Data-driven thermal and percolation analyses of 3D composite structures with interface resistance

Abstract

Data-driven thermal and percolation analyses are conducted to elucidate the effects of various characteristics on the effective thermal conductivity of complex 3D composite structures. These characteristics include the thermal and geometric properties of the composite constituents, the interface resistance, and the existence of percolation paths. A series of voxel-wise microstructure samples with various characteristics are generated. Their effective thermal conductivities are evaluated using a diffuse-interface-based computational homogenization method. A voxel-based algorithm is employed to identify the potential percolation paths in the structures. The homogenization results show particularly significant effects of the percolation path in composite samples with higher aspect ratios and interface resistances. The importance of different thermal and geometric features to the effective thermal conductivity is analyzed using a data-driven sensitivity study. The analysis also demonstrates that the particle volume fraction and interface thermal resistance are the most influential characteristics for determining the effective thermal conductivity. Finally, employing a surrogate-based classification model, microstructures with and without percolation can be distinguished with an accuracy of 93%.