Estimation of heat transfer parameters of shell and helically coiled tube heat exchangers by machine learning

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dc.contributor.authorÇolak, Andaç Batur
dc.contributor.authorAkgül, Doğan
dc.contributor.authorMercan, Hatice
dc.contributor.authorDalkılıç, Ahmet Selim
dc.contributor.authorWongwises, Somchai
dc.date.accessioned2023-05-05T10:48:24Z
dc.date.available2023-05-05T10:48:24Z
dc.date.issued2023en_US
dc.departmentRektörlük, Bilişim Teknolojileri Uygulama ve Araştırma Merkezien_US
dc.description.abstractShell and helically coiled tube heat exchangers (SHCTHEXs) are heat exchangers that only take up a small space and enable greater heat transfer area compared to traditional models. Information on 21 different SHCTHEXs obtained from catalog was considered for the modeling. Two other artificial neural network structures have been created to forecast the heat transfer coefficient, pressure drop, Nusselt number, and performance evaluation criteria values as outputs. In contrast, tubing and coil diameters, Reynolds and Dean numbers, curvature ratio, and mass flow rate are designed as inputs. In the network structures with 105 data points, 70% of the data was used for training, 15% for validation, and 15% for the testing stages. The Levenberg-Marquardt procedure was evaluated as the training algorithm in multi-layer perceptron network models. The coefficient of determination was as higher than 0.99. The mean deviation was less than 0.01%. The results show that the created artificial neural network structures can acqurately estimate the outputs.en_US
dc.identifier.doi10.1016/j.csite.2023.102713en_US
dc.identifier.scopus2-s2.0-85147735861en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11467/6591
dc.identifier.urihttps://doi.org/10.1016/j.csite.2023.102713
dc.identifier.volume42en_US
dc.identifier.wosWOS:000922640700001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofCase Studies in Thermal Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectANN, MLP, Levenberg-Marquardt, Heat exchanger, Helically coiled tubeen_US
dc.titleEstimation of heat transfer parameters of shell and helically coiled tube heat exchangers by machine learningen_US
dc.typeArticleen_US

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