Rheological behavior of engine oil based hybrid nanofluid containing MWCNTs and ZnO nanopowders: Experimental analysis, developing a novel correlation, and neural network modeling

In this study, the rheological behavior and dynamic viscosity of 5 W30 engine oil based ZnO-MWCNT (30:70) hybrid nanofluid with various volume fractions (VFs) ranging from 0.05 to 1 vol%, temperatures in the range of 5-55 degrees C, and shear rates altering from 50 to 1000 rpm are experimentally evaluated. The measured viscosity values at different shear rates (SRs) and temperatures revealed that the hybrid nanofluid under study has a nonNewtonian behavior. For example, with increasing SR from 50 to 300 rpm, the maximum viscosity reduction (= 25.6%) occurs at temperature of 5 degrees C and VF of 1%, while with increasing SR from 700 to 1000 rpm, the minimum viscosity reduction (= 7.6%) occurs at temperature of 55 degrees C and VF of 0.05%. Moreover, the power law index is observed less than unity indicating pseudoplastic behavior of hybrid nanofluid under study in all VFs and temperatures. The experimental results show that the increase of the VF of nanoparticles leads to the elevated viscosity. By raising the nanofluid temperature, the viscosity of hybrid nanofluid reduces, while engine oil without additives has a high viscosity at elevated temperatures. As an example, considering constant SR of 300 rpm, the increase of temperature from 5 to 55 degrees C leads to the 78.4% and 85.5% viscosity reduction for the hybrid nanofluid with VF of 0.5% and 0.75%, respectively. In order to predict the experimental data, a three-variable correlation (depending on the temperature, VF of nanoparticles, and SR) and artificial neural network modeling are developed. It is concluded that an excellent agreement exists between the experimental data, correlation results, and predicted data by neural network, however, neural network modeling demonstrated its capability to estimate the viscosity of 5 W30 engine oil based ZnO-MWCNT hybrid nanofluid more precisely rather than the proposed correlation. Hence, the neural network modeling is highly recommended for prediction of viscosity of the hybrid nanofluid under study.