Using Neural Network for Determination of Viscosity in Water-TiO2 Nanofluid

Abstract

Using nanofluids is a novel solution to enhance heat transfer. This study tries to extract the model of viscosity changes in water-TiO2 nanofluid through examining the effect of temperature and volume fraction on the viscosity. Results were recorded and analyzed within temperature range of 25 to 70°C with increments of five for 0.1, 0.4, 0.7, and 1% volume fractions. The obtained results demonstrated that the viscosity of this nanofluid decreases by increasing the temperature and increases by raising the volume fraction. The results show that conventional correlations are unable to properly predict nanofluid viscosity especially at high volume fractions. A model was developed by the data obtained from experiments to estimate viscosity of water-TiO2 nanofluid based on two variables of temperature and volume fraction using neural network. The proposed model was qualified as highly competent for determination of nanofluid viscosity.