Prediction of dynamic viscosity of a hybrid nano-lubricant by an optimal artificial neural network

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

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Official URL: http://dx.doi.org/10.1016/j.icheatmasstransfer.201...

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
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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