scholarly journals The Classical Nature of Thermal Conduction in Nanofluids

2010 ◽  
Vol 132 (10) ◽  
Author(s):  
Jacob Eapen ◽  
Roberto Rusconi ◽  
Roberto Piazza ◽  
Sidney Yip
Keyword(s):  
Experimental Data ◽  
Thermal Conductivity ◽  
Strong Evidence ◽  
Thermal Conduction ◽  
Base Fluid ◽  
Liquid Mixtures ◽  
Large Set ◽  
Conductivity Data ◽  
Homogeneous Systems ◽  
Thermal Conductivity Data

We show that a large set of nanofluid thermal conductivity data falls within the upper and lower Maxwell bounds for homogeneous systems. This indicates that the thermal conductivity of nanofluids is largely dependent on whether the nanoparticles stay dispersed in the base fluid, form large aggregates, or assume a percolating fractal configuration. The experimental data, which are strikingly analogous to those in most solid composites and liquid mixtures, provide strong evidence for the classical nature of thermal conduction in nanofluids.

Mean-Field Bounds and the Classical Nature of Thermal Conduction in Nanofluids

2008 ◽  
Author(s):  
Jacob Eapen
Keyword(s):  
Thermal Conductivity ◽  
Thermal Conduction ◽  
Conduction Mechanism ◽  
Linear Chain ◽  
Mean Field ◽  
Liquid Mixtures ◽  
Large Set ◽  
Inhomogeneous Systems ◽  
Conduction Behavior ◽  
Intermediate Configuration

The initial promise of nanofluids as an advanced, nanoengineered coolant has been tempered in the recent years by a conspicuous lack of consensus on its thermal conduction mechanism. Several new mechanisms have been hypothesized in the recent years to characterize the thermal conduction behavior in nanofluids. In this presentation, we show that a large set of nanofluid thermal conductivity data is enveloped by the well-known Hashin and Shtrikman (H-S) mean-field bounds for inhomogeneous systems. The thermal conductivity in nanofluids, therefore, is largely dependent on whether the nanoparticles stays dispersed in the base fluid, form linear chain-like configurations, or assume an intermediate configuration. The experimental data, which is strikingly analogous to those in most solid composites and liquid mixtures, provides a strong evidence for the classical nature of thermal conduction in nanofluids.

A Method for Predicting the Mean Thermal Conductivity of Insulating Materials at Other Than Experimental Conditions

1963 ◽  
Vol 85 (2) ◽  
pp. 185-186
Author(s):  
C. E. Jones
Keyword(s):  
Experimental Data ◽  
Thermal Conductivity ◽  
Operating Conditions ◽  
Conductivity Data ◽  
Experimental Conditions ◽  
Insulating Materials ◽  
Thermal Conductivity Data ◽  
Test Conditions ◽  
The Mean

A procedure whereby experimental thermal conductivity data can be readily extrapolated to operating conditions quite different from test conditions is presented. Use of this technique can also lessen the amount of experimental data that must be collected and ease experimental problems.

Thermal conductivity data for refrigerant mixtures containing R1234yf and R1234ze(E)

2019 ◽  
Vol 133 ◽  
pp. 135-142 ◽  
Author(s):  
Sofia K. Mylona ◽  
Thomas J. Hughes ◽  
Amina A. Saeed ◽  
Darren Rowland ◽  
Juwoon Park ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Conductivity Data ◽  
Thermal Conductivity Data ◽  
Refrigerant Mixtures

A Review of Thermal Conductivity Data, Mechanisms and Models for Nanofluids

2010 ◽  
Vol 1 (4) ◽  
pp. 269-322 ◽  
Author(s):  
Ji-Hwan Lee ◽  
Seung-Hyun Lee ◽  
Chul Choi ◽  
Seok Jang ◽  
Stephen Choi
Keyword(s):  
Thermal Conductivity ◽  
Conductivity Data ◽  
Thermal Conductivity Data

Estimating thermal diffusivity and specific heat from needle probe thermal conductivity data

2006 ◽  
Vol 77 (4) ◽  
pp. 044904 ◽  
Author(s):  
William F. Waite ◽  
Lauren Y. Gilbert ◽  
William J. Winters ◽  
David H. Mason
Keyword(s):  
Thermal Conductivity ◽  
Thermal Diffusivity ◽  
Specific Heat ◽  
Conductivity Data ◽  
Thermal Conductivity Data ◽  
Needle Probe
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