Swarm intelligence integrated micromechanical model to investigate thermal conductivity of multi-walled carbon nanotube-embedded cyclic butylene terephthalate thermoplastic nanocomposites

With the recent demand for miniaturization and integration of electronic devices, there has been a growing interest in device malfunction due to high temperature. In this study, a experimental and theoretical study on the composites with improved thermal conductivity by dispersing multi-walled carbon nanotubes (MWCNTs) in the thermoplastic resin was carried out. A micromechanical model was derived based on the ensemble volume-averaging method and the modified Eshelby's tensor reflecting the interface properties. The effects of the waviness, interface, and orientation of fillers on the thermal conductivity of composites were numerically analyzed. A computational intelligence-based particle swarm optimization (PSO) algorithm was adopted to the proposed model for optimizing the model constants. The thermal conductivity of the polymerized cyclic butylene terephthalate (pCBT)/MWCNT composites was experimentally measured according to the content of MWCNT. Finally, the experimentally measured data were utilized in the PSO to improve the predictive capability of the proposed model.