Thermal and rheological behavior of aluminium-doped zinc oxide nanofluids: Experimental study and application of artificial neural network model

Abstract

In this study, the influence of Al-doping concentration on the thermal and rheological characteristics of pristine zinc oxide (ZnO) nanofluids was investigated across a range of temperatures and nanoparticle concentrations. A multilayer perceptron (MLP) artificial neural network (ANN) with an optimized topology was employed to model the thermal conductivity and viscosity of nanofluids. The addition of 0.13 M of Al-doped zinc oxide nanoparticles at 1 % rate led to a remarkable 64 % increase in the thermal conductivity of the base fluid (50:50 ratio water + ethylene glycol mixture). Furthermore, incorporating 1 % of these Al-doped nanoparticles outperformed pure zinc oxide, resulting in a 44 % boost in thermal conductivity. The enhanced heat transfer capabilities of Al-doped zinc oxide were evident across different doping and nanoparticle concentrations. The viscosity of nanofluids increases with an increase in nanoparticle concentration and decreases with an increase in temperature. ZnO and Al-doped ZnO nanoparticles have been prepared and studied their structural, morphological and elemental properties using XRD, SEM and EDS respectively. The predicted thermal and rheological behaviours are in close agreement with the experimental values.