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Research Papers

Thermal Nanofluid Property Model With Application to Nanofluid Flow in a Parallel-Disk System—Part I: A New Thermal Conductivity Model for Nanofluid Flow

[+] Author and Article Information
Clement Kleinstreuer1

Yu Feng

Department of Mechanical and Aerospace Engineering,  North Carolina State University, Raleigh, NC 27695-7910

1

Corresponding author.

J. Heat Transfer 134(5), 051002 (Apr 13, 2012) (11 pages) doi:10.1115/1.4005632 History: Received April 05, 2010; Revised November 05, 2010; Published April 11, 2012; Online April 13, 2012

This is a two-part paper, which proposes a new theory explaining the experimentally observed enhancement of the thermal conductivity, knf , of nanofluids (Part I) and discusses simulation results of nanofluid flow in a radial parallel-plate channel using different knf -models (Part II). Specifically, Part I provides the derivation of the new model as well as comparisons with benchmark experimental data sets and other theories, focusing mainly on aluminum and copper oxide nanoparticles in water. The new thermal conductivity expression consists of a base-fluid static part, kbf , and a new “micromixing” part, kmm , i.e., knf  = kbf  + kmm . While kbf relies on Maxwell’s theory, kmm encapsulates nanoparticle characteristics and liquid properties as well as Brownian-motion induced nanoparticle fluctuations, nanoparticle volume fractions, mixture-temperature changes, particle–particle interactions, and random temperature fluctuations causing liquid-particle interactions. Thus, fundamental physics principles include the Brownian-motion effect, an extended Langevin equation with scaled interaction forces, and a turbulence-inspired heat transfer equation. The new model predicts experimental data for several types of metal-oxide nanoparticles (20 < dp  < 50 nm) in water with volume fractions up to 5% and mixture temperatures below 350 K. While the three competitive theories considered match selectively experimental data, their needs for curve-fitted functions and arbitrary parameters make these models not generally applicable. The new theory can be readily extended to accommodate other types of nanoparticle-liquid pairings and to include nonspherical nanomaterial.

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Copyright © 2012 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Comparison between KKL model and benchmark experimental data

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Figure 2

Sketch for multinanoparticle Brownian-motion influence on base fluid

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Figure 3

Sketch for capturing sphere around fluid package

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Figure 4

Sketch for induced velocities due to nanoparticles at different temperatures

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Figure 5

Spherical coordinates and Cartesian coordinates for calculating Stokes flow around a particle

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Figure 6

F-K model predictions for nanofluids dependence on temperature

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Figure 7

Comparisons between F-K model and benchmark experimental data for Al2 O3 -water nanofluids dependence on volume fraction ϕ

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Figure 8

Comparisons between F-K model and benchmark experimental data for Al2 O3 -water nanofluids dependence on temperature T

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Figure 9

Comparisons between F-K model and benchmark experimental data for CuO-water nanofluids dependence on volume fraction ϕ

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Figure 10

Comparisons between F-K model and benchmark experimental data for ZrO2 -water and TiO2 -water nanofluids dependence on volume fraction ϕ

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Figure 11

Comparisons between F-K model and MSB model [11]

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Figure 12

Comparisons between F-K model and Bao’s model [12]

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