Effect of Particle Migration on Flow and Convective Heat Transfer of Nanofluids Flowing Through a Circular Pipe

2010 ◽  
Vol 132 (6) ◽  
Author(s):  
M. M. Heyhat ◽  
F. Kowsary
Keyword(s):  
Heat Transfer ◽  
Shear Stress ◽  
Heat Transfer Coefficient ◽  
Wall Shear Stress ◽  
Transfer Coefficient ◽  
Convective Heat Transfer ◽  
Convective Heat ◽  
Particle Migration ◽  
Circular Pipe ◽  
Wall Shear

This paper aims to study the effect of particle migration on flow and heat transfer of nanofluids flowing through a circular pipe. To do this, a two-component model proposed by Buongiorno (2006, “Convective Transport in Nanofluids,” ASME J. Heat Transfer, 128, pp. 240–250) was used and a numerical study on laminar flow of alumina-water nanofluid through a constant wall temperature tube was performed. The effects of nonuniform distribution of particles on heat-transfer coefficient and wall shear stress are shown. Obtained results illustrate that by considering the particle migration, the heat-transfer coefficient increases while the wall shear stress decreases, compared with uniform volume fraction. Thus, it can be concluded that the enhancement of the convective heat transfer could not be solely attributed to the enhancement of the effective thermal conductivity, and beside other reasons, which may be listed as this higher enhancement, particle migration is proposed to be an important reason.

Flow and Thermal Fields in a Pendant Droplet Moving on Lyophobic Surface

2010 ◽  
Author(s):  
Basant Singh Sikarwar ◽  
K. Muralidhar ◽  
Sameer Khandekar
Keyword(s):  
Heat Transfer ◽  
Contact Angle ◽  
Reynolds Number ◽  
Shear Stress ◽  
Heat Transfer Coefficient ◽  
Wall Shear Stress ◽  
Transfer Coefficient ◽  
Maximum Shear Stress ◽  
Computational Domain ◽  
Wall Shear

Clusters of liquid drops growing and moving on physically or chemically textured lyophobic surfaces are encountered in drop-wise mode of vapor condensation. As opposed to film-wise condensation, drops permit a large heat transfer coefficient and are hence attractive. However, the temporal sustainability of drop formation on a surface is a challenging task, primarily because the sliding drops eventually leach away the lyophobicity promoter layer. Assuming that there is no chemical reaction between the promoter and the condensing liquid, the wall shear stress (viscous resistance) is the prime parameter for controlling physical leaching. The dynamic shape of individual droplets, as they form and roll/slide on such surfaces, determines the effective shear interaction at the wall. Given a shear stress distribution of an individual droplet, the net effect of droplet ensemble can be determined using the time averaged population density during condensation. In this paper, we solve the Navier-Stokes and the energy equation in three-dimensions on an unstructured tetrahedral grid representing the computational domain corresponding to an isolated pendant droplet sliding on a lyophobic substrate. We correlate the droplet Reynolds number (Re = 10–500, based on droplet hydraulic diameter), contact angle and shape of droplet with wall shear stress and heat transfer coefficient. The simulations presented here are for Prandtl Number (Pr) = 5.8. We see that, both Poiseuille number (Po) and Nusselt number (Nu), increase with increasing the droplet Reynolds number. The maximum shear stress as well as heat transfer occurs at the droplet corners. For a given droplet volume, increasing contact angle decreases the transport coefficients.

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

2014 ◽  
Vol 31 (5) ◽  
pp. 843-863 ◽  
Author(s):  
Mehdi Bahiraei ◽  
Seyed Mostafa Hosseinalipour ◽  
Morteza Hangi
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Convective Heat Transfer ◽  
Convective Heat ◽  
Single Phase ◽  
Convective Heat Transfer Coefficient ◽  
Particle Migration ◽  
Content Type

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.

Sign in / Sign up

Export Citation Format

Share Document