Single-Phase Thermal Transport of Nanofluids in a Minichannel

2009 ◽  
Author(s):  
Leyuan Yu ◽  
Dong Liu
Keyword(s):  
Heat Transfer ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Thermal Transport ◽  
Single Phase ◽  
Convective Heat Transfer Coefficient ◽  
Promising Candidate ◽  
Experimental Conditions ◽  
Heat Transfer Fluids ◽  
Inner Diameter

As a promising candidate for heat transfer fluids in advanced cooling technologies, nanofluids have been studied extensively in the past decade. Despite the tremendous research efforts, it is still unclear if and how the presence of dispersed nanoparticles alters the thermal transport and leads to enhanced thermal performance of nanofluids. An experimental investigation was conducted to explore the single-phase forced convection of Al2O3-water nanofluids in a circular minichannel with 1.09 mm inner diameter. The Reynolds number studied ranges from approximately 600 to 2300. The friction factor and convective heat transfer coefficient were measured for nanofluids with volume concentrations of up to 2%. The effects of nanoparticle concentration and flow rate on the local and average heat transfer coefficient as well as Nusselt number are examined. It was found that, once the thermophysical properties of the nanofluids are properly accounted for, the established pressure drop and heat transfer correlations can offer satisfactory predictions of the single-phase thermal transport of nanofluids under the experimental conditions considered in this study.

Two-Phase Heat Transfer Performance of Dielectric Fluid HFE-7100 Within Multiport Microchannel

2008 ◽  
Author(s):  
Lung-Yi Lin ◽  
Yeau-Ren Jeng ◽  
Chi-Chuan Wang
Keyword(s):  
Heat Transfer ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Single Phase ◽  
Heat Transfer Performance ◽  
Convective Heat Transfer Coefficient ◽  
Dielectric Fluid ◽  
Transfer Performance ◽  
Two Phase ◽  
Two Phase Heat Transfer

This study presents convective single-phase and boiling two-phase heat transfer performance of HFE-7100 coolant within multi-port microchannel heat sinks. The corresponding hydraulic diameters are 450 and 237 μm, respectively. For single-phase results, the presence of inlet/outlet locations inevitably gives rise to considerable increase of total pressure drop of a multi-port microchannel heat sink whereas has virtually no detectable influence on overall heat transfer performance provided that the effect of entrance has been accounted for. The convective boiling heat transfer coefficient for the HFE-7100 coolant shows a tremendous drop when vapor quality is above 0.6. For Dh = 450 μm, it is found that the mass flux effect on the convective heat transfer coefficient is rather small.

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.

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