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

• Published in: International journal of heat and mass transfer Vol. 131; pp. 863 - 872
• Main Authors: Kim, Jeonggeon, Goo, Yong-Rack, Choi, Indae, Kim, Songkil, Lee, Donggeun
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.03.2019; Elsevier BV
• Subjects: Accuracy; Composite materials; Computer simulation; Design engineering; Effective thermal conductivity; Energy harvesting; Energy storage; Heat conductivity; Heat transfer; Lattice Boltzmann simulations; Mathematical analysis; Mathematical models; Model accuracy; Particle-reinforced composites; Particulate composites; Practical model prediction; Shape effects; Storage systems; Thermal conductivity; Effective thermal conductivity; Practical model prediction; Particle-reinforced composites; Lattice Boltzmann simulations
• ISSN: 0017-9310; 1879-2189
• DOI: 10.1016/j.ijheatmasstransfer.2018.11.107

•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. 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.