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A Comparative Study of Multiple Regression and Machine Learning Techniques for Prediction of Nanofluid Heat Transfer

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dc.contributor.author Koçak, Eyüp
dc.contributor.author Aylı, Ece
dc.contributor.author Türkoğlu, Haşmet
dc.date.accessioned 2023-11-24T11:44:00Z
dc.date.available 2023-11-24T11:44:00Z
dc.date.issued 2022
dc.identifier.citation Koçak, Eyüp; Aylı, Ece; Türkoğlu, Haşmet (2022). "A Comparative Study of Multiple Regression and Machine Learning Techniques for Prediction of Nanofluid Heat Transfer", JOURNAL OF THERMAL SCIENCE AND ENGINEERING APPLICATIONS, Vol. 14, No. 6. tr_TR
dc.identifier.issn 1948-5085
dc.identifier.uri http://hdl.handle.net/20.500.12416/6632
dc.description.abstract The aim of this article is to introduce and discuss prediction power of the multiple regression technique, artificial neural network (ANN), and adaptive neuro-fuzzy interface system (ANFIS) methods for predicting the forced convection heat transfer characteristics of a turbulent nanofluid flow in a pipe. Water and Al2O3 mixture is used as the nanofluid. Utilizing fluent software, numerical computations were performed with volume fraction ranging between 0.3% and 5%, particle diameter ranging between 20 and 140 nm, and Reynolds number ranging between 7000 and 21,000. Based on the computationally obtained results, a correlation is developed for the Nusselt number using the multiple regression method. Also, based on the computational fluid dynamics results, different ANN architectures with different number of neurons in the hidden layers and several training algorithms (Levenberg-Marquardt, Bayesian regularization, scaled conjugate gradient) are tested to find the best ANN architecture. In addition, ANFIS is also used to predict the Nusselt number. In the ANFIS, number of clusters, exponential factor, and membership function (MF) type are optimized. The results obtained from multiple regression correlation, ANN, and ANFIS were compared. According to the obtained results, ANFIS is a powerful tool with a R-2 of 0.9987 for predictions. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.1115/1.4052344 tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.subject Nanofluid tr_TR
dc.subject ANN tr_TR
dc.subject FCM tr_TR
dc.subject ANFIS tr_TR
dc.subject Empirical Correlation tr_TR
dc.subject Al2O3 tr_TR
dc.subject Forced Convection tr_TR
dc.subject Heat and Mass Transfer tr_TR
dc.subject Thermal Systems tr_TR
dc.title A Comparative Study of Multiple Regression and Machine Learning Techniques for Prediction of Nanofluid Heat Transfer tr_TR
dc.type article tr_TR
dc.relation.journal JOURNAL OF THERMAL SCIENCE AND ENGINEERING APPLICATIONS tr_TR
dc.contributor.authorID 283455 tr_TR
dc.contributor.authorID 265836 tr_TR
dc.contributor.authorID 12941 tr_TR
dc.identifier.volume 14 tr_TR
dc.identifier.issue 6 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Makine Mühendisliği Bölümü tr_TR


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