Prediction of convective heat transfer of Al2O3-water nanofluid considering particle migration using neural network

Abstract

Purpose – The purpose of this paper is to attempt to investigate the particle migration effects on nanofluid heat transfer considering Brownian and thermophoretic forces. It also tries to develop a model for prediction of the convective heat transfer coefficient. Design/methodology/approach – A modified form of the single-phase approach was used in which an equation for mass conservation of particles, proposed by Buongiorno, has been added to the other conservation equations. Due to the importance of temperature in particle migration, temperature-dependent properties were applied. In addition, neural network was used to predict the convective heat transfer coefficient. Findings – At greater volume fractions, the effect of wall heat flux change was more significant on nanofluid heat transfer coefficient, whereas this effect decreased at higher Reynolds numbers. The average convective heat transfer coefficient raised by increasing the Reynolds number and volume fraction. Considering the particle migration effects, higher heat transfer coefficient was obtained and also the concentration at the tube center was higher in comparison with the wall vicinity. Furthermore, the proposed neural network model predicted the heat transfer coefficient with great accuracy. Originality/value – A review of the literature shows that in the single-phase approach, uniform concentration distribution has been used and the effects of particle migration have not been considered. In this study, nanofluid heat transfer was simulated by adding an equation to the conservation equations to consider particle migration. The effects of Brownian and thermophoretic forces have been considered in the energy equation. Moreover, a model is proposed for prediction of convective heat transfer coefficient.