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Research Papers: Micro/Nanoscale Heat Transfer

Thermal Transport Mechanisms in Carbon Nanotube-Nanofluids Identified From Molecular Dynamics Simulations

[+] Author and Article Information
Jonathan W. Lee

Department of Mechanical Engineering,
Rice University,
Houston, TX 77005
e-mail: jonathanwlee5@gmail.com

Andrew J. Meade, Jr.

Department of Mechanical Engineering,
Rice University,
Houston, TX 77005
e-mail: meade@rice.edu

Enrique V. Barrera

Department of Materials Science &
Nanoengineering,
Rice University,
Houston, TX 77005
e-mail: ebarrera@rice.edu

Jeremy A. Templeton

Thermal/Fluid Sciences &
Engineering Department,
Sandia National Laboratories,
Livermore, CA 94550
e-mail: jeremy.templeton@sandia.gov

Contributed by the Heat Transfer Division of ASME for publication in the JOURNAL OF HEAT TRANSFER. Manuscript received July 11, 2014; final manuscript received February 18, 2015; published online March 24, 2015. Assoc. Editor: Robert D. Tzou.

J. Heat Transfer 137(7), 072401 (Jul 01, 2015) (8 pages) Paper No: HT-14-1459; doi: 10.1115/1.4029913 History: Received July 11, 2014; Revised February 18, 2015; Online March 24, 2015

Atomistic simulations of carbon nanotubes (CNTs) in a liquid environment are performed to better understand thermal transport in CNT-based nanofluids. Thermal conductivity is studied using nonequilibrium molecular dynamics (MD) methods to understand the effective conductivity of a solvated CNT combined with a novel application of Hamilton–Crosser (HC) theory to estimate the conductivity of a fluid suspension of CNTs. Simulation results show how the presence of the fluid affects the CNTs ability to transport heat by disrupting the low-frequency acoustic phonons of the CNT. A spatially dependent use of the Irving–Kirkwood relations reveals the localized heat flux, illuminating the heat transfer pathways in the composite material. Model results can be consistently incorporated into HC theory by considering ensembles of CNTs and their surrounding fluid as being present in the liquid. The simulation-informed theory is shown to be consistent with existing experimental results.

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References

Figures

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Fig. 1

Time-averaged temperature gradients of two CNT in fluid simulations. Depicted here are (6,6) solvated CNTs from (a) case 2 and (b) case 3. The flat gray data points refer to the CNT temperature while the lighter data points indicate the fluid temperature. The system temperature is shown in black, which largely overlaps with the fluid temperature. Note the piecewise nature of the profile due to the presence of the CNT, particularly in case 3 where the solvating fluid has a smaller cross section as compared with case 2. A least-squares linear line is used to fit the data sufficiently far away from the source/sink regions. (a) Case 2: 25 nm (6,6) CNT in 6 × 6 nm2 solvated box and (b) case 3: 100 nm (6,6) CNT in 2.856 × 2.856 nm2 solvated box.

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Fig. 2

HC model applied to enhancement results from MD simulation cases with the experimental data is overlaid. While the model still suggests a roughly linear trend in the region of interest, it produces values that are on a similar order of magnitude as the experimental data. Cases 2 and 3 represent more physically motivated examples than the rest, and nonlinearity becomes particularly apparent in these cases at larger volume fractions.

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Fig. 3

The (a) longitudinal and (b) transversal DOS of a 100 nm (6,6) CNT simulated at 300 K. The solid-line spectra are from the CNT in vacuum simulations while the dashed-line spectra are the corresponding solvated case. The CNT in vacuum case presented here was subjected to a 4.806 × 10−7 W heat flux using the Ikeshoji Method. While the overall features remain the same, the low-frequency peaks are highly attenuated in the solvated case. The inset shows the comparison at low frequencies. A distinct feature of the vacuum simulation is a large first peak below 1 THz. The aqueous simulation specifically does not share this feature. The large peak near 50 THz is narrowed and most features appear to be blueshifted and smoothed.

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Fig. 4

An example of the time-averaged longitudinal heat flux density of a CNT in fluid simulation. Depicted here is a 25 nm (6,6) CNT, case 2 from Table 1. The longitudinal components of the heat flux density vector show boundary conditions accurately persisting through the fluid until the CNT disrupts the “bulk” behavior. The CNT carries much of the heat transport load, particularly closer to the center. (a) Fluid–fluid and solid–solid heat flux densities, (b) fluid–solid heat flux density, and (c) solid–fluid heat flux density.

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Fig. 5

Computed heat flux through the CNT. The solid line is the nodal data from the MD simulation. The dashed line is a quadratic least squares fit. In (b), the flux maxes out as a result of the longer CNT, so the quadratic fit is not appropriate for the entire length of the CNT. Instead, the fit is based only on the left-most and right-most 119 Å of the CNT (equivalent to the full length of the shorter CNTs in the study). (a) Case 2: 25 nm (6,6) CNT in 6 × 6 nm2 solvated box and (b) case 3: 100 nm (6,6) CNT in 2.856 × 2.856 nm2 solvated box.

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Fig. 6

The temperature distribution from the (a) 25 nm (6,6) CNT example above and (b) 100 nm (6,6) CNT from case 3. Most of the temperature gradient exists in the fluid due to the lower thermal conductivity of the fluid. In the region near the CNT, the temperature gradient is diminished due to heat being primarily transported through the highly conducting CNT. (a) Case 2: 25 nm (6,6) CNT in 6 × 6 nm2 solvated box and (b) case 3: 100 nm (6,6) CNT in 2.856 × 2.856 nm2 solvated box.

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