Optimization assisted divide-combine approach to model cooling of a PV module equipped with TEG by using a trapezoidal shaped hybrid nano-enhanced cooling channel and performance estimation with generalized neural networks

dc.contributor.authorSelimefendigil, Fatih
dc.contributor.authorÖztop, Hakan Fehmi
dc.date.accessioned2026-08-12T18:11:17Z
dc.date.issued2025
dc.departmentFırat Üniversitesi
dc.description.abstractInnovative cooling strategies and efficient thermal management techniques are needed to increase the efficiency of photovoltaic (PV) modules. In the current work, a novel cooling channel method and computational approach is utilized for thermal management of PV module combined with thermoelectric generator (TEG) unit. The method uses an optimization assisted divide-combine computational approach while a trapezoidal wavy cooling channel is utilized. Hybrid nanofluid is used in the cooling channel. Simulations for cooling channel and PV-TEG unit are conducted by using finite element method while COBYLA algorithm is considered for optimization of trapezoidal wavy channel. It is shown that the corrugation amplitude has the largest effect on a trapezoidal wavy channel's cooling effectiveness, while the inclination angle has the least effect. The range of average Nu improvements by adjusting the trapezoidal wavy channel's amplitude, wave number, and inclination are obtained as 36%-42%, 13.5%-15%, and 2.5%-3%. The average PV-cell temperature decreases by approximately 2.7oC to 3.4oC when the cooling channel is connected to the PV-TEG unit. It also decreases by approximately 1oC to 1.3oC when the wave number is changed. The optimum corrugation height (b/H) and inclination (0) for the best cooling performance are found as (b/H, 0)=(0.5, 36) when using 3 waves and (b/H, 0)=(0.5, 13.16) when using 11 waves. The PV-cell temperature drops with optimal channel configurations with wave numbers of 3 and 11 are obtained as 4.3oC and 6oC, respectively, in comparison to the reference cooling channel (flat channel employing only pure fluid). While the PV-TEG unit is coupled with parametric simulation of the cooling channel, generalized neural network models are used to successfully estimate the PV-cell temperature and TEG power. More complex channel assemblies and consideration of multiple PV-TEG combined units can be developed using the proposed optimization-assisted divide-combine methodology.
dc.identifier.doi10.1016/j.ijheatmasstransfer.2025.126757
dc.identifier.issn0017-9310
dc.identifier.issn1879-2189
dc.identifier.scopus2-s2.0-85216935088
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.ijheatmasstransfer.2025.126757
dc.identifier.urihttps://hdl.handle.net/11508/63625
dc.identifier.volume241
dc.identifier.wosWOS:001424422500001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofInternational Journal of Heat and Mass Transfer
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260511
dc.subjectPV-TEG unit
dc.subjectTrapezoidal wavy
dc.subjectCooling channel
dc.subjectOptimization
dc.subjectComputational fluid dynamics
dc.subjectGeneralized neural networks
dc.titleOptimization assisted divide-combine approach to model cooling of a PV module equipped with TEG by using a trapezoidal shaped hybrid nano-enhanced cooling channel and performance estimation with generalized neural networks
dc.typeArticle

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