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Exploring the potential of artificial intelligence tools in enhancing the performance of an inline pipe turbine

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dc.contributor.author Çelebioğlu, Kutay
dc.contributor.author Aylı, Ece
dc.contributor.author Çetintürk, Hüseyin
dc.contributor.author Taşçıoğlu, Yiğit
dc.contributor.author Aradağ, Selin
dc.date.accessioned 2024-05-30T13:07:06Z
dc.date.available 2024-05-30T13:07:06Z
dc.date.issued 2024
dc.identifier.citation Çelebioğlu, Kutay...et al. "Exploring the potential of artificial intelligence tools in enhancing the performance of an inline pipe turbine", Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering. tr_TR
dc.identifier.issn 0954-4089
dc.identifier.uri http://hdl.handle.net/20.500.12416/8459
dc.description.abstract In 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. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.1177/09544089231224324 tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.subject CFD tr_TR
dc.subject Efficiency tr_TR
dc.subject Francis Turbine tr_TR
dc.subject Hill Chart tr_TR
dc.subject Inline Turbine tr_TR
dc.title Exploring the potential of artificial intelligence tools in enhancing the performance of an inline pipe turbine tr_TR
dc.type article tr_TR
dc.relation.journal Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering tr_TR
dc.contributor.authorID 265836 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Makine Mühendisliği Bölümü tr_TR


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