Dynamic viscosity of Titania nanotubes dispersions in ethylene glycol/water-based nanofluids: Experimental evaluation and predictions from empirical correlation and artificial neural network

The emerging applications of nanofluids in heat transfer makes it imperative to study their viscous properties. The knowledge and assessment of physical properties with changes in concentration and temperature are essential for the practical applications of nanofluids. The first part of the current study is the synthesis of Titania (TiO₂) nanotubes via a conventional method. The experimental investigation of viscosity behavior of TiO₂ nanotubes dispersed in ethylene glycol/water-based nanofluid by different process parameters such as the mass concentration of nanotubes (0 to 1%), temperature (25–65 °C) and shear rate (150–500 s⁻¹). The results showed a 30% increase in viscosity at 55 °C by increasing the mass concentration of nanotubes from 0 to 1%, while 22% increase was observed at 25 °C. In the second part, a multivariable correlation, and Artificial Neural Network (ANN) have been used to predict the viscosity at varying temperatures and shear rates based on the experimental data. Statistical analyses were done to investigate the accuracy of both empirical correlation and ANN modeling. It was observed from the results that ANN prediction is highly accurate, with 0.1981 AAD% and 0.999 R² as compared to empirical correlations (2.68 AAD%, 0.9872 R²).