A micro-convection model for thermal conductivity of nanofluids

Patel, Hrishikesh; Sundararajan, T.; Pradeep, Thalappil; Dasgupta, Arnab; Dasgupta, Nandita; Das, Sarit K.

doi:10.1007/bf02704086

Cited by 293 publications

(123 citation statements)

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“…• particle Brownian motion agitates the fluid, thus creating a microconvection effect that increases energy transport; [29][30][31][32][33] 1996 1998 • clusters or agglomerates of particles form within the nanofluid, and heat percolates preferentially along such clusters; [34][35][36][37][38][39] and • basefluid molecules form a highly ordered highthermal-conductivity layer around the particles, thus augmenting the effective volumetric fraction of the particles. 34,38,40,41 Experimental confirmation of these mechanisms has been weak; some mechanisms have been openly questioned.…”

Section: Introductionmentioning

confidence: 99%

A benchmark study on the thermal conductivity of nanofluids

Buongiorno

Venerus

Prabhat

et al. 2009

Journal of Applied Physics

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986

423

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This article reports on the International Nanofluid Property Benchmark Exercise, or INPBE, in which the thermal conductivity of identical samples of colloidally stable dispersions of nanoparticles or "nanofluids," was measured by over 30 organizations worldwide, using a variety of experimental approaches, including the transient hot wire method, steady-state methods, and optical methods. The nanofluids tested in the exercise were comprised of aqueous and nonaqueous basefluids, metal and metal oxide particles, near-spherical and elongated particles, at low and high particle concentrations. The data analysis reveals that the data from most organizations lie within a relatively narrow band ͑Ϯ10% or less͒ about the sample average with only few outliers. The thermal conductivity of the nanofluids was found to increase with particle concentration and aspect ratio, as expected from classical theory. There are ͑small͒ systematic differences in the absolute values of the nanofluid thermal conductivity among the various experimental approaches; however, such differences tend to disappear when the data are normalized to the measured thermal conductivity of the basefluid. The effective medium theory developed for dispersed particles by Maxwell in 1881 and recently generalized by Nan et al. ͓J. Appl. Phys. 81, 6692 ͑1997͔͒, was found to be in good agreement with the experimental data, suggesting that no anomalous enhancement of thermal conductivity was achieved in the nanofluids tested in this exercise.

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Section: Introductionmentioning

confidence: 99%

A benchmark study on the thermal conductivity of nanofluids

Buongiorno

Venerus

Prabhat

et al. 2009

Journal of Applied Physics

Self Cite

986

423

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“…15 are presented and discussed. Numerical experiments are performed for varying values of pertinent physical parameters over a finite domain [0,12] instead of infinite domain [0, ∞). In all these computational experiments the numerical values of different physical parameters are calculated using Eqs.5-11 and the thermo-physical properties from Table I.…”

Section: Numerical Results and Discussionmentioning

confidence: 99%

“…After Choi 1 made principal measurement of thermal conductivity of nanofluids large number of exploratory and hypothetical studies has been done by various researchers. [7][8][9][10][11][12] Higher thermal conductivity of nanofluid does not block stream channels and prompts a little pressure drop as compared to conventional liquid and conventional two-phase mixture. Solid particles improve heat conduction in liquids.…”

Section: Introductionmentioning

confidence: 99%

Heat transfer enhancement of automobile radiator using H2O–CuO nanofluid

Khan

Dil

2017

AIP Advances

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In this article, we study heat transfer enhancement of water based nanofluids with application to automotive radiators. In this respect, we consider here three types of different nanoparticles viz. copper oxide (CuO), Titanium dioxide (TiO2) and Aluminum oxide (Al2O3). The dynamics of the flow in a radiator is governed by set of partial differential equations (PDEs) along with boundary conditions which are formulated. Suitable similarity transformations are utilized to convert the PDEs into their respective system of coupled nonlinear ordinary differential equations (ODEs). The boundary value problem is solved using Shooting method embedded with Runge-Kutta-Fehlberg (RK-5) numerical scheme. Effects of different physical parameters are studied on profiles of velocity and temperature fields at boundary. In addition, influence of nanoparticle concentration factor on the local coefficient of skin-friction and Nusselt number is analyzed. We conclude that water based nanofluids with copper oxide nano-particles have a much higher heat transfer rate than the Al2O3-water and TiO2-water nanofluids. Moreover, larger the concentration of the CuO nanoparticles in the base fluid higher is the heat transfer rate of CuO-water nanofluid.

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“…Patel et al [32] proposed a model for thermal conductivities of nanofluids by taking into account the specific surface area of nanoparticles and nanoconvection induced by Brownian nanoparticles. In their model, they considered kinetic theory-based micro-convection as well as liquid layering in addition to particle concentration.…”

Section: ( )mentioning

confidence: 99%