Jeffrey Giglio

This work applies reconstruction methods based on a genetic algorithm to derive 3D material properties, namely porosity and percolation fraction, in irradiated U-Pu-Zr fuel with minor actinides. We provide two-dimensional experimental data regarding the radial distribution of fission gas bubbles in the fuel and apply the algorithm successfully developed in a companion paper to reconstruct the fuel pore structure in 3D which is unknown a priori. The algorithm returned a set of best structures that constituted the best candidate solutions representing the pore phase. From these, it was possible to extract statistics on the 3D percolation fraction of the reference medium and infer a mean value, the related uncertainty, and an upper and lower bound of the percolation fraction. The algorithm proved able to infer this 3D property from 2D information of the metallic fuel with confidence intervals, thus establishing a path to infer 3D properties directly from 2D experimental images. The knowledge of such a relationship can be used to extrapolate the percolation threshold with confidence interval, which is a crucial property in defining microstructure-based fission gas release models of metallic fuels.