Fuel Performance Design Basis for the Versatile Test Reactor
Not Available
SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Not Available
The poster entitled "TRISO Fuel for High-Temperature Passively-Safe Nuclear Reactors" presents reasons why TRISO fuel is a safe nuclear fuel. The poster contains four blocks: Multiple barriers to fission product release, Thermal stability, Chemical compatibility and Safety and Performance. Each block present information on the TRISO fuel in terms of the mechanical, chemical and thermal properties. In summary the safety and performance of the TRISO fuel based on the experimental results is shown.
High-density fuels have been proposed as a possible replacement for uranium-dioxide as a fuel for light water reactors (LWRs) due to their increased loading of fissionable material. The objectives of this proposal are (1) to increase reactor cycle length and reactor power, and (2) to offset any neutronic penalty associated with advanced cladding systems. Of the high-density fuels under consideration, there is particular interest in triuranium disilicide (U 3 Si 2 ) due to its increased metal density and favorable thermal properties as compared to UO 2 . However, there are concerns regarding the chemical compatibility of U 3 Si 2 with water and steam as used for LWR coolant.This paper summarizes research on fuel-coolant chemical compatibility for UO 2 LWR fuel during a cladding breach and highlights that generally, because of its chemical inertness, UO 2 -coolant reactions are of little consequence to reactor operation. However, the volumetric expansion associated with the reaction of UO 2 and oxygen is a concern for possible conditions encountered during air ingress of dry storage. These same concerns arise for U 3 Si 2 , which exhibits greater volumetric expansion than UO 2 when exposed to water or steam. These reactions ultimately result in increased fuel volume that the cladding must accommodate, as well as additional heat generated as the fuel reacts.The BISON fuel performance code was used to perform a comparative analysis on the behavior of UO 2 and U 3 Si 2 under normal operation. Silicide fuel simulations were then extended to demonstrate how varying thermodynamic and chemical kinetics influence fuel expansion and subsequent cladding performance during a cladding breach. These simulations were further extended to a 3D subsection of a fuel rod to demonstrate the characteristics of the resulting cladding crack.
The growth rate of structural defects in nuclear fuels under irradiation is intrinsically related to the diffusion rates of the defects in the fuel lattice. The generation and growth of atomistic structural defects can significantly alter the performance characteristics of the fuel. This alteration of functionality must be accurately captured to qualify a nuclear fuel for use in reactors. Predicting the diffusion coefficients of defects and how they impact macroscale properties such as swelling, gas release, and creep is therefore of significant importance in both the design of new nuclear fuels and the assessment of current fuel types. In this article, we apply data-driven methods focusing on machine learning (ML) to determine various diffusion properties of two nuclear fuels—uranium oxide and uranium nitride. We show that using ML can increase, often significantly, the accuracy of predicting diffusivity in nuclear fuels in comparison to current analytical models. We also illustrate how ML can be used to quickly develop fuel models with parameter dependencies that are more complex and robust than what is currently available in the literature. In conclusion, these results suggest there is potential for ML to accelerate the design, qualification, and implementation of nuclear fuels.
This report documents work performed under the Spent Fuel and Waste Disposition’s Spent Fuel and Waste Science and Technology program for the US Department of Energy (DOE) Office of Nuclear Energy (NE). This work was performed to fulfill Level 2 Milestone M2SF-21OR010201032, “ORNL High Burnup Confirmatory Demo Sibling Rod Testing Results,” within work package SF-21OR01020103 and is an update to the work reported in M2SF-19ORO010201026 and M2SF-19OR010201028.
TRistructural ISOtropic (TRISO) particle fuel is a type of nuclear fuel known for its high-temperature and high-burnup performance. Each sub-millimeter diameter TRISO particle consists of uranium-oxycarbide (UCO) or UO2 fuel kernel, coated with buffer, inner pyrolytic carbon (IPyC), silicon carbide (SiC), and outer pyrolytic carbon (OPyC) layers. The SiC layer acts as the main containment barrier for the TRISO particle to retain the fission products, while the IPyC and OPyC layers provide additional barriers to the release of fission products, especially fission gases. During irradiation, phenomena like kernel swelling, buffer densification, and IPyC fracture may impact fuel performance. Post-irradiation microscopy on entire compact cross sections or samples of individual particles deconsolidated from compacts is often used to identify these irradiation-induced changes in morphology. However, each fuel compact generally contains thousands of TRISO particles. To get statistical information on these phenomena, it is cumbersome work if done manually. For example, to get information about swelling/densification behaviors of different layers or kernels after irradiation, researchers previously manually measured the perimeter of each TRISO layer in hundreds of particles after four rounds of iterative grinding and polishing encompassing more than 2000 cross-section images for a total of four fuel compacts. To attempt to reduce the subjectivity inherent in that process and accelerate data analysis, we conducted a study on the automatic TRISO layer segmentation on cross-sectional microscopic images using Convolutional Neural Networks (CNNs). CNNs are a class of machine learning algorithms specifically designed for processing structured grid data that have gained popularity in recent years due to their remarkable performance in various computer vision tasks, including image classification, object detection, and image segmentation. In this research, we have generated the large irradiated TRISO layer dataset with more than 2000 cross-section TRISO microscopic images and the corresponding annotated images. Based on these annotated images, we have employed different CNNs for automatic segmentation of different TRISO layers. These include RU-Net (developed in this study), as well as three existing architectures: U-Net, Residual Network (ResNet), and Attention U-Net. The preliminary results show that the model based on RU-Net has the best performance in terms of intersection-over-union (IoU). Through the aid of these CNN models, we can expedite the analysis of TRISO particle cross-sections, significantly reducing the manual labor involved and improving the objectivity of the segmentation results.
Oak Ridge National Laboratory (ORNL) is investigating respirable aerosol fractions from failed high-burnup (HBU) commercial spent nuclear fuel (CSNF) rods (Montgomery et al., 2022). The work aims to characterize the source term of radioactive aerosols that could result from mechanical failure and handling of failed fuel rods during storage and/or transportation. The experiment involves capturing and characterizing aerosols from a four-point bending experiment of fueled rod segments at ORNL’s Irradiated Fuels Examination Laboratory (IFEL). For this, a Marple cascade impactor (Series 290) with the ability to collect particles up to aerodynamic equivalent diameters of ~15 μm was tested for feasibility. The commercially available Marple cascade impactor design was modified for the experiment, where the cascade inlet was replaced with a 3D printed inlet nozzle. Collection efficiency curves were then generated with the modified impactor, and the cascade’s performance was tested using ISO dust capture experiments followed by SEM analysis of the collection and computational modeling. The current work highlights the performance testing and experimental validation of this modified Marple Impactor for spent nuclear fuel aerosol collection.
H-Canyon is blending solutions from the dissolution of High Flux Isotope Reactor (HFIR) and Material Test Reactor (MTR) fuels with Target Residue Material (TRM) to prepare feed solution for the 1st Cycle of solvent extraction. The initial acid concentration for HFIR fuel dissolution is limited to 7.5 M by the flowsheet; however, an increase in the initial concentration is desirable to more easily achieve the target acidity for solvent extraction using the current blending strategy. To provide flexibility in batching the highly enriched uranium (HEU) solutions, the Savannah River National Laboratory (SRNL) was requested to evaluate the potential for increasing the maximum HNO3 concentration for HFIR fuel dissolution from 7.5 to 8.5 M. In response to this request, a literature review was performed to evaluate the impact of a higher starting HNO3 concentration on the dissolution of aluminum spent nuclear fuels (ASNF) including both HFIR and MTR fuels.
This report represents the milestone deliverable M4SF-22SN010309092 “Modeling Activities Related to Waste Form Degradation: Progress Report” that describes the progress of R&D activities of ongoing modeling investigations specifically on nuclear waste glass degradation, Density Functional Theory (DFT) studies on clarkeite structure and stability, and electrochemical model development of spent nuclear fuel (SNF). These activities are part of the Waste Form Testing, Modeling, and Performance work package at Sandia National Laboratories (SNL). This work package is part of the “Inventory and Waste Form Characteristics and Performance” control account that includes various experimental and modeling activities on nuclear waste degradation conducted at Oak Ridge National Laboratory (ORNL), SNL, Argonne National Laboratory (ANL), and Pacific Northwest National Laboratory (PNNL).
Here we assessed the hydrogen migration and redistribution model implemented in the BISON fuel performance code for component-level tritium transport applications in fusion and fission example scenarios. We developed BISON models of an ITER heat exchanger, light water reactor fuel cladding, and a fluoride salt cooled high temperature reactor heat exchanger. Reasonable agreement between reported values in reference studies and BISON predictions demonstrated the ability of the BISON models to predict tritium transport behavior through steel materials. Next, sensitivity and uncertainty analysis was used to understand key parameter sensitivities within the BISON model for evaluation of tritium migration and redistribution. . The sensitivity analysis showed that the diffusivity activation energy was the most important parameter and that the heat of transport was the least important parameter for tritium migration in steels. Overall, the capabilities of the BISON code for component level modeling of tritium transport are promising.
This report documents an initial evaluation to support future use of disposal control rod assemblies (DCRAs) for post-closure criticality control in dual purpose canisters (DPCs). The work described herein is an extension of previous efforts performed by Walker using inputs generated by the Used Nuclear Fuel-Storage, Transportation & Disposal Analysis Resource and Data System (UNFST&DARDS) for the Zion site DPCs with as-loaded isotopic compositions. The results of this analysis demonstrate that there are multiple pathways to using DCRA for post-closure criticality control. Various configurations of DCRA material, diameter, number of rods per DCRA, and number and location of DCRAs within a DPC were shown to be effective in varying degrees for the set of DPCs analyzed by Walker. Because of the variations in DPC as-loaded isotopic compositions considered in an array of DCRA parametric sweeps, it can be concluded that a DPC-specific methodology is feasible (i.e., a one-size-fits-all approach may not be needed). Instead, the utility program created for this work can be expanded to develop capabilities to provide DPC-specific DCRA arrangements to limit cost and weight and to allow for operational considerations.
The international safeguards regime desires methods to efficiently verify that facilities are only performing declared activities. Electropotential verification (EPV) is a newly proposed technique that was tested for its feasibility to perform facility design information verification (DIV) and verification of spent nuclear fuel while in a cooling pool. EPV works by passing a constant, low voltage current through a conductive system (facility infrastructure of nuclear fuel assembly) and measuring the resulting voltage at various places throughout the infrastructure in order to establish a baseline. Changes made to the system affect these voltage readings, which will deviate from the baseline and indicate that a change to the system was made. For facility DIV, it appears feasible that changes in configuration of the system’s grounding can be detected in real-time, and the location of the change can be inferred from the measured intensity of the change in voltage. Determination of whether or not spent fuel was present in a fuel rod, as well as the presence/absence of a fuel rod from an assembly using EPV, proved unsuccessful with the sensitivity of instrumentation used in this study.
A refined BISON-Griffin workflow for designing targeted TRISO transient analyses in the Transient Reactor Test Facility
Tripling the nation’s nuclear energy capacity is a critical component for significantly increasing energy production and reducing energy costs for American families and businesses. Achieving this vision requires fuel cycle technologies that maximize resource utilization while minimizing radioactive waste generation. Advanced sulfur chloride–based chlorination technologies are being developed to enable efficient recycling of fuel cladding materials, which account for a significant fraction of used nuclear fuel. However, the impacts of ionizing radiation on the longevity and performance of these sulfur chloride compounds are not well established. Here, we will explore the effects of gamma and electron-beam irradiation on the chemical composition of select sulfur chloride reagents, specifically sulfur monochloride (S2Cl2) and thionyl chloride (SOCl2), and the impacts of pre-irradiation on the chlorination yield/chemical dissolution of surrogate aluminum alloy 6061 (AA6061-T6) materials.
This report documents work performed under the Spent Fuel and Waste Disposition’s Spent Fuel and Waste Science and Technology program for the US Department of Energy (DOE) Office of Nuclear Energy (NE). This work was performed to fulfill Level 2 Milestone M2SF-21OR010201032, “ORNL High Burnup Confirmatory Demo Sibling Rod Testing Results,” within work package SF-21OR01020103 and is an update to the work reported in M2SF-19ORO010201026 and M2SF-19OR010201028.
The U.S. Department of Energy is funding research into studying the consequences of postclosure criticality on the performance of a generic repository by (1) identifying the features, events, and processes (FEPs) that need to be considered in such an analysis, (2) developing the tools needed to model the relevant FEPs in a postclosure performance assessment, and (3) conducting analyses both with and without the occurrence of a postclosure criticality and comparing the results. Here, this paper describes progress in this area of research and presents the results to date of analyzing the consequences of a postulated steady-state criticality in a hypothetical saturated shale repository. Preliminary results indicate that postclosure criticality would not affect repository performance.
The economic viability of light-water reactors (LWRs) in the United States is declining in heavily subsidized markets, and as a result, the nuclear industry is looking for opportunities to enhance the economic competitiveness of nuclear power. This is not a foreign concept to the nuclear industry: in the mid-2000s, the nuclear industry set out to achieve zero fuel failures by 2010. The goal in this effort was to drive down the cost of reactor shut down by replacing a pin or bundle in response to fuel rod failure. 2010 brought about the initiative to deliver the nuclear promise to reduce operating cost by 30% to improve nuclear energy’s economic competitiveness before 2020. The emergence of accident-tolerant fuel also offers the nuclear industry an opportunity to build on these past successes and deliver affordable, clean energy. Accident-tolerant fuel has been shown to provide superior performance compared to traditional Zircaloy/UO2 fuel concepts, offering the unique ability to remove operational limitations that inhibit the economic viability of nuclear power. This has led the industry to begin building a technical case to extend the peak rod average burnup beyond 62 GWd/tU to extend pressurized water reactor cycle lengths to 24 months and to develop more efficient boiling water reactor core designs. The Nuclear Energy Advanced Modeling and Simulation (NEAMS) program mission is to develop advanced modeling and simulation tools and capabilities to accelerate the deployment of advanced nuclear energy technologies. The primary safety concern inhibiting the nuclear industry from extending burnup is related to high-burnup fuel fragmentation, relocation, and dispersal. Therefore, the NEAMS program developed a targeted 5-year plan to support the industry’s efforts to extend burnup. This milestone report summarizes the 5-year plan that was enacted in FY20, followed by a discussion of the ongoing activates required to fulfill the 5-year plan, as well as the approach to address the current modeling gaps. Additionally, an LWR stakeholder meeting was held to communicate work performed in the NEAMS program over the past three years, to assess the LWR community’s perspective on the impact of the program, and to identify remaining significant gaps in the NEAMS suite of capabilities. This engagement will be documented by the Electric Power Research Institute and used by NEAMS to redirect current LWR scope as needed and to develop the next phase for LWR research and development.
Presentation for the LWR Fuels Research Workshop on work INL has performed related to RIA modeling and experiments