Toward high-accuracy and high-applicability of a practical model to predict effective thermal conductivity of particle-reinforced composites. (March 2019) - Kim, Jeonggeon; Goo, Yong-Rack; Choi, Indae; Kim, Songkil; Lee, Donggeun
Highlights: Comprehensive evaluation showed that existing models had their conditional limitations. The models were unacceptably degraded under conditions for electronics applications. We developed a new model to accurately predict effective thermal conductivity. The new model was self-consistent and described asymptotic behaviors of existing models. With an additional correlation, the new model was reasonably applicable to any conditions. Abstract: 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.