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DOE OSTI2022

High Flux Isotope Reactor Low Enriched Uranium U-10Mo Fuel Design Parameters

Activities to convert the HFIR from HEU to LEU are ongoing as part of the US Department of Energy (DOE) National Nuclear Security Administration (NNSA) nuclear nonproliferation mission. Design activities to study the conversion of HFIR from HEU to LEU fuel explored different fuel design features and shapes with a uranium-molybdenum (U-10Mo) monolithic alloy fuel. This high-density alloy contains 90 wt % uranium and 10 wt % molybdenum and has a uranium density of 15.318gU/cm 3 . The goal of these studies is to generate several candidate HFIR LEU fuel designs of varying fuel fabrication complexity that meet the current HEU performance metrics and safety requirements. Recent advancements in modeling and simulation tools and design methods enabled a thorough analysis of the available design space with U-10Mo fuel. A surrogate model used this analysis as training data to quickly determine the performance of a design given specific design parameters. An optimization module used this surrogate model to quickly search this multidimensional search space given specific desired performance characteristics. This approach was made possible by the large available design space with U-10Mo fuel. Shift, a Monte Carlo tool optimized for high-performance computing (HPC) architectures, was used for faster calculation and better data management for reactor physics simulations. Once most of these design studies were complete, a new suite called the Python HFIR Analysis and Measurement Engine (PHAME) was developed to connect all fuel design analysis steps, making design studies more efficient and reproducible. The post-processing capabilities of these new tools are leveraged for the information provided herein. Leveraging these tools, several candidate fuel designs were selected with varying levels of feature complexity and reactor performance. This report provides design feature details for four selected HFIR LEU U-10Mo fuel designs and their corresponding performance and safety metrics. Nominal best-estimate design parameters and irradiation conditions, including fission rate densities, power densities, heat fluxes, and cumulative fission densities, are provided. Simulations show that the high uranium density of U-10Mo fuel provides a large potential design space that enables various LEU designs to meet HEU core performance metrics and safety requirements with a power increase from 85 MW (HEU) to 95 MW or 100 MW (LEU).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Towards Lightweight Data Integration Using Multi-Workflow Provenance and Data Observability

Modern large-scale scientific discovery requires multidisciplinary collaboration across diverse computing facilities, including High Performance Computing (HPC) machines and the Edge-to-Cloud continuum. Integrated data analysis plays a crucial role in scientific discovery, especially in the current AI era, by enabling Responsible AI development, FAIR, Reproducibility, and User Steering. However, the heterogeneous nature of science poses challenges such as dealing with multiple supporting tools, cross-facility environments, and efficient HPC execution. Building on data observability, adapter system design, and provenance, we propose MIDA: an approach for lightweight runtime Multi-workflow Integrated Data Analysis. MIDA defines data observability strategies and adaptability methods for various parallel systems and machine learning tools. With observability, it intercepts the dataflows in the background without requiring instrumentation while integrating domain, provenance, and telemetry data at runtime into a unified database ready for user steering queries. We conduct experiments showing end-to-end multi-workflow analysis integrating data from Dask and MLFlow in a real distributed deep learning use case for materials science that runs on multiple environments with up to 276 GPUs in parallel. We show near-zero overhead running up to 100,000 tasks on 1,680 CPU cores on the Summit supercomputer.

Santos Souza, Renan↗

DOE OSTI2024

NEA HTTR LOFC Project Test#3 Benchmark Results

In the second half of FY23, the High Temperature Engineering Test Reactor (HTTR) Loss Of Forced Cooling (LOFC)#3 data for the 9 MW test case with Vessel Cooling System (VCS) off were made available through the Nuclear Energy Agency (NEA) LOFC project framework; the neutronic model developed for the initial test (LOFC#1) achieved a satisfactory level of maturity, demonstrating its accuracy in predicting power evolution and core re-criticality, but LOFC#3 should be used primarily to investigate thermal hydraulic phenomena, as the reactor was shut down prematurely due to overheating in the upper reactor components, which prevented re-criticality; this report focuses on advancing the HTTR thermal hydraulic model to accurately simulate the LOFC#3 scenario, including simulating the LOFC#3 benchmark and generating the corresponding benchmark specifications, aiming to ensure consistency across participant models and provide essential data for future participants, including private industry stakeholders seeking to validate their computational tools.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Quantum Theory of the Classical: Einselection, Envariance, Quantum Darwinism and Extantons

Core quantum postulates including the superposition principle and the unitarity of evolutions are natural and strikingly simple. I show that—when supplemented with a limited version of predictability (captured in the textbook accounts by the repeatability postulate)—these core postulates can account for all the symptoms of classicality. In particular, both objective classical reality and elusive information about reality arise, via quantum Darwinism, from the quantum substrate. This approach shares with the Relative State Interpretation of Everett the view that collapse of the wavepacket reflects perception of the state of the rest of the Universe relative to the state of observer’s records. However, our “let quantum be quantum” approach poses questions absent in Bohr’s Copenhagen Interpretation that relied on the preexisting classical domain. Thus, one is now forced to seek preferred, predictable, hence effectively classical but ultimately quantum states that allow observers keep reliable records. Without such (i) preferred basis relative states are simply “too relative”, and the ensuing basis ambiguity makes it difficult to identify events (e.g., measurement outcomes). Moreover, universal validity of quantum theory raises the issue of (ii) the origin of Born’s rule, p k =|ψ k | 2 , relating probabilities and amplitudes (that is simply postulated in textbooks). Last not least, even preferred pointer states (defined by einselection—environment—induced superselection)—are still quantum. Therefore, unlike classical states that exist objectively, quantum states of an individual system cannot be found out by an initially ignorant observer through direct measurement without being disrupted. So, to complete the ‘quantum theory of the classical’ one must identify (iii) quantum origin of objective existence and explain how the information about objectively existing states can appear to be essentially inconsequential for them (as it does for states in Newtonian physics) and yet matter in other settings (e.g., thermodynamics). I show how the mathematical structure of quantum theory supplemented by the only uncontroversial measurement postulate (that demands immediate repeatability—hence, predictability) leads to preferred states. These (i) pointer states correspond to measurement outcomes. Their stability is a prerequisite for objective existence of effectively classical states and for events such as quantum jumps. Events at hand, one can now enquire about their probability—the probability of a pointer state (or of a measurement record). I show that the symmetry of entangled states—(ii) entanglement—assisted invariance or envariance—implies Born’s rule. Envariance also accounts for the loss of phase coherence between pointer states. Thus, decoherence can be traced to symmetries of entanglement and understood without its usual tool—reduced density matrices. A simple and manifestly noncircular derivation of p k =|ψ k | 2 follows. Monitoring of the system by its environment in course of decoherence typically leaves behind multiple copies of its pointer states in the environment. Only pointer states can survive decoherence and can spawn such plentiful information-theoretic progeny. This (iii) quantum Darwinism allows observers to use environment as a witness—to find out pointer states indirectly, leaving systems of interest untouched. Quantum Darwinism shows how epistemic and ontic (coexisting in epiontic quantum state) separate into robust objective existence of pointer states and detached information about them, giving rise to extantons—composite objects with system of interest in the core and multiple records of its pointer states in the halo comprising of environment subsystems (e.g., photons) which disseminates that information throughout the Universe.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Preliminary analysis of TREAT free-field experiments using openmc

This work analyses activation calculations for dosimetry materials during a steady-state irradiation in the Transient Reactor Test (TREAT) reactor core. Hence, we developed a workflow based on the Monte Carlo code OpenMC alongside a custom depletion solver. The irradiation-induced activity as a function of time is computed, and several sensitivity studies are performed to evaluate uncertainty. This study has shown activity computations are sensitive to flux amplitude, irradiation time, atoms quantity and microscopic cross sections. Stochastic uncertainties have been propagated to evaluate the activity uncertainty for each dosimetry material. Most uncertainties are below our target of 3%, which demonstrates OpenMC as a powerful predictive and analysis tool. The precise results obtained through this newly developed computation scheme will be used in future experiments to characterize quantities of interest when operating the TREAT reactor in new configurations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Preliminary analysis of TREAT free-field experiments using OpenMC

61 - RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2023

Characterization of Fe-6Si Soft Magnetic Alloy Produced by Laser-Directed Energy Deposition Additive Manufacturing

Abstract Commercial electrical steels, Fe-Si alloys with < 4 wt.% Si, are inexpensive and efficient materials for electrical power conversion. Further efficiency improvements require increasing the silicon concentration to 6 wt.%, at which point the material becomes brittle and difficult to form by conventional rolling and sheet fabrication methods. Additive manufacturing stands to overcome challenges with commercial manufacturing techniques by leveraging near-net-shape fabrication. The wide array of process conditions provides additive manufacturing with increased flexibility, enabling control over the microstructure and mechanical properties. This work explores the microstructures and magnetic properties of ring-shaped Fe-Si alloys produced using concentric and cross-hatch tool paths on a laser-directed energy deposition additive manufacturing system. Concentric-built samples exhibit elongated grain structures while cross-hatch-built samples comprise lower aspect ratio grain structures. Thermal finite element analysis simulations model the stress conditions produced by the different scan path geometries. Microhardness measurements probe the mechanical properties as a function of anneal temperature, providing a qualitative understanding of the intergranular defect density. Soft magnetic properties measured under quasistatic and AC conditions show frequency- and microstructure-dependent coercivity and permeability. Finally, analysis of the core loss quantifies how the build strategies and thermal treatments influence efficiency in electrical power conversion applications. Understanding the influences of scan path geometry and thermal treatment provides a pathway towards application of additively manufactured soft magnetic materials.

Adamczyk, Jesse M.↗

DOE OSTI2022

Development of an Integrated Multidiagnostic to Assess the High-Z Impurity Fluxes in the Metallic Environment of WEST Using IMAS

WEST is an actively cooled, long-pulse tokamak with nearly all plasma-facing components (PFC) made of tungsten. One of the aims of WEST is to study plasma operations with tungsten PFCs in preparation for long-pulse operations on high-Z divertor devices, such as ITER. For long-pulse operation, the high-Z impurity content and transport to the core plasma are critical concerns that require further measurement and interpretation to improve plasma performance and PFC durability. This work details the impurity influxes in WEST during a series of discharges in which the lower hybrid (LH) injected power was incrementally increased. An analysis has been performed of measurements collected from an array of edge diagnostics. Visible spectroscopy was utilized to measure the spectral radiances generated by fuel particles (D) and impurities (W, O, and C) at the divertor and at the antennas with a newly developed spectral peak-fitting tool used to analyze the data in WEST. The scrape-off layer (SOL) plasma conditions (n e and T e ) measured at the divertor target with flush Langmuir probes and near the outer mid-plane (OMP) using reciprocating Langmuir probes (RCPs) are used to evaluate the number of ionizations per photons (S/XB) coefficients required to estimate the impurity fluxes obtained with the collisional-radiative model ColRadPy. Here, the array of edge diagnostics discussed in this work, coupled with SOL plasma modeling tools, represents a multidiagnostic interpretative modeling workflow that will continue to be applied to upcoming experimental campaigns on the WEST experiment to assess high-Z impurity transport.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

A Novel Machine Learning Approach to Disentangle Multitemperature Regions in Galaxy Clusters

The hot intracluster medium (ICM) surrounding the heart of galaxy clusters is a complex medium that comprises various emitting components. Although previous studies of nearby galaxy clusters, such as the Perseus, the Coma, or the Virgo cluster, have demonstrated the need for multiple thermal components when spectroscopically fitting the ICM’s X-ray emission, no systematic methodology for calculating the number of underlying components currently exists. In turn, underestimating or overestimating the number of components can cause systematic errors in the emission parameter estimations. In this paper, we present a novel approach to determining the number of components using an amalgam of machine learning techniques. Synthetic spectra containing a various number of underlying thermal components were created using well-established tools available from the Chandra X-ray Observatory. The dimensions of the training set was initially reduced using principal component analysis and then categorized based on the number of underlying components using a random forest classifier. Our trained and tested algorithm was subsequently applied to Chandra X-ray observations of the Perseus cluster. Our results demonstrate that machine learning techniques can efficiently and reliably estimate the number of underlying thermal components in the spectra of galaxy clusters, regardless of the thermal model (MEKAL versus APEC). We also confirm that the core of the Perseus cluster contains a mix of differing underlying thermal components. We emphasize that although this methodology was trained and applied on Chandra X-ray observations, it is readily portable to other current (e.g., XMM-Newton, eROSITA) and upcoming (e.g., Athena, Lynx, XRISM) X-ray telescopes. The code is publicly available at https://github.com/XtraAstronomy/Pumpkin.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

FY22 Grid Modernization & Energy Storage Program: Accomplishments & Impacts

Sandia’s Grid Modernization and Energy Storage program works to advance a national vision of a secure, resilient, and sustainable electric system for all users. Our achievements reflect a strategic approach combining technology development; modeling, simulation, and data analytics; and partnered demonstrations and outreach to further the adoption of advanced grid and storage technologies. Our FY22 efforts leverage the strengths of our partnerships—spanning Sandia’s core science and technology competencies as well as external technology leaders—to develop the solutions today which enable the grid of tomorrow. Much of the material in this report comes from the separate 2022 Accomplishments Report compiled by our Energy Storage subprogram team, a cornerstone of our grid research and achievements. The Grid Energy Storage Program at Sandia is focused on making energy storage cost-effective through research and development (R&D) in new battery technologies, advanced power electronics and power conversion systems, improved safety and reliability for energy storage systems, analytical tools for the valuation of energy storage, and the validation of new energy storage technologies through demonstration projects. During the 2022 fiscal year, Sandia executed R&D work supported by the U.S. Department of Energy’s (DOE) Office of Electricity – Energy Storage Program under the leadership of Dr. Imre Gyuk. This report indicates key areas of research and engagement and summarizes the impact of Sandia’s contributions through notable accomplishments, journal publications, patents, and technical conferences and presentations. It is provided with the hope that readers discover ways we can further team to create our modern grid and apply the outcomes of our efforts. The bulk of work described herein is funded by the DOE Office of Electricity and key programs within the DOE Office of Energy Efficiency and Renewable Energy. As we indicated in our report from last year, the contributors to our successes are too numerous to name here, though our team wishes to express our deep gratitude to the numerous program and project sponsors at the US Department of Energy, who often function equally as technical collaborators; our many partners in industry, academia, utilities, and other national labs; and fellow researchers and business partners at Sandia whose leadership and creativity have enabled the accomplishments described herein.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Exploring the Evolution of Stellar Rotation Using Galactic Kinematics

The rotational evolution of cool dwarfs is poorly constrained after ∼1–2 Gyr due to a lack of precise ages and rotation periods for old main-sequence stars. In this work, we use velocity dispersion as an age proxy to reveal the temperature-dependent rotational evolution of low-mass Kepler dwarfs and demonstrate that kinematic ages could be a useful tool for calibrating gyrochronology in the future. We find that a linear gyrochronology model, calibrated to fit the period–T{sub eff} relationship of the Praesepe cluster, does not apply to stars older than around 1 Gyr. Although late K dwarfs spin more slowly than early-K dwarfs when they are young, at old ages, we find that late K dwarfs rotate at the same rate or faster than early-K dwarfs of the same age. This result agrees qualitatively with semiempirical models that vary the rate of surface-to-core angular momentum transport as a function of time and mass. It also aligns with recent observations of stars in the NGC 6811 cluster, which indicate that the surface rotation rates of K dwarfs go through an epoch of inhibited evolution. We find that the oldest Kepler stars with measured rotation periods are late K and early M dwarfs, indicating that these stars maintain spotted surfaces and stay magnetically active longer than more massive stars. Finally, based on their kinematics, we confirm that many rapidly rotating GKM dwarfs are likely to be synchronized binaries.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Modeling and Calibration of Supplier Selection Problem in Freight Agent-Based Simulations

Freight transportation modeling often struggles with data limitations, especially in accurately representing complex supplier selection processes and their impact on network flows. This research addresses this critical gap by developing a large-scale, calibrated agent-based model for supplier selection, complemented by a probabilistic heuristic for international shipments. Our approach integrates trade relationships between industry sectors, transportation costs, and a supplier-rating model adapted from existing literature. The model’s core objective is to minimize the discrepancy between modeled and observed commodity flows while ensuring a close match to regional shipping distance distributions. Implemented and tested across four major U.S. metropolitan areas—Atlanta, Chicago, Dallas–Fort Worth, and Los Angeles—the model demonstrates high fidelity in replicating observed freight patterns. Key findings reveal consistent alignment with national shipping distance trends and highlight significant spatial variations in commodity trade assignments and demand across the study regions. This behaviorally informed and transport-sensitive framework is designed to approximate real-world decision making, providing a robust tool for policymakers and planners to evaluate targeted interventions, assess infrastructure investments, and enhance supply chain resilience in the face of disruptions.

Ismael, Abdelrahman (ORCID:0000000303712110)↗

DOE OSTI2026

Enabling kilometer-scale E3SM land model simulation over North America: A new integrated framework solution

This study introduces a novel framework designed to enhance the performance, scalability, and portability of the kilometer-scale E3SM Land Model (km-ELM) within the E3SM modeling infrastructure. By seamlessly integrating cutting-edge data tools, we address existing challenges such as slow performance, limited scalability, and difficulties in software integration in current data-driven ELM simulation over large geographic areas. Our innovative approach leverages the KiloCraft data toolkit to generate unified inputs for simulations ranging from a single-cite case, to a 72,083-cell regional case to a continental configuration encompassing 21.6 million land grid cells at a 1 km × 1 km resolution. We conduct extensive strong- and weak-scaling experiments on three state-of-the-art supercomputers, utilizing up to 100,800 CPU cores across 2400 compute nodes to evaluate end-to-end metrics including wall-clock time, simulation-years-per-day (SYPD), initialization costs, and I/O throughput. Our results reveal the land (LND) component’s efficient scaling, demonstrating near-ideal weak scaling and strong-scaling parallel efficiencies reaching up to 87% at 50,400 cores. We confirm portability and reproducibility through bitwise-equivalent outputs across different machines using identical inputs over supported machines. Notably, at extreme scales, we identify I/O as a critical bottleneck and that leads to effective solution with the SCORPIO/ADIOS stack. Collectively, these findings validate the deployment of km-ELM at a continental scale with high parallel efficiency and provide essential guidance on configuration, decomposition, and I/O settings for optimized kilometer-scale land simulations in E3SM. This work emphasizes the innovative design and practical solutions that enhance the operational capabilities of km-ELM, focusing on software performance and scalability while leaving detailed scientific evaluations of simulated land processes for future investigations.

E3SM land model (ELM), km-ELM, scalability, perfor↗

DOE OSTI2021

High Flux Isotope Reactor Low-Enriched Uranium Low Density Silicide Fuel Design Parameters

High Flux Isotope Reactor (HFIR) highly enriched uranium (HEU) to low-enriched uranium (LEU) conversion activities are ongoing as part of the Department of Energy (DOE) National Nuclear Security Administration (NNSA)’s nuclear nonproliferation mission. Design activities studying the conversion of HFIR from HEU to LEU fuel explored different fuel design features and shapes with a low density uranium-silicide dispersion (U 3 Si 2 -Al) fuel, which has a uranium density of 4.8 gU/cm 3 . The goal of these studies is to generate several HFIR LEU fuel designs of varying fuel fabrication complexity that meet the current HEU performance metrics and safety requirements. The documented designs will serve as references for fuel fabrication and qualification activities. Recent advancements in modeling and simulation tools enable quick prototyping of fuel designs. Shift, a Monte Carlo neutron transport and depletion tool optimized for high-performance computing (HPC) architectures, is used for efficient fuel cycle and performance metrics calculations. The HFIR Steady State Heat Transfer Code (HSSHTC) is used to vet the thermal safety margin. Also, a new automation tool that connects all fuel design analysis steps, named Python HFIR Analysis and Measurement Engine (PHAME), has been developed to expedite the design study in an efficient and reproducible manner. Leveraging these tools, several candidate fuel designs were selected for varying fabrication complexity. This report provides design feature details for four selected HFIR LEU low density U 3 Si 2 -Al fuel designs and their corresponding performance and safety metrics. Nominal, best-estimate design parameters and irradiation conditions, including fission rate densities, power densities, heat fluxes, and cumulative fission densities are provided for candidate fuel designs relevant to framing irradiation experiments to support fuel qualification efforts. Simulations show that the low density U 3 Si 2 -Al, with design features to enhance safety, can meet HEU core performance metrics and safety requirements if the reactor power is increased from 85 MW (HEU) to 95 MW (LEU) and if the active fuel length is increased from 50.80 cm (HEU) to 55.88 cm (LEU).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Reliable and Efficient Machine Learning (Final Technical Report)

Modern scientific experiments generate massive amounts of data at a pace much faster than humans can manually analyze. While machine learning has revolutionized commercial data analysis (such as recommending movies or recognizing faces), applying these tools to complex scientific discovery is challenging because scientific answers must be precise, interpretable, and adhere to physical laws. The research under this project aims to develop new mathematical tools and computer algorithms specifically designed for scientific applications. Major progress has been made in automatically cleaning and deconstructing messy experimental data, analyzing the visual information of physical phenomena, determining the underlying physical variables, and providing rig orous mathematical analysis of interesting algorithms and concepts widely used in machine learning. This project addressed the critical gap between our ability to generate massive scientific data and our ability to extract interpretable information from it. We established mathematical foundations for Scientific Machine Learning (SciML) aimed at effective data analytics and automated discovery. Our work focused on three core objectives: (1) developing reliable feature extraction methods for dynamic high-dimensional data, (2) establishing mathematical foundations for discovering dynamics via neural networks, and (3) creating rigorous optimization techniques for these models. Key outcomes come from two fronts. On the practical side, they include the development of algorithms that significantly enhance the extraction of signals from field data, as well as the capability to handle situations that exhibit smooth variations or physical stretching due to temperature changes. They also include the creation of an automated framework for discovering fundamental state variables from raw experimental data, demonstrating the ability to identify intrinsic physical dimensions without prior knowledge of the governing laws. On the theoretical front, the research results in theoretical advances in Optimal Transport, a widely used notion in SciML, specifically regarding functions with fixed-size nodal sets, provide sharp bounds relevant to uncertainty quantification. Meanwhile, the outcomes also include the establishment of convergence theories for nonlocal gradient descent methods, enabling robust optimization with noisy data in high-dimensional settings commonly encountered in scientific modeling. The project also helps creating opportunities to train the next generation of researchers, equipping them with the necessary technical skills for today’s workplace and preparing them for future advances.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

ppdx : Automated modeling of protein–protein interaction descriptors for use with machine learning

This paper describes ppdx, a python workflow tool that combines protein sequence alignment, homology modeling, and structural refinement, to compute a broad array of descriptors for characterizing protein–protein interactions. The descriptors can be used to predict various properties of interest, such as protein–protein binding affinities, or inhibitory concentrations (IC 50 ), using approaches that range from simple regression to more complex machine learning models. The software is highly modular. It supports different protocols for generating structures, and 95 descriptors can be currently computed. More protocols and descriptors can be easily added. The implementation is highly parallel and can fully exploit the available cores in a single workstation, or multiple nodes on a supercomputer, allowing many systems to be analyzed simultaneously. As an illustrative application, ppdx is used to parametrize a model that predicts the IC 50 of a set of antigens and a class of antibodies directed to the influenza hemagglutinin stalk.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Diversion Path Analysis: A Proposed Methodology to Develop an MC&A Approach for Liquid-Fueled Molten Salt Reactors

Nuclear material control and accounting (MC&A) is a critical element of both the US Nuclear Regulatory Commission (NRC) and US Department of Energy (DOE)’s domestic safeguards and security requirements. NRC licensees are required, under Title 10 of the Code of Federal Regulations (10 CFR) Part 74 to establish and maintain an MC&A program that captures and records the quantities and locations of special nuclear material (SNM) at the facility. Along with physical protection, MC&A is a key element of domestic nuclear material safeguards that enables the NRC to ensure that SNM is controlled and accounted for. SNM, per 10 CFR Part 74, refers to plutonium, 233 U, and uranium enriched in the isotope 233 U or 235 U, but does not include source material. Periodic physical inventories, coupled with material balance evaluations, are effective and demonstrated tools to account for and detect theft or diversion of SNM in facilities containing SNM in bulk material form (i.e., not in discrete, countable items). Historically in the United States, these types of facilities have included fuel fabrication, conversion, and enrichment facilities. In comparison, reactors have relied on item counting of assemblies and control of SNM while in containment (e.g., a sealed reactor pressure vessel) because, to date, reactor fuel has been in item form. In liquid-fueled molten salt reactors (MSRs), unlike traditional light water reactors (LWRs) or bulk facilities, bulk SNM quantities can change significantly during operation as a result of depletion and transmutation. This introduces challenges to the use of traditional periodic physical inventories and material balance evaluations to detect theft or diversion of SNM in reactors that use SNM in bulk material form. Liquid-fueled (i.e., salt-fueled) MSR facilities are MSRs that use SNM within a salt eutectic as the fuel. The SNM is in a bulk material form any time it is outside of fresh or spent fuel storage containers. Some examples of when SNM will be in bulk form in the facility are during addition of fuel to the reactor system, while fuel is circulating in operation, and while fuel is in a drain tank. Periodic physical inventories and material balance evaluations can likely be effectively applied to many portions of an MSR facility, including all areas where depletion and transmutation are not significantly changing the quantities of SNM within the control area. Within an MSR facility, this would include fresh fuel receipt and loading, waste streams that may contain SNM, irradiated fuel storage outside of the reactor core, and any irradiated fuel processing that may happen after SNM has been removed from the reactor. All of these process steps could rely on measurements of SNM quantities compared with documented inventories. Any discrepancies from predicted (i.e., book) inventories and measured inventories could be quantified as inventory differences, consistent with traditional MC&A guidance from the NRC (e.g., in NUREG-1065 Revision 2, NUREG-2159 Revision 1, and RG 5.29 Revision 2). Within the reactor system, additions and removals to the book inventory include depletion of the SNM (e.g., fission of 235 U), which complicates the use of physical inventories. SNM control, however, can also likely be effectively applied to detect theft of SNM throughout a liquid-fueled MSR facility. To complement these approaches, prior technical reports have identified that a diversion path analysis may be a useful, risk-informed, and performance-based tool to determine suitable elements of an MC&A approach for the reactor system within a liquid-fueled MSR facility.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

REopt Lite User Manual

REopt Lite evaluates the economic viability of grid-connected solar photovoltaics, wind, combined heat and power, and electric and thermal storage at commercial and small industrial sites. It allows building owners to identify the system sizes and dispatch strategies that minimize the site’s life cycle cost of energy. REopt Lite also estimates the amount of time on-site generation and storage can sustain the site's critical load during a grid outage and allows the user the choice of optimizing for energy resilience. It is primarily used to inform project development decisions and to support research on the factors that drive project feasibility for market development and policy analysis. It is available through a web interface, application programing interface, and open-source code. This user manual provides an overview of the model, including its capabilities and typical applications; inputs and outputs; economic calculations; technology descriptions; and model parameters, variables, and equations. The model is highly flexible and is continually evolving to meet the needs of each analysis. Therefore, this report is not an exhaustive description of all capabilities, but rather a summary of the core components of the model.

24 POWER TRANSMISSION AND DISTRIBUTION↗