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Research Papers: Micro/Nanoscale Heat Transfer

Effect of Brownian Motion on Thermal Conductivity of Nanofluids

[+] Author and Article Information
Ratnesh K. Shukla

Mechanical and Aerospace Engineering Department, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, CA 90095

Vijay K. Dhir

Mechanical and Aerospace Engineering Department, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, CA 90095vdhir@seas.ucla.edu

J. Heat Transfer 130(4), 042406 (Mar 18, 2008) (13 pages) doi:10.1115/1.2818768 History: Received December 13, 2006; Revised May 01, 2007; Published March 18, 2008

Nanofluids, i.e., liquids containing nanometer sized metallic or nonmetallic solid particles, show an increase in thermal conductivity compared to that of the pure liquid. In this paper, a simple model for predicting thermal conductivity of nanofluids based on Brownian motion of nanoparticles in the liquid is developed. A general expression for the effective thermal conductivity of a colloidal suspension is derived by using ensemble averaging under the assumption of small departures from equilibrium and the presence of pairwise additive interaction potential between the nanoparticles. The resulting expression for thermal conductivity enhancement is applied to the nanofluids with a polar base fluid, such as water or ethylene glycol, by assuming an effective double layer repulsive potential between pairs of nanoparticles. It is shown that the model predicts a particle size and temperature dependent thermal conductivity enhancement. The results of the calculation are compared with the experimental data for various nanofluids containing metallic and nonmetallic nanoparticles.

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Copyright © 2008 by American Society of Mechanical Engineers
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Figures

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Figure 1

Schematic showing coordinate system for N identical spheres in a volume V

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Figure 2

(a) Comparison of the theoretical predictions with the experimental data for alumina (a=19.2nm)/water nanofluid, Z=475. (b) Corresponding nondimensional inverse Debye length used in the calculations.

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Figure 3

(a) Comparison of the theoretical predictions with the experimental data for copper oxide (a=12.1nm)/water nanofluid, Z=475. (b) Corresponding nondimensional inverse Debye length used in the calculations.

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Figure 4

Pair distribution function g(r) as a function of nondimensional distance r for computations using (a) λ*=5.417, T*=3.462×10−2 and (b) λ*=6.934, T*=7.272×10−2, corresponding to alumina/water nanofluid at 300K with nanoparticle volume fractions of 1% and 4%, respectively

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Figure 5

(a) Comparison of the theoretical predictions with the experimental data for copper (a=4nm)/ethylene glycol nanofluid. (b) Corresponding nondimensional inverse Debye length used in the calculations.

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Figure 6

(a) Comparison of the theoretical predictions with the experimental data for gold (a=8.5nm)/water nanofluid, Z=1150. (b) Corresponding nondimensional inverse Debye length used in the calculations.

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Figure 7

(a) Comparison of the theoretical predictions with the experimental data for silver (a=30nm)/water nanofluid, Z=1150. (b) Corresponding nondimensional inverse Debye length used in the calculations.

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Figure 8

Pair distribution function g(r) calculations using λ*=3.74 and nondimensional temperatures of (a) T*=3.23×10−3 and (b) T*=1.61×10−3

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