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

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Author list: Afrand M., Nazari Najafabadi K., Sina N., Safaei M.R., Kherbeet A., Wongwises S., Dahari M.

Publisher: Elsevier

Publication year: 2016

Journal: International Communications in Heat and Mass Transfer (0735-1933)

Volume number: 76

Start page: 209

End page: 214

Number of pages: 6

ISSN: 0735-1933

eISSN: 1879-0178

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84971673295&doi=10.1016%2fj.icheatmasstransfer.2016.05.023&partnerID=40&md5=acdde3c537031583221427862bc547ce

Languages: English-Great Britain (EN-GB)

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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. ฉ 2016 Elsevier Ltd.

Keywords

Empirical correlation, Relative viscosity