The focus of this study is to predict the thermal properties of new materials that can be synthesized with carbon nanotubes (e.g., thin nanotube composite layers to be used as thermal shields). Random walk simulations of thermal walkers are used to study the effect of interfacial resistance on heat flow in dispersed carbon nanotube composites. Effective heat conductivity through CN composites, as a function of nanotube length, orientation and percent composition was also calculated. The work has generated a parallelizable off-lattice Monte Carlo code of a large number of random walkers traveling in the computational cell for a relatively long time. The off-lattice Monte Carlo simulation successfully shows how the system can be replaced with an effective medium approximation, as well as the role of the thermal boundary resistance at the CN surface in diminishing the impact of the CN's effect on the heat conduction. The adopted algorithm effectively makes the thermal conductivity of the nanotubes themselves infinite. Our algorithm is more efficient than a typical random walk algorithm, and much faster than a Molecular Dynamics algorithm. Even though it cannot provide results at the fundamental molecular level as Molecular Dynamics can, it can be used to model physico-chemical properties of randomly-dispersed nanotube materials quite successfully.
In addition to the development of the numerical algorithm, the paper will discuss the effects of carbon nanotube orientation, aspect ratio, volume fraction, and Kapitza resistance on the composite effective conductivity. We find that orientation is crucial in maximizing the impact of the CNs on heat conduction. It was found that the effective thermal conductivities of nanotube composites are much lower than those calculated from the modified Maxwell theory. The effect of the Kapitza resistance becomes important when the surface area of the CNs and their aspect ratios are small.
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