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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.