Afrand, M. and Nazari Najafabadi, K. and Sina, N. and Safaei, M.R. and Kherbeet, A.Sh. and Wongwises, S. and Dahari, M. (2016) Prediction of dynamic viscosity of a hybrid nano-lubricant by an optimal artificial neural network. International Communications in Heat and Mass Transfer, 76. pp. 209-214. ISSN 0735-1933, DOI https://doi.org/10.1016/j.icheatmasstransfer.2016.05.023.
Full text not available from this repository.Abstract
In this paper, at first, a new correlation was proposed to predict the relative viscosity of MWCNTs-SiO2/AE40 nano-lubricant using experimental data. Then, considering minimum prediction error, an optimal artificial neural network was designed to predict the relative viscosity of the nano-lubricant. Forty-eight experimental data were used to feed the model. The data set was derived to training, validation and test sets which contained 70%, 15% and 15% of data points, respectively. The correlation outputs showed that there is a deviation margin of 4%. The results obtained from optimal artificial neural network presented a deviation margin of 1.5%. It can be found from comparisons that the optimal artificial neural network model is more accurate compared to empirical correlation.
| Item Type: | Article |
|---|---|
| Funders: | High Impact Research Grant “UM.C/HIR/MOHE/ENG/23”, “Research Chair Grant” National Science and Technology Development Agency (NSTDA), the Thailand Research Fund (TRF) and the National Research University Project (NRU) |
| Uncontrolled Keywords: | Nanofluid; Relative viscosity; Empirical correlation; Artificial neural network |
| Subjects: | T Technology > TJ Mechanical engineering and machinery T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 24 Oct 2017 02:24 |
| Last Modified: | 24 Oct 2017 02:24 |
| URI: | http://eprints.um.edu.my/id/eprint/18078 |
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