SUMMARYThis study evaluated the computational efficiency and accuracy of different upscaling methods used in predicting thermal conductivity in loaded nuclear waste forms, which are heterogeneous material systems. Thermal conductivity in loaded nuclear waste form is an important property specific to scientific researchers, involved in waste form integrated performance and safety code (IPSC). The effective thermal conductivity, obtained from microstructure information and local thermal conductivity of different components, is critical in predicting the life and performance of waste form during storage. Thermal conductivity is directly related to temperature increase during storage, which determines mechanical deformation behavior, corrosion resistance, and aging performance.Several methods, including the Taylor model, Sachs model, self-consistent model, and statistical upscaling method, were developed and implemented. Due to the absence of experimental data, finite element method (FEM) prediction results were used to determine the accuracy of the different upscaling methods. Micrographs from waste forms with varying waste loadings were used in the prediction of thermal conductivity. Prediction results demonstrated that in term of efficiency, boundary models (e.g., Taylor model and Sachs model) are better than the self-consistent model, statistical upscaling method, and finite element method. However, when balancing computational efficiency and accuracy, statistical upscaling is a useful method in predicting effective thermal conductivity for nuclear waste forms.