Nanofluids are engineered colloids made of a base fluid and nanoparticles (1-100 nm) Nanofluids have higher thermal conductivity' and single-phase heat transfer coefficients than their base fluids In particular the heat transfer coefficient increases appear to go beyond the mere thermal-conductivity effect, and cannot be predicted by traditional pure-fluid correlations such as Dittus-Boelter's In the nanofluid literature this behavior is generally attributed to thermal dispersion and intensified turbulence, brought about by nanoparticle motion To test the validity of this assumption, we have considered seven slip mechanisms that can produce a relative velocity between the nanoparticles and the base fluid These are inertia, Brownian diffusion, thermophoresis, diffusioplwresis, Magnus effect, fluid drainage, and gravity We concluded that, of these seven, only Brownian diffusion and thermophoresis are important slip mechanisms in nanofluids Based on this finding, we developed a two-component four-equation nonhomogeneous equilibrium model for mass, momentum, and heat transport in nanofluids A nondimensional analysis of the equations suggests that energy transfer by nanoparticle dispersion is negligible, and thus cannot explain the abnormal heat transfer coefficient increases Furthermore, a comparison of the nanoparticle and turbulent eddy time and length scales clearly indicates that the nanoparticles move homogeneously with the fluid in the presence of turbulent eddies so an effect on turbulence intensity is also doubtful Thus, we propose an alternative explanation for the abnormal heat transfer coefficient increases: the nanofluid properties may vary significantly within the boundary layer because of the effect of the temperature gradient and thermophoresis For a heated fluid, these effects can result in a significant decrease of viscosity within the boundary layer, thus leading to heat transfer enhancement A correlation structure that captures these effects is proposed

Convective Transport in Nanofluids

Low thermal conductivity is a primary limitation in the development of energy-efficient heat transfer fluids that are required in many industrial applications. In this paper we propose that an innovative new class of heat transfer fluids can be engineered by suspending metallic nanoparticles in conventional heat transfer fluids. The resulting {open_quotes}nanofluids{close_quotes} are expected to exhibit high thermal conductivities compared to those of currently used heat transfer fluids, and they represent the best hope for enhancement of heat transfer. The results of a theoretical study of the thermal conductivity of nanofluids with copper nanophase materials are presented, the potential benefits of the fluids are estimated, and it is shown that one of the benefits of nanofluids will be dramatic reductions in heat exchanger pumping power.

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Investigation on Convective Heat Transfer and Flow Features of Nanofluids

Turbulent friction and heat transfer behaviors of dispersed fluids (i.e., uttrafine metallic oxide particles suspended in water) in a circular pipe were investigated experimentally. Viscosity measurements were also conducted using a Brookfield rotating viscometer. Two different metallic oxide particles, γ-alumina (Al2O3) and titanium dioxide (TiO2), with mean diameters of 13 and 27 nm, respectively, were used as suspended particles. The Reynolds and Prandtl numbers varied in the ranges l04-I05 and 6.5-12.3, respectively. The viscosities of the dispersed fluids with γ-Al2O3 and TiO2 particles at a 10% volume concentration were approximately 200 and 3 times greater than that of water, respectively. These viscosity results were significantly larger than the predictions from the classical theory of suspension rheology. Darcy friction factors for the dispersed fluids of the volume concentration ranging from 1% to 3% coincided well with Kays' correlation for turbulent flow of a single-phase fluid. The Nusselt n...

Hydrodynamic and heat transfer study of dispersed fluids with submicron metallic oxide particles

Effective thermal conductivity of mixtures of e uids and nanometer-size particles is measured by a steady-state parallel-plate method. The tested e uids contain two types of nanoparticles, Al 2O3 and CuO, dispersed in water, vacuum pump e uid, engine oil, and ethylene glycol. Experimental results show that the thermal conductivities of nanoparticle ‐e uid mixtures are higher than those of the base e uids. Using theoretical models of effective thermal conductivity of a mixture, we have demonstrated that the predicted thermal conductivities of nanoparticle ‐e uid mixtures are much lower than our measured data, indicating the dee ciency in the existing models when used for nanoparticle ‐e uid mixtures. Possible mechanisms contributing to enhancement of the thermal conductivity of the mixtures are discussed. A more comprehensive theory is needed to fully explain the behavior of nanoparticle ‐e uid mixtures. Nomenclature cp = specie c heat k = thermal conductivity L = thickness Pe = Peclet number P q = input power to heater 1 r = radius of particle S = cross-sectional area T = temperature U = velocity of particles relative to that of base e uids ® = ratio of thermal conductivity of particle to that of base liquid ¯ = .® i 1/=.® i 2/ ° = shear rate of e ow Ω = density A = volume fraction of particles in e uids Subscripts

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Thermal Conductivity of Nanoparticle -Fluid Mixture

Thermal conductivity of carbon nanotubes and their polymer nanocomposites: A review

The application of plasma directly in water has been studied in recent years, because of its practical applications in water sterilization and decontamination. Although the mechanism is not fully understood, it is expected that the physical effects such as high electric field, Shockwaves and ultraviolet radiation, as well as reactive species produced from the discharge, significantly contribute in the inactivation of microorganisms. In this work, the results of recent experiments of sterilization of E. Coli with the use of pulsed spark discharge in a continuous water circulation system are presented. The main objective was to discuss the optimization of water solution sterilization by plasma for more realistic applications and its scalability for industrial use, taking into account the influence of energy density of plasma and various water parameters. An energy requirement of about 500 J/L was observed to achieve three log reduction of E. coli in the circulation system, which is higher than the energy requirement for batch process systems 1. Comparison of inactivation efficiencies determined at different initial bacterial concentration will be made. Also the influence of water parameters including conductivity, flow rate and total water volume et. al. will be discussed.

Sterilization of E. coli by pulsed spark discharge in continuous water circulation system

This study presents an impedance method of in-vitro characterization of the thrombotic potential of whole blood. Whole blood samples of 0.2 cc were put into a micro-cell with embedded three electrodes immediately after venepuncture at 37℃. Anti-coagulated blood samples were also collected for hematocrit and blood viscosity analyses. The rate of change of electron flow was measured, which indicates the inverse of the thrombotic potential. A sudden decrease in the rate of change of electron flow was observed at a time equal to approximately 110 seconds. This sudden decrease was significantly delayed in anti-coagulated samples. After the sudden decrease, the rate continued to decrease, reaching a minimum value in unadulterated samples while the change in the rate in the anti-coagulated ones was found rather moderate. Based on these preliminary findings, the present method may be of used as a new tool for the diagnosis of the thrombotic potential of whole blood.

Young I. Cho

Papers

Sterilization of E. coli by pulsed spark discharge in continuous water circulation system

In-vitro characterization of the thrombotic potential of whole blood using an impedance method

Could Hemodynamic Analyses Be Helpful In Predicting Blood Flow Signal Levels In MRI