Experimental investigation and model development for effective viscosity of Al2O3-glycerol nanofluids by using dimensional analysis and GMDH-NN methods

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.