Experimental investigation and artificial neural networks (ANNs) based prediction of thermal performance of solar air heaters with different surface geometry

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dc.contributor.authorCerci, Kamil Neyfel
dc.contributor.authorSaydam, Dogan Burak
dc.contributor.authorHurdogan, Ertac
dc.contributor.authorOzalp, Coskun
dc.date.accessioned2025-03-17T12:25:56Z
dc.date.available2025-03-17T12:25:56Z
dc.date.issued2024
dc.departmentTarsus Üniversitesi
dc.description.abstractSolar air heaters (SAHs) are one of the most utilized tools to obtain heat energy from the sun. A variety of SAH models exist with different geometries used for improving the heat transfer between the absorber plate and the air in the SAHs. In today ' s world, many researchers are focusing on designs that occupy the same dimensions but can generate more useful energy. In this study, two SAH with the same external volume (same base area and height), but different absorber surface geometries: a V -corrugated type (V -Type) and an internal baffle -installed type (B -Type), were designed, manufactured and experimentally tested in the climatic conditions of Osmaniye, T & uuml;rkiye. B -Type SAH, unlike the literature, is constructed with 11 inner baffles to allow the air to circulate more within the collector, aiming to achieve higher temperatures. The performances of the SAHs were compared by means of energy, exergy and enviro-economic (3E) analyses in the experiments lasting four consecutive days without interruption. The results show that the average amount of useful heat, energy efficiency ( eta I ) and exergy efficiency ( eta II ) of the V -Type SAH was 35.71%, 38.00% and 80.11% higher, respectively, than those obtained with the B -Type SAH, and also that the V -Type SAH was more environmentally friendly than the B -Type SAH. Additionally, in this study, common ANN models predicting the performance parameters of both SAHs were developed, constituting the another novelty of the research. Among the common ANN models developed for the outlet temperature ( T o ) , besides the eta I and eta II parameters, the best results were obtained with ANN 15, ANN 13 and ANN 18, respectively. Therefore, it is possible to use these developed models safely.
dc.identifier.doi10.1016/j.solener.2024.112499
dc.identifier.issn0038-092X
dc.identifier.issn1471-1257
dc.identifier.scopus2-s2.0-85189619861
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.solener.2024.112499
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1947
dc.identifier.volume273
dc.identifier.wosWOS:001227026200001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofSolar Energy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250316
dc.subjectSolar air heater
dc.subjectEnergy
dc.subjectExergy
dc.subjectEnviro-economic
dc.subjectArtificial neural networks
dc.titleExperimental investigation and artificial neural networks (ANNs) based prediction of thermal performance of solar air heaters with different surface geometry
dc.typeArticle

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