Predicting the viscosity of multi-walled carbon nanotubes/water nanofluid by developing an optimal artificial neural network based on experimental data
Regarding the viscosity of the fluids which is an imperative parameter for calculating the required pumping power and convective heat transfer, based on experimental data, an optimal artificial neural network was designed to predict the relative viscosity of multi-walled carbon nanotubes/water nanof...
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| Format: | Article |
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Elsevier
2016
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| _version_ | 1825721046147268608 |
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| author | Afrand, M. Ahmadi Nadooshan, A. Hassani, M. Yarmand, H. Dahari, M. |
| author_facet | Afrand, M. Ahmadi Nadooshan, A. Hassani, M. Yarmand, H. Dahari, M. |
| author_sort | Afrand, M. |
| collection | UM |
| description | Regarding the viscosity of the fluids which is an imperative parameter for calculating the required pumping power and convective heat transfer, based on experimental data, an optimal artificial neural network was designed to predict the relative viscosity of multi-walled carbon nanotubes/water nanofluid. Solid volume fraction and temperature were used as input variables and relative viscosity was employed as output variable. Accurate and efficient artificial neural network was obtained by changing the number of neurons in the hidden layer. The dataset was divided into training and test sets which contained 80 and 20% of data points respectively. The results obtained from the optimal artificial neural network exhibited a maximum deviation margin of 0.28%. Eventually, the ANN outputs were compared with results obtained from the previous empirical correlation and experimental data. It was found that the optimal artificial neural network model is more accurate compared to the previous empirical correlation. |
| first_indexed | 2024-03-06T05:44:22Z |
| format | Article |
| id | um.eprints-18077 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:44:22Z |
| publishDate | 2016 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | um.eprints-180772017-10-24T02:13:44Z http://eprints.um.edu.my/18077/ Predicting the viscosity of multi-walled carbon nanotubes/water nanofluid by developing an optimal artificial neural network based on experimental data Afrand, M. Ahmadi Nadooshan, A. Hassani, M. Yarmand, H. Dahari, M. TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering Regarding the viscosity of the fluids which is an imperative parameter for calculating the required pumping power and convective heat transfer, based on experimental data, an optimal artificial neural network was designed to predict the relative viscosity of multi-walled carbon nanotubes/water nanofluid. Solid volume fraction and temperature were used as input variables and relative viscosity was employed as output variable. Accurate and efficient artificial neural network was obtained by changing the number of neurons in the hidden layer. The dataset was divided into training and test sets which contained 80 and 20% of data points respectively. The results obtained from the optimal artificial neural network exhibited a maximum deviation margin of 0.28%. Eventually, the ANN outputs were compared with results obtained from the previous empirical correlation and experimental data. It was found that the optimal artificial neural network model is more accurate compared to the previous empirical correlation. Elsevier 2016 Article PeerReviewed Afrand, M. and Ahmadi Nadooshan, A. and Hassani, M. and Yarmand, H. and Dahari, M. (2016) Predicting the viscosity of multi-walled carbon nanotubes/water nanofluid by developing an optimal artificial neural network based on experimental data. International Communications in Heat and Mass Transfer, 77. pp. 49-53. ISSN 0735-1933, DOI https://doi.org/10.1016/j.icheatmasstransfer.2016.07.008 <https://doi.org/10.1016/j.icheatmasstransfer.2016.07.008>. http://dx.doi.org/10.1016/j.icheatmasstransfer.2016.07.008 doi:10.1016/j.icheatmasstransfer.2016.07.008 |
| spellingShingle | TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering Afrand, M. Ahmadi Nadooshan, A. Hassani, M. Yarmand, H. Dahari, M. Predicting the viscosity of multi-walled carbon nanotubes/water nanofluid by developing an optimal artificial neural network based on experimental data |
| title | Predicting the viscosity of multi-walled carbon nanotubes/water nanofluid by developing an optimal artificial neural network based on experimental data |
| title_full | Predicting the viscosity of multi-walled carbon nanotubes/water nanofluid by developing an optimal artificial neural network based on experimental data |
| title_fullStr | Predicting the viscosity of multi-walled carbon nanotubes/water nanofluid by developing an optimal artificial neural network based on experimental data |
| title_full_unstemmed | Predicting the viscosity of multi-walled carbon nanotubes/water nanofluid by developing an optimal artificial neural network based on experimental data |
| title_short | Predicting the viscosity of multi-walled carbon nanotubes/water nanofluid by developing an optimal artificial neural network based on experimental data |
| title_sort | predicting the viscosity of multi walled carbon nanotubes water nanofluid by developing an optimal artificial neural network based on experimental data |
| topic | TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering |
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