4.7 Article

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

Journal

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.icheatmasstransfer.2016.05.023

Keywords

Nanofluid; Relative viscosity; Empirical correlation; Artificial neural network

Funding

  1. High Impact Research Grant, University of Malaya, Malaysia [UM.C/HIR/MOHE/ENG/23]
  2. Faculty of Engineering, University of Malaya, Malaysia
  3. Research Chair Grant National Science and Technology Development Agency (NSTDA)
  4. Thailand Research Fund (TRF)
  5. National Research University Project (NRU)

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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. (C) 2016 Elsevier Ltd. All rights reserved.

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