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DOE OSTI2015

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused toward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assessment capability to RELAP-7, currently under development at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activaties are currently ongoing for coupling RAVEN with software such as RELAP5-3D, etc. The aim of this document is the explanation of the input requirements, focalizing on the input structure.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2015

Specification of the Advanced Burner Test Reactor Multi-Physics Coupling Demonstration Problem

This document specifies the multi-physics nuclear reactor demonstration problem using the SHARP software package developed by NEAMS. The SHARP toolset simulates the key coupled physics phenomena inside a nuclear reactor. The PROTEUS neutronics code models the neutron transport within the system, the Nek5000 computational fluid dynamics code models the fluid flow and heat transfer, and the DIABLO structural mechanics code models structural and mechanical deformation. The three codes are coupled to the MOAB mesh framework which allows feedback from neutronics, fluid mechanics, and mechanical deformation in a compatible format.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Results and Analysis of the Research and Development Work Scope Request for Information (DE-SOL-0008246)

The Department of Energy (DOE) Office of Nuclear Energy (NE) released a request for information (RFI) (DE-SOL-0008246) for “University, National Laboratory, Industry and International Input to the Office of Nuclear Energy’s Competitive Research and Development Work Scope Development” on April 13, 2015. DOE-NE solicited information for work scopes for the four main program areas as well as any others suggested by the community. The RFI proposal period closed on June 19, 2015. From the 124 responses, 238 individual work scopes were extracted. Thirty-three were associated with a DOE national laboratory, including Argonne National Laboratory (ANL), Brookhaven National Laboratory (BNL), Idaho National Laboratory (INL), Los Alamos National Laboratory (LANL), Pacific Northwest National Laboratory (PNNL) and Oak Ridge National Laboratory (ORNL). Thirty US universities submitted proposals as well as ten industrial/commercial institutions. Four major R&D areas emerged from the submissions, appearing in more than 15% of the proposed work scopes. These were: nuclear fuel studies, safety and risk analysis, nuclear systems analysis and design and advanced instrumentation and controls. Structural materials for nuclear power plants, used nuclear fuel disposition and various types of systems analysis were also popular, each appearing in more than 10% of the proposals. Nuclear Energy Enabling Technologies (NEET) was the most popular program area with 42% of the proposals referencing the NEET-CTD program. The order of the remaining programs was Fuel Cycle Technologies (FC) at 34%, Nuclear Energy Advanced Modeling and Simulation (NEAMS) at 29% and Reactor Concepts at 17%.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2016

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by input files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused toward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assessment capability to RELAP-7, currently under development at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with software such as RELAP5-3D, etc. The aim of this document is the explanation of the input requirements, focusing on the input structure.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

SHARP Multiphysics Tutorials

SHARP, developed under the NEAMS Reactor Product Line, is an advanced modeling and simulation toolkit for the analysis of advanced nuclear reactors. SHARP is comprised of three physics modules currently including neutronics, thermal hydraulics, and structural mechanics. SHARP empowers designers to produce accurate results for modeling physical phenomena that have been identified as important for nuclear reactor analysis. SHARP can use existing physics codes and take advantage of existing infrastructure capabilities in the MOAB framework and the coupling driver/solver library, the Coupled Physics Environment (CouPE), which utilizes the widely used, scalable PETSc library. This report aims at identifying the coupled-physics simulation capability of SHARP by introducing the demonstration example called sahex in advance of the SHARP release expected by Mar 2016. sahex consists of 6 fuel pins with cladding, 1 control rod, sodium coolant and an outer duct wall that encloses all the other components. This example is carefully chosen to demonstrate the proof of concept for solving more complex demonstration examples such as EBR II assembly and ABTR full core. The workflow of preparing the input files, running the case and analyzing the results is demonstrated in this report. Moreover, an extension of the sahex model called sahex_core, which adds six homogenized neighboring assemblies to the full heterogeneous sahex model, is presented to test homogenization capabilities in both Nek5000 and PROTEUS. Some primary information on the configuration and build aspects for the SHARP toolkit, which includes capability to auto-download dependencies and configure/install with optimal flags in an architecture-aware fashion, is also covered by this report. A step-by-step instruction is provided to help users to create their cases. Details on these processes will be provided in the SHARP user manual that will accompany the first release.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Dakota Uncertainty Quantification Methods Applied to the CFD code Nek5000

This report presents the state of advancement of a Nuclear Energy Advanced Modeling and Simulation (NEAMS) project to characterize the uncertainty of the computational fluid dynamics (CFD) code Nek5000 using the Dakota package for flows encountered in the nuclear engineering industry. Nek5000 is a high-order spectral element CFD code developed at Argonne National Laboratory for high-resolution spectral-filtered large eddy simulations (LESs) and unsteady Reynolds-averaged Navier-Stokes (URANS) simulations.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

Assessment of SFR Wire Wrap Simulation Uncertainties

Predictive modeling and simulation of nuclear reactor performance and fuel are challenging due to the large number of coupled physical phenomena that must be addressed. Models that will be used for design or operational decisions must be analyzed for uncertainty to ascertain impacts to safety or performance. Rigorous, structured uncertainty analyses are performed by characterizing the model’s input uncertainties and then propagating the uncertainties through the model to estimate output uncertainty. This project is part of the ongoing effort to assess modeling uncertainty in Nek5000 simulations of flow configurations relevant to the advanced reactor applications of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. Three geometries are under investigation in these preliminary assessments: a 3-D pipe, a 3-D 7-pin bundle, and a single pin from the Thermal-Hydraulic Out-of-Reactor Safety (THORS) facility. Initial efforts have focused on gaining an understanding of Nek5000 modeling options and integrating Nek5000 with Dakota. These tasks are being accomplished by demonstrating the use of Dakota to assess parametric uncertainties in a simple pipe flow problem. This problem is used to optimize performance of the uncertainty quantification strategy and to estimate computational requirements for assessments of complex geometries. A sensitivity analysis to three turbulent models was conducted for a turbulent flow in a single wire wrapped pin (THOR) geometry. Section 2 briefly describes the software tools used in this study and provides appropriate references. Section 3 presents the coupling interface between Dakota and a computational fluid dynamic (CFD) code (Nek5000 or STARCCM+), with details on the workflow, the scripts used for setting up the run, and the scripts used for post-processing the output files. In Section 4, the meshing methods used to generate the THORS and 7-pin bundle meshes are explained. Sections 5, 6 and 7 present numerical results for the 3-D pipe, the single pin THORS mesh, and the 7-pin bundle mesh, respectively.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2011

Progress on Plant-Level Components for Nuclear Fuel Recycling: Commonality

Progress made in developing a common mathematical modeling framework for plant-level components of a simulation toolkit for nuclear fuel recycling is summarized. This ongoing work is performed under the DOE Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, which has an element focusing on safeguards and separations (SafeSeps). One goal of this element is to develop a modeling and simulation toolkit for used nuclear fuel recycling. The primary function of the SafeSeps simulation toolkit is to enable the time-dependent coupling of separation modules and safeguards tools (either native or third-party supplied) that simulate and/or monitor the individual separation processes in a separations plant. The toolkit integration environment will offer an interface for the modules to register in the toolkit domain based on the commonality of diverse unit operations. This report discusses the source of this commonality from a combined mathematical modeling and software design perspectives, and it defines the initial basic concepts needed for development of application modules and their integrated form, that is, an application software. A unifying mathematical theory of chemical thermomechanical network transport for physicochemical systems is proposed and outlined as the basis for developing advanced modules. A program for developing this theory from the underlying first-principles continuum thermomechanics will be needed in future developments; accomplishment of this task will enable the development of a modern modeling approach for plant-level models. Rigorous, advanced modeling approaches at the plant-level can only proceed from the development of reduced (or low-order) models based on a solid continuum field theory foundation. Such development will pave the way for future programmatic activities on software verification, simulation validation, and model uncertainty quantification on a scientific basis; currently, no satisfactory foundation exists for considering these activities. The exploitation of mathematical commonality for unit operations with the intent of developing generic and comprehensive software for nuclear reprocessing has not been considered to this author's knowledge. Past attention has been given to plant-level processes on an individual and isolated basis, which has led to various models and corresponding codes implementing non-systematic approaches based on elementary principles of chemical processing. This practice has built an initial knowledge base, but it was not fruitful in producing a lasting and extensible simulation capability. In contrast, a common, rigorous, mathematical modeling framework for all plant-level operations, as proposed here, has a tremendous practical and theoretical value because it will reduce the software implementation work, creates a well-defined modeling standard to compare past and future models, and, more importantly, opens the doors for scientific considerations of simulation fidelity; the latter has an obvious beneficial impact in supporting experimental validation programs. Therefore, the proposed framework is likely to generate a solid foundation for modeling plant-level processes for physicochemical nuclear (and non-nuclear) applications. Demonstration of concrete module implementation is the subject of future communications including prototypes for several modules, namely, voloxidation, dissolver, digester, accountability tank, and solvent extraction. Unit-operation commonality is a key aspect to be explored for a successful implementation of these modules aimed at realizing various flowsheets and plant configurations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

A physics-based crystallographic modeling framework for describing the thermal creep behavior of Fe-Cr alloys

This Report addresses the Milestone M2MS-16LA0501032 of NEAMS Program (“Develop hardening model for FeCrAl cladding), with a deadline of 09/30/2016. Here we report a constitutive law for thermal creep of FeCrAl. This Report adds to and complements the one for Milestone M3MS-16LA0501034 (“Interface hardening models with MOOSE-BISON”), where we presented a hardening law for irradiated FeCrAl. The last component of our polycrystal-based constitutive behavior, namely, an irradiation creep model for FeCrAl, will be developed as part of the FY17 Milestones, and the three regimes will be coupled and interfaced with MOOSE-BISON.

36 MATERIALS SCIENCE↗
DOE OSTI2017

A thermal conductivity model for U-­Si compounds

U 3 Si 2 is a candidate for accident tolerant nuclear fuel being developed as an alternative to UO 2 in commercial light water reactors (LWRs). One of its main benefits compared to UO 2 is higher thermal conductivity that increases with temperature. This increase is contrary to UO 2 , for which the thermal conductivity decreases with temperature. The reason for the difference is the electronic origin of thermal conductivity in U 3 Si 2 , as compared to the phonon mechanism responsible for thermal transport in UO 2 . The phonon thermal conductivity in UO 2 is unusually low for a fluorite oxide due to the strong interaction with the spins in the paramagnetic phase. The thermal conductivity of U 3 Si 2 as well as other U-­Si compounds has been measured experimentally [1-­4]. However, for fuel performance simulations it is also critical to model the degradation of the thermal conductivity due to damage and microstructure evolution caused by the reactor environment (irradiation and high temperature). For UO 2 this reduction is substantial and it has been the topic of extensive NEAMS research resulting in several publications [5, 6]. There are no data or models for the evolution of the U 3 Si 2 thermal conductivity under irradiation. We know that the intrinsic thermal conductivities of UO 2 (semi-conductor) and U 3 Si 2 (metal) are very different, and we do not necessarily expect the dependence on damage to be the same either, which could present another advantage for the silicide fuel. In this report we summarize the first step in developing a model for the thermal conductivity of U-­Si compounds with the goal of capturing the effect of damage in U 3 Si 2 . Next year, we will focus on lattice damage. We will also attempt to assess the impact of fission gas bubbles.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

Multiscale Modeling and Uncertainty Quantification for Nuclear Fuel Performance

In this project, we will address the challenges associated with constructing high fidelity multiscale models of nuclear fuel performance. We (*) propose a novel approach for coupling mesoscale and macroscale models, (*) devise efficient numerical methods for simulating the coupled system, and (*) devise and analyze effective numerical approaches for error and uncertainty quantification for the coupled multiscale system. As an integral part of the project, we will carry out analysis of the effects of upscaling and downscaling, investigate efficient methods for stochastic sensitivity analysis of the individual macroscale and mesoscale models, and carry out a posteriori error analysis for computed results. We will pursue development and implementation of solutions in software used at Idaho National Laboratories on models of interest to the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

SAM Theory Manual

The System Analysis Module (SAM) is an advanced and modern system analysis tool being developed at Argonne National Laboratory under the U.S. DOE Office of Nuclear Energy’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. SAM development aims for advances in physical modeling, numerical methods, and software engineering to enhance its user experience and usability for reactor transient analyses. To facilitate the code development, SAM utilizes an object-oriented application framework (MOOSE), and its underlying meshing and finite-element library (libMesh) and linear and non-linear solvers (PETSc), to leverage modern advanced software environments and numerical methods. SAM focuses on modeling advanced reactor concepts such as SFRs (sodium fast reactors), LFRs (lead-cooled fast reactors), and FHRs (fluoride-salt-cooled high temperature reactors) or MSRs (molten salt reactors). These advanced concepts are distinguished from light-water reactors in their use of single-phase, low-pressure, high-temperature, and low Prandtl number (sodium and lead) coolants. As a new code development, the initial effort has been focused on modeling and simulation capabilities of heat transfer and single-phase fluid dynamics responses in Sodium-cooled Fast Reactor (SFR) systems. The system-level simulation capabilities of fluid flow and heat transfer in general engineering systems and typical SFRs have been verified and validated. This document provides the theoretical and technical basis of the code to help users understand the underlying physical models (such as governing equations, closure models, and component models), system modeling approaches, numerical discretization and solution methods, and the overall capabilities in SAM. As the code is still under ongoing development, this SAM Theory Manual will be updated periodically to keep it consistent with the state of the development.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analy- sis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncer- tainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused to- ward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is in- terested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assess- ment capability to RELAP-7, currently under develop-ment at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with soft- ware such as RELAP5-3D, etc. The aim of this document is the explaination of the input requirements, focalizing on the input structure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

MARMOT update for oxide fuel modeling

This report summarizes the lower-length-scale research and development progresses in FY16 at Idaho National Laboratory in developing mechanistic materials models for oxide fuels, in parallel to the development of the MARMOT code which will be summarized in a separate report. This effort is a critical component of the microstructure based fuel performance modeling approach, supported by the Fuels Product Line in the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. The progresses can be classified into three categories: 1) development of materials models to be used in engineering scale fuel performance modeling regarding the effect of lattice defects on thermal conductivity, 2) development of modeling capabilities for mesoscale fuel behaviors including stage-3 gas release, grain growth, high burn-up structure, fracture and creep, and 3) improved understanding in material science by calculating the anisotropic grain boundary energies in UO$_2$ and obtaining thermodynamic data for solid fission products. Many of these topics are still under active development. They are updated in the report with proper amount of details. For some topics, separate reports are generated in parallel and so stated in the text. The accomplishments have led to better understanding of fuel behaviors and enhance capability of the MOOSE-BISON-MARMOT toolkit.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

Web-Based Computational Model Builder For Nuclear Energy Advanced Modeling and Simulation (Final Report)

According to the World Nuclear Association, the U.S. is the largest producer of nuclear power worldwide. In fact, the U.S. provides over 30 percent of the world's nuclear generation of electricity. Although few new reactors have been built in the past 30 years, the association anticipates that four to six reactors may be built by 2020. The first of the reactors will be built in response to 16 license applications, which have been completed since 2007, to build 24 additional nuclear reactors. Currently, both government and industry are working to accelerate approval for the construction and design of new nuclear energy plants. The U.S. nuclear industry has already achieved significant developments in the implementation of nuclear power plants due to advancements in refueling, maintenance, and safety systems at current power plants. Meanwhile, changes in government policy, which have occurred since the late 1990s, have provided for noteworthy expansion in nuclear capacity. For example, the Energy Policy Act of 2005 evoked investment in electricity infrastructure such as nuclear power. To design future systems and continue operational improvements at existing U.S. plants, advanced modeling and simulation of nuclear reactors are crucial. We developed a Cloud/Web-based tool, HPCCloud, that enables nuclear energy firms, or other manufacturing and engineering companies, to innovate using advanced modeling and simulation. Through the Web-Based Computational Model Builder for Nuclear Energy Advanced Modeling and Simulation grant, we develop HPCCloud to simplify and democratize advanced modeling and simulation in the nuclear energy industry on a range of nuclear engineering applications. It leverages millions of investment dollars from the Department of Energy's Consortium for Advanced Simulation of Light Water Reactors (CASL) and the Office of Nuclear Energy's research and development in Nuclear Energy Advanced Modeling and Simulation (NEAMS). Our approach simplifies the workflow, alleviates the need for in-house computational science and engineering experts, and lowers the capital investments required for advanced modeling and simulation. The Cloud/Web-based simulation environment guides end-users and developers through the advanced modeling and simulation lifecycle. In addition to providing significantly improved, intuitive software, the environment offers reproducible workflows where one can save, share, and even publish the full path of data from input to final analyzed results. HPCCloud addresses identified challenges to the nuclear energy industry's use of advanced modeling and simulation. With this unique Cloud/Web-based environment, we enable an ecosystem of synergistic activities, fueling new research and development for nuclear energy. HPCCloud lays the groundwork for commercialization, through customization, technology integration, and advanced research and development services.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Warthog: Coupling Status Update

The Warthog code was developed to couple codes that are developed in both the Multi-Physics Object-Oriented Simulation Environment (MOOSE) from Idaho National Laboratory (INL) and SHARP from Argonne National Laboratory (ANL). The initial phase of this work, focused on coupling the neutronics code PROTEUS with the fuel performance code BISON. The main technical challenge involves mapping the power density solution determined by PROTEUS to the fuel in BISON. This presents a challenge since PROTEUS uses the MOAB mesh format, but BISON, like all other MOOSE codes, uses the libMesh format. When coupling the different codes, one must consider that Warthog is a light-weight MOOSE-based program that uses the Data Transfer Kit (DTK) to transfer data between the various mesh types. Users set up inputs for the codes they want to run, and then Warthog transfers the data between them. Currently Warthog supports XSProc from SCALE or the Sub-Group Application Programming Interface (SGAPI) in PROTEUS for generating cross sections. It supports arbitrary geometries using PROTEUS and BISON. DTK will transfer power densities and temperatures between the codes where the domains overlap. In the past fiscal year (FY), much work has gone into demonstrating two-way coupling for simple pin cells of various materials. XSProc was used to calculate the cross sections, which were then passed to PROTEUS in an external file. PROTEUS calculates the fission/power density, and Warthog uses DTK to pass this information to BISON, where it is used as the heat source. BISON then calculates the temperature profile of the pin cell and sends it back to XSProc to obtain the temperature corrected cross sections. This process is repeated until the convergence criteria (tolerance on BISON solve, or number of time steps) is reached. Models have been constructed and run for both uranium oxide and uranium silicide fuels. These models demonstrate a clear difference in power shape that is not accounted for in a stand-alone BISON run. Future work involves improving the user interface (UI), likely through integration with the Nuclear Energy Advanced Modeling and Simulation (NEAMS) Workbench. Furthermore, automating the input creation would ease the user experience. The next priority is to continue coupling the work with other codes in the SHARP package. Efforts on other projects include work to couple the Nek5000 thermo-hydraulics code to MOOSE, but this is in the preliminary stages.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Development of Macroscale Models of UO 2 Fuel Sintering and Densification using Multiscale Modeling and Simulation

Light water reactor fuel pellets are fabricated using sintering to final densities of 95% or greater. During reactor operation, the porosity remaining in the fuel after fabrication decreases further due to irradiation-assisted densification. While empirical models have been developed to describe this densification process, a mechanistic model is needed as part of the ongoing work by the NEAMS program to develop a more predictive fuel performance code. In this work we will develop a phase field model of sintering of UO 2 in the MARMOT code, and validate it by comparing to published sintering data. We will then add the capability to capture irradiation effects into the model, and use it to develop a mechanistic model of densification that will go into the BISON code and add another essential piece to the microstructure-based materials models. The final step will be to add the effects of applied fields, to model field-assisted sintering of UO 2 . The results of the phase field model will be validated by comparing to data from field-assisted sintering. Tasks over three years: 1. Develop a sintering model for UO 2 in MARMOT 2. Expand model to account for irradiation effects 3. Develop a mechanistic macroscale model of densification for BISON

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

Overview of lower length scale model development for accident tolerant fuels regarding U 3 Si 2 fuel and FeCrAl cladding

U 3 Si 2 and FeCrAl have been proposed as fuel and cladding concepts, respectively, for accident tolerance fuels with higher tolerance to accident scenarios compared to UO 2 . However, a lot of key physics and material properties regarding their in-pile performance are yet to be explored. To accelerate the understanding and reduce the cost of experimental studies, multiscale modeling and simulation are used to develop physics-based materials models to assist engineering scale fuel performance modeling. In this report, the lower-length-scale efforts in method and material model development supported by the Accident Tolerance Fuel (ATF) high-impact-problem (HIP) under the NEAMS program are summarized. Significant progresses have been made regarding interatomic potential, phase field models for phase decomposition and gas bubble formation, and thermal conductivity for U 3 Si 2 fuel, and precipitation in FeCrAl cladding. The accomplishments are very useful by providing atomistic and mesoscale tools, improving the current understanding, and delivering engineering scale models for these two ATF concepts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗