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

• Main Authors: Afrand, M., Nazari Najafabadi, K., Sina, N., Safaei, M.R., Kherbeet, A.Sh., Wongwises, S., Dahari, M.
• Format: Article
• Published: Elsevier 2016
• Subjects: TJ Mechanical engineering and machinery; TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.um.edu.my/18078/; http://dx.doi.org/10.1016/j.icheatmasstransfer.2016.05.023

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.