Experimental study on thermal conductivity of water-based Fe3O4 nanofluid: Development of a new correlation and modeled by artificial neural network

Abstract

In this paper, the thermal conductivity of Fe3O4 magnetic nanofluids has been investigated experimentally. The nanofluid samples were prepared using a two-step method by dispersing Fe3O4 nanoparticles into the water with the solid volume fractions of 0.1%, 0.2%, 0.4%, 1%, 2% and 3%. Thermal conductivity measurements were performed by employing a KD2 Pro thermal properties analyser under temperatures ranging from 20°C to 55°C. Then, using experimental data, a new correlation was proposed to predict the thermal conductivity ratio of the magnetic nanofluid. Finally, an optimal artificial neural network was designed to predict the thermal conductivity ratio of the magnetic nanofluid. The experimental results indicated that the maximum enhancement of thermal conductivity of nanofluid was about 90%, which occurred at solid volume fraction of 3.0% and temperature of 55°C. The comparative results showed that there are deviations of 5% and 1.5%, respectively, for correlation and ANN from the experimental data. It was found from comparisons that the optimal artificial neural network model is more accurate compared to empirical correlation.