Abstract
In this paper, the rheological performance and dynamic viscosity of hybrid nanofluid containing SiO2 and multi-walled carbon nanotubes (MWCNTs) nanoparticles (90:10) with 5W30 engine oil as base fluid is experimentally evaluated under different shear rates (SRs) in the range of 50–1000 rpm. The hybrid nanofluid volume fractions (VFs) and temperatures are considered in the ranges of 0.05–1.00 vol% and 5–65 °C, respectively. It was found that the hybrid nanofluid under study behaves as a non-Newtonian fluid. In addition, the calculated power law index was lower than unity, resulting in pseudoplastic features of hybrid nanofluid in all VFs and temperatures. It was observed that the rise of nanofluid temperature from 5 to 65 °C leads to the dynamic viscosity reduction (a 93% decrease in viscosity was observed in a VF of 0.2%), while the increase of nanofluid VF brings about the dynamic viscosity elevation (By increasing VF from 0.05% to 1% at SR of 800 rpm and temperature of 25 °C, the viscosity increases by 29.21%). Based on measured data, an innovative three-variable correlation was established that can more accurately estimate the experimental data than published correlations in the literature. Moreover, the capabilities of GMDH-type neural network (NN) and response surface methodology (RSM) to predict the relative viscosity of the hybrid nanofluid were evaluated. It was concluded that both NN and RSM approaches have a superior ability to forecast the dynamic viscosity behavior of the corresponding hybrid nanofluid, having R2 values of 0.999656 and 0.9955. Furthermore, the optimization was performed and the best solution for achieving the minimum dynamic viscosity with the maximum desirability (1.00) was obtained. Eventually, the dynamic viscosity sensitivity to changes in VF, temperature, and SR was evaluated. It was observed that the dynamic viscosity sensitivity increases as the nanofluid temperature and concentration increase considering a constant SR of 800 rpm.
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