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

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• 作者：Masoud Afrand^a ; ^{masoud.afrand@pmc.iaun.ac.ir" class="auth_mail" title="E-mail the corresponding author} ; ^{masoud_afrand@yahoo.com" class="auth_mail" title="E-mail the corresponding author} ; Karim Nazari Najafabadi^a ; Nima Sina^a ; Mohammad Reza Safaei^b ; A.Sh. Kherbeet^c ; Somchai Wongwises^d ; ^{somchai.won@kmutt.ac.th" class="auth_mail" title="E-mail the corresponding author} ; Mahidzal Dahari^e
• 关键词：Nanofluid ; Relative viscosity ; Empirical correlation ; Artificial neural network
• 刊名：International Communications in Heat and Mass Transfer
• 出版年：2016
• 出版时间：August 2016
• 年：2016
• 卷：76
• 期：Complete
• 页码：209-214
• 全文大小：1108 K

文摘

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.