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  1. Demonstration of new fracture criteria based on micro-structure and compare to empirical model and measurements

    The US nuclear industry is currently exploring extending the peak rod average burnup limits above the current regulatory limit of 62 GWd/tU. A potential concern for fuel exceeding the burnup limit is the fragmentation during a temperature transient such as a loss-of-coolant accident (LOCA). In the event of cladding failure, the fragmented fuel could relocate and disperse within the reactor. The potential impact of fuel fragmentation, relocation and dispersal (FFRD) on licensing assumptions for burnup extension has focused a great amount of efforts and research on this topic.
  2. Building a DFT+U machine learning interatomic potential for uranium dioxide

    Despite uranium dioxide (UO2) being a widely used nuclear fuel, fuel performance models rely extensively on empirical correlations of material behavior, leveraging the historical operating experience of UO2. Mechanistic models that consider an atomistic understanding of the processes governing fuel performance (such as fission gas release and creep) will enable a better description of fuel behavior under non-prototypical conditions such as in new reactor concepts or for modified UO2 fuel compositions. To this end, molecular dynamics simulation is a powerful tool for rapidly predicting physical properties of proposed fuel candidates. However, the reliability of these simulations depends largely on themore » accuracy of the atomic forces. Traditionally, these forces are computed using either a classical force field (FF) or density functional theory (DFT). While DFT is relatively accurate, the computational cost is burdensome, especially for f-electron elements, such as actinides. By contrast, classical FFs are computationally efficient but are less accurate. For these reasons, we report a new accurate machine learning interatomic potential (MLIP) for UO2 that provides high-fidelity reproduction of DFT forces at a similar low cost to classical FFs. We employ an active learning approach that autonomously augments the DFT training data set to iteratively refine the MLIP. To further improve the quality of our predictions, we utilize transfer learning to retrain our MLIP to higher-accuracy DFT+U data. We validate our MLIPs by comparing predicted physical properties (e.g., thermal expansion and elastic properties) with those from existing classical FFs and DFT/DFT+U calculations, as well as with experimental data when available.« less
  3. Diffusional creep in UO2 informed by lower length scale simulations

    Using molecular dynamics, we predict information at the atomistic scale used to develop a mechanistic UO2 creep model for use in higher length-scale fuel performance codes. The ultimate objective of the model is to not only to capture the creep rates of UO2 but to determine the dominant mechanism in the diffusional regime, which is still debated in the literature. It is important to have a model to capture the correct mechanisms for creep in UO2 as this can be used as the foundation when applying to other fuels, such as doped UO2, and when irradiation is accounted. In lastmore » years NEAMS milestone (FY22), we developed a prelimnary model, however there were issues, for example, excessively high values of uranium vacancy concentrations at the grain boundary. This year we have addressed the issues with the previous version of the model, added a new term that accounts for the nucleation of dislocations at stress raisers (e.g., triple junctions) within the microstructure and discussed where there was disagreement in the literature about the underpinning physics (uranium self-diffusion at the grain boundary).« less
  4. Simulations of self- and Xe diffusivity in uranium mononitride including chemistry and irradiation effects

    A combination of density functional theory and empirical potential atomic scale simulations have been used to determine a model for defect stability and mobility in uranium mononitride (UN), as a function of temperature ($$T$$) and N2 partial pressure ($$p$$N$$_{2}$$). Using the model, predictions of hypo-stoichiometry under U-rich conditions compare favorably to CALPHAD calculations using the TAF-ID database. Furthermore, our predictions of U and N self-diffusivity are in good agreement with experiments carried out as a function of $$T$$ at specific partial pressures under thermal equilibrium. The validated atomic scale data have then been implemented within a cluster dynamics method tomore » simulate irradiation-enhanced defect concentrations. All defects and clusters studied have significantly enhanced concentrations, with respect to thermal equilibrium, as $$T$$ is lowered. The irradiation-enhanced Xe diffusivity is compared to post-irradiation annealing and in-pile experiments. In conclusion, the contributions of various defects and clusters to non-stoichiometry, self-diffusivity, and Xe diffusivity are discussed.« less
  5. Development of bubble evolution model for new mechanistic transient fission gas release capability in BISON

    This report summarizes efforts within NEAMS to investigate the mechanisms that govern fission gas behavior in UO2. In particular, the focus is on understanding how fission gas behavior causes transient fission gas release and fragmentation/pulverization of high burnup structure (HBS) in UO2. HBS forms in the periphery of the pellet where temperatures are relatively low. Previously, MD simulations were performed to determine the reaction energies for various Xe and U defects with bubbles, as a function of Xe to vacancy ratio or, equivalently, pressure. As had been shown in FY22, it was found that the unmodified version of the Simplemore » Integrated Fission Gas Release and Swelling (SIFGRS) model within BISON greatly over-predicted the number gas atoms per vacancy in the bubbles in the outer rim of the pellet (a ratio of > 1 million). This was due to slow grain boundary vacancy diffusivity and not accounting for the pressure-dependent reaction energy for Xe interstitials with bubbles. The application of the pressure dependent reaction energies was able to restrict Xe to vacancy ratios to 2:1, which is far more realistic than those originally obtained from SIFGRS.« less
  6. Molecular dynamics simulations of fission gas xenon (Xe) diffusion at UO2 grain-boundaries (Rev.1)

    The diffusivity of fission gas xenon (Xe) at UO2 grain-boundaries is one of the most important parameters in mechanistic modeling of fission gas diffusion in UO2 based nuclear fuels. In this report, we use molecular dynamics simulations to investigate the Xe diffusivity in UO2 grain-boundaries, employing the many-body potential developed by Cooper, Rushton and Grimes for UO2. Three different types of grain-boundaries are investigated, twist Σ5, tilt Σ5, and a random grain-boundary. Diffusion activation energies in the range of 0.39 – 1.46 eV are obtained for the Xe diffusivity. Comparison to results for the uranium vacancy diffusivity from MD simulationsmore » employing the same methodology suggests a weak to moderate attractive Xe-uranium vacancy binding energy depending on the grain-boundary type.« less
  7. Diffusion in undoped and Cr-doped amorphous UO2

    UO2 fuel pellets are often doped with chromium oxide to obtain favourable properties such as higher density, improved thermal stability, large grain sizes, improved pellet-clad interaction margins, and increased fission gas retention during transients. Chromium has a low solubility limit in UO2, with past experimental work reporting solubility limits ranging between 0.004 to 0.06 wt.% Cr. Due to its low solubility, segregation of Cr ions to the grain boundary may occur. Further, the complexity of these boundaries may be high as observed in other ceramics resulting in disordered or amorphous regions along the boundary, affecting a range of material andmore » operational properties of the fuel pellet. To assess these disordered regions, in this work we study amorphous undoped and Cr doped UO2 systems (containing 10–50 at.% Cr3+) that have been modelled using classical molecular dynamics methods incorporating Cr3+ into the well-used CRG potential library. Diffusion coefficients, pre-exponential factors, and activation energies for diffusion were computed for oxygen ions, assessing the impact of structure and extrinsic species on migration. Oxygen diffusion was observed to be much faster in the undoped amorphous system compared to its crystalline counterpart. Oxygen diffusion in doped systems decreased with increasing Cr concentration, highlighting the importance of additives to retain fission products and other migratory species.« less
  8. Correlations for the specific heat capacity of ( U x Pu 1 - x ) 1 - y Gd y O 2 - z derived from molecular dynamics

    We report UO2 is the primary conventional fuel used in most nuclear reactors with Gd2O3 commonly added as a burnable absorber to produce a more level power distribution in the reactor core at the beginning of operation. It can also be mixed with other actinide oxides to produce mixed oxide (MOx) fuel. In this study, molecular dynamics simulations were used to predict the specific heat capacity of Gd-doped PuO2, UO2 and (U, Pu)O2 MOx accommodating Gd3+ substituted at cation sites via two charge compensation mechanisms - oxygen vacancy formation and the oxidation of U4+ to U5+. The specific heat capacitymore » values for PuO2 and UO2 are in good agreement with other studies showing a distinct peak at high temperatures - above 1800 K. As Gd3+ is added, the peak height reduces for each composition considered. An analytical fit was applied to the data where Gd3+ was fully charge compensated by either oxygen vacancies or U5+. The expression was then validated by predicting the specific heat capacity for three compositions of (UxPu1-x)1-yGdyO2-z containing both oxygen vacancies and U5+, and compared to molecular dynamics data.« less
  9. Development of a creep model informed by lower-length scale simulations to simulate creep in doped UO2

    Using molecular dynamics, we predict information at the atomistic scale used to develop a mechanistic UO2 creep model for use in higher length-scale fuel performance codes. The ultimate objective of the model is to better describe the grain size dependence and therefore impact of doping on creep rates in UO2. In a previous NEAMS milestone, we found that Nabarro-Herring (bulk diffusional) creep was too low to capture the experimentally observed creep rates in standard UO2. Moreover, in that milestone, other mechanisms were explored, such as Coble (grain boundary) creep and dislocation climb, with each mechanism exhibiting different grain size dependencies.more » Again, these were orders of magnitude too low to describe the experimental creep rates. In this work, we address the previous assumptions made for the Coble creep mechanism by investigating the diffusivity of various defects at grain boundaries in UO2 and, critically, to determine if enhanced grain boundary diffusivity allows the model to better reproduce experimental results. The diffusivity as a function of temperature for different concentrations of uranium vacancies and interstitials for bulk UO2 have also been examined using cluster dynamics. Furthermore, using a concentration dependent segregation model, the concentration of defects at the grain boundary were predicted. This atomistic data was then input into the various creep mechanisms and the creep rates compared to the empirical MATPRO correlation (used in BISON) and experiment.« less
  10. Machine learning molecular dynamics simulations toward exploration of high-temperature properties of nuclear fuel materials: case study of thorium dioxide

    Predicting materials properties of nuclear fuel compounds is a challenging task in materials science. Their thermodynamical behaviors around and above the operational temperature are essential for the design of nuclear reactors. However, they are not easy to measure, because the target temperature range is too high to perform various standard experiments safely and accurately. Moreover, theoretical methods such as first-principles calculations also suffer from the computational limitations in calculating thermodynamical properties due to their high calculation-costs and complicated electronic structures stemming from f-orbital occupations of valence electrons in actinide elements. Here, we demonstrate, for the first time, machine-learning molecular-dynamics tomore » theoretically explore high-temperature thermodynamical properties of a nuclear fuel material, thorium dioxide. The target compound satisfies first-principles calculation accuracy because f-electron occupation coincidentally diminishes and the scheme meets sampling sufficiency because it works at the computational cost of classical molecular-dynamics levels. We prepare a set of training data using first-principles molecular dynamics with small number of atoms, which cannot directly evaluate thermodynamical properties but captures essential atomistic dynamics at the high temperature range. Then, we construct a machine-learning molecular-dynamics potential and carry out large-scale molecular-dynamics calculations. Consequently, we successfully access two kinds of thermodynamic phase transitions, namely the melting and the anomalous λ transition induced by large diffusions of oxygen atoms. Furthermore, we quantitatively reproduce various experimental data in the best agreement manner by selecting a density functional scheme known as SCAN. Our results suggest that the present scale-up simulation-scheme using machine-learning techniques opens up a new pathway on theoretical studies of not only nuclear fuel compounds, but also a variety of similar materials that contain both heavy and light elements, like thorium dioxide.« less
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