An improved random sequential absorption method for thermal conductivity study of diamond-reinforced composites

With the advancement of numerical simulation technology, the representative volume element has been widely applied to the analysis and design of composite structures. In this work, an improved random sequential absorption method was developed for batch modeling and simulation of diamond-reinforced composites. The proposed algorithm incorporates the core principles of the Cell-list method while integrating a weighted displacement adjustment strategy, achieving packing densities of up to 54 % for spherical particles and 42 % for hexoctahedral particles within the representative volume elements. To ensure the periodic boundary conditions of the representative volume element models, an auxiliary space is employed. Using the method, thermal transfer models for diamond/Cu and diamond/Al were developed. The study analyzed the impact of diamond thermal conductivity, volume fraction, particle size, and interfacial thermal conductance on the overall thermal conductivity. The data curves reveal that the thermal conductivity of diamond/Cu and diamond/Al exhibits a linear relationship with the thermal conductivity and volume fraction of the reinforcements. However, there is a clear nonlinear relationship with particle size and interfacial thermal conductance, and no significant correlation with the sphericity of the diamond particles. The results were compared with predictions from the Maxwell, Hasselman and Johnson and the differential effective medium models, which finds that the differential effective medium model provides more accurate predictions of the thermal conductivity of diamond-reinforced composites, although the prediction error increases with the increase in volume fraction.