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DOE OSTI2026

Refinement and demonstration of a coupled BISON-Griffin workflow for designing targeted TRISO transient experiments in TREAT

The U.S. nuclear industry is expected to deploy tristructural isotropic (TRISO) particle fuel technologies for commercial reactors within the next decade. In previous work, we defined a preliminary transient design space for TRISO fuels, identified potential gaps in the available data, and began to develop multiphysics modeling tools that could be applied to design targeted Transient Reactor Test Facility (TREAT) experiments to fill these gaps. Here, this work builds on that foundation by (1) updating BISON fuel performance and Griffin reactor physics models to reflect the current TREAT experiment tube and capsule designs, (2) coupling the codes to improve the accuracy and usability of the transient design analyses, and (3) demonstrating their use over an expanded design space that includes fuel burnup. The simulated mechanical responses of the TRISO particles were complex functions of fission product accumulation, fission gas release, and irradiation-induced dimensional change in the pyrolytic carbon layers. The predicted tangential stresses in the particles' silicon carbide layers were least compressive for preheated tests involving fresh fuels but remained compressive throughout the ranges of temperature, heat rate, and burnup considered in this work. Finally, comparisons between the potential TREAT transients and historical test reactor irradiations showed that the TREAT tests would produce significantly lower average energy deposition rates, yielding less severe transients with greater relevance to near-term commercial applications. The use of these predictive capabilities has the potential to increase the value of each test, improving the overall efficiency and cost-effectiveness of transient testing for TRISO and other advanced fuels.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

A process to verify numerical models for seismic fluid-structure interaction in advanced reactor vessels

Seismic design and qualification of a liquid-filled advanced nuclear reactor will have to account for fluid-structure interaction (FSI). Interaction between the tank, internal components, and contained liquid will rely on analysis of numerical models that must be verified and validated. Here this study demonstrates a verification process for models of a base-supported cylindrical tank by comparing numerical predictions and analytical solutions. The numerical models are consistent with the assumptions made to derive analytical solutions, namely, either a rigid or a linear elastic tank, ideal fluid, and small-amplitude, unidirectional, horizontal inputs. One software platform is used to illustrate the process. Seismic FSI analysis is performed using the Arbitrary Lagrangian-Eulerian (ALE) and Incompressible Computational Fluid Dynamics (ICFD) solvers in LS-DYNA. Reported responses are those used for design, including hydrodynamic pressures on the tank wall, shear forces and moments at the tank base, and wave heights of the contained liquid. The accuracy of the numerical results is discussed. The numerical models are verified for calculating the pressures on the tank wall and reactions at its base. Accurate simulation of wave action is challenging for both solvers. Recommendations for modeling, code development, and steps for verification are provided. Although focused on reactor vessels and one software platform, the verification process described herein is broadly applicable to liquid-filled vessels and other finite element codes.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Optimization of Conductive Fins to Minimize UO2 Fuel Temperature and Radial Temperature Gradient

To further the development of low-enriched uranium fuels, precedence has been placed on delivering the same amount of power while lowering the fuel temperature and radial temperature gradient. To address this, modeling efforts have resulted in a novel design featuring conductive fins of varying thermal conductivities and geometries inserted into the fuel matrix. These conductive inserts were not allowed to exceed 6% of the original fuel volume. This constraint was imposed due to other designs displacing 10% of fuel volume. A parametric study was performed that consisted of 2.56 million BISON simulations involving varying fin characteristics (i.e., fin thermal conductivity, number, and geometry) to determine the optimal geometric configuration for a desired amount of fuel volume displaced. The results from this study show that the thickness and length of each fin affect the fuel temperature and temperature gradient more than varying the number and thermal conductivity of the fins. The parametric study resulted in the development of an optimized combination to produce the lowest peak fuel temperature, lowest radial temperature gradient, and highest temperature reduction for the amount of original fuel volume displaced. The simulations presented in this work will eventually be compared with irradiation experiments of similar fuel designs at Idaho National Laboratory’s Advanced Test Reactor.

Paaren, Kyle M.↗

NASA NTRSDate not supplied

Nuclear Space System Analysis and Modelling (NSSAM): A Software Tool to Efficiently Analyze the Design Space of Space Reactor Systems

Space reactors have the potential to play a key role in future NASA exploration activities due to their capability to enable sustainable power and advanced propulsion systems. To enable assessment of the space reactor design space, the nuclear space system analysis and modelling (NSSAM) software was developed by Analytical Mechanics Associates. NSSAM leverages a scalable and extensible software architecture which automates reactor analysis to perform coupled engine-reactor and reactor physics-thermal hydraulics calculations. This allows space reactor systems to be evaluated by a wider number of users with a consistent analysis approach to compare designs. NSSAM has been developed with multiple use cases to tailor the analysis to the level of detail desired by the user and computing resources. This summary overviews the NSSAM architecture and development approach, current capabilities (including design variants and use cases) and analysis approach for reactor and system component models.

nuclear thermal propulsion↗

DOE OSTI2021

Metallic fuel cladding degradation model development and evaluation for BISON

Fuel cladding chemical interaction (FCCI) and coolant cladding chemical interaction (CCCI), both consume stainless-steel cladding in sodium-cooled fast reactors (SFRs), and effectively degrade cladding mechanical performance through thickness reduction. Therefore, FCCI/CCCI and corresponding cladding degradation models are essential for advanced fuel performance codes, such as BISON, to reliably predict cladding behavior in SFRs. Here, we report the development and implementation efforts for BISON's FCCI/CCCI correlations and cladding degradation models based on U.S. legacy metallic fuel data. The models were evaluated using Integral Fast Reactor (IFR) program X447/A experiment data supported by the ongoing integration project enabling coordinated application of BISON and EBR-II fuel irradiation and physics database (FIPD). Furthermore, the implemented models were demonstrated to improve BISON's capabilities of predicting cladding damage and degradation behavior that is more consistent with post-irradiation examination observations. Additionally, some limitations of current BISON modules are identified, which are to be overcome through the BISON-FIPD integration efforts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

SULI Intern Final Report: Computationally Investigating Hydrogen Thermo-Diffusion in Yttrium Hydride Using Multiscale Methods

The renaissance of nuclear energy has arrived, heralding an age of abundant inexpensive clean energy, and renewed space exploration. In nuclear-powered spacecraft and microreactors, safety and size are of utmost importance. Yttrium Hydride (YHx) is being researched for its utility as a neutron moderator in nuclear reactors; the hydrogen in YHx slows down neutrons, enabling a continuous nuclear reaction in the reactor. This has the benefit of allowing reactors to be more safe, compact, and efficient. The goal of this effort is to computationally predict the coefficient of temperature-dependent hydrogen diffusion within YHx, the Soret coefficient. This parameter is essential for determining the safe operating modes of YHx moderators. Zirconium Hydride (ZrHx) is used in the Training, Research, Isotopes, General Atomics (TRIGA) reactor, is the reference material for these calculations. In this work, nanoscale atomic modeling in the Vienna Ab initio Simulation Package (VASP) is combined with the mesoscale finite element phase-field module in the Multiphysics Object-Oriented Simulation Environment (MOOSE); this culminates in a new multiscale computational method to simulate Soret diffusion of hydrogen in YHx. This data is useful for predicting experimental outcomes. This workflow involves convergence testing followed by static, Nudged Elastic Band (NEB), Quasi-Harmonic Approximation (QHA), and Molecular Dynamics (MD) calculations - linked with phase field simulation. NEB simulates hydrogen migration, while QHA and MD predict temperature-dependent properties. The static calculations align with literature, and preliminary NEB and QHA calculations yield accurate results. Once the atomic calculations are complete, we will incorporate Electron Backscatter Diffraction (EBSD) images and VASP-generated parameters into the phase field module to simulate intra- and intergranular transport of hydrogen in ZrHx and YHx. Future research will extend our approach to fuel-moderator materials systems such as Uranium-Yttrium Hydride (U-YHx). This work contributes to the development of advanced nuclear energy solutions for space travel.

36 - MATERIALS SCIENCE↗

DOE OSTI2022

SAM-ML: Integrating data-driven closure with nuclear system code SAM for improved modeling capability

Advanced reactors often involve complicated thermal-fluid (T-F) phenomena. Modeling such phenomena with the traditional one-dimensional (1-D) system code is a challenging task. The System Analysis Module (SAM), a modern nuclear system code, has developed a coarse mesh multi-dimensional (multi-D) flow model to capture the spatial effect of T-F phenomena in advanced reactors. As a coarse mesh solver, constitutive relations are required for SAM's multi-D model for unresolved fine-scale physics, such as turbulence. Here this work presents a novel approach that integrates neural networks as data-driven closure for SAM's multi-D flow model. The data-driven closure is trained with fine-resolution data to ensure its accuracy while maintaining a coarse mesh setup to ensure its efficiency and consistency with SAM. We demonstrate the applicability of this SAM-ML capability in an open volume thermal stratification problem, where a neural network model serves as the eddy viscosity closure. A customized interface between the neural network and SAM is developed to ensure flexible and efficient data exchange. The SAM-ML results demonstrate superior performance compared to SAM's built-in zero-equation eddy viscosity closure. The case study shows that although the generalization capability of the data-driven closure still needs to be improved for different transient case or different geometric setup, SAM -ML demonstrates good potential for challenging simulation problems with improved accuracy and computational efficiency.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

Radiation Damage in Materials—Helium Effects

Despite its scarcity in terrestrial life, helium effects on microstructure evolution and thermo-mechanical properties can have a significant impact on the operation and lifetime of applications, including: advanced structural steels in fast fission reactors, plasma facing and structural materials in fusion devices, spallation neutron target designs, energetic alpha emissions in actinides, helium precipitation in tritium-containing materials, and nuclear waste materials. The small size of a helium atom combined with its near insolubility in almost every solid makes the helium–solid interaction extremely complex over multiple length and time scales. This Special Issue, “Radiation Damage in Materials—Helium Effects”, contains review articles and full-length papers on new irradiation material research activities and novel material ideas using experimental and/or modeling approaches. These studies elucidate the interactions of helium with various extreme environments and tailored nanostructures, as well as their impact on microstructural evolution and material properties.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Assessment of Condition Monitoring Methods and Technologies for Inservice Inspection and Testing of Nuclear Power Plant Components

This report was prepared for the U.S. Nuclear Regulatory Commission (NRC) to explore the application of advanced technologies toward meeting the current and future regulatory requirements for maintenance and condition monitoring of structures, systems, and components. The advanced technologies considered in this work are advanced sensors and instrumentation, data analytics, machine learning and artificial intelligence (ML/AI), physics-based models, and digital twins (DT). The interest in the application of advanced technologies for condition monitoring in nuclear power plants continues to grow, and current and future licensees are expected to implement advanced technologies as part of their inservice inspection (ISI) and inservice testing (IST) programs. This report delineates the outcomes of an exploratory investigation into the implementation of advanced condition monitoring technologies to address ISI and IST requirements. A thorough review was conducted of the existing regulatory requirements for ISI and IST, along with an analysis of associated industry practices. Additionally, a state-of-the-art assessment was performed on advanced condition monitoring technologies frequently employed in non-nuclear sectors. This research incorporated two nuclear-specific case studies to illustrate the application of these technologies within the current nuclear fleet. The report provides an exhaustive discussion on the technical challenges, considerations, and opportunities associated with the deployment of advanced condition monitoring technologies. The following are key considerations in the application of advanced technologies for the ISI and IST of nuclear power plant components: • Developing adequate verification and validation procedures to confirm the functional and non-functional requirements, • Developing technical capabilities to conduct real-time asset condition monitoring, • Establishing guidance and protocol for modeling and simulation tools to continuously meet regulatory requirements, • Addressing trustworthiness, explainability, and interpretability of ML/AI methods, • Evaluating maintenance activities to maintain an adequate safety margin and avoid undesirable conditions, • Establishing cybersecure condition monitoring programs associated with a computer-based software system, and • Establishing standardized evaluation metrics for advanced condition monitoring programs. Interest in the use of advanced technologies for condition monitoring in ISI and IST programs continues to grow, and the technology is expected to experience rapid and wide industry adoption in the near future. Adoption of advanced technologies for condition monitoring could have novel and unique impacts on regulatory activities associated with ISI and IST programs. The NRC is continuing to explore the regulatory aspects of advanced technologies as part of ISI and IST programs by pursuing additional research in this technical area.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Development of Decay Heat Sensitivity Analysis Capability in SCALE/ORIGEN

In this work, a decay heat sensitivity analysis capability was developed and implemented in the ORIGEN code of the SCALE nuclear modeling and simulation suite. This capability introduces improved numerical integration schemes, which overcome the challenges associated with accurately modeling the behavior of adjoint nuclide amounts during coarse time steps for both nuclide amount and decay heat sensitivity calculations. This capability significantly improves the accuracy of calculations without compromising computational efficiency compared to the existing method. Extensive verification was conducted for various benchmark problems, including a 238 Pu decay and an irradiation problem involving 135 Xe, evaluated with both coarse and fine time grids. The results show excellent agreement with reference direct perturbation solutions, reaffirming the computational accuracy of the newly proposed numerical integration methods. Furthermore, sensitivity analyses were performed for fission product inventories ( 147 Sm, 150 Sm, 155 Gd) in pressurized water reactor UO 2 and MOX fuel assemblies. These analyses demonstrated that the ORIGEN sensitivity analysis capability can capture detailed sensitivity coefficients and underlying physics in real applications. Additionally, a decay heat sensitivity analysis for high-assay low-enriched uranium fuel, including various initial 235 U enrichment and burnup points, highlights the extended capabilities of SCALE/ORIGEN in comprehensively assessing the factors influencing total decay heat. These advancements in ORIGEN offer valuable insights for reactor analysis, fuel design, and safety assessments, especially in the context of advanced nuclear fuel development and design changes.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

Toward fusion plasma scenario planning for NSTX-U using machine-learning-accelerated models

One of the most promising devices for realizing power production through nuclear fusion is the tokamak. To maximize performance, it is preferable that tokamak reactors achieve advanced operating scenarios characterized by good plasma confinement, improved magnetohydrodynamic (MHD) stability, and a largely non-inductively driven plasma current. Such scenarios could enable steady-state reactor operation with high \emph{fusion gain} --- the ratio of produced fusion power to the external power provided through the plasma boundary. Precise and robust control of the evolution of the plasma boundary shape as well as the spatial distribution of the plasma current, density, temperature, and rotation will be essential to achieving and maintaining such scenarios. The complexity of the evolution of tokamak plasmas, arising due to nonlinearities and coupling between various parameters, motivates the use of model-based control algorithms that can account for the system dynamics. In this work, a learning-based accelerated model trained on data from the National Spherical Torus Experiment Upgrade (NSTX-U) is employed to develop planning and control strategies for regulating the density and temperature profile evolution around desired trajectories. The proposed model combines empirical scaling laws developed across multiple devices with neural networks trained on empirical data from NSTX-U and a database of first-principles-based computationally intensive simulations. The reduced execution time of the accelerated model will enable practical application of optimization algorithms and reinforcement learning approaches for scenario planning and control development. An initial demonstration of applying optimization approaches to the learning-based model is presented, including a strategy for mitigating the effect of leaving the finite validity range of the accelerated model. The approach shows promise for actuator planning between experiments and in real-time.

machine learning↗

DOE OSTI2025

Implementation of Perturbation Theory and Sensitivity Capabilities in Griffin

Griffin is a Multiphysics Object-Oriented Simulation Environment (MOOSE) based reactor Multiphysics analysis application, jointly developed by Argonne and Idaho National Laboratories under the DOE-NE NEAMS program. This fiscal year, capabilities for reactivity and sensitivity evaluation using perturbation methods were implemented and verified. The First Order Perturbation Method (FOPT) was employed to compute reactivity worth resulting from small perturbations in input parameters, while the Generalized Perturbation Theory (GPT) was used to evaluate sensitivities of a range of response types, including reaction rate ratio, k-eigenvalue, neutron generation time, and effective delayed neutron fraction. These perturbation methods enable users to quantify how response quantities change due to a perturbation in a input parameter without explicitly performing an additional transport simulation for each perturbed state. In particular, the GPT formulation accounts for indirect effects arising from flux changes by solving generalized inhomogeneous equations, for which a Neumann series-based iterative solution method was developed and implemented in Griffin. The implemented reactivity and sensitivity evaluation capabilities were verified using two test problems: an infinite homogeneous system and a two-dimensional hexagonal core. The results showed excellent agreement with reference solutions obtained by a direct method based on finite difference approximation as well as GPT-based results from the PERSENT code, confirming the accuracy of both reactivity and sensitivity evaluations. Additionally, preliminary uncertainty quantification (UQ) results were obtained by combining the sensitivity values computed using GPT and external covariance data, demonstrating that the implemented sensitivity results can be reliably used for uncertainty calculations. To further demonstrate the generality and practical strength of the implementation, the sensitivity evaluation capability was successfully applied to the Empire microreactor with a geometrically complex design that poses significant modeling challenges. The results confirm that Griffin enables sensitivity evaluations even for irregular and highly heterogeneous reactor configurations, thereby establishing a foundation for UQ applications in advanced reactor designs and analyses.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS2020

Modeling Electrolytic Conversion of Metabolic CO2 and Optimizing a Macrofluidic Electrochemical Reactor for Advanced Closed Loop Life Support Systems

The International Space Station (ISS) is currently equipped with a complex, heavy, and power consuming system that recovers approximately 50% of O2 from metabolic CO2. Future long duration missions will require a sustainable and highly efficient system capable of yielding a minimum of 75% O2 recovery. A Macrofluidic Electrochemical Reactor (MFECR) technology development effort is currently underway at NASA Marshall Space Flight Center (MSFC) to significantly increase current O2 recovery efficiency and reduce complexity of the system. This paper presents a comprehensive multi-physic 3D model developed at MSFC on CO2 conversion to O2 and C2H4 at standard conditions via MFECR. The 3D spatial domain of the model is a replica of the actual MFECR’s 3D drawing generated for the MFECR fabrication and operated to recover O2 from CO2 yielding C2H4 as byproduct. Electrochemical (EC) physics that includes EC multicomponent reaction mechanisms, mass transport, and current density distributions is coupled in the model with all the other physics phenomena involved in the process, such as free and porous fluid flow, multicomponent mass transfer, heat transfer, and DC electrical current generation along with Joule heating effect. The authors plan to use experimental results to validate this comprehensive and rigorous model and build a reliable simulator that will not only assist the authors on the MFECR design but also optimize its operation.

Dominguez, Jesus A.↗

DOE OSTI2024

Nuclear safety Enhanced: A Deep dive into current and future RAVEN applications

As the horizon of nuclear energy expands with the advent of small modular reactors, IV generation reactors, and fusion reactors, there is a growing perspective that the licensing process could benefit from a more comprehensive approach. Moving beyond traditional deterministic and PRA analysis might pave the way for a novel safety analysis paradigm propelled by the increasing computational power at our disposal. This paper explores different methodologies that can improve the outcomes of nuclear safety analysis. These range from uncertainty quantification techniques, aimed at enhancing the precision of safety margins, to deploying dynamic event trees by driving system code simulations, capturing the potential evolutions of severe accidents. These methodologies introduce innovative dimensions to safety analysis, considering the consequences of postulated events and the dynamics of accident sequences. However, they also bring forth challenges, especially in managing the complexity and sheer volume of potential scenarios. The paper touches upon some strategies to counter these challenges, emphasizing the importance of adaptability and continuous evolution in the face of emerging nuclear safety concerns. Additionally, the paper sheds light on the need for advanced tools to apply these methodologies. Among these tools is RAVEN, an open-source software designed for parametric and probabilistic analyses. Its core components, including distribution, sampler, and reduced order model, enable various applications, from risk assessment and mitigation to dynamic learning and plant control logic simulations.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Results and lessons learned from accelerating radio frequency modeling using machine learning [slides]

The “advanced tokamak” reactor concept is a leading candidate for a steady state fusion pilot plant. An advanced tokamak (AT) sustains a majority of the required plasma current with effects resulting from maintenance of the peaked pressure at the device center. This current is augmented by auxiliary current drive sources. These auxiliary actuators may consist of neutral particle beams and/or radio frequency (RF) systems such as lower hybrid current drive (LHCD) and high harmonic fast wave (HHFW) current drive using radio and microwaves from antennas. The primary focus of this work is to develop models of RF current profile control suitable for use in integrated modeling frameworks and for real-time control in experiments. Direct physics models of RF current drive can be computationally intensive. In order to achieve predictive times appropriate for the thousands of calls needed in real-time control of experiments and for use in integrated models, we will apply modern machine learning (ML) techniques to accelerate these models and interpolate their results. To generate the fast and accurate models for use in control level algorithms and integrated modeling we need to replace present models with high dimensional interpolation of their results. We will perform additional simulations across a broader parameter range for EAST and other tokamaks in different physics regimes (Alcator C-Mod, DIII-D, WEST, CFETR, ARC, ITER) and combine them into a larger database for training and testing of the ML models. Further testing of the control level models with experimental current profile data from EAST and C-Mod tokamaks will provide additional confirmation of the control level model before integration in a tokamak control system or integrated modeling suite. ML will be used to optimize the selection of training data consisting of RF current driven at different values of density profile, temperature profile, plasma current, and wavenumber. ML will also be used to facilitate classification of current drive from these input data. The output of this effort will be a validated classifier capable of determining the current drive profiles for HHFW CD and LHCD on a mille-second timescale. This will provide a breakthrough capability enabling real-time control of RF driven current profiles in experiments including ITER ICRF and use integrated modeling frameworks requiring thousands of current profile calculations in discharge simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRSDate not supplied

Affordable Development Strategy for NEP Nuclear Systems

One nuclear electric propulsion (NEP) reactor systems under consideration is a hydride moderated thermal spectrum reactor fueled by high assay low enriched uranium (HALEU). While such a reactor is expected to yield the lightest HALEU reactor design, its development challenges grow exponentially with increasing mission demands, most notably power output, specific weight of the overall system (which may require operation at temperatures exceeding 1200 K), service lifetime, and human-rated reliability. Two of the greatest cost drivers are full-powered nuclear demonstrations and extensive material development campaigns, so it is important to consider options that can minimize the need for or complexity of such tasks. This paper discusses a structured framework being developed for assessing how NEP design choices, such as materials selection, neutronic features, and heat-removal technologies, can translate into project risk and how project performance goals can be traded with development cost.Reactors operating at high temperatures often require cutting-edge heat transfer technologies and creep-resistant materials. Use of new materials in high temperature reactors brings additional complication beyond those common to any new space materials development campaign. For example, such materials may not possess necessary neutronic cross-sectional or neutronic irradiation data. Similarly, use of new materials may significantly influence core neutronics; in some extreme cases, neutronic reactivity feed-back of certain new materials can vary during their service life as radiation damage impacts the scattering cross-section. In an affordable development approach, high fidelity modeling and simulation tools are used to identify and characterize potential ‘knees-in-the-curves’ in the relationship that exists between the mission characteristics and the project risk. Of particular significance is use of modern uncertainty management and variance reduction methods to perform gap analyses that feed into phenomena identification and ranking tables (PIRT) commonly used to communicate nuclear readiness levels. Model-based measurements techniques are used to design sub-scale experiments as a substitute to minimize orcompletely eliminate the need for nuclear demonstrations.This paper will describe the approach and present preliminary results. It will lay the groundwork for developing a set of metrics that can be broadly characterized as system nuclear readiness levels and advancement degree of difficulty for nuclear systems. Equally importantly, a goal of this paper is to initiate a dialogue among stakeholders on what is the sufficient level of maturity that is required for launching a demonstration unit.

Dasari V Rao↗

DOE OSTI2022

Sensitivity Study of Multiscale and Phenomenological Elasto-Viscoplastic Grade 91 Material Models for Component-Scale Response

Many advanced nuclear reactor concepts currently being developed are targeting higher operating temperatures relative to the current fleet of light water nuclear reactors, for efficiency gains and other operational considerations. The design of high temperature structural components with reliable long-term operational performance will depend on material models that accurately capture the inelastic deformation mechanisms active in these environments. In this work, we perform a detailed parameter sensitivity analysis of two unified elasto-viscoplastic Grade 91 material models capable of capturing long term high temperature creep deformation. The first model is a phenomelogical material model from the Nuclear Engineering Material Library (NEML) developed at Argonne National Lab. The NEML model parameters and their uncertainty were fit to a range of Grade 91 experimental data using Bayesian Markov Chain Monte Carlo analysis. The second model is a LAROMance data-driven surrogate material model developed at Los Alamos National Lab. The LAROMance model is fit to a large database of responses produced by a mechanistic crystal plasticity based polycrystal model. Parameters for the LAROMance surrogate material model reflect the pedigree of the Grade 91 microstructure. Both material models have been integrated into the Grizzly code, based on the open-source MOOSE multiphysics simulation framework, to simulate both the progression of aging mechanisms and the effects of that aging on nuclear power plant structures. Grizzly is used analyze a three-dimensional Grade 91 piping system to compare the long-term inelastic response predicted by these two fundamentally different models and assess the sensitivity of the material model input parameters on this quantity of interest.

42 ENGINEERING↗

DOE OSTI2021

Irradiation-enhanced diffusion and diffusion-limited creep in U 3 Si 2

U 3 Si 2 is an advanced fuel candidate due to its relatively high fissile density and attractive thermal properties. Compared to standard UO 2 fuel, there are significant data gaps for the thermophysical and thermomechanical properties of U 3 Si 2 . Point defect concentrations and mobilities under irradiation govern a number of important fuel performance properties, such as creep and fission gas release. In this work, we utilized density functional theory (DFT) data to inform a cluster dynamics framework to predict point defect concentrations in U 3 Si 2 under irradiation. Molecular dynamics (MD) simulations were used to examine the contribution of atomic mixing during ballistic cascades to diffusion, as well as the diffusivity of U and Si at grain boundaries. These atomic scale models for diffusivity were then used to inform a creep model based on bulk (Nabarro-Herring) and grain boundary (Coble) diffusional creep, and climb-limited dislocation creep. The model compares well against available experimental data and has been implemented in the BISON fuel performance code. Finally, a demonstration case using simple power profiles has been carried out, showing that negligible creep occurs due to the low temperatures experienced by U 3 Si 2 in-reactor, a consequence of its high thermal conductivity.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗