Toward high-accuracy and high-applicability of a practical model to predict effective thermal conductivity of particle-reinforced composites

A particle-reinforced composite material is a matrix with thermally conductive particles that has a diverse range of applications from electronics to energy harvesting/storage systems. In the engineering design of a particle-reinforced composite material for application, it is crucial to accurately and practically predict its effective thermal conductivity. Here, we report the development of a simple analytical model for predictions with improved accuracy and applicability. Comprehensive evaluation of existing models was first conducted to clarify their limitations in prediction accuracy and applicability to various experimental conditions. To overcome the challenges of the existing models, our new model was derived to consider the effect of shape, particle aggregation, and mutual interaction of particles on effective thermal conductivity. Lattice Boltzmann simulations were conducted to obtain a quasi-universal coefficient representing interactions of particles, whereas a shape coefficient characterizing microstructures of aggregated particles was obtained from experimental data available from literature. As a result, our model prediction outperformed the existing models in its prediction accuracy, and it could be applicable to any experimental circumstances where previous model predictions are inappropriate to use.