contributor.authorPasupuleti J.en_US
dc.contributor.authorYaw C.T.en_US
dc.contributor.authorYusaf T.en_US
dc.contributor.authorAbdalla A.N.en_US
dc.contributor.authorCai Y.en_US
dc.contributor.authorid58839436200en_US
dc.contributor.authorid58168727000en_US
dc.contributor.authorid11340187300en_US
dc.contributor.authorid36560884300en_US
dc.contributor.authorid23112065900en_US
dc.contributor.authorid25646071000en_US
dc.contributor.authorid58787406000en_US
dc.date.accessioned2025-03-03T07:48:37Z
dc.date.available2025-03-03T07:48:37Z
dc.date.issued2024
dc.description.abstractEfficient energy conversion and utilization remain paramount in addressing the growing energy demand and environmental concerns. Concentrating photovoltaic thermal (CPVT) systems have emerged as promising solutions by integrating photovoltaic (PV) cells with thermal components for simultaneous electricity and heat generation. In this paper, we propose the application of the Boosted Regression Tree (BRT) algorithm to predict the entropy generation rate in a CPVT system equipped with a perforated twisted tube turbulator. Brief introduction of numerical analysis of local and global rates of frictional (S?fr) and thermal (S?th) irreversibilities in a CPVT system equipped with a perforated twisted tube turbulator. The results approve the efficacy of the BRT algorithm in predicting the entropy generation rate. Through comprehensive simulations and data analysis, we establish a predictive model that considers factors such as solar irradiance, fluid flow rate, tube geometry, and turbulator characteristics. The BRT model exhibits remarkable accuracy in capturing the nuanced interplay of these factors, enabling reliable estimations of entropy generation rate. ? 2023en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo103729
dc.identifier.doi10.1016/j.csite.2023.103729
dc.identifier.scopus2-s2.0-85181073956
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85181073956&doi=10.1016%2fj.csite.2023.103729&partnerID=40&md5=c400b3771de014341cff7b06c6997dfa
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/37203
dc.identifier.volume53
dc.publisherElsevier Ltden_US
dc.relation.ispartofAll Open Access; Gold Open Access; Green Open Access
dc.sourceScopus
dc.sourcetitleCase Studies in Thermal Engineering
dc.subjectEntropy
dc.subjectFlow of fluids
dc.subjectRegression analysis
dc.subjectSolar power generation
dc.subjectTrees (mathematics)
dc.subjectBoosted regression trees
dc.subjectConcentrating photovoltaic
dc.subjectEnergy
dc.subjectEnergy demands
dc.subjectEntropy generation
dc.subjectEntropy generation rate
dc.subjectEnvironmental concerns
dc.subjectPhotovoltaic/thermal systems
dc.subjectRegression tree algorithms
dc.subjectTurbulators
dc.subjectSolar energy
dc.titlePredication of entropy generation rate in a concentrating photovoltaic thermal system with twisted tube turbulator using Boosted regression tree algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication
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