GCR Methods Area Overview & International Collaborations
Presentation of GCR Methods Area Overview & International Collaborations
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
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Summary Report of the FY24 DOE Contributions to the GIF VHTR CMVB
The Generation-IV Forum (GIF) Very-High-Temperature Reactor-Computational Methods Validation and Benchmark (VHTR-CMVB) initiative, involving organizations from Korea Atomic Energy Research Institute (KAERI) (South Korea), Institute of Nuclear and New Energy Technology of Tsinghua University (INET) (China), U.S. Department of Energy (DOE) (U.S.), Joint Research Centre (JRC) (Europe), and Japan Atomic Energy Agency (JAEA) (Japan), is dedicated to the verification and validation of tools for High-Temperature Gas-Cooled Reactors (HTGRs) analysis, using data shared by Computational Methods Validation and Benchmark (CMVB) signatories. For FY24, the US DOE CMVB has committed to several critical activities. Under WP1, led by the US, the integration of the High Temperature Gas Cooled Reactor - Pebble-Bed Module (HTR-PM) Phenomena Identification and Ranking Table (PIRT) into the comparative PIRT is progressing, with a new draft of the comparison tables issued earlier this year and currently being utilized by INET for their contribution. Neutronic validation efforts under WP3 include the preparation of the burnup analysis benchmark, preliminary calculations, and the development of reference models and results. In WP2, a validation exercise for hot gas mixing in the lower plenum of HTR-PM is in progress, using experimental data from INET (China) to validate modeling approaches. A model of the experimental facility has been developed using StarCCM+, with initial calculations slated for presentation at the GIF CMVB meeting this fall. Another WP2 activity focuses on validating numerical models for air-cooled Reactor Cavity Cooling System (RCCS) with experimental data from the Wisconsin Madison RCCS facility. A high-fidelity model, developed using NEK-RS, is currently being validated with available data from a low power forced convection test. These efforts are aimed at enhancing and confirming the accuracy of HTGR analysis tools, ensuring their alignment with experimental data and regulatory requirements.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Temperature sensitivity of the equilibrium neutronics and accident analysis of the HTR-10
Pebble-Bed High-Temperature Gas-cooled Reactors (PB-HTGR) are moderated by the graphite in the fuel pebbles and the graphite reflector surrounding the pebble-bed. Because graphite is by far the most abundant material in PB-HTGRs and the primary moderator, accurate modeling of the graphite material, including density, impurities, and temperatures, is crucial for accurate computational modeling and simulation of these reactors. While main characteristics of the graphite components are often known, the local temperature is less well known and often averaged over all components. Here, this work studies the impact of considering accurate temperature profiles in the graphite material on the generation of a small PB-HTGR model at the state of equilibrium operation and on short-term accident progression. The fuel compositions for the PB-HTGR were determined using a jump-in equilibrium modeling method, the Axial Radial Zone Equilibrium Modeling (AR-ZEM) method. In contrast to previous work, the AR-ZEM method was used considering thermal-hydraulic feedback from the MELCOR code to determine temperatures of the fuel pebbles and the surrounding graphite reflector. The consideration of an axial and radial temperature profile in the core and reflector, as opposed to uniform material temperatures, had an impact of almost 1,300 pcm on the equilibrium core eigenvalue and caused significant differences in the discharged plutonium fuel inventory with up to 4.9% and 11.0% for Pu-239 and Pu-242, respectively. To assess the impact on short-term accident progression, two Anticipated Transient Without SCRAM (ATWS) events, a Pressurized Loss of Forced Coolant (PLOFC) and a Control Rod Withdrawal (CRW) with loss of flow, were simulated with MELCOR. The use of temperature profiles in the equilibrium core models did not reveal a significant impact on the temperature, power, or reactivity responses during the transients. In conclusion, a need for consideration of accurate temperature profiles, in particular for the graphite reflector, was found for the generation of equilibrium PB-HTGRs core models using jump-in methods, but detailed temperature profiles may not be necessary when performing conservative transient analysis.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
RELAP5-3D Capabilities for High-Temperature Gas-Cooled Reactor Analysis
RELAP5-3D is the premier systems thermal-hydraulic code in the world. The code has been used significantly for high-temperature gas-cooled reactor (HTGR) analyses over the last 10 years, but there has not been a comprehensive summary of RELAP5-3D capabilities and methods for HTGR modeling. In this paper, we provide a guide for how novice users can build models for prismatic HTGRs. This guide includes best practices for developing heat structures and setting up radiation and conduction heat transfer. We also provide a summary of relevant validation studies. These studies are divided into three categories: separate effects tests, integral effects tests, and nuclear reactor studies. We present a comprehensive review of RELAP5-3D HTGR applications for reactor modeling over the last 10 years. These applications include work for the Modular High-Temperature Gas-Cooled Reactor benchmark, design activities for a prismatic HTGR as part of the U.S. Department of Energy’s Advanced Demonstration and Test Reactor Options Study, and studies performed to design gas-cooled microreactors. Following the comprehensive literature review, we present a gap analysis highlighting capabilities of RELAP5-3D that are available and missing for a complete analysis of HTGR thermal hydraulics. We also discuss the limitations of existing code capabilities and potential improvements that could improve analyses. Current research efforts have yet to fully validate RELAP5-3D for a prismatic HTGR analysis; however, some of this is attributable to confounding factors in the available experimental data as opposed to code capabilities. In conclusion, this paper presents the most comprehensive review to date on RELAP5-3D capabilities, limitations, and applications for HTGR analysis.
22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
Preliminary Process and Instrumentation Design of Advanced Reactor Integration with Refineries and Hydrogen Production Facilities
With the ongoing push to decarbonize energy use and especially greenhouse gas emissions across all sectors, there are incentives to investigate how nuclear reactors may be used to generate clean energy and be used in various energy economies beyond just the electrical grid. Two initial integrations, high temperature steam electrolysis (HTSE) and oil refineries, are investigated in this first DOE Integrated Energy Systems (IES) program detailed industrial integration design report. Increasingly detailed reports are anticipated both for the industries discussed in this report and for additional industries in the future of the program. This report is a robust starting point showing how integration thermodynamic analysis establishes the requirements on the reactor and methods by which those requirements can be evaluated for specific reactor designs. Two Advanced Reactor Demonstration Program awardees are selected as representative designs for their respective technologies: NuScale for light-water reactors (LWRs) and X-Energy for high temperature gas reactors (HTGRs). Other reactor technologies or specific reactor configurations would require specific analysis similar to what is done in this report, thus this report can be a reference point by which to extend this work to other nuclear plant designs.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Equilibrium core modeling of a pebble bed reactor similar to the Xe-100 with SCALE
As the nuclear industry moves towards licensing and constructing advanced reactors, new attention has been focused on the advanced reactor designs that have past operational experience, such as pebble-bed high-temperature gas-cooled reactors (PB-HTGRs). Pebble-bed reactor designs have many advantages, such as their higher operating temperatures and online refueling capabilities. However, high-fidelity computational modeling of pebble-bed reactor designs, from reactor startup to operation at equilibrium, is more challenging compared to conventionally fueled reactors due to the continuous movement of the fuel pebbles through the reactor during operation. In previous work at Oak Ridge National Laboratory (ORNL), the SCALE Leap-In method for Cores at Equilibrium (SLICE) was developed around tools within the SCALE code system. This iterative method can effectively generate pebble-bed reactor zone-wise fuel inventories at equilibrium core operation within a reasonable computational time. The objective of this work was to further verify the ORNL SLICE method and to investigate the impact of considering temperature profiles during the application of the method. The SLICE method was applied to a modular high-temperature gas-cooled reactor design based upon publicly available design specifications of the Xe-100 pebble-bed reactor. Upon comparing the results from the SLICE method to published literature, the differences in the eigenvalue k effective were on the order of several hundred pcm (percent millirho). To investigate one possible cause of these differences, a study looking at the sensitivity of the full-core equilibrium k effective and discharge nuclide inventory to temperature was performed by developing equilibrium cores of two additional temperature profiles. From this temperature study, differences on the order of hundreds of pcm for the full-core equilibrium k effective , and up to 15% difference for the discharge inventories were found. In conclusion, these results indicated the strong dependence on temperature that needs to be considered for future work in equilibrium modeling of PB-HTGRs.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Reduced-Order Modeling of Multigroup Neutron Cross Sections for High-Temperature Gas-cooled Reactors
Abstract – Deterministic neutronics calculations rely on multigroup neutron cross section libraries, which consist of databases of tabulated values, used to calculate the neutron cross sections through multivariate linear interpolation. However, interpolation of the multidimensional cross section data becomes memory inefficient and time consuming as the number of tabulations increases, significantly slowing down the neutronics calculation, especially in the case of microscopic cross section libraries where every isotope (on the order of hundreds) has its own set of specific reactions and cross sections. In order to address this challenge, this work constructs efficient and robust reduced-order models (ROMs) of the multi-group cross sections to support the Griffin simulation of high-temperature gas-cooled reactors (HTGRs). The first part of the study investigates the linearity of the multigroup cross section data across isotopes, reaction types, and energy groups on pre-generated datasets for the purpose of dimensionality reduction. Secondly, a down-selection of ROM techniques is presented on representative classical machine learning (ML) techniques, including variants of linear regression, kernel-based methods, tree-based algorithms, and artificial neural networks. The selection criteria jointly consider the memory efficiency, predictive accuracy, prediction speed, and scalability in comparison to the multidimensional interpolation. Among all the ML techniques, deep neural networks (DNNs) have proven to be the best selection with sufficient accuracy, high robustness, good memory efficiency, great scalability, and superior flexibility. DNNs have been trained for all isotopes in this work and systematic Griffin testing is ongoing to ensure the feasibility of this ROM technique for predicting cross section and reducing memory requirements without a significant sacrifice in computational performance.
42 - ENGINEERING↗
Advanced Cross Section Library Generation using Reduced Order Models
Deterministic neutronics calculations rely on multigroup neutron cross section libraries, which consist of databases of tabulated values, used to calculate the neutron cross sections through multivariate linear interpolation. However, interpolation of the multidimensional cross section data becomes memory inefficient and time consuming as the number of tabulations increases, significantly slowing down the neutronics calculation, especially in the case of microscopic cross section libraries where every isotope (on the order of hundreds) has its own set of specific reactions and cross sections. In order to address this challenge, this work constructs efficient and robust reduced-order models (ROMs) of the multi-group cross sections to support the Griffin simulation of high-temperature gas-cooled reactors (HTGRs). The first part of the study investigates the linearity of the multigroup cross section data across isotopes, reaction types, and energy groups on pre-generated datasets for the purpose of dimensionality reduction. Secondly, a down-selection of ROM techniques is presented on representative classical machine learning (ML) techniques, including variants of linear regression, kernel-based methods, tree-based algorithms, and artificial neural networks. The selection criteria jointly consider the memory efficiency, predictive accuracy, prediction speed, and scalability in comparison to the multidimensional interpolation. Among all the ML techniques, deep neural networks (DNNs) have proven to be the best selection with sufficient accuracy, high robustness, good memory efficiency, great scalability, and superior flexibility. DNNs have been trained for all isotopes in this work and systematic Griffin testing is ongoing to ensure the feasibility of this ROM technique for predicting cross section and reducing memory requirements without a significant sacrifice in computational performance.
42 - ENGINEERING↗
Reduce-Order Modeling of Multigroup Neutron Cross Sections for High-Temperature Gas-cooled Reactors
Deterministic neutronics calculations rely on multigroup neutron cross section libraries, which usually consists of a database of tabulated values, used to calculate the cross sections through multivariate linear interpolation. However, interpolation of the multidimensional cross section data becomes memory inefficient and time consuming as the number of tabulations increases, significantly slowing down the neutronics calculation, especially in the case of micro cross section libraries where every isotope (on the order of hundreds) has its own set of specific reactions and cross sections. To address this challenge, this work constructs efficient and robust reduced-order models (ROMs) of the multi-group cross sections to support the Griffin simulation of high-temperature gas-cooled reactors (HTGRs). The first part of the study investigates the linearity of the multi-group cross section data across isotopes, reaction types and energy groups on pre-generated datasets for the purpose of dimensionality reduction. Secondly, a down-selection of ROM techniques is presented on representative classical machine learning (ML) techniques, including variants of linear regression, kernel-based methods, tree-based algorithms, and artificial neural networks. The selection criteria jointly consider the memory efficiency, predictive accuracy, prediction speed, and scalability in comparison to the multidimensional interpolation. Among all the ML techniques, deep neural networks (DNNs) have proven to be the best selection with sufficient accuracy, high robustness, good memory efficiency, great scalability, and superior flexibility. DNNs for have been trained for all isotopes in this work and systematic Griffin testing is ongoing at this moment to ensure the feasibility of this ROM technique for cross section predictions.
42 - ENGINEERING↗
Technical and Economic Assessment and Gap Analysis of Advanced Nuclear Reactor Integration with a Reference Methanol Synthesis Plant
Efforts continue to identify the most-economic methods to decarbonize several sectors of the United States (U.S.) economy. Industrial processes such as synfuel synthesis and high value commodity chemicals rely heavily on energy-dense and easily stored and transported fossil fuels, which power and feed their operations. Steam methane reforming (SMR) is a widely used process for producing methanol. In this process, methane (CH 4 ) from natural gas (NG) reacts with steam (H 2 O) over a catalyst at high temperatures (700-1,000°C) to produce syngas, a mixture of hydrogen (H 2 ) and carbon monoxide (CO). The syngas is then converted into methanol (CH 3 OH) through a second catalytic reaction. This method is known for being an efficient and commonly employed pathway for industrial methanol production. The high-temperature heat needed for SMR, which is currently used in the natural-gas-to-methanol process, cannot be supplied by small modular nuclear reactor (SMNR) direct heating; the temperatures required for the SMR process exceed those of the main steam produced by near-market high-temperature gas reactors (HTGRs). For the conventional methanol process, this leaves possible nuclear-integration opportunities that include: (1) blending nuclear hydrogen into the SMR NG fuel, or (2) assessing alternative synthesis routes leveraging nuclear capabilities and steam electrolysis outputs. In the reference methanol plant, SMR provides the methanol-synthesis reactor with H 2 and co. In Case (2), the state-of-the-art reverse water gas shift (RWGS) pathway achieves the same, sourcing carbon from an industrial CO 2 source.
08 HYDROGEN↗
Technical and Economic Assessment and Gap Analysis of Advanced Nuclear Reactor Integration with a Reference Methanol Synthesis Plant
Efforts continue to identify the most-economic methods to decarbonize several sectors of the United States (U.S.) economy. Industrial processes such as synfuel synthesis and high value commodity chemicals rely heavily on energy-dense and easily stored and transported fossil fuels, which power and feed their operations. Steam methane reforming (SMR) is a widely used process for producing methanol. In this process, methane (CH 4 ) from natural gas (NG) reacts with steam (H 2 O) over a catalyst at high temperatures (700°1,000°C) to produce syngas, a mixture of hydrogen (H 2 ) and carbon monoxide (CO). The syngas is then converted into methanol (CH 3 OH) through a second catalytic reaction. This method is known for being an efficient and commonly employed pathway for industrial methanol production. The high-temperature heat needed for SMR, which is currently used in the natural-gas-to-methanol process, cannot be supplied by small modular nuclear reactor (SMNR) direct heating; the temperatures required for the SMR process exceed those of the main steam produced by near-market high-temperature gas reactors (HTGRs). For the conventional methanol process, this leaves possible nuclear-integration opportunities that include: (1) blending nuclear hydrogen into the SMR NG fuel, or (2) assessing alternative synthesis routes leveraging nuclear capabilities and steam electrolysis outputs. In the reference methanol plant, SMR provides the methanol-synthesis reactor with H 2 and co. In Case (2), the state-of-the-art reverse water gas shift (RWGS) pathway achieves the same, sourcing carbon from an industrial CO 2 source.
08 - HYDROGEN↗
Scoping Analysis of Pebble-Bed Reactors for the Destruction of the Transuranic Inventory of LWR Spent Nuclear Fuel
With the forecasted increase in the construction and operation of nuclear reactors, there will be a corresponding increase in the quantity of spent nuclear fuel (SNF) that requires long-term storage. In SNF, transuranic isotopes contribute the most to the long-term radiotoxicity of the fuel and pose a proliferation risk. One option that has been explored to address these issues is the removal of the transuranic isotopes from SNF and the conversion of these isotopes into transuranic fuel (TRU fuel). Here, this work sought to determine how effective a micro-modular Pebble-Bed High-Temperature Gas-Cooled Reactor (PB-HTGR); the 10-MW High Temperature Gas-cooled Test Reactor (HTR-10); and a salt-cooled small-modular pebble-bed reactor (PBR), i.e. the generic Fluoride-cooled High-temperature Reactor (gFHR), are at reducing the inventory of transuranic isotopes while still maintaining the intrinsic safety features of the PBR designs, such as negative temperature coefficients of reactivity. Optimized pebble designs utilizing TRU fuel were found for both reactors through the adjustment for the packing fraction of fuel in each pebble. The Axial Zone Equilibrium Modeling (A-ZEM) method was used in this work to help select the optimized pebble design. Once an optimized pebble design was selected and an equilibrium model was produced, the results from the deep burn (DB) HTR-10 and gFHR designs were compared to the results of two models from the literature. While both the DB gFHR and the DB HTR-10 were able to reduce the weapons-usable transuranic inventory, the performance of these reactors did not match that of the small-modular PB-HTGRs in the literature. Therefore, a need was identified for further refinement of the gFHR design using TRU fuel, as the results for this model were more promising than those of the DB HTR-10, which was strongly limited by the high leakage intrinsic to micro-modular PB-HTGRs.
Transuranic fuel↗
HTGR Multiphysics Application Drivers FY26 Updates
This report summarizes FY26 progress under the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program's high-temperature gas-cooled reactor (HTGR) application driver work, covering a wide range of activities such as code validation and multi-physics code assessment. 1) A detailed SAM model of the High-Temperature Engineering Test Reactor (HTTR) was developed using a unique-block grouping approach, with an extended parallel thermal network method to capture block-to-block conduction and radiation heat transfer, and applied to steady-state simulations of the HTTR 30~MW and 9~MW cases. 2) In another activity, SAM's newly implemented multi-component gas flow model was validated against the Natural convection Shutdown heat removal Test Facility (NSTF) argon ingress experiment, correctly capturing the density-driven suppression and thermal recovery of natural circulation observed when argon is introduced into the air-cooled Reactor Cavity Cooling System (RCCS) loop. 3) For the OECD/NEA High Temperature Test Facility (HTTF) benchmark, we co-led the international benchmark activities as well as the OECD/NEA final benchmark report to be released at the end of this year. 4) Finally, the coupled Griffin-SAM modeling capability for pebble-bed HTGRs was advanced by verifying the Griffin neutronics solution against Serpent Monte Carlo for a realistic non-uniform temperature distribution, resolving several deficiencies in the SAM-to-Griffin temperature transfer scheme, and enabling distinct fuel kernel, moderator, and coolant temperatures for cross section feedback. These new features were demonstrated in a PBR load-following transient.
Lee, Alvin↗
Hydrogen uptake in graphite matrix at high temperature
Tritium management is a critical challenge for the next generation of nuclear reactors, such as Fluoride Salt Cooled High Temperature Reactors (FHRs) and High Temperature Gas-cooled Reactors (HTGRs), due to the higher production rate (up to 10,000 times) than conventional Light Water Reactors (LWRs). Graphitic materials employed as moderator, reflector, and fuel pebbles offer a potential pathway for tritium recovery by serving as a sink for tritium. Prediction of uptake capacity under reactor relevant conditions remains a challenge due to a lack of low partial pressure data and significant inter-grade variability of graphite. This study addresses these gaps by providing a comprehensive characterization of hydrogen (as a tritium surrogate) uptake and release behavior in the A3-3 graphite matrix (GM) used in fuel pebbles. Uptake measurements are performed at reactor relevant temperatures of 600- 800 °C, 1-200 Torr hydrogen pressure, and 15-120 min equilibration time, followed by thermal desorption spectroscopy up to 1100 °C. Uptake experiments at different equilibration times demonstrate the role of kinetics in hydrogen uptake, which can be modeled as a diffusion-with-trapping process. In the thermodynamics limit, the Sips adsorption model is shown to capture the uptake in A3-3 GM well. Our campaign provides a set of new results for hydrogen uptake in A3-3, including limiting uptake capacity at 600 °C, apparent diffusion coefficient at 600 °C, and the first estimates of the FHR/HTGR relevant (600 °C, 20 Pa partial pressure) equilibrium uptake capacity and time to saturation. Desorption data highlights a new site for hydrogen uptake, not observed in nuclear graphite, which we attribute to the non-graphitized binder. Using the Kissinger method, we estimate activation energy for release from the desorption peaks, confirming the activation energy for release from the basal planes and providing the first estimate for the activation energy of release from the binder.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗