Staff View: Assessment of thermal conductivity enhancement of nano-antifreeze containing single-walled carbon nanotubes: Optimal artificial neural network and curve-fitting

Assessment of thermal conductivity enhancement of nano-antifreeze containing single-walled carbon nanotubes: Optimal artificial neural network and curve-fitting

The neural network is a technique to reduce cost and time that can be a good alternative to practical testing. This technique, which has become more important with the advancement of computer science, can also be used to predict the properties of nanofluids. To prove this claim, in this research, an...

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Main Authors:	Moradikazerouni, Alireza, Hajizadeh, Ahmad, Safaei, Mohammad Reza, Afrand, Masoud, Yarmand, Hooman, Zulkifli, Nurin Wahidah Mohd
Format:	Article
Published:	Elsevier 2019
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://eprints.um.edu.my/23962/ https://doi.org/10.1016/j.physa.2019.01.051
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id	my.um.eprints.23962
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spelling	my.um.eprints.239622020-03-04T03:51:11Z http://eprints.um.edu.my/23962/ Assessment of thermal conductivity enhancement of nano-antifreeze containing single-walled carbon nanotubes: Optimal artificial neural network and curve-fitting Moradikazerouni, Alireza Hajizadeh, Ahmad Safaei, Mohammad Reza Afrand, Masoud Yarmand, Hooman Zulkifli, Nurin Wahidah Mohd TJ Mechanical engineering and machinery The neural network is a technique to reduce cost and time that can be a good alternative to practical testing. This technique, which has become more important with the advancement of computer science, can also be used to predict the properties of nanofluids. To prove this claim, in this research, an optimal artificial neural network (ANN) was designed to evaluation the thermal conductivity enhancement of the SWCNTs/EG-water nanofluid using experimental data. For this goal, reported experimental enhancement for various concentrations and temperatures were employed. 35 measured data obtained from experiments have been applied to utilize ANN modeling. 80% data were chosen for network training and the remaining data were adopted for network testing. Based on the minimum mean square error (MSE), ANN model with two hidden layers and 4 neurons in each layer was selected. In addition, a new correlation was presented for predicting the thermal conductivity enhancement. Comparative results showed ANN model can forecast the thermal conductivity enhancement of nanofluids appropriately. © 2019 Elsevier B.V. Elsevier 2019 Article PeerReviewed Moradikazerouni, Alireza and Hajizadeh, Ahmad and Safaei, Mohammad Reza and Afrand, Masoud and Yarmand, Hooman and Zulkifli, Nurin Wahidah Mohd (2019) Assessment of thermal conductivity enhancement of nano-antifreeze containing single-walled carbon nanotubes: Optimal artificial neural network and curve-fitting. Physica A: Statistical Mechanics and its Applications, 521. pp. 138-145. ISSN 0378-4371 https://doi.org/10.1016/j.physa.2019.01.051 doi:10.1016/j.physa.2019.01.051
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	TJ Mechanical engineering and machinery
spellingShingle	TJ Mechanical engineering and machinery Moradikazerouni, Alireza Hajizadeh, Ahmad Safaei, Mohammad Reza Afrand, Masoud Yarmand, Hooman Zulkifli, Nurin Wahidah Mohd Assessment of thermal conductivity enhancement of nano-antifreeze containing single-walled carbon nanotubes: Optimal artificial neural network and curve-fitting
description	The neural network is a technique to reduce cost and time that can be a good alternative to practical testing. This technique, which has become more important with the advancement of computer science, can also be used to predict the properties of nanofluids. To prove this claim, in this research, an optimal artificial neural network (ANN) was designed to evaluation the thermal conductivity enhancement of the SWCNTs/EG-water nanofluid using experimental data. For this goal, reported experimental enhancement for various concentrations and temperatures were employed. 35 measured data obtained from experiments have been applied to utilize ANN modeling. 80% data were chosen for network training and the remaining data were adopted for network testing. Based on the minimum mean square error (MSE), ANN model with two hidden layers and 4 neurons in each layer was selected. In addition, a new correlation was presented for predicting the thermal conductivity enhancement. Comparative results showed ANN model can forecast the thermal conductivity enhancement of nanofluids appropriately. © 2019 Elsevier B.V.
format	Article
author	Moradikazerouni, Alireza Hajizadeh, Ahmad Safaei, Mohammad Reza Afrand, Masoud Yarmand, Hooman Zulkifli, Nurin Wahidah Mohd
author_facet	Moradikazerouni, Alireza Hajizadeh, Ahmad Safaei, Mohammad Reza Afrand, Masoud Yarmand, Hooman Zulkifli, Nurin Wahidah Mohd
author_sort	Moradikazerouni, Alireza
title	Assessment of thermal conductivity enhancement of nano-antifreeze containing single-walled carbon nanotubes: Optimal artificial neural network and curve-fitting
title_short	Assessment of thermal conductivity enhancement of nano-antifreeze containing single-walled carbon nanotubes: Optimal artificial neural network and curve-fitting
title_full	Assessment of thermal conductivity enhancement of nano-antifreeze containing single-walled carbon nanotubes: Optimal artificial neural network and curve-fitting
title_fullStr	Assessment of thermal conductivity enhancement of nano-antifreeze containing single-walled carbon nanotubes: Optimal artificial neural network and curve-fitting
title_full_unstemmed	Assessment of thermal conductivity enhancement of nano-antifreeze containing single-walled carbon nanotubes: Optimal artificial neural network and curve-fitting
title_sort	assessment of thermal conductivity enhancement of nano-antifreeze containing single-walled carbon nanotubes: optimal artificial neural network and curve-fitting
publisher	Elsevier
publishDate	2019
url	http://eprints.um.edu.my/23962/ https://doi.org/10.1016/j.physa.2019.01.051
_version_	1662755203701538816
score	13.211869

Assessment of thermal conductivity enhancement of nano-antifreeze containing single-walled carbon nanotubes: Optimal artificial neural network and curve-fitting

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