Modeling of viscosity of composite of TiO₂–Al₂O₃ and ethylene glycol nanofluid by artificial neural network: experimental correlation

The viscosity of TiO₂-Al₂O₃/EG composite nanofluid was examined using a correlation fitted from experiment and an artificial neural network approach. Using a 15 and 13 nm nominal surface-area weighted diameters sized TiO₂ and Al₂O₃ nanoparticle, respectively, the two-step method was utilized to formulate TiO₂-Al₂O₃/EG nanofluid at 0.04, 0.06, 0.07, 0.08, 0.13, 0.19 and 0.24% volume fractions. The viscosity of the composite as well as the individual nanofluids was experimentally examined. Also, at 0.03% volume fraction, the viscosities of the nanofluids were determined at varied temperature range of 303 to 373 K. Thereafter, theoretical correlation centered on experimental data was developed. Also, a multilayer perceptron neural network was used in predicting the composite nanofluid viscosity as a dependence on nanoparticle volume fraction and temperature. Experimental outcomes show that the nanofluids viscosity increase with increase in nanoparticle volume fraction and decrease with increase in temperature. The mean coefficient of determination relative error for the Levenberg–Marquardt algorithm and the proposed correlation were 0.1% and 4.8%, respectively when compared to the experimental result. This study reveals that the proposed correlation and the artificial neural network have a high predicting ability for the nano-composite viscosity with minimal relative error.