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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| Format: | Article |
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Elsevier
2019
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| _version_ | 1825722159281995776 |
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| 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 |
| collection | UM |
| 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. |
| first_indexed | 2024-03-06T06:01:23Z |
| format | Article |
| id | um.eprints-23962 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T06:01:23Z |
| publishDate | 2019 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | 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, DOI https://doi.org/10.1016/j.physa.2019.01.051 <https://doi.org/10.1016/j.physa.2019.01.051>. https://doi.org/10.1016/j.physa.2019.01.051 doi:10.1016/j.physa.2019.01.051 |
| 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 |
| title | 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_short | 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 |
| topic | TJ Mechanical engineering and machinery |
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