Abstract:
Nanofluids are new heat transfer fluids which aimed to improve the poor heat removal efficiency of
conventional heat transfer fluids. The dispersion of nanoparticles into traditional heat transfer fluids such
as ethylene glycol, glycerol, engine oil, gear oil and water has become widely applicable in engineering
systems because of their superior heat transfer properties. However, viscosity increase due to nanoparticle
dispersion is an issue which needs attention and proper experimental investigation. Therefore, in this
study, it is experimentally optimized the two-step preparation procedure for Al2O3-glycerol nanofluids
consisting of 19, 139 and 160 nm particle sizes, and then studied the effective viscosity between 20-70 oC
for the range of 0 to 5% volume fractions. The nanofluids’ viscosity showed a characteristic increase as
volume fraction increases; decrease as the working temperature increases; and the smallest nanoparticles
showed the highest shear resistance. Based on the available experimental data, an empirical correlation
has been offered using dimensional analysis. Thereafter, a hybrid neural network based on the group
method of data handling (GMDH-NN) has been employed for modeling the effective viscosity of Al2O3-
glycerol nanofluid. The correlations obtained from both modeling procedures showed higher accuracy in
the prediction of the present experimental data when compared to most cited models from the open
literature.
