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

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 e...

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