Measurement of the dynamic viscosity of hybrid engine oil-Cuo-MWCNT nanofluid, development of a practical viscosity correlation and utilizing the artificial neural network

Abstract

The main objectives of this study have been measurement of the dynamic viscosity of CuO–MWCNTs/SAE 5w–50 hybrid nanofluid, utilization of artificial neural networks (ANN) and development of a new viscosity model. The new nanofluid has been prepared by a two-stage procedure with volume fractions of 0.05, 0.1, 0.25, 0.5, 0.75 and 1%. Then, utilizing a Brookfield viscometer, its dynamic viscosity has been measured for temperatures of 5, 15, 25, 35, 45, 55 °C. The experimental results demonstrate that the viscosity increases by increasing the nanoparticles volume fraction and decreases by increasing temperature. Based on the experimental data the maximum and minimum nanofluid viscosity enhancements, when the volume fraction increases from 0.05 to 1, are 35.52% and 12.92% for constant temperatures of 55 and 15 °C, respectively. The higher viscosity of oil engine in higher temperatures is an advantage, thus this result is important. The measured nanofluid viscosity magnitudes in various shear rates show that this hybrid nanofluid is Newtonian. An ANN model has been employed to predict the viscosity of the CuO–MWCNTs/SAE 5w-50 hybrid nanofluid and the results showed that the ANN can estimate the viscosity efficiently and accurately. Eventually, for viscosity estimation a new temperature and volume fraction based third-degree polynomial empirical model has been developed. The comparison shows that this model is in good agreement with the experimental data.