In this proposal, four individual nuclear materials-based projects will be presented and discussed. Each project presents a unique problem within the field of nuclear materials engineering and can demonstrate the application of computational methods in support of existing experimental framework or in the guidance or suggestion of new experimental investigation.
The first project seeks to understand the atomic ordering behavior in U-Zr, an alloy being investigated for use as a metallic fuel in advanced fast reactors. In these metals there is a potential for phase decomposition and a redistribution of fissile U atoms. A complete understanding of the atomic ordering behavior is therefore needed. Density Functional Theory (DFT), a first-principles electronic structure modelling framework, is employed to investigate this ordering and results are compared with high-resolution synchrotron diffraction data. In the second project, point defect formation energies in Th and Th-U metal are modeled using DFT. These defects often have a large effect on a material’s mechanical properties and in the highly radioactive environment of a reactor core defects are readily created during the displacement cascades of primary knock-on atoms (PKAs). The third project investigates the energetics associated with Cr depletion in Ni-Cr surfaces contacting a molten salt. The depletion of Cr atoms in the surface regions of these materials has been observed as the dominant form of corrosion but the underlying mechanisms and driving forces are not well understood. In this project, DFT is used to model the effect adsorbed salt atoms have on the surface segregation behavior of Cr atoms near the surface of a Ni-Cr alloy. In the fourth project, the role of crystallographic texture evaluation in a metal in the field of nuclear forensics is discussed and demonstrated. In the event of seizure of an illegally trafficked nuclear material the establishment of the origin of that material would be of great importance. In a metal, there is a possibility for texture to contain clues about of the processing history. In this work, through a collaborative effort of many contributors, the texture evolution of a metal is measured and analyzed for a thermo-mechanical process. The measured data is then used with an inverse model methodology to predict specific processing conditions and initial texture states.