Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/11021
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dc.contributor.authorÇelebioglu, Kutay-
dc.contributor.authorAyli, Ece-
dc.contributor.authorCetintürk, Huseyin-
dc.contributor.authorTaşcıoglu, Yiğit-
dc.contributor.authorAradağ, Selin-
dc.date.accessioned2024-02-11T17:17:32Z-
dc.date.available2024-02-11T17:17:32Z-
dc.date.issued2024-
dc.identifier.issn0954-4089-
dc.identifier.issn2041-3009-
dc.identifier.urihttps://doi.org/10.1177/09544089231224324-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/11021-
dc.description.abstractIn this study, investigations were conducted using computational fluid dynamics (CFD) to assess the applicability of a Francis-type water turbine within a pipe. The objective of the study is to determine the feasibility of implementing a turbine within a pipe and enhance its performance values within the operating range. The turbine within the pipe occupies significantly less space in hydroelectric power plants since a spiral casing is not used to distribute the flow to stationary vanes. Consequently, production and assembly costs can be reduced. Hence, there is a broad scope for application, particularly in small and medium-scale hydroelectric power plants. According to the results, the efficiency value increases on average by approximately 1.5% compared to conventional design, and it operates with higher efficiencies over a wider flow rate range. In the second part of the study, machine learning was employed for the efficiency prediction of an inline-type turbine. An appropriate Artificial Neural Network (ANN) architecture was initially obtained, with the Bayesian Regularization training algorithm proving to be the best approach for this type of problem. When the suitable ANN architecture was utilized, the prediction was found to be in good agreement with CFD, with an root mean squared error value of 0.194. An R2 value of 0.99631 was achieved with the appropriate ANN architecture.en_US
dc.description.sponsorshipTurkish Ministry of Developmenten_US
dc.description.sponsorshipThe authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Turkish Ministry of Development.en_US
dc.language.isoenen_US
dc.publisherSage Publications Ltden_US
dc.relation.ispartofProceedings of The Institution of Mechanical Engineers Part E-Journal of Process Mechanical Engineeringen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFrancis turbineen_US
dc.subjectinline turbineen_US
dc.subjectCFDen_US
dc.subjectefficiencyen_US
dc.subjecthill charten_US
dc.subjectFrancis Turbineen_US
dc.subjectAnnen_US
dc.subjectModelsen_US
dc.titleExploring the potential of artificial intelligence tools in enhancing the performance of an inline pipe turbineen_US
dc.typeArticleen_US
dc.departmentTOBB ETÜen_US
dc.identifier.wosWOS:001141929100001en_US
dc.identifier.scopus2-s2.0-85182199612en_US
dc.institutionauthor-
dc.identifier.doi10.1177/09544089231224324-
dc.authorscopusid37661052300-
dc.authorscopusid55371892800-
dc.authorscopusid56444345000-
dc.authorscopusid16231633500-
dc.authorscopusid11440423900-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
item.cerifentitytypePublications-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypeArticle-
item.fulltextNo Fulltext-
item.grantfulltextnone-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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