A Comparative Study of Multiple Regression and Machine Learning Techniques for Prediction of Nanofluid Heat TransferSource: Journal of Thermal Science and Engineering Applications:;2021:;volume( 014 ):;issue: 006::page 61002-1DOI: 10.1115/1.4052344Publisher: The American Society of Mechanical Engineers (ASME)
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 R2 of 0.9987 for predictions.
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contributor author | Kocak, Eyup | |
contributor author | Aylı, Ece | |
contributor author | Turkoglu, Hasmet | |
date accessioned | 2022-05-08T08:49:55Z | |
date available | 2022-05-08T08:49:55Z | |
date copyright | 10/12/2021 12:00:00 AM | |
date issued | 2021 | |
identifier issn | 1948-5085 | |
identifier other | tsea_14_6_061002.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4284397 | |
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 R2 of 0.9987 for predictions. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | A Comparative Study of Multiple Regression and Machine Learning Techniques for Prediction of Nanofluid Heat Transfer | |
type | Journal Paper | |
journal volume | 14 | |
journal issue | 6 | |
journal title | Journal of Thermal Science and Engineering Applications | |
identifier doi | 10.1115/1.4052344 | |
journal fristpage | 61002-1 | |
journal lastpage | 61002-17 | |
page | 17 | |
tree | Journal of Thermal Science and Engineering Applications:;2021:;volume( 014 ):;issue: 006 | |
contenttype | Fulltext |