Flow boiling heat transfer analysis of Al2O3 and TiO2 nanofluids in horizontal tube using artificial neural network (ANN)

Abstract

A nanofluid is a suspension of nanometer-sized particles in a base fluid. In the last decade, flow boiling of nanofluid has gained much attention. However, only a few correlations on flow boiling are available. In this paper, an experimental study for HTC (heat transfer coefficient) of water-based TiO2 and Al2O3 nanofluids flowing in an annulus has been carried out at 1 bar. The volumetric concentration of the nanofluid was varied from 0.05 to 0.20%, and heat flux and the mass flux were varied from 6.25 to 143.2 kW m−2 and 338 to 1014 kg m−2 s−1, respectively. It was observed that HTC for both the nanofluids was greater than that of the base fluid water, and it increased with increase in the concentration of the nanoparticles, the heat flux and the mass flux. The highest HTC was obtained for Al2O3 nanofluid at 0.20% concentration for the heat flux of 143.2 kW m−2 and mass flux of 1014 kg m−2 s−1. It was found that nanofluid made from Al2O3 nanoparticles had better HTC than nanofluid made from TiO2 nanoparticles. The HTC ratios, i.e., the ratio of HTC of the nanofluid to the HTC of the base fluid, also increased with the increase in concentration, heat flux and mass flux. In the later part of the paper, new correlations were developed for predicting HTC for TiO2 and Al2O3 nanofluids. Finally, an ANN model was developed to predict the heat transfer coefficient. Experimental values were found to be in good agreement with ANN predictions.