Prediction of dynamic viscosity of a hybrid nano-lubricant by an optimal artificial neural network
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....
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Published: |
Elsevier
2016
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/18078/ http://dx.doi.org/10.1016/j.icheatmasstransfer.2016.05.023 |
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| Summary: | 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. |
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