MATEC Web of Conferences
Volume 13, 2014ICPER 2014 - 4th International Conference on Production, Energy and Reliability
|Number of page(s)||5|
|Section||Materials and Manufacturing|
|Published online||17 July 2014|
Experimental Preparation and Numerical Simulation of High Thermal Conductive Cu/CNTs Nanocomposites
1 Department of Mechanical Engineering, Universiti Teknologi PETRONAS (UTP), Malaysia
2 Centre of Innovative Nanostructures and Nanodevices (COINN), UTP, Malaysia
3 Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia
a Corresponding author: firstname.lastname@example.org
Due to the rapid growth of high performance electronics devices accompanied by overheating problem, heat dissipater nanocomposites material having ultra-high thermal conductivity and low coefficient of thermal expansion was proposed. In this work, a nanocomposite material made of copper (Cu) reinforced by multi-walled carbon nanotubes (CNTs) up to 10 vol. % was prepared and their thermal behaviour was measured experimentally and evaluated using numerical simulation. In order to numerically predict the thermal behaviour of Cu/CNTs composites, three different prediction methods were performed. The results showed that rules of mixture method records the highest thermal conductivity for all predicted composites. In contrast, the prediction model which takes into account the influence of the interface thermal resistance between CNTs and copper particles, has shown the lowest thermal conductivity which considered as the closest results to the experimental measurement. The experimentally measured thermal conductivities showed remarkable increase after adding 5 vol.% CNTs and higher than the thermal conductivities predicted via Nan models, indicating that the improved fabrication technique of powder injection molding that has been used to produced Cu/CNTs nanocomposites has overcome the challenges assumed in the mathematical models.
© Owned by the authors, published by EDP Sciences, 2014
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 2.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.