Date of Award
Santa Clara : Santa Clara University, 2016.
Master of Science (MS)
On Shun Pak
Carbon Nanotubes (CNT) are seen as promising materials for thermal transport applications. The high thermal conductivity and structural flexibility of the CNT present them as very attractive components to be used as particle fillers in thermal interface materials. It is important to understand the effective thermal conductivity for CNT-matrix composites at high CNT volume fraction.
In prior work, an effective medium approach (EMA) has been developed to evaluate composite physical properties such as thermal conductivity, dielectric function or elastic modulus (C-W Nan, Prog. Mat. Sci. V 37, 1993). This model combined with the Kapitza interface resistance can predict the effective thermal conductivity of randomly dispersed long fibers for a very low volume fraction (f < 0.01). The interfacial contact resistance is a combination of poor mechanical or chemical adherence at the interface and thermal expansion mismatch between the particle and the matrix. Many studies have demonstrated that the Kapitza resistance has an important impact on the effective thermal conductivity of composites.
The present study compares finite-element (FEA) computations and the EMA model for CNT-matrix compositions with low to moderate volume fractions, 0.001 to 0.02. The value of the Kapitza radius used for the estimation of the interface resistance between the CNT and the matrix is obtained from values calculated in literature. In the simulation, the thermal conductivity of the particle filler is considered orthotropic due to the added Kapitza resistance. A comparison is calculated according to the EMA model. To determine the particle to particle interaction the different geometric configurations are evaluated by using Voronoi cells. This is a tool for characterization of composite materials, identifying the closest particles or near neighbors.
The FEA results obtained show that the EMA model underestimates the effective thermal conductivity of the composite when the particles are very close to each other. The present work proposes a general correction function for the dependence on the particle to particle interaction based on the near neighbor distances and the number of near neighbors. This correction function for particle to particle interaction is tested for various configurations and reduces the EMA over prediction to within several percent (< 5%) in most cases.
Grandio, Diana, "Effects of the Fiber Distribution and Number of Nearby Fibers on the Thermal Conductivity for Aligned CNT-Silicon Oil Composites" (2016). Mechanical Engineering Master's Theses. 3.