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DOE OSTI2022

Multilevel Analysis, Design, and Modeling of Coupling Advanced Nuclear Reactors and Thermal Energy Storage in an Integrated Energy System

This report discusses the different options for coupling thermal energy storage (TES) systems to advanced nuclear power plants (A-NPPs) in order to enable flexible and hybrid plant operation. An advanced light-water reactor (A LWR), a high-temperature gas-cooled reactor (HTGR) and a liquid-metal fast reactor (LMFR) were selected as the initial use cases for demonstrating a thermally balanced energy storage coupling design for thermal power extraction. The models presented herein showcase several design considerations, focusing on optimal deployment methodologies for achieving steady-state and transient-state operation with minimum disruption to the nuclear power cycle. This first part of the study presents steady-state models developed using Aspen HYSYS®, with the thermal energy bypass for NPP-TES coupling being varied at up to 50%. The various components were sized using the Aspen Process Economic Analyzer (APEA) and Aspen Exchanger Design and Rating (EDR), when applicable. Cost functions from these models were developed using the latest publicly available data obtained from APEA V11. The TES-coupled A-NPP steady-state models and cost functions then provided a baseline for dynamic operation and process optimization by using Idaho National Laboratory (INL)’s Framework for Optimization of Resources and Economics (FORCE) tools. A stochastic optimization of the various energy storage systems coupled to the A-NPPs was then performed using the Risk Analysis Virtual Environment (RAVEN) and its dispatch optimization plugin, the Holistic Energy Resource Optimization Network (HERON). The signal processing and synthetic history capabilities of RAVEN were used to account for the unpredictable behavior of electricity markets. An autoregressive moving average (ARMA) model was used to analyze price signals from the Pennsylvania-New Jersey-Maryland (PJM) market and were applied to the HERON analysis in order to optimize a system with the best economics. Transient modeling evaluation was then performed using Modelica models within the HYBRID repository, which was developed at INL for the Department of Energy Integrated Energy Systems program for the characterization of dynamic integrated system behavior and feedback. This includes evaluation of the TES-coupled A-LWR systems’ impact on physical and thermal system response during imposed system demands. Additional TES-coupled reactor types, coupling approaches, markets, and TES technologies will be evaluated in future work.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Documentation on How to Run the NEAMS Workbench GUI on Sawtooth

This document provides guidelines on how to run Moose-based applications from the NEAMS WorkbenchGUI on the HPC platform Sawtooth located at Idaho National Laboratory. The different steps are illustrated with a multi-app example modeling a sodium-cooled fast reactor (SFR) taken from the virtual test bed (VTB) website. The workflow consists of four steps to demonstrate the capabilities of the NEAMS Workbench GUI, that covers log into Sawtooth, editing and validating input files, submission of the job to the queue and visualization of the numerical solution and the geometry. Each step is illustrated with figures in the text, and a demonstration video is also available.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Annual Summary Report for the Remote-Handled Low-Level Waste Disposal Facility—FY2021

This Fiscal Year (FY) 2021 annual summary report (ASR) documents the continued adequacy of the performance assessment (PA), the composite analysis (CA) and associated operating disposal-authorization statement (ODAS) technical basis documents for the Remote Handled (RH) Low-Level Waste (LLW) Disposal Facility at Idaho National Laboratory. Annual review of the adequacy of the PA and CA for the RHLLW Disposal Facility ensures that conclusions of the analyses remain valid in accordance with requirements of Department of Energy (DOE) Order 435.1, “Radioactive Waste Management.” In FY 2021, no significant operational changes or other activities occurred that would cause deviation from the assumptions in the PA and CA pertaining to disposal geometry, verification of waste characteristics, tracking disposal inventories against total limits, facility closure design, or institutional controls. Sixteen total waste-canister shipments were received at the RHLLW Disposal Facility, and 16 total waste canisters were emplaced.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

FORCE-DISPATCHES Integration - Initial Demonstration

Integrated energy systems (IES) combine, in mutually beneficial ways, power from variable renewable energy sources and nuclear power plants (NPP) to improve economic viability under uncertain market and weather conditions. The open-source Framework for Optimization of Resources and Economics (FORCE) tool suite, developed at Idaho National Laboratory (INL), has enabled comprehensive modeling and simulation of IES. The capabilities within FORCE include grid portfolio optimization through the Holistic Energy Resource Optimization Network (HERON) and the transient process model analysis library HYBRID, among others. Continuous efforts and investments from the IES programs have been made to expand and improve the versatility of the FORCE toolset in fiscal year 2022. Code-coupling and cross-tool communication have been important methods for improving this versatility. This report focuses on an additional workflow in the HERON tool for capacity and dispatch stochastic optimization through integration with the external tool Design Integration and Synthesis Platform to Advance Tightly Coupled Hybrid Energy Systems (DISPATCHES). DISPATCHES was primarily developed by the National Energy Technology Laboratory, in collaboration with other national laboratories, which included INL, universities, and industry partners. It is coupled to a library of algebraic models for specific plant components, and to a framework for stochastic optimization different from that provided in the current Risk Analysis Virtual Environment (RAVEN)-running-RAVEN algorithm in HERON. HERON currently conducts stochastic optimization via an outer-inner loop: it optimizes over variable capacity on the outer loop, and at each step within the capacity parameter space, conducts an inner optimization over scenarios (of market signals, demand, and/or weather patterns) and hourly dispatch throughout a user-specified number of years. On the other hand, DISPATCHES conducts stochastic optimization via an “all-at-once” strategy in which capacity variables are optimized at the same level as dispatch variables, as all scenarios are considered at once. The latter method works especially well for projects of limited size and project length, as the necessary computational power and memory increases with the number of variables and scenarios. The new capability to use the DISPATCHES workflow in HERON enhances standalone simulations by leveraging FORCE tools—namely, the economic metrics from the Tool for Economic Analysis (TEAL) and reduced-order model (ROM) sampling from RAVEN. The initial demonstration of the DISPATCHES workflow simulates an existing nuclear-case flowsheet within the DISPATCHES repository—this models a NPP with a secondary revenue stream for hydrogen production. Electrical output from the plant is converted to hydrogen via a proton-exchange membrane (PEM) electrolyzer, hydrogen tanks are used for storage, and an additional turbine is added for hydrogen combustion. Continued work regarding this FORCE-DISPATCHES integration will include automatic generation of DISPATCHES models from HERON inputs, offering analysts the option of using either the RAVEN-runsRAVEN or DISPATCHES workflow to solve technoeconomic optimization problems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Acquire and Test IDAES Framework Components

The cooperation under the Grid Modernization Initiative and the Applied Energy Tri-Laboratory umbrellas requires mutual understanding of capabilities and a common effort to identify gaps existing in the available computational modeling and analysis tools. In this work, we report acquisition and testing of the National Energy Technology Laboratory’s Institute for the Design of Advanced Energy Systems (IDAES) framework, particularly including both electric power systems production cost model simulator Prescient as well as system optimization IDAES itself. Further, we consider the methods in which developments in Prescient and IDAES for grid-energy simulation and optimization may be included in the Framework for Optimization of ResourCes and Economics (FORCE) ecosystem that has been under collaborative development by Idaho National Laboratory, Argonne National Laboratory, and Oak Ridge National Laboratory. We propose two integration methods to introduce Prescient and IDAES into existing FORCE automated workflows. One integration involves using Prescient as a market reduced-order model and IDAES models similar to how existing Modelica models are used. The other integration involves wrapping the Prescient-IDAES dispatch optimization process with the stochastic capacity optimization workflow in FORCE.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Production of Fischer-Tropsch Synfuels at Nuclear Plants

A case study analysis was performed to evaluate nuclear-powered synthetic fuel production in the midwestern United States (U.S.). A Fischer-Tropsch (FT) fuel synthesis plant design was used as the basis for the analysis. The FT plant design was configured to produce a product slate consisting of diesel fuel, jet fuel, and motor gasoline blend stocks from carbon dioxide (CO 2 ) and hydrogen (H 2 ) feedstocks. The CO 2 feedstock for the FT plant was assumed to be sourced from biorefineries in the region around a Midwest light water reactor (LWR) nuclear power plant (NPP). The analysis specifies that power from the LWR is used to produce H 2 via high-temperature steam electrolysis and to operate the FT synfuel production plant. Capital costs were estimated for the FT plant while capital costs for the electrolysis plant were based on previous Idaho National Laboratory (INL) studies. In addition to labor and maintenance costs for the FT and electrolysis plants, operating costs also include the costs for CO 2 feedstock transport. An analysis was performed to determine the cost of transporting CO 2 from the distributed biorefinery sources to the centralized fuel synthesis plant as a function of the synfuel plant capacity and corresponding CO 2 demand. The primary revenue streams are associated with sales of the synthetic fuel products. The synthetic fuel products will likely follow the same market trends as the conventional fuel products. The synfuel price data was thus based on projections made by the U.S. Energy Information Administration (EIA) 2021 Annual Energy Outlook (AEO) for conventional fuel products minus federal and state taxes, as well as marketing and distribution costs. The economic analysis also considered cases that included and excluded revenues from the 2022 Inflation Reduction Act (IRA) clean hydrogen production tax credit (PTC) of $\$ $3.00/kg for the first ten years of operation. The economic analysis calculated the net present value (NPV) for cases involving steady-state synfuel production for comparison with the NPV for a business-as-usual case in which NPP continues to sell only electric power to the grid. A synfuel production “Reference Case” was considered in addition to sensitivity cases in which the plant capacity, electricity price, and synthetic fuel product prices were perturbed. The synfuel production Reference Case considered a scenario in which the electrolysis and synfuel plants utilized a combined electrical load of 1000 megawatt electrical (MWe) from the LWR with the balance of the LWR power output being sold to the electric grid. The economic analysis suggests that the synfuel production Reference Case evaluated in this analysis would lead to considerable economic potential for near-term deployment of a nuclear-based synfuel production plant. Specifically, the economic analysis suggests that the deployment of a 1000 megawatt (MW) nuclear-powered synfuel plant could result in a NPV increase of approximately $\$ $1.7 billion for a case with no clean synfuel price premium relative to conventional petroleum fuels when accounting for the additional revenues from the 2022 IRA clean hydrogen PTCs of $\$ $3/kg. Sensitivity analysis was performed to evaluate the effect of perturbation of selected model input parameters on the NPV for the synfuel production Reference Case. The sensitivity analysis indicates that the plant capacity has the largest impact on the differential NPV, with a smaller synfuel production capacity resulting in a decrease in revenue when a larger fraction of the power from the NPP is sold to the grid and a smaller fraction of the power is used to produce synthetic fuel products. The synfuel product pricing has the next largest impact on the differential NPV, with lower synfuel prices resulting in decreased NPV from decreased synfuel sales revenue while higher synfuel prices result in increased NPV from increased synfuel sales revenue. Electricity pricing has a smaller effect on the NPV than the fuel sales price since, in the Reference Case, most of the energy from the NPP is used for synfuel production and a smaller amount of the system revenues are associated with electrical power sales. However, the electricity price sensitivity does indicate that the Synfuel Integrated Energy System (IES) would have a greater NPV than the business-as-usual case (e.g., grid power sales only) when electricity market prices are low, suggesting that synfuel production could provide a strategy for decreasing the economic risks to NPPs posed by a loss of revenues attributed to falling electricity market prices.

10 SYNTHETIC FUELS↗
DOE OSTI2021

Scalable Technologies Achieving Risk-Informed Condition-Based Predictive Maintenance Enhancing the Economic Performance of Operating Nuclear Power Plants

The primary objective of the research presented in this report is to develop scalable technologies that are deployable across plant assets and across the nuclear fleet to achieve risk-informed predictive maintenance (PdM) strategies at commercial nuclear power plants (NPPs). Over the years, the nuclear fleet has relied on labor-intensive and time-consuming preventive maintenance (PM) programs, driving up operation and maintenance (O&M) costs to achieve high capacity factors. A well-constructed risk-informed PdM approach for an identified plant asset has been developed in this research, taking advantage of advancements in data analytics, machine learning (ML), artificial intelligence (AI), physics-informed modeling, and visualization. These technologies would allow commercial NPPs to reliably transition from current labor-intensive PM programs to a technology driven PdM program, eliminating unnecessary O&M costs. The work presented in the report is being developed as part of a collaborative research effort between Idaho National Laboratory and Public Service Enterprise Group Nuclear, LLC. This report (1) reflects the results of work by LWRS Program researchers with PSEG, Nuclear LLC-owned Salem and Hope Creek Nuclear Power Plants; (2) presents utilization of circulating water system (CWS) heterogeneous data and fault modes from both the Salem and Hope Creek nuclear power plant sites to develop salient fault signatures associated with each fault mode; (3) describes the integration of component-level predictive models into a robust system-level model enabled by the federated-transfer learning; (4) describes the development of physics-informed model of circulating water pump and motor; (5) develops a scalable risk and economic model; and (6) outlines the development of a user-centric visualization application. The outcomes presented in this report lays the foundation and provides a much-needed technical basis to focus on explainability and trustworthiness of ML and AI-based technologies, as part of future research.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Energy Equity and Environmental Justice Summit Report 2022

Pacific Northwest National Laboratory (PNNL) hosted a limited-engagement Energy Equity and Environmental Justice (EEEJ) Summit on September 28, 2022, with the Department of Energy (DOE) Office of Economic Impact and Diversity (DOE-ED) and DOE national laboratories: Argonne National Laboratory, Idaho National Laboratory, Lawrence Livermore National Laboratory, National Renewable Energy Laboratory, Oak Ridge National Laboratory, Sandia National Laboratories, and Savannah River National Laboratory. The goal of the Summit was for labs to highlight their capabilities in the EEEJ space and connect them to DOE-ED’s mission and goals as well as share lessons learned and best practices in our various programmatic spaces. The event commenced with an opening address from Director Shalanda Baker (DOE-ED), which included a call to action for the national labs. Three presentation panels followed, giving insights to the programs stewarded by national laboratories that are currently taking steps to enhance the way science is implemented through equity and justice lenses. A roundtable discussion concluded the Summit and highlighted lessons learned, gaps, challenges, opportunities, and future needs that will improve the impact and partnership of EEEJ work at DOE, national laboratories, at other agencies and institutions, and in other programmatic areas. Interest among participants to stay connected and coordinated and to partner on EEEJ work across labs was emphasized, including a suggestion to establish an intra-laboratory community of practice.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Autonomous System Subversion Tactics: Prototypes and Recommended Countermeasures

One of the fielding requirements for Advanced and Small Modular Reactors (AR/SMR) is the ability to support remote and autonomous operations. Autonomous Control Systems (ACS) are found on platforms such as Autonomous Space Vehicles, Cruise Missiles, and advanced driver-assistance systems. Each of these ACS implementations depends upon a set of decision support subsystems responsible for supporting Autonomous Mission Managers (names vary based upon field and author preferences). These Autonomous Mission Managers receive inputs from system sensors (e.g., LIDAR collection from an automobile travelling down a street; transients from a nuclear reactor), and perform a set of classifications (e.g., Red Traffic Light; Small Pedestrian at 10m; Load Rejection; Single Coolant Pump Trip), and then use these classifications in combination with recommendation algorithms to achieve platform goals (e.g., Stop the Vehicle at the Traffic Light, Avoid the Small Pedestrian; Trip the Reactor to prevent a Safety Event). The design, implementation, and fielding of an ACS capability will alter the cyber-attack surface such that existing risk management plans will need to be updated to include how to protect and defend against data-science and decision-support-system attack classes. These attack classes would include protection of the design and training environments where algorithm selection and testing and training data would be obvious attack vectors. These attack classes would also require an informed set of detection and response procedures to identify anomalous behaviors and document best practices for anomaly assessment and vulnerability mitigation and remediation. Last year we published a Cyber Threat Assessment Methodology for Autonomous and Remote Operations for AR/SMRs along with a companion publication on Cyber Attack and Defense Use Cases. The focus of the methodology was on describing and enumerating ACS processes, components, and functions such that security engineers could: evaluate subversion options against the target; identify threat actor attributes and capabilities derived from each subversion option; and identify security controls and response countermeasures. The Use Cases document offered detailed methodology examples including an assessment of a Military Base SMR, an Autonomous System Decision Loop, and implementation of AR/SMR Machine Learning algorithms. Our proposal at the end of last year was to focus on implementation of subversion prototypes related to the last Use Case area: AR/SMR Machine Learning (ML) Algorithms. We included six attack scenarios in our Use Cases paper: a Poisoning Attack against ML functions implemented using an FPGA; a Trojaning Attack against ML classifiers exploiting the excitability of Nuclear Engineers; a Backdooring Attack against ML Training environments to ensure persistence of an attack vector; a False Positive Evasion Attack against multi-factor Access Control Systems using clever inputs; an Inference Attack against ML models by an Insider with access to the Operational environment; and an Adversarial Reprogramming Attack against a Material Access Control Video Surveillance System. At the beginning of this year these six attack scenarios were provided to our research teams at Georgia Tech and Idaho State University and each team successfully implemented a subversion attack against a ML implementation to include transient misclassifications. While this is a notable outcome from this type of research, this paper offers the reader insight into not only how to structure and execute these types of attacks, but into the thought process behind how the researcher investigated the problem space, performed initial algorithm implementation, and the trial-and-error behind arriving at the successful subversion prototypes. We include in this paper a set of associated Scenarios on how these subversion prototypes could be implemented and an initial set of guidance for AR/SMR architects, Nuclear Regulators, and Cyber Defenders to implement awareness and defense capabilities into their current operational portfolios.

42 ENGINEERING↗
DOE OSTI2022

Development of Temperature Compensation Tools for SPNDs Operating in High Temperature Environments

Two rhodium-based self-powered neutron detectors (Rh-SPNDs) were irradiated at the Massachusetts Institute of Technology Reactor as a follow-on experiment to the heated irradiation previously conducted at the Neutron Radiography facility at Idaho National Laboratory. The experiment was conducted over the temperature ranges of 600-850°C to further examine the effects of temperature on the Rh-SPNDs. Four tests of varying temperature and power were performed. The tests identified two types of temperature effects consistent with historical evaluations. One effect is a prompt proportionality to temperature at steady-state reactor power due to the decrease in Rh-SPND insulation resistance. The other effect is a delayed effect generated from displacement currents generated by changing the space charge within the insulator as a function of temperature. The result of this experiment demonstrates a characterizable responses to temperature that is feasible for developing a temperature compensation tool for SPND operating in high temperatures. The characteristics identified in this experiment will be integrated within the delayed-response compensation techniques for FY23 evaluation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Woody Feedstocks 2021 State of Technology Report

The U.S. Department of Energy promotes production of advanced liquid transportation fuels from lignocellulosic biomass by funding fundamental and applied research that advances the state of technology (SOT). As part of its involvement with this overall mission, Idaho National Laboratory completes annual SOT reports for n th -plant biomass feedstock logistics. The purpose of the SOTs is to provide the status of feedstock supply system technology development for biomass to biofuels, based on actual data and experimental results relative to technical targets and cost goals from specific design cases.

09 BIOMASS FUELS↗
DOE OSTI2021

Herbaceous Feedstock 2021 State of Technology Report

The U.S. Department of Energy (DOE) promotes the production of advanced liquid transportation fuels from lignocellulosic biomass by funding fundamental and applied research that advances the State of Technology (SOT). As part of its involvement with this mission, Idaho National Laboratory (INL) completes an annual SOT report for biomass feedstock logistics. This report summarizes supply system impacts of Bioenergy Technologies Office (BETO)-funded research and development efforts at INL and INL collaboration with external partners (e.g. Forest Concepts, Purdue University ) that lead to improvements in feedstock supply systems. These include improvements to and observed performance of innovative harvest and collection methods, storage technologies, transportation and handling approaches, and advanced preprocessing technologies. Biomass quality and variability, and the interface between feedstock quality and conversion performance are key drivers in addition to delivered feedstock cost. In this report, we estimate the benefits of R&D technology improvements to individual supply system unit operations and present the status of feedstock logistics technology development for converting herbaceous biomass into biofuels. These analyses are supported by experimental data where possible and help to align the SOT relative to the cost goals defined in the Multi-Year Plan.

09 BIOMASS FUELS↗
DOE OSTI2023

Physical Sciences Vistas: Perspectives on Simultaneous Excellence (Issue 1, 2023)

The issue begins with an article highlighting Physics Division’s involvement in diagnostic development that led to the measurement of ignition at the National Ignition Facility at Lawrence Livermore National Laboratory. Subsequent articles transition to some of the materials development occurring in Sigma, Materials Science and Technology, and Materials Physics and Applications divisions. This is inclusive of the development of next generation moderator materials as an enabling technology of small modular reactors. These materials will most likely be qualified at a facility at Idaho National Laboratory. The issue also highlights development of novel, additively manufactured foams for next generation weapons. This work is being done in close collaboration with the Kansas City National Security Campus to enable transition of materials development to production in a more agile way. Finally, an article describes Accelerator Operations and Technology Division’s hard work to replace the legacy remote instrumentation and control equipment at the Los Alamos Neutron Science Center (LANSCE) with a modern control system. The story discusses how that engineering success will lead to improved sustainability of the beam during operation and thus a better experience for all of our collaborators and partners in the LANSCE user program.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Materials and Fuels Complex FY-23 - FY-27: Five-Year Investment Strategy

The Department of Energy Office of Nuclear Energy (DOE-NE) vision is to “Advance nuclear energy science and technology to meet U.S. energy, environmental, and economic needs.” The Materials and Fuels Complex (MFC) serves as the foundation of a nuclear RD&D enabling test bed at Idaho National Laboratory (INL) and is an integral part of a National Reactor Innovation Center (NRIC) strategy. MFC facilities focus on developing and maintaining RD&D capabilities that can increase research throughput, reduce barriers to deployment, and facilitate commercialization of new ideas and technologies for clean and secure sources of energy.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

An Integrated Framework for Risk Assessment of High Safety Significant Safety-related Digital Instrumentation and Control Systems in Nuclear Power Plants: Methodology and Demonstration

This report documents the activities performed by Idaho National Laboratory (INL) during Fiscal Year (FY) 2022 for the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk Informed Systems Analysis (RISA) Pathway, digital instrumentation and control (DI&C) risk assessment project. In FY 2019, the RISA Pathway initiated a project to develop a risk assessment strategy for delivering a technical basis to support effective and secure DI&C technologies for digital upgrades/designs. A framework was proposed for this strategy, which aims to (1) provide a best-estimate, risk-informed capability to quantitatively and accurately estimate the risk impact of plant modernization, considering the introduction of high safety-significant safety-related (HSSSR) DI&C systems, (2) support and supplement existing risk-informed DI&C design guides by providing quantitative risk information and evidence, (3) offer a capability of design architecture evaluation of various DI&C systems, (4) assure the long-term safety and reliability of HSSSR DI&C systems, and (5) reduce uncertainty in costs and support integration of DI&C systems in the plant. To achieve these technical goals, the framework provides a means to address relevant technical issues by: (1) defining a risk-informed analysis process for DI&C upgrade, that integrates hazard analysis, reliability analysis, and consequence analysis, (2) applying risk-informed tools to address common cause failures (CCFs) and quantify corresponding failure probabilities for DI&C technologies, particularly software CCFs, (3) evaluating the impact of digital failures at the component level, system level, and plant level, and (4) providing insights and suggestions on designs to manage the risks, thus to support the development and deployment of advanced DI&C technologies on nuclear power plant (NPPs).

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

National Reactor Innovation Center (NRIC) Grid Connection Roadmap

In an effort to define the roadmap for establishing the necessary electrical infrastructure at the Idaho National Laboratory to fully support connection of NRIC reactors, this roadmap serves to identify the current forecasted reactors planned for installation at INL and provide a consolidated list of the identified infrastructure improvements that will be necessary to support testing of NRIC reactors. Additionally, the roadmap will leverage this information to establish an integrated timeline to support detailed planning and execution of system improvements. To drive this planning, requirements, as derived from authoritative sources, will be identified. A clear definition of impacted stakeholders will be established, as well as a list of risks that will need to be managed as the program moves forward.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Assembly of MiniFuel Targets for Irradiation of U-Mo Fuel Specimens in the High Flux Isotope Reactor

To support the development of advanced low-enriched uranium for use in nuclear reactors, irradiation testing of U-Mo disk specimens was performed at the Oak Ridge National Laboratory (ORNL) High Flux Isotope Reactor (HFIR) to collect experimental irradiation data on this type of fuel at pressurized water reactor–relevant temperatures. U-Mo is a uranium alloy that has superior dimensional stability relative to alpha-phase uranium metal and has a substantially higher uranium density compared to UO 2 . U-Mo disks specimens were fabricated at Idaho National Laboratory and inserted into MiniFuel targets for HFIR irradiation. Three MiniFuel targets were successfully assembled, welded, tested, and delivered to HFIR, along with their quality assurance documentation. The targets were inserted into HFIR’s inner vertical experiment facility within the permanent beryllium reflector. Each target contains six disk specimens and will be irradiated in HFIR for three, four, and eight cycles, with target temperatures between 250 and 500°C. This report summarizes the experiment design, test matrix, pre-characterization of specimens, and experiment assembly.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

LLNL Macroscopic Anisotropic Explosives Research at INL National Security Test Range - Test Results

A select team of 23 engineers, scientists, and explosives specialists from LLNL, LANL, INL, and Marine Raiders from Marine Special Operations Command (MARSOC) and U.S. Special Operations Command (SOCOM) assembled during the second week of November at the INL National Security Test Range near Idaho Falls to investigate and demonstrate fundamental principles of explosives anisotropy. Today's explosives are isotropic in their detonation performance. That is, no matter what direction a detonation runs through bulk explosive, the performance is the same; whereas, anisotropic explosives exhibit different performance, depending on which direction the detonation wave moves through the explosive. The ANISO Team worked in subfreezing temperatures on the Snake River Plain, carrying out 55 experimental explosives shots in four days that lead to a clear understanding of the performance and behavior of an assembly of small, linerless, C4 shaped charges. These shots clearly demonstrated, for the first time, on a macroscopic scale, the principle of anisotropy in measured progression of the detonation through the explosive assembly. The outputs of nine piezo timing pins in the explosive assembly clearly showed detonation progressing through the assembly faster than nominal detonation velocity and moving slower than nominal detonation velocity in the opposite direction. Basic data from these experiments will be used to design and construct explosives assemblies that will be shot in the LLNL High Explosives Applications Facility's (HEAF). These experimental tests will provide refined basic data that will then be used by modelers to develop high explosives models. Computer simulations using these models will then be run to predict performance and design inhomogeneous, anisotropic bulk explosive charges that will be tested at LLNL.

33 ADVANCED PROPULSION SYSTEMS↗