A novel comparative analysis between the experimental and numeric methods on viscosity of zirconium oxide nanofluid: Developing optimal artificial neural network and new mathematical model

Abstract

In this study, the viscosity of five different ZrO2/Water nanofluids of 0.0125%, 0.025%, 0.05%, 0.1% and 0.2% prepared by the two-step method were experimentally investigated. Using the experimental data obtained, a multi-layer perceptron feed-forward back-propagation artificial neural network and a new mathematical correlation have been developed in order to predict the viscosity of ZrO2/Water nanofluid. Experimental results revealed that viscosity decreases with increasing temperature and increases with increasing concentration. The outputs obtained from the developed artificial neural network and the new correlation were compared with the experimental results and analyzed. The results show that the developed artificial neural network can predict the viscosity of ZrO2/Water nanofluid with an average error rate of −0.11%. The new mathematical model developed has been able to calculate the viscosity of ZrO2/Water nanofluid with an error rate of −0.74%.