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Analysis of heat transfer enhancement of passive methods in tubes with machine learning

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dc.contributor.author Özgirgin Yapıcı, Ekin
dc.contributor.author Aylı, Ece
dc.contributor.author Türkoğlu, Haşmet
dc.date.accessioned 2024-05-28T13:27:45Z
dc.date.available 2024-05-28T13:27:45Z
dc.date.issued 2024-04
dc.identifier.citation Özgirgin Yapıcı, Ekin; Aylı, Ece; Türkoğlu, Haşmet (2024). "Analysis of heat transfer enhancement of passive methods in tubes with machine learning", Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, Vol. 238, No. 8, pp. 3613-3633. tr_TR
dc.identifier.issn 0954-4062
dc.identifier.uri http://hdl.handle.net/20.500.12416/8416
dc.description.abstract This study investigates the efficacy of machine learning techniques and correlation methods for predicting heat transfer performance in a dimpled tube under varying flow conditions, including the presence of nanoparticles. A comprehensive numerical analysis involving 120 cases was conducted to obtain Nusselt numbers and friction factors, considering different dimple depths and velocities for both pure water and water-Al2O3 nanofluid at 1%, 2%, and 3% volume concentrations. Utilizing the data acquired from the numerical simulations, a correlation equation, SVM ANN architectures were developed. The predictive capabilities of the statistical approach, ANN, and SVM models for Nusselt number distribution and friction factor were meticulously assessed through mean average percentage error (MAPE) and correlation coefficients (R2). The research findings reveal that machine learning techniques offer a highly effective approach for accurately predicting heat transfer performance in a dimpled tube, with results closely aligned with Computational Fluid Dynamics (CFD) simulations. Particularly noteworthy is the superior performance of the ANN model, demonstrating the most precise predictions with an error rate of 2.54% and an impressive R2 value of 0.9978 for Nusselt number prediction. In comparison, the regression model achieved an average error rate of 6.14% with an R2 value of 0.8623, and the SVM model yielded an RMSE value of 2.984% with an R2 value of 0.9154 for Nusselt number prediction. These outcomes underscore the ANN model’s ability to effectively capture complex patterns within the data, resulting in highly accurate predictions. In conclusion, this research showcases the promising potential of machine learning techniques in accurately forecasting heat transfer performance in dimpled tubes. The developed ANN model exhibits notable superiority in predicting Nusselt numbers, making it a valuable tool for enhancing thermal system analyses and engineering design optimization. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.1177/09544062231200959 tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.subject ANN tr_TR
dc.subject Computational Analysis tr_TR
dc.subject Dimples tr_TR
dc.subject Heat Transfer Enhancement tr_TR
dc.subject Nanofluid tr_TR
dc.title Analysis of heat transfer enhancement of passive methods in tubes with machine learning tr_TR
dc.type article tr_TR
dc.relation.journal Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science tr_TR
dc.contributor.authorID 31329 tr_TR
dc.contributor.authorID 265836 tr_TR
dc.contributor.authorID 12941 tr_TR
dc.identifier.volume 238 tr_TR
dc.identifier.issue 8 tr_TR
dc.identifier.startpage 3613 tr_TR
dc.identifier.endpage 3633 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Makine Mühendisliği Bölümü tr_TR


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