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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning

Deep learning has shown great success in settings with massive amounts of data but has struggled when data is limited. Few-shot learning algorithms, which seek to address this limitation, are designed to generalize well to new tasks with limited data. Typically, models are evaluated on unseen classes and datasets that are defined by the same fundamental task as they are trained for (e.g. category membership). One can also ask how well a model can generalize to fundamentally different tasks within a fixed dataset (for example: moving from category membership to tasks that involve detecting object orientation or quantity). To formalize this kind of shift we define a notion of “independence of tasks” and identify three new sets of labels for established computer vision datasets that test a model's ability to generalize to tasks which draw on orthogonal attributes in the data. We use these datasets to investigate the failure modes of metric-based few-shot models. Based on our findings, we introduce a new few-shot model called Fuzzy Simplicial Networks (FSN) which leverages a construction from topology to more flexibly represent each class from limited data. In particular, FSN models can not only form multiple representations for a given class but can also begin to capture the low-dimensional structure which characterizes class manifolds in the encoded space of deep networks. We show that FSN outperforms state-of-the-art models on the challenging tasks we introduce in this paper while remaining competitive on standard few-shot benchmarks.

deep learning↗
DOE OSTI2022

Improvements to the Griffin Transport Solvers

Griffin is a Multiphysics Object-Oriented Simulation Environment (MOOSE) based reactor multiphysics analysis application jointly developed by Idaho National Laboratory and Argonne National Laboratory. The code includes a variety of steady-state solvers for fixed-source, k-eigenvalue, adjoint, and subcritical multiplication, as well as transient solvers for point-kinetics, improved quasi-static, and spatial dynamics. This document summarizes the transport solver development efforts pursued during Fiscal Year 2022. We added the multiphysics transient capability for the coarse-mesh finite difference accelerated Richardson iteration for discontinuous finite element method discrete ordinates (DFEM-SN) scheme to support high-order heterogeneous transport simulations. HFEM (hybrid finite element method) - PN (spherical harmonics expansion) was completed and red-black iteration was added for solving the HFEM-PN system with both preconditioned Jacobian-free Newton Krylov and Richardson iteration solvers. The HFEM-PN scheme, as one of the low-order transport schemes, is expected for supporting routine design simulations. Pin power reconstruction capability was also designed and implemented with the Griffin ISOXML module to enhance all the low-order transport solvers for more accurate multiphysics simulations. Numerical results are presented for demonstrating the capabilities and verifying their performance, and future works are discussed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Comparison of Fission Product Release Predictions using PARFUME and BISON with Results from the AGR-3/4 Irradiation Experiment

The PARFUME (PARticle Fuel ModEl) fuel performance modeling code and the BISON nuclear fuel performance application built on the Multiphysics Object-Oriented Simulation Environment (MOOSE) finite element library were used to predict the fission product release from tristructural isotropic (TRISO) coated fuel particles and compacts during the third and fourth irradiation experiment of the Advanced Gas Reactor (AGR-3/4) Fuel Development and Qualification Program. The fuel performance modeling codes PARFUME and BISON modeled the AGR-3/4 irradiation experiment using the fuel compact time-averaged volume averaged (TAVA) daily temperatures for a total irradiation duration of 369.1 effective full power days (EFPD) to predict the release fraction of the fission product silver (Ag-110m) from a representative TRISO-coated fuel particle from AGR-3/4 compacts. Post-irradiation examination (PIE) measurements provided data on the release of these fission products in the compacts outside of the silicon carbide (SIC) layer. The PARFUME and BISON results were then compared to the silver release measured from compact gamma scanning. The results showed good agreement between PARFUME and BISON but both codes under-predicted the silver release fraction for all the compacts. In addition, BISON was used to model and predict the fission product concentration radial profile outside of the compacts in capsules’ inner and outer rings. These rings were either comprised of matrix and/or structural graphite. To obtain the concentration profiles of silver, cesium, and strontium, a sorption isotherm model was developed in BISON to capture the effects of fission product transport across the gaps between the concentric rings. The general shape of the concentration radial profiles as calculated by BISON were similar in the inner ring (IR) but varied in the outer ring (OR) depending on the fission product of interest or capsule temperature. Using this methodology and model, BISON now has the capability to aid in developing new fission product diffusion coefficients for matrix or structural graphite materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

BISON As-run AGR-3/4 Irradiation Test Predictions

BISON, a nuclear fuel performance application built using the Multiphysics Object-Oriented Simulation Environment (MOOSE) finite element library, was used to model the Advanced Gas Reactor (AGR)-3/4 irradiation test using as-run physics and thermal hydraulics data. The AGR-3/4 test consists of the combined third and fourth planned irradiations of the AGR Fuel Development and Qualification Program. The AGR-3/4 test train consists of twelve separate and independently controlled and monitored capsules. Each capsule contains four compacts filled with both uranium oxycarbide (UCO) unaltered “driver” fuel particles and UCO designed-to-fail (DTF) fuel particles. The DTF fraction was specified to be 1×10-2. This report documents the calculations performed to predict the failure probability of tristructural isotropic (TRISO) coated driver fuel particles during the AGR-3/4 experiment on a single compact. This report will demonstrate the capabilities of BISON to model the complex AGR-3/4 irradiation test and identify further development needed to capture the fuel particle failure probability and source term on every compact for further comparison from post-irradiation examination (PIE) data. The calculations include the modeling of the AGR-3/4 irradiation that occurred from December 2011 to April 2014 in the Advanced Test Reactor (ATR) over a total of 10 ATR cycles including seven normal cycles, one low power cycle, one unplanned outage cycle, and one Power Axial Locator Mechanism (PALM) cycle.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Demonstrate Improved Ag Diffusion and Describe the Basis for Pd Penetration Modeling in SiC

In past work, an effective diffusivity coefficient was determined for Ag transport through the silicon carbide layer of a tristructural isotropic fuel particle. The effective diffusivity coefficient accounts for the microstructure of the silicon carbide and includes both bulk diffusion and grain boundary diffusion of Ag. In this report, the model has been improved by accounting for the enhanced concentration of vacancies in the bulk due to irradiation, which substantially influence bulk diffusivity at low temperatures. To improve the BISON model and make it fission rate dependent, effective diffusivity calculations have been performed that incorporate the radiation modified bulk diffusivity. The microstructure and irradiation-dependent effective diffusivity has also been implemented into BISON, and its predictions for Ag release from tristructural isotropic fuel have been successfully compared to AGR-1 post irradiation measurements. Moreover, a new feature has been developed in the Multiphysics Object-Oriented Simulation Environment (MOOSE) to account for different grain boundary types. The Ag diffusivity in 5 (210)/[001] grain boundaries has been computed and was found to be greater than in random high-angle grain boundaries. The presence of the fission product Pd can also have an important effect on the properties of the silicon carbide layer in tristructural isotropic particles. The penetration of Pd into the silicon carbide layer causes a corrosion reaction that can lead to the failure of the silicon carbide layer; however, this corrosion reaction is not well understood. To enable an improved understanding of the mechanism, ab-initio molecular dynamics simulations of Pd interaction with bulk silicon carbide have been performed. The improved understanding of the reaction will form a basis for future improvements to the BISON’s Pd penetration failure model

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Continued Validation Studies using the MOOSE Framework for Plasma Simulation with Electromagnetics

Resources and tools for the modeling and simulation of low-temperature plasma (LTP) discharges are increasingly vital to progress in the field in order to properly characterize and study complex source designs and plasma chemistries beyond the scope of traditional diagnostics. Open-source software provides powerful platforms for this work and can enable community-driven LTP R&D. Within the Multiphysics Object-Oriented Simulation Environment (MOOSE) open-source framework [1], capabilities have been demonstrated in the areas of plasma fluids (Zapdos [2]), plasma chemistry (CRANE [3]), and general electromagnetic wave theory (Electromagnetic Library for Kinetics & fluids [ELK] [4]). ELK has since been coupled to Zapdos/CRANE to enable fully coupled electromagnetic plasma simulations. This talk will detail the continued validation efforts and discuss Zapdos-ELK-CRANE code coupling with various-low temperature plasma sources. The impact of fully coupled electromagnetics on process parameters (e.g., temperature and electron/ion energy) versus an electrostatic description will also be discussed. [1] Permann et al., SoftwareX, 11 (2020) 100430. [2] Lindsay et al., J. Phys. D: Appl. Phys. 49 (2016) 235204. [3] Keniley et al., Talk, GEC Session DT3.00002 (2019). [4] Icenhour et al., Talk, GEC Session RR1.00006 (2019). Acknowledgements: INL Graduate Fellowship Program, NSF SI2 Grant 1740300, U.S. Dept. of Energy Office of Science Graduate Student Research Program

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

On Practical Aspects of Variational Consistency in Contact Dynamics

Usage of contact mechanics methodologies is a pervasive modeling requirement in dynamic simulations. While for some trivial problems, solutions taken from analytical geometry are available, use of a finite element framework is common to achieve formulation generality. This work explores two dynamic contact formulations: one based on the traditional node-to-segment (NTS) approach, and a variationally consistent segment-to-segment (STS) mortar formulation. The NTS formulation employed here enforces the constraints kinematically (i.e., the interpenetration is enforced to the solver tolerance), whereas the mortar approach uses Lagrange multipliers to enforce the contact constraints. Both approaches are implemented in the open-source finite element framework Multiphysics Object-Oriented Simulation Environment (MOOSE). The results highlight two relevant contact-interface-related dynamic phenomena in finite element simulations. First, stabilization of contact constraints is discussed, taking into account the evolution of the total energy in a benchmark problem. Second, the influence of finite element discretization on both of the aforementioned contact formulations is analyzed by exercising a large-deformation example with continuous relative sliding. Variationally consistent contact approaches such as the mortar formulation lead to improved energy preservation and avoid spurious excitation of the system's frequencies. This is especially relevant in settings where inertia and vibrations are of importance.

42 ENGINEERING↗
DOE OSTI2023

Gas-Liquid Flow Modeling for Renewable Fuels Production

Aerobic/anaerobic and gas fermentation pathways have emerged as promising new technologies for the generation of renewable fuels/chemicals from biomass derived sugars, and mixtures of greenhouse/energy rich gas streams (CO2/CH4/H2/CO) via microbial action. Example pathways include sugars-to-ethanol conversion, biomethanation (CO2/H2 to CH4), biogas upgrading, CO fermentation and wet-waste conversion. Gas and liquid phase transport, mass-transfer, and mixing physics at large length scales can significantly affect microbial conversion rates, particularly when the microbial reaction requires a narrow set of conditions. These phenomena are difficult to study in small-scale bench-top reactors that are typically well-mixed. Predictive computational fluid dynamics (CFD) based simulations can therefore aid in the scale-up, design and optimization of these reactors. This work presents multiphase Euler-Euler CFD simulations of at-scale (~500 m3) bioreactors. Our mathematical model treats the gas and liquid as interpenetrating phases. This approach reduces the computational complexity of tracking individual gas bubbles that are several orders of magnitude smaller than reactor dimensions. We solve the Reynolds averaged Navier-Stokes (RANS) multiphase equations that account for phase and chemical species transport, interphase mass and momentum transfer and uses a phenomenological model for gas uptake by microbes. We use a customized solver derived from open-source CFD toolbox, OpenFOAM [1], to perform these simulations, which has been validated against small-scale reactors in our previous work [2]. There is currently a knowledge-gap regarding bubble-size distributions when using gas mixtures with vastly different properties, which can have a significant impact overall mass-transfer. For example, hydrogen bubbles are more buoyant compared to other relatively heavier gases (CO2/CH4/CO), resulting in a large distribution of residence times and bubble sizes. This work therefore develops a deeper understanding of bubble dynamics and interphase mass transfer in such heterogenous gas mixtures through well-resolved computational models. We use a population balance model (PBM) for bubble-size-distribution modeling that is validated against small-scale experiments in our solver with an uncertainty quantification study for bubble coalescence and break-up model parameters. Results pertaining to multiple simulations of gas-fermentation reactors are presented where gas mixtures with varying compositions of CO2/CH4/CO/H2 are imposed at the sparger boundaries. The spatio-temporal variations in bubble-size distribution and mass transfer coefficient are analyzed for varying superficial velocities and gas-compositions for varying sizes of bubble-column and airlift reactors. This work will also examine the performance of different reactor designs, viz. bubble column reactor, airlift reactor with an internal draft tube, and a stirred-tank reactor with Rushton impellers. Reactor mass-transfer coefficient, gas hold-up, and dissolved gas distribution are critically analyzed among reactors, and sensitivity studies pertaining to gas flow rates and reactor geometry will be presented. [1] Weller, H., Tabor, G., Jasak, H. and Fureby, C., A tensorial approach to computational continuum mechanics using object-oriented techniques, Computers in physics, 12, 6, 620--631, 1998. [2] Rahimi, M., Sitaraman, H., Humbird, D. and Stickel, J., Computational fluid dynamics study of full-scale aerobic bioreactors: Evaluation of gas-liquid mass transfer, oxygen uptake, and dynamic oxygen distribution, Chemical Engineering Research and Design, 139: 283-295.

BIOMASS FUELS↗
DOE OSTI2020

BISON Robustness and Performance Improvements

BISON is a modern finite-element based nuclear fuel performance code that has been under development at the Idaho National Laboratory (USA) since 2009 [1]. The code is applicable to both steady and transient fuel behavior and can be used to analyze 1D (spherically symmetric), 2D (axisymmetric and generalized plane strain) or 3D geometries. BISON is the fuel performance code used within CASL for LWR fuel under both normal operating and accident conditions. BISON is built using the INL Multiphysics ObjectOriented Simulation Environment, or MOOSE [2, 3]. MOOSE is a massively parallel, finite element-based framework to solve systems of coupled non-linear partial differential equations using the Jacobian-Free Newton Krylov (JFNK) method [4]. This enables investigation of computationally large problems, for example a full stack of discrete pellets in a LWR fuel rod, or every rod in a full reactor core. MOOSE supports the use of complex two and three-dimensional meshes and uses implicit time integration, important for the widely varied time scale in nuclear fuel simulation. An object-oriented architecture is employed which greatly minimizes the programming effort required to add new material and behavioral models. The flexibility of the implicit and fully coupled multiphysics approach comes with a need for constructing suitable approximations for the Jacobian matrix of the coupled system used for either preconditioning a Krylov solve or in a direct Newton solve. Preconditioning options for Bison problems need to be revisited with new preconditioning methods becoming available.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Multi-scale modeling of the evolution of structure and properties in materials for nuclear energy applications

Nuclear energy is an important component of an overall strategy to address climate change. Idaho National Laboratory (INL) is the U.S. Department of Energy’s primary facility for research and development in nuclear science and technology for energy generation, supporting the improvement and life extension of the existing reactor fleet and the development and licensing of new reactor designs. Computational modeling is an important component of these activities, particularly in the area of materials for nuclear applications, where experimental data can be very challenging and expensive to acquire, and where data is especially scarce for new reactor designs. INL has used multi-scale modeling – linking atomistic, mesoscale, and engineering scales – to improve the ability to predict the performance of materials for nuclear energy applications. These modeling efforts make extensive of MOOSE (Multiphysics Object-Oriented Simulation Environment), a general-purpose open source finite element framework developed at INL. In this talk, I will give an overview of the approach and tools used, and several examples of application, including performance of nuclear fuels, understanding radiation-driven formation of nanoscale void and gas bubble superlattices, and powder densification through electric field assisted sintering.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Structure and Process of Managing the UO2 Dry In-Pile Fracture Test Irradiation Experiment

The management and structure of a novel research and development (R&D) project can be hard to navigate. This is especially true when it involves nuclear fuel. Familiarity with institutional safety and procedural requirements, a well defined scope, and accurate cost estimates can all help ensure successful project management from start to finish. The Dry In-Pile Fracture Test (DRIFT) experiments performed at the Transient Test Reactor Facility are an excellent example of managing an R&D project within its scope and budget constraints. The objective of the DRIFT test was to develop fracture propagation data in a manner consistent with light-water reactors to validate and improve the fracture propagation models of uranium oxide fuels in the MOOSE (Multiphysics Object-Oriented Simulation Environment)-BISON-Marmot code framework [1].

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Multi-scale modeling of the evolution of structure and properties in materials for nuclear energy applications

Nuclear energy is an important component of an overall strategy to address climate change. Idaho National Laboratory (INL) is the U.S. Department of Energy’s primary facility for research and development in nuclear science and technology for energy generation, supporting the improvement and life extension of the existing reactor fleet and the development and licensing of new reactor designs. Computational modeling is an important component of these activities, particularly in the area of materials for nuclear applications, where experimental data can be very challenging and expensive to acquire, and where data is especially scarce for new reactor designs. INL has used multi-scale modeling – linking atomistic, mesoscale, and engineering scales – to improve the ability to predict the performance of materials for nuclear energy applications. These modeling efforts make extensive of MOOSE (Multiphysics Object-Oriented Simulation Environment), a general-purpose open source finite element framework developed at INL. In this talk, I will give an overview of the approach and tools used, and several examples of application, including performance of nuclear fuels, understanding radiation-driven formation of nanoscale void and gas bubble superlattices, and powder densification through electric field assisted sintering.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Utilizing GEOPHIRES-X Beyond Electricity

The GEOPHIRES tool is a techno-economic simulator for evaluating the thermal performance and cost-competitiveness of geothermal plants for electricity, heating, and/or cooling. The tool combines reservoir, wellbore, and surface plant cost and performance models to estimate overall techno-economic metrics such as net present value or levelized cost of electricity, heating, or cooling. We recently upgraded the tool to an object-oriented Python framework, presented in an accompanying paper. As part of the upgrade, we enhanced the capability to simulate the performance of geothermal plants for heating and cooling, which is the topic of this paper. Specifically, we (1) integrated absorption chillers to investigate the performance of utilizing geothermal heat for cooling, (2) integrated a heat pump module to boost the geothermal temperature and thermal output, (3) integrated a district heating module to estimate heating demand for a district based on local weather data, and simulated heat supply with geothermal energy and peaking boilers, and (4) integrated GEOPHIRES as an engine in the dGeo simulator to perform a geospatial analysis of geothermal district heating feasibility across a large region (e.g., a state or the entire United States) utilizing resource and thermal demand maps. This paper presents background information and case studies for several of these heating and cooling end-use options in GEOPHIRES.

absorption chiller↗
DOE OSTI2019

A Python Package for Fuel Cycle and Waste Management Analysis - 19545

The future of nuclear power utilization in the United States may involve a transition from the once-through light water reactor fuel cycle to a so-called advanced fuel cycle. An advanced fuel cycle may offer improvements in natural resource utilization, reactor safety, and environmental impact. In addition, adoption of an advanced fuel cycle will result in changes in the amount, composition, and disposal of the nuclear wastes they produce. Characteristics intrinsic to the wastes themselves, such as mass and radioactivity at specific times, were used as waste management metrics in the Department of Energy 2014 Fuel Cycle Evaluation and Screening (FCES) study. Binning of these metrics informed fuel cycle according to relative potential benefit. However, waste properties alone cannot qualify management strategy. Therefore, comparing fuel cycles from the perspective of waste management can provide additional insights. To that end, an object-oriented Python package has been developed to perform fuel cycle waste management calculations. The package, named nwpy (short for Nuclear Waste analysis in Python), mimics the mass flow operations reported in the FCES study and interfaces with the ORIGEN-S code to assess metric data as reported in the FCES. In addition, this Python package determines the loading of waste streams into forms suitable for final disposal in a geological repository and uses such data for repository performance assessment. This work describes the Python package methodology, assumptions, and capabilities. As input data, the package requires information about the fuel cycle such as the mass of fuel discharged, the cooling time between reactor discharge and subsequent operations, and the separations process efficiencies and waste forms it produces. In addition, the isotopic composition of the reactor discharge stream, such as that available from the FCES database, is required. With that, it can perform prescribed mass flow and decay operations throughout the back end of the fuel cycle, the latter carried out by the ORIGEN-S code distributed with the SCALE6.2 software suite. The result is time-dependent data characterizing the waste on a per-package basis. Package loading for waste streams depends on the stream form (i.e. UNF vs. HLW, or glass HLW from aqueous reprocessing vs. ceramic HLW from electrochemical processing) and is based on literature review and engineering assumption. As an example of the application of the code, calculations for the areal footprint of direct-contact repositories for different fuel cycles is shown. Development is ongoing, and the package is expected to grow incorporating capabilities for waste management analyses such as waste transportation and radionuclide transport through the biosphere. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

235-F GoldSim Fate and Transport Model: Uncertainty Quantification

Building 235-F was configured with two missions in mind: Actinide Billet Line (ABL) and the fabrication of Pu-238 oxide for space program applications. ABL produced Np-237 billets for use in SRS reactors, whereas the design process, fabrication, and examination of Pu-238 oxide powder occurred within the following areas, respectively: Plutonium Experimental Facility (PEF), Plutonium Fuel Form (PuFF), and Old Metallography Lab (OML). By 1990 production ceased and by 2006 de-inventory occurred; however, assays have shown significant holdup remains within ABL and PuFF. As a result, 235-F is a Category 2 nuclear facility, with plans to undergo deactivation and decommission (D and D) via In-Situ Disposal (ISD). The purpose of this project is to ensure United States Environmental Protection Agency (USEPA) groundwater radiation maximum contaminant level (MCL) and dosage standards are met during the D and D of 235-F by quantifying uncertainty through probabilistic modeling and evaluation of various ISD alternatives. GoldSim is a dynamic modeling software package with a graphical, object-oriented interface capable of capturing the influence of complex system input variability on probabilistic system outcomes. A GoldSim stochastic fate and transport model for 235-F was developed and matched with a PORFLOW deterministic model to simulate probabilistic release and flow of radionuclides from ABL and PuFF into the vadose zone, the Upper Three Runs (UTR) Aquifer, and UTR Creek. The GoldSim model was used to probabilistically evaluate four ISD scenarios against USEPA groundwater radiation MCLs and dosage standards. The deterministic 235-F GoldSim fate and transport model continues to be refined to match the results of the PORFLOW deterministic model to ensure the model accurately represents radionuclide movement through the groundwater system. The stochastic variables that are utilized within the GoldSim model are founded on the most current data; a conservative perspective is taken where needed. Alignment with the PORFLOW deterministic model, coupled with input stochastic variability, allows the probabilistic 235-F GoldSim model to capture the conservative breadth of possible outcomes for radionuclide fate within this particular system. This ensures that the USEPA MCLs and dosage limits hold even in the worst case scenarios.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Scientific Machine Learning using MOOSE

A machine learning interface has been recently developed for the Multiphysics Object-Oriented Simulation Environment (MOOSE). This presentation summarizes the motivation behind using machine learning in scientific computing together with several examples for applications.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Sockeye Heat Pipe Analysis Code Verification and Validation

Some of the most promising microreactor designs currently under development utilize heat pipe technology to transfer heat from the reactor core to the secondary side heat exchanger, due to the technology’s compactness, efficiency, passivity, and reliability. Sockeye is an engineering-scale heat pipe tool developed under the Nuclear Energy Advanced Modeling and Simulation Program to be used for the design and safety analysis of microreactors. Sockeye’s core capability lies in a 1D, two-phase, compressible flow model, used to simulate the working fluid inside the heat pipe. Sockeye is built on the Multiphysics Object-Oriented Simulation Environment framework, which allows for seamless multiphysics coupling with other Nuclear Energy Advanced Modeling and Simulation tools and can thus be used in a full-scale simulation of a microreactor assembly, which can include hundreds of heat pipes. This paper presents some initial verification and validation assessments performed for Sockeye. We demonstrate good agreement between Sockeye’s numerical results and steady-state analytic solutions for velocity and pressure drop, with differences attributable to the underlying assumptions made by the analytic solutions. We also show that Sockeye reproduces analytic predictions of key operational limits, such as the capillary limit and the sonic limit. Finally, we compare Sockeye results to experimental data for the SAFE-30 heat pipe module test.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Multigroup cross section generation capability in GRIFFIN

GRIFFIN is an advanced reactor multiphysics application built on the object-oriented simulation environment (MOOSE) and is jointly developed by Idaho National Laboratory and Argonne National Laboratory. The cross section application programming interface, originally developed for the PROTEUS code, has been integrated into GRIFFIN to prepare cross sections for thermal reactor applications with heterogeneous geometries. Additional improvements have been made by implementing an on-the-fly slowing down method, a double heterogeneity treatment capability, and updating the procedure to generate the fine multigroup library. The cross section preparation capability in GRIFFIN was verified for graphite-moderated TRISO fuel-based reactor benchmark problems: unit-cell problems of VHTR and EMPIRE micro reactor and HTTR assembly problems. Eigenvalues and multigroup cross sections of GRIFFIN agreed very well with those of the continuous-energy Monte Carlo code Serpent2 within 200 pcm in eigenvalue and 2% in cross sections. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗