Benchmarking the Current Capability of BISON to Model Constituent Redistribution and Swelling in U-Zr Annular Fuels Without a Sodium Bond
Abstract
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Engineering topics
Tonks, Michael
Publications and source records attributed to Tonks, Michael.
Understanding the effect of crystal anisotropy on grain growth, texturing and transport via the orthorhombic ?-U system [Slides]
The alpha phase of uranium exhibits the orthorhombic crystal structure, resulting in significant physical property anisotropy. These complex properties make the material challenging to work with in engineering settings but provide a rich arena to investigate the fundamental, multi-scale effects of anisotropy on physical behaviors such as grain growth and microstructure evolution under irradiation. We apply molecular dynamics and phase field modeling to study mass transport and grain growth in alpha-uranium. We characterize the mobilities of crystalline defects and the interfacial energy, and We find that the grain boundary energy is highly variable depending on the interface structure and that the relative rates of defect transport change with temperature. We also find that anisotropic thermal expansion has a significant impact on grain growth kinetics and texture development. Our results are used to explain experimental observations and to provide a basis for further hypotheses into the complex physical behavior of alpha-uranium.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Development of a Model for Irradiation-Induced Grain Growth in UO2 thin films
In this work, we develop a model of irradiation-induced grain growth in UO2 using the MARMOT mesoscale nuclear materials simulation tool. We couple the existing thermally activated grain growth model with a heat conduction model that includes random heat sources representing thermal spikes. We compare the results with the irradiation data on UO2 thin films.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Elucidating Abnormal Grain Growth in Thermomagnetic Processed Materials with Transfer Learning and Reinforcement Learning
The goal of this research program is to establish the mechanism governing local grain boundary motion, which is needed to design and process desirable microstructures for better performance, by identifying the relative contributions of grain boundary (GB) energy and mobility to grain growth. Classical models for grain growth assume that the primary mechanism for reducing the total interfacial energy is area reduction and that GB restructuring is not significant. This assumption implies that grain growth is locally driven by curvature. However, recent experimental observations using new non-destructive 3D x-ray diffraction microscopy techniques (3D-XRM) reveal that classic descriptors (i.e., curvature, number of neighbors, grain size) do not predict real grain growth. Instead, local GB motion appears to be governed by its energy relative to its neighbors such that low-energy boundaries replace those of higher energy. However, simulations that incorporate GB energy anisotropy still fail to reproduce these observations. These discrepancies suggest that the common assumption for grain growth theory must be re-examined to predict and, thus, control microstructure evolution in real polycrystals. A significant challenge to testing this assumption is due to anisotropic GB mobility. Mobility may cause abnormal grain growth or affect the final grain shapes or growth rate but its true contributions are unknown because it is difficult to measure. For example, observations in Fe have found that grains associated with high energy and high mobility boundaries tend to experience abnormal grain growth, whereas abnormal grain growth is associated with low energy and high mobility boundaries in alumina. As mobility and energy both control GB motion, it is challenging to isolate the local driving forces necessary to test the common assumption that the primary mechanism is area reduction. The novelty of this work is the use of machine learning tools to capture GB mobility and energy from 3D-XRM measurements in polycrystals to test the common assumption used in grain growth models. Machine learning can capture high-order correlations in dynamic systems like those found in the evolving GB topology. The PIs have developed a physics-regularized interpretable machine learning microstructure evolution (PRIMME) model that accurately replicates the grain growth behavior of its trained data set.
36 MATERIALS SCIENCE↗
Marmot V2
MARMOT is a robust numerical tool for mesoscale modeling of fuel performance developed under the NEAMS Fuels technical area to predict the coevolution of microstructure and properties in fuel and cladding materials. MARMOT accomplishes this using the phase field method coupled with finite strain mechanics and heat conduction. MARMOT is based on the open source Multiphysics Object-Oriented Simulation Environment (MOOSE) and solves the coupled partial differential equations defining the physics using the finite element method. MARMOT is being developed in order to facilitate the development of improved materials models for fuel performance, but it is also being developed as a powerful tool in and of itself for the simulation of mesoscale fuel performance.
Aagesen, LarryK.↗