Prediction of dynamic viscosity of a hybrid nano-lubricant by an optimal artificial neural network
Document Type
Article
Publication Date
1-1-2016
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.
Keywords
Nanofluid, Relative viscosity, Empirical correlation, Artificial neural network
Divisions
fac_eng
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)
Publication Title
International Communications in Heat and Mass Transfer
Volume
76
Publisher
Elsevier