A Randomly Distributed Filler Model for Heat Conductivity Prediction in Filled Composite Materials Considering Fillers Aggregation

Abstract

Polymeric heat exchangers are widely used in various industries. However, their low thermal conductivity has limited their practical applications. Adding fillers with high thermal conductivity to the base materials has been recognized as an efficient way to increase the thermal conductivity of polymers. Filler contents have great impacts on the overall effective thermal conductivity of composite materials. Increasing filler content is the most direct and efficient way to prepare materials of super-high thermal conductivity. Previous researches were confined to volumetric filler content less than 20%. Higher filler contents were not considered due to possible filler aggregation. In this study, the effects of filler contents higher than 20% are investigated, with simultaneous considerations of random distribution of fillers in the base material and fillers aggregation. The interactions between the fillers, and between the fillers and the base material, are studied to predict the effective thermal conductivity of the composite material. It is found that the thermal conductivity of composite material grows rapidly with filler content. It has a sudden increase when the filler volume content increases beyond 30%. Under such circumstance, new “larger fillers” are formed by the aggregation of small fillers. They have tremendous impacts on the thermal conductivity of composites, impacts that were not disclosed by previous models for thermal conductivity predictions.