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NASA NTRS1998

The Soft X-Ray Emission Component of Spiral Galaxies

Work included the analysis of the HRJ observations of the Sombrero galaxy (Fabbiano and Juda) published in Ap.J. This paper discussed the discovery of a point-like x-ray source at the nucleus of the galaxy, which is suspected to host a massive black hole. More work was done on the analysis of the Observation of M94 in support of an AXAF proposal. We have also analyzed the M81 data by adding to our observation the entire set of the archival ROSAT data. We plan to write up the results for publication. Both galaxies have nuclei optically similar to that of the Sombrero galaxy. The nucleus of M81 is a known x-ray source. The M94 data has revealed a point-like nuclear source superposed on more diffuse emission.

Fabbiano, Giuseppina↗

DOE OSTI2021

DESIGN AND PROTOYPING OF A FISSILE-BEARING CHLORIDE SALT IRRADIATION EXPERIMENT

Limited data exists on the properties of irradiated fueled chloride-based salts for use in advanced nuclear reactors. The design and prototyping of a salt irradiation experiment is underway. Mechanical design will integrate the experiment into existing assemblies in TRIGA type cores. Neutronics modeling demonstrated how targeted power densities can be reached and provide insight on radionuclide source term generation within the experiment. Thermal-hydraulic and computational fluid dynamics (CFD) analysis indicated that temperature ranges in the salt can satisfy both safety and programmatic requirements. Initial prototyping tests provided confidence in the ability to extract the salt post-irradiation, and in the heater performance. Future planned prototyping will provide an integral assessment of the proposed design prior to insertion in the reactor.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Approach for Inferring Full-Scope Human Reliability Data Based on Simplified Simulator Data

This paper proposes a method for inferring full-scope human reliability data based on the Simplified Human Error Experimental Program (SHEEP) data. It mainly focuses on the human errors observed when using simulators with different complexity levels. In the proposed method, the manner in which human error probabilities (HEPs) change as a result of increasing simulator complexity and how simulator complexity levels are quantified represent key information for inferring full-scope data. In the present study, SHEEP error data pertaining to actual professional operators using Rancor Microworld (Rancor) (i.e., a more simplified simulator) and Compact Nuclear Simulator (CNS) (i.e., a less simplified simulator) were compared with the HuREX error data. An approach to quantifying simulator complexity levels was then proposed based on information theory and acquired eye-tracker data.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2021

Considerations regarding the Use of Computer Vision Machine Learning in Safety-Related or Risk-Significant Applications in Nuclear Power Plants

With the advancements made to date in the field of artificial intelligence (AI), significant potential exists to utilize AI capabilities for nuclear power plant (NPP) applications. AI can replicate human decision making and it is usually faster and more accurate than humans. For implementations that impact critical NPP applications (e.g., safety-related or non-safety systems that potentially affect overall plant risk), a deeper safety analysis of the AI methods is necessary. AI applied to NPP operations could resemble the use of digital I&C (DI&C) because such applications involve digital computer hardware and custom-designed software that input plant data, execute complex software algorithms, and output the results to a system or licensed human operator to potentially provoke an action. For AI methods to be compliant with current safety requirements for DI&C, AI compatibility must be evaluated, and AI-related gaps may exist that prevent the prompt deployment of AI in NPPs. This effort aims to evaluate how example AI technologies align with the DI&C safety framework, and discusses how they could be analyzed, modeled, tested, and validated in a manner similar to typical DI&C technologies. Because AI is a broad field that encompasses areas such as machine learning (ML), natural language processing, and computer vision, this research focused on a subset of methods categorized as the computer vision ML (CVML) methods. This report explores two CVML use cases, gauge reading and fire watch, considered relevant to the DI&C standards, as they could play a safety-critical role. For the gauge reading use case, a CVML-enabled technology that can read gauges at oblique angles is utilized. For the fire watch use case, a CVML-enabled technology is utilized that migrates fire watch from a manual (human) approach to automated fire detection. These use cases are mainly intended to give context to the CVML system discussion. This effort assumes the worst-case scenario, with the CVML system being used to replace a safety-related or risk-significant system, thus requiring evaluation. Evaluating CVML against most of the relevant safety requirements for DI&C yielded several CVML-specific considerations due to the uniqueness of its characteristics in comparison with typical DI&C systems. For example, CVML models often employ commonly used (open-source) datasets, and it is not always possible to determine the level of overlap among open-source datasets. Therefore, the independence of the developed CVML models when demonstrating diversity is questionable, therefore creating vulnerability to common cause failure (CCF). The design verification process is also impacted since the data overlap could result in overestimation of the software validation and verification (V&V) performance results. Section 2 of this report evaluates a list of the identified CVML-specific characteristics and discusses the resulting considerations and potential solutions in the context of each referenced requirement. A summation is provided in Section 3. This report is not to be used as a guideline. It was developed to identify and consider issues in the implementation of ML technologies used to augment activities that may have a bearing on plant operation. The report draws parallels to the use of DI&C technologies, for which many standards are available to guide their use in nuclear plant operation. It considers the technologies and some of the potential implications of their use in safety-related applications but is not intended to address regulatory or licensing related issues.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Analysis with the ORIGEN Module of PNAR Spent Fuel Measurements for Nuclear Safeguards Applications

This report documents the analysis of the Passive Neutron Albedo Reactivity (PNAR) measurements for 23 boiling water reactor spent fuel assemblies that were performed in Finland under Action Sheet 65, which is an international collaboration on spent fuel safeguards verification methods in the context of the Finnish spent fuel encapsulation/repository system. PNAR measures the passive neutron and gamma emission rates from each of the spent fuel assemblies like a Fork detector, and it also measures the PNAR ratio, which is expected to correlate with the net neutron multiplication of the measured fuel assembly. The analysis was performed with the Oak Ridge Isotope Generation and Depletion (ORIGEN) Data Analysis Module, which was originally developed for predicting Fork detector spent fuel measurement signals in real time and has been integrated into the Integrated Review and Analysis Package developed by Euratom and the International Atomic Energy Agency. The module includes the ORIGEN burnup analysis code and integrates detector response functions pregenerated using the MCNP Monte Carlo transport code to predict the detector signals in several seconds per assembly. In this study, new response functions specific to the analyzed PNAR measurements were generated for the ORIGEN Module. The study also analyzes the impact of using detailed fuel design and operation information vs. standard safeguards information on the results calculated with the ORIGEN Module. Using detailed information reduced the standard deviation of the relative differences between calculated and measured neutron count rates among the 23 assemblies from ~10% to ~4%. The results obtained using standard safeguards information for these PNAR measurements were similar to those obtained for the Fork detector. A clear trend was found between the calculated net neutron multiplications and the measured PNAR ratios of the 23 assemblies. An uncertainty assessment of the calculations was also performed to estimate the potential impact of the accuracy and the completeness of declaration information on the calculated results for the count rates and net neutron multiplications. The ORIGEN Module predictions for PNAR signals and net neutron multiplication are expected to directly support the safeguards inspectors’ efforts to verify operator declarations of a spent fuel assembly in real time in safeguards practices.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Counter-Current Flow Limitation Studies in Complex Geometries Utilizing Interface Capturing Simulations Coupled with PID Flow Rate Controller

In nuclear thermal-hydraulic studies, counter-current flow limitation (CCFL) typically refers to steam rising at a fast rate such that it prevents coolant from draining down within a confined channel. CCFL is a crucial issue in nuclear reactor safety analysis. This study investigates CCFL in debris bed channels using high-resolution interface-capturing simulations. A novel proportional-integral-derivative flow rate controller is developed to efficiently achieve the CCFL conditions. Verification studies confirm that CCFL occurs under the same conditions with or without the controller, demonstrating that PID control ensures accurate prediction. Three debris bed channel geometries were examined: a cylindrical channel, a channel with small obstacles, and a channel with large obstacles. Results show that obstacles significantly impact flow behavior, interfacial shear, wall shear, and pressure gradients required for CCFL. Furthermore, the comparison with experimental data confirmed that simulations incorporating geometric complexities align more closely with experimental CCFL conditions. A pressure gradient correlation was also developed for CCFL prediction.

Counter-current flow limitation↗

DOE OSTI2021

Development of Digital Twin Predictive Model for PWR Components: Updates on Multi Times Series Temperature Prediction Using Recurrent Neural Network, DMW Fatigue Tests, System Level Thermal-Mechanical-Stress Analysis

The long-term operation (LTO) of nuclear power plant (NPP) beyond their original design life of 40 years, can lead to more material damage associated with cyclic fatigue under thermal-mechanical loading cycles and associated long-term exposure of reactor material to the deleterious reactor-coolant environments. However, under this LTO condition the reactor components can still safely operate but may require more frequent Nondestructive Evaluation (NDE) of reactor components. Frequent NDE requirement may lead to frequent shutdown of the NPP. This in turn can lead to power outage and additional NDE-inspection-cost related economic loss. The economic loss can be minimized by reducing uncertainty in life estimation of safety-critical pressure boundary components and by implementing more digital approach such as by using upcoming digital-twin (DT) technology for predicting the structural states (e.g., time and location dependent inside/outside thickness temperature, stress, strain, plastic deformation, etc.) and associated fatigue life of a component in real time. Towards this goal Argonne National Laboratory (ANL) with the sponsorship of DOE Light Water Reactor Sustainability (LWRS) program is working on the development of a DT framework that can be used for real time environmental fatigue prediction of reactor components. The DT framework is based on limited experiment-data, Artificial-intelligence (AI) – Machine-Learning (ML) - Deep-Learning (DL) based techniques and Multiphysics-computational-mechanics such as finite element (FE) based modeling tools. Towards this overall goal, following are some of the major contributions made during the FY21: 1) Multiple 82/182 dissimilar metal weld (DMW) specimens (both solid-weld and joint-weld representing the actual reactor multi-metal nozzles) were fatigue tested. The resulting fatigue lives were compared to the NUREG-6909 based best-fit and design fatigue curves. Additionally, the results of 52/152 DMW fatigue specimens (which were recently tested at Republic of Korea under the sponsorship of International Nuclear Energy Research Initiative - INERI program) were compared to the NUREG-6909 based best-fit and design fatigue curves. From the comparison of 82/182 and 52/152 DMW test data with NUREG-6909 best-fit curve, most of the reported test data fall way away from the NUREG-6909 suggested best-fit or mean curve. The NUREG-6909 suggested best-fit curve is the best-fit curve of austenitic stainless steel and due to lack of enough data on Nickel-based welds, this is currently being used for predicting the life of Nickel-alloy-based welded components. However, the above observation may require higher scaling factor (e.g., ASME suggested factor of 20 on cycles rather than the current NUREG-6909 suggested factor of 12 on cycles) for scaling the austenitic-stainless-steel best-fit-curve for estimating the design or safe-life of a welded component. Accordingly, for example, if a DMW component experience a strain amplitude of 0.6% the PWR-water life of the component would be 52 cycles instead of 85 cycles. However, more DMW tests are required to further ascertain the above-mentioned observations. 2) A system level CAD and finite element model were developed which consists of reactor pressure vessel (RPV), part of steam generator (SG), part of pressurizer (PRZ), hot leg (HL), and surge line (SL). This is with detailed nozzle geometry and thermal-mechanical material properties of different metals to simulate realistic thermal-mechanical stress under connected system global thermal-mechanical boundary conditions. 3) Different system level heat transfer analyses were performed with estimation of relevant heat transfer coefficients. The resulting data were used in subsequent system level thermal-mechanical stress analysis and for generating spatial-temporal training and validation data for a system level digital-twin based temperature predictor. Transient heat transfer analyses were performed considering thermal boundary condition under design-basis (DB) loading and EDF (Électricité de France) data-based grid-load-following (EDF-GLF) loading cycles. 4) System level thermal-mechanical stress analysis was performed for identifying damage-prone hotspots and for future extension of the model for cyclic state prediction. From the system-level model simulation under DB loading cycle it is found that HL and the SL nozzle that connect to the HL can experience significant stress and strain and could be one of the weakest links in the overall reactor coolant system (RCS). 5) An AI/ML based DT model was developed for multi-time-series temperature prediction at any inside/outside thickness locations of PWR pressure boundary components. This is by using Recurrent-neural-network (RNN) and keras machine learning libraries. The RNN model was validated against two laboratory test-based data sets with one obtained through ANL’s in-air fatigue test system and other through PWR-water test loop. The experimentally validated DT model further validated against FE model results to predict thermal scarification related spatialtemporal temperatures at random locations of a component. The well validated DT model was then used for demonstrating spatial-temporal temperature prediction under 100+ years of reactor operation subjected to combined DB, EDF-GLF and randomized grid-load-following (RANDOMGLF) loading Cycles. The expert-elicitation DT model framework was developed assuming field/input/process measurements can be available from a few existing plant sensors and can readily be used by the NPP operators. The above temperature prediction model will feed to the next-step stress analysis model based on which the life of a component can be predicted in realtime, which is one of our future works.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Evidence of a $J/ψΛ$ structure and observaton of excited $Ξ_{b}^{-}$ → $J/ψΛΚ^{-}$ decay

First evidence of a structure in the $J/ψΛ$ invariant mass distribution is obtained from an amplitude analysis of $Ξ_{b}^{-}$ → $J/ψΛΚ^{-}$ decays. The observed structure is consistent with being due to a charmonium pentaquark with strangeness with a significance of 3.1σ including systematic uncertainties and look elsewhere effect. Its mass and width are determined to be 4458.8 ± $2.9_{-1.1}^{+4.7}$ MeV and 17.3 ± $6.5_{-5.7}^{+8.0}$ MeV respectively, where the quoted uncertainties are statistical and systematic. The structure is also consistent with being due to two resonances. In addition, the narrow excited $Ξ^{-}$ states, $Ξ(1690)^{-}$ and $Ξ(1820)^{-}$, are seen for the first time in a $Ξ_{b}^{-}$ decay, and their masses and widths are measured with improved precision. The analysis is performed using $pp$ collision data corresponding to a total integrated luminosity of 9 fb -1 , collected with the LHCb experiment at centre-of-mass energies of 7, 8 and 13 TeV.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Study of charged-particle emission in neutron reactions on 10 B

The 10 B + n system has been studied by measuring charged particles from neutron-induced reactions in the 1 to 20 MeV energy range. Protons, deuterons, tritons, and α particles were measured at four angles using the pulsed white neutron spectrum at the Los Alamos Neutron Science Center Weapons Neutron Research facility. Differential cross sections for each species are reported. These new data are combined with literature data in an R-matrix analysis of the 11 B compound system. Furthermore, a comparison is made to the molecular and cluster states candidates observed in these reactions.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

Fatigue Testing and Characterization of Pre-hydrided Zircaloy-4 Cladding Tubes

The solubility of hydrogen in Zircaloy-4 (Zry-4) at the cladding operating temperature is near 100 wt. ppm. Above this solubility limit, excess hydrogen precipitates as δ-hydride platelets in the cladding material. Because of the effect of the thermal gradient across the cladding thickness on the migration and precipitation of hydrogen, hydride rims are often observed at the cladding outer surface; under excessive corrosion conditions hydride blisters are possible. It is well known that the fatigue of metal alloys, especially high cycle fatigue, is sensitive to the status of surface including the microstructure, roughness, and residual stress. Thus, the question considered herein, is whether excessive hydrogen pickup modifies the microstructure such that it has a degradation effect on the fatigue performance of cladding during operation. This report describes the evaluation of fatigue performance of a pre-hydrided Zry-4 cladding. A commercial Zry-4 was polished and pre-hydrided to 800 ppm and 1300 ppm H contents. The fatigue testing was conducted under strain control at 5 Hz with fully-reversed bending by using a cyclic integrated reversible bending fatigue tester (CIRFT). Six specimens with 1300 ppm and one specimen with 800 ppm were tested. Significant variation in test results were observed. While two of the 1300 ppm specimens failed with less than 1 ×10 5 cycles (at 0.32% and 0.41%), the four other samples did not fail over the strain amplitude range of 0.25% to 0.38%. Interestingly, the fracture initiation site of the failed samples was on the outside diameter (OD) surface rather than on the inside diameter (ID) surface as is typical for the as-polished cladding. This suggested a degrading effect of the hydriding process. Subsequently, three of the unfailed specimens were then further tested at ~0.43% to observe where failure initiation occurred. Significant variation was also observed in these three specimens. One specimen failed at ~9000 cycles while the two others failed at 43000 and 1.06 ×10 5 cycles. The performance here correlated to the location of failure initiation; failure initiated on the OD in the sample that failed after 9000 cycles while it initiated on the ID in the sample that failed after 43000 and 1.06 ×10 5 cycles. Flat features at the OD initiation sites suggest a brittle hydride feature on the surface of those samples was the cause of the degradation in fatigue performance, though the overall hydrogen levels in all samples was similar. A summary of all the observations is provided below: • The un-failed specimens with 1300 ppm H were cycled to failure with a higher amplitude near 0.43%. In addition, the fatigue-treated specimen tended to have a longer fatigue at the same induced amplitude. • Fractography revealed a mixed failure mode for the pre-hydrided specimen. Particularly, the specimen with fracture initiation site (FIS) located on the outer diameter surface of tube tended to have a shorter fatigue life than that of FIS on the inner diameter surface. • Etched cross section was shown to have hydride platelets aligned with tube longitudinal axis as expected, and the density of hydrides is at the similar level as in literature data. Meanwhile, a LECO procedure was applied for hydrogen concentration measurement, which showed the measured hydrogen contents are close to the nominal value. • With the polished cladding tube as baseline, O’Donnell-Lager (O-L) analysis showed that a decrease of about 50% in reduction-of-area (RA) would be needed for the O-L fitting to the fatigue data of 1300 ppm cladding. The suggestion for the next steps is provided.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Small Reactors in Microgrids: Technology Modeling and Selection (Net-Zero Microgrid Program Project Report)

This report demonstrates the capabilities of the net-zero microgrid (NZM) Xendee platform for modeling an SR module with electricity, heat extraction and thermal storage in microgrids configurations. The model effectively captures the most important technical and economic considerations for SR technology specific analysis: cost and operational characteristics of SR technology and financial costs and incentives. The model can analyze multiple scenarios to establish metrics for cost-competitive and zero-carbon microgrids connected to the grid or completely isolated. The model is fully integrated within the Xendee platform for modeling and analysis of clean energy microgrids with storage and generation from renewable energy sources. The model captures the capabilities, constraints, and nuances of SR by incorporating parameters related to plant economics, design efficiency and performance, plant operation and component and fuel lifespan. The cost and operational parameters modeled in the SR module are specific to the technology selected for integration in the microgrid. Cost parameters recognize advanced nuclear technology for modular production and installation based on economies of scale from factory manufacture and related commissioning, and cost reduction through technology maturation—first-of-a-kind (FOAK) and nth-of-a-Kind (NOAK). The cost parameters include installation, operations and maintenance (O&M), fuel refueling cycle, and reactor life. Installation cost reflects economies of scale due to unit sizing at scale and colocation. O&M economies of scale for both fixed- and variable-cost fuel life-cycle costs are incurred at every refueling interval, with separate front- and back-end fuel costs, as well as waste-handling and disposition costs. This report investigates key characteristics of different SR technologies suitable for microgrid applications, including design principles, sizing, coolant properties, temperature ratings, fuel structures, and life-cycle considerations. This also includes fuel technologies applicable to these SR systems, alongside strategies for nuclear-waste and spent-fuel management and approaches to address safety, security, and proliferation challenges. Four primary groups of SR technologies are examined: water-cooled, liquid-metal-cooled, high-temperature gas-cooled, and molten-salt-cooled systems. In this report, an initial guideline for technology selection is established, aligning the characteristics of the technologies with the requirements of microgrids. The selection of technology in a microgrid is influenced by various factors, including financial capacity, location and accessibility, demand type and characteristics, reliability and resilience requirements, area constraints, and the lifespan of the microgrid. The types of electrical and non-electrical applications within the microgrid also play a significant role in technology selection. The characteristics of SRs, such as their smaller size, modularity, transportability, long refueling interval, improved safety features, ability to operate in autonomous or semi-autonomous mode, and provision of high-grade heat, are particularly appealing for microgrids. Furthermore, a list of considerations for implementing SRs in microgrids is outlined. The SR model is created to be continuously improved with the acquisition of actual data on investment and operational costs, experience with supply chains, production at scale, and field deployments. In the near term, performance data on applications in microgrids will become available from lessons learned from laboratory tests, such as those planned for the Microreactor Applications Research Validation and Evaluation Project (MARVEL), led by Idaho National Laboratory (INL). The SR model incorporates scenario data and known SR design specifications, enabling technoeconomic analysis for SR deployment in microgrids. It specifically considers the distinctive attributes of SRs as generators in technoeconomic studies. SRs can be modeled and analyzed with generation from renewable-energy sources, energy storage, and flexible loads over a range of functionality and applications. This offers a comprehensive tool for feasibility studies, scenario development, and sensitivity analysis for “what-if” consideration of any range of assumptions about SRs in microgrids and other aggregations of distributed-energy resources, including virtual power plants.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2024

16 O ⁡(𝑒,𝑒′⁢𝛼)⁢ 12 C measurements and the 12 C ⁡(𝛼,𝛾)⁢ 16 O astrophysical reaction rate

The 12 C ⁡(𝛼,𝛾)⁢ 16 O reaction, an important component of stellar helium burning, has a key role in nuclear astrophysics. It has significant impact on the evolution and final state of heavy to low mass stars, maximum mass of stellar formed black holes and also shapes the elemental abundances resulting from nucleosynthesis in such stars. Providing a reliable estimate for the energy dependence of this reaction at stellar helium burning temperatures has been a longstanding and important goal. In this work, we study the role of potential new 𝐸⁢1 and 𝐸⁢2 measurements of the 16 O ⁡(𝑒,𝑒′⁢𝛼)⁢ 12 C reaction in reducing the overall uncertainty in the astrophysical 𝑆 factor for the 12 C ⁡(𝛼,𝛾)⁢ 16 O 𝐸⁢1 and 𝐸⁢2 ground state capture extrapolated to a stellar energy of 300 keV. A multilevel 𝑅-matrix analysis is used to make extrapolations of the 𝑆 𝐸⁢1 (300 keV) and 𝑆 𝐸⁢2 (300 keV) factors for the 12 C ⁡(𝛼,𝛾)⁢ 16 O reaction from existing ground state capture data. Bayesian analysis is used to quantify the uncertainties in the extrapolations for both the existing data alone and also when possible new experimental data are included. In particular, we consider a new experiment that would make use of a high-intensity low-energy electron beam that impinges on a windowless oxygen gas target as a means to determine the total 𝐸⁢1 and 𝐸⁢2 ground state cross sections for this reaction. Here we find that the new data could significantly reduce the 𝑆⁡(300) uncertainties. Splitting the new data into high and low energy regions shows that both low and high energy data are effective in reducing the uncertainty.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

Toward the performance assessment of advanced nuclear waste forms: temperature dependence of lanthanide borosilicate glass dissolution

Lanthanide borosilicate (LaBS) glasses are among the most promising waste forms for the immobilization of high-level radioactive waste generated from advanced nuclear fuel cycles. However, the temperature dependence of their dissolution kinetics remains poorly understood and constrained, limiting the integration of these materials into established performance assessment models. Here, we investigate the dissolution behavior of the legacy AmCm2-19 LaBS glass and the benchmark alkali aluminoborosilicate ISG-1 in deionized water between 50 °C and 250 °C using ASTM C1285 (Product Consistency Test-B) protocols. For AmCm2-19 LaBS glass, normalized elemental release rates for boron and silicon increase with temperature before plateauing near 150 °C, consistent with solubility-limited behavior. From data obtained at 50 °C and 100 °C, Arrhenius analysis yields activation energies of E a (B) = 24.8 ± 0.3 kJ mol⁻¹ and E a (Si) = 14.4 ± 0.2 kJ mol⁻¹, similar or slightly lower than those previously reported for two other compositions of LaBS glasses. No secondary phases or alteration layers were detected by SEM-EDX or pXRD. These results establish one of the first temperature-dependent kinetic datasets for LaBS glass dissolution, providing quantitative parameters to inform mechanistic corrosion models and predictive simulations of glass degradation in geological disposal environments.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

Cluster Structures with Machine Learning Support in Neutron Star M-R relations

Neutron stars (NS) are compact objects with strong gravitational fields, and a matter composition subject to extreme physical conditions. The properties of strongly interacting matter at ultra-high densities and temperatures impose a big challenge to our understanding and modelling tools. Some difficulties are critical, since one cannot reproduce such conditions in our laboratories or assess them purely from astronomical observations. The information we have about neutron star interiors are often extracted indirectly, e.g., from the star mass-radius relation. The mass and radius are global quantities and still have a significant uncertainty, which leads to great variability in studying the micro-physics of the neutron star interior. This leaves open many questions in nuclear astrophysics and the suitable equation of state (EoS) of NS. Recently, new observations appear to constrain the mass-radius and consequently has helped to close some open questions. In this work, utilizing modern machine learning techniques, we analyze the NS mass-radius (M-R) relationship for a set of EoS containing a variety of physical models. Our objective is to determine patterns through the M-R data analysis and develop tools to understand the EoS of neutron stars in forthcoming works.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

OpenCSP v.1.0

SAND2024-08987O OpenCSP analyzes and improves the performance of optical mirrors that reflect sunlight and concentrate it onto a receiver. The algorithms in this library analyze the flow of light from an original source—such as the sun—reflecting off one or more mirrors, and onto a receiver, optical screen, or camera. OpenCSP also analyzes optical systems using other techniques, such as shape or slope comparison. The software calculates aggregate properties of the optical system, such as its efficiency, manufacturing error, pointing control error, expected performance, and other useful parameters. OpenCSP also includes high-level tools, such as Open SOFAST, a program that obtains high-resolution measurement of CSP mirrors, full heliostats, and other CSP collectors. OpenCSP supports producing meaningful output data to help CSP designers and field operators improve the performance of their systems. OpenCSP also includes classes and functions that support these calculations, including input utilities, analysis output libraries, geometric constraint analysis for drone flight planning, and more. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Brost, Randolph↗

DOE OSTI2025

CEC Quest: Long Duration Energy Storage Impact Analysis Tool

SAND2025-14389O CEC Quest is a Python tool with a user interface designed to analyze the greenhouse gas impacts of long-duration energy storage projects in California. The tool automates data collection from public sources and uses an Application Programming Interface (API) to enable users to download photovoltaic resource availability, marginal operating emissions rate, and utility rate data. It guides users in inputting parameters for a battery energy storage model and uploading site electrical load data, while also prompting for relevant analysis parameters like timestep and grid limits. CEC Quest performs monthly optimization of one year of data to assess impacts on the site’s electrical bill and the grid’s greenhouse gas emissions. Finally, it conducts a lifecycle analysis to evaluate changes over a defined quantification period, with results aggregated through automated report generation. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Rosewater, David [Sandia National Lab. (SNL-CA), L↗

NASA NTRS1977

Elastic scattering of deuterons by carbon

Deuteron-carbon elastic scattering is studied within the framework of the Glauber approximation. The full Glauber multiple scattering series is evaluated with simplified nuclear wave functions. An approximation in which deuteron-nucleus scattering is expressed in terms of nucleon-nucleus scattering amplitudes is shown to be accurate for small momentum transfers. The effects of the Coulomb field and the deuteron D state are investigated. The impressive fit to the 650 MeV d-(C-12) elastic scattering data, obtained in a recent calculation, is shown to be due to additional approximations made in that analysis.

Varma, G. K.↗

DOE OSTI2026

Physicochemical evolution of uranium nitride kernel microstructure with varying carbon distribution for advanced TRISO fuel forms

Uranium nitride (UN) has emerged as a fuel candidate for advanced nuclear reactor concepts due to its superior uranium density, thermal conductivity, and high melting temperature. However, the fabrication route for converting UO 2 to UN is complex and difficult to standardize. Although the chemistry of this conversion process is well-studied, more insight into the physicochemical dynamics of this conversion using advanced characterization techniques can help further our understanding of this material system. This work leveraged thermogravimetric analysis (TGA), X-ray diffraction (XRD), and nondestructive 3D X-ray computed tomography (XCT) to characterize dynamic microstructural changes in the UO 2 → UCO → UN fabrication pathway for two kernels with a varying carbon distribution in the starting composition. TGA and XRD were used to quantify changes in the mass, density, and chemical composition of the two kernels, while three-dimensional image processing and segmentation of XCT data were used to quantify the volume, surface area, and spatial distribution of features within each kernel for multiple steps along the fabrication pathway. The analysis indicates distinct differences between the two kernels that are correlated to downstream conversion efficiency. In conclusion, this work is among the first to perform 3D quantification of physicochemical evolution during UN conversion, providing quantitative correlation between processing, properties, and expected fuel performance.

Nuclear fuel↗