Thermal Conductivity Prediction of Metal Matrix Particulate Composites: Theoretical Methodology and Application

Abstract

Abstract To make more accurate predictions of the effective thermal conductivity of the composites, a systematic method for predicting the effective thermal conductivity of metal matrix particle composites with arbitrarily shaped particles was proposed, and the geometry of random particles with controlled shape characteristics is reconstructed. In addition, the geometric vertices of the reconstructed particles are used to characterize the morphology of inclusions with complex profile in two-dimensional isotropic elasticity, and its explicit expression for the Eshelby tensor are explored. Moreover, the material mismatch between the particles and the matrix phase is simulate using a continuously distributed source field based on the Eshelby's equivalent inclusion method. The relationship between micro-structure and effective performance is established. Finally, the effective thermal conductivity of CuCr alloys was predicted using the ETC prediction model. Through the comparison of the numerical simulations, experiments, and calculations, the results show that the ETC model has reliable predictive capability.