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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Countering Weapons of Mass Destruction (CWMD) Device Cybersecurity Characterization Process and Profile

Countering Weapons of Mass Destruction (CWMD) recognizes that threats in the cyberspace domain continue to grow, which requires CWMD devices and supporting systems to be both cybersecure (ability to protect or defend from cyber-attacks) and resilient (ability to maintain required capability in the face of adversity) to cyber threats. The CWMD cybersecurity characterization approach in this document supports existing cyber resilience activities within the Acquisition Lifecycle Framework. Similarly, this process supports existing Department of Homeland Security Cyber Resilience Test and Evaluation activities, which consist of iterative processes, starting at the initiation of system acquisition and continuing throughout the entire device and system life cycle. Cyber resilience is the ability of an information system to continue to operate while under attack, even if in a degraded or debilitated state, and to rapidly recover operational capabilities for essential functions after a successful attack. The goal of the security characterization task for CWMD is to support the development of a CBRN device-dependent profile that aligns with device network capabilities and maps to recommended security controls to create a characterization security profile impact levels. The impact levels for CWMD devices should be characterized as Low (L), Moderate (M), High (H) to align with the low, moderate, high control baselines. To estimate the impact levels, the device’s security-related attributes are translated into the security objectives: Confidentiality (C), Integrity (I), and Availability (A), known as the CIA triad. The potential impact for each device can be L, M, H, for devices that connect and transmit different types of data and may have different impact levels. National Institute of Standards and Technology Federal Information Processing Standards Publication 199 states, “the potential impact values assigned to the respective security objectives shall be the highest value from among those security categories that have been determined for each type of information resident on the information system.” As CWMD is determining the cybersecurity impact levels of CBRN devices based on network connections and data transfers, the impact levels are aligned with the associated attributes of network connections and communications. For example, if the device system is connected to a wireless network and transmits different data types based on the confidentiality of the data, the highest impact value for each security objective should represent the device’s CIA impact level. This document is intended to be used by test managers, test team, and program managers.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

Well-Log Derived Geomechanical Analysis of Microseismicity in the Mt. Simon Saline Aquifers (Illinois Basin - Decatur Project)

The Illinois Basin Decatur Project (IBDP) successfully demonstrated the safe geologic storage of carbon dioxide at a commercial scale. Within the IBDP project three deep wells (injection (CCS1), monitoring (VW1), geophysical (GM1)) were competed and geophysical logs were recorded. During injection and post-injection periods microseismic monitoring was conducted to create a miscoseismic catalog. The correlations between microseimic attributes and geomechanical well logs define major geomechanical drivers of microseismic expression to understand a reservoir response to CO2 injection in geological context. Utilizing standard sonic and density well logs, the dynamic elastic moduli were calculated and employed to correlate with microseismic pseudo-logs. A multi-dimensional Mu-rho and Lambda-rho (MRLR) hyperdimensional plots display of meaningful data and uncovered subtle relationships between elastic properties of sandstones and the seismological attributes of recorded microseismicity.

Myshakin, Evgeniy↗
DOE OSTI2023

Active High Assurance Authentication Protocol (AHAAP)

The AHAAP Maturation Project involves maturation and evaluation of a patented zero-trust tamper-resistant high-assurance session-less dynamic and active device authentication protocol that simultaneously authenticates identity and provides integrity verification in a single step, substantially reducing the risk of cyberattack, and eliminating the need for costly and complex conventional communication security systems requirements (i.e., cryptography, Public Key Infrastructure (PKI), and key management). These cybersecurity attributes of the technology must be preserved when applying the technology to different cybersecurity solutions, including Command & Control (C&C), Over-the-Air (OTA) update, Common Access Card (CAC), and distributed energy resource (DER) implementations, among others. The technology research objective is to test and verify that the cybersecurity attributes of the technology are not degraded in different cybersecurity applications. The primary technology development objective is to build minimum viable products to demonstrate the technology addresses today’s cybersecurity threats so that prospective investors, strategic partners, regulatory agencies, and commercial customers can interact with and assess the protection assured by the technology. The AHAAP Maturation Project goal is to develop, test, and validate one or more AHAAP implementations. The AHAAP Maturation Project tasks are: (i) engineer AHAAP implementation software, (ii) build a functional prototype that implements the AHAAP software for demonstration, testing, analysis, and evaluation purposes, and (iii) generate a report detailing the results of the AHAAP C&C software and hardware implementation. The final project deliverables are: (i) AHAAP software implementation and prototype, (ii) a report from Sandia National Laboratories detailing the results of the AHAAP implementations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Translation of "Contribution a l'etude du diagramme pression-temperature du beryllium pur" (Contribution to the Study of the Pressure-Temperature Diagram of Pure Beryllium)

An unusual feature of beryllium’s phase diagram is the transition from a hexagonal close packed (hcp, or α) to a body-centered cubic (bcc, or β) structure ~20 K below the normal melting temperature of T melt ≈ 1560 K. An early thermal analysis of 99.7% pure Be found two arrests1 : one at 1533-1536 K and another at 1551-1557 K. Without ruling out an allotropic transformation in Be itself, Sloman attributed the first of these to solidification of a Be-BeO eutectic. Losano observed only the higher of the two in 99.962% Be, seeming to confirm the notion that the lower feature was an artifact of impurity. Teitel (99.4%) also measured two arrests and attributed the lower to impurity, but questioned whether oxygen was the culprit.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

FY24 Progress Report: SRNL Analysis of ICCWR LCM and WAMS data for Corrosion and Cracking

Algorithms for Machine Learning (ML) and data analysis for the 3013 Surveillance Program have been developed in an ongoing collaborative effort by the Savannah River National Laboratory (SRNL) and the University of South Carolina (USC). The objective of the algorithms is to automate the identification of corrosion and crack formation in the Inner Container Closure Weld Region (ICCWR) of the canister system used to store Pu-bearing material. Data for corrosion and cracking is collected from large binary files generated by a Laser Confocal Microscope (LCM), the Wide Area 3D Measurement System (WAMS), or,in a recent proposal, by a Scanning Electron Microscope (SEM). The ML software uses the physical attributes in the data files (e.g., one or more of: height, color, and 16-bit grayscale values as functions of position in a plane projection) to detect signs of surface corrosion and cracking after being trained on similar data, with the features to be detected. Although the initial scope included screening for broader indicators of corrosion, e.g., pitting, identification of potential cracks was prioritized for the past several years at the request of program leadership. Labeled training data is essential to developing the ML algorithm, and enhancements to data labeling capability have been developed to address this essential precursor to application of ML routines. Efficient labeling is particularly important in view of the large volume of data required to train ML algorithms and the relative rarity of cracks in the ICCWR data set. The updated program will read binary data from either LCM, WAMS or SEM files, interrogate data attributes, facilitate user labeling of data for training ML algorithms, execute ML algorithms, output parameters from trained ML algorithms, report ML model accuracy with respect to labeled data, and generate graphical representations for various analyses. In FY24, hourglass neural networks (HNNs) that were initiated in FY22 were further developed and tested using available LCM data, and their performance was tested against that of the alternative U-Net Neural Network algorithm structure. HNNs along with previously developed Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs) comprise a suite of ML tools for identification of cracks in the ICCWR

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Proteomics Analysis of Human Contaminant Proteins

Complete characterization of unknowns via proteomics remains challenging. There exist regions of mass spectrometry-based proteomics data where empirical measurements are not attributed to peptides, and/or sequenced peptides from mass spectra are not attributed to any source. These uncharacterized regions are known as the “dark” proteome. Many proteomics tools rely on some a priori knowledge of sample composition; few tools allow for investigation of unknowns without relying on composition assumptions. Further, the potential low abundance of minor traces in these uncharacterized regions can make elucidation of the “dark” proteome challenging. Herein, we describe the development and evaluation of approaches to study the “dark” proteome and move towards an untargeted approach for more complete characterization, namely by studying minor human protein traces in non-human samples and combining that approach with non-human source organism identification without relying on assumptions. Human protein markers, in the form of genetically variant peptides, have been extensively examined in a variety of human matrices, including blood, plasma, and hair, but have yet to be investigated in non-human samples, such as cell cultures, as human contaminant traces. Genetically variant peptides are those that are found in proteins carrying single nucleotide polymorphisms. In this work, we aimed to (1) investigate the feasibility of detecting human contaminant genetically variant peptides (GVPs) in a diverse set of non-human organisms using public proteomics data and a computational pipeline, as well as to (2) develop a combined capability for untargeted source organism characterization and GVP detection. To our knowledge, this is the first report of applying these approaches towards a more complete proteomic characterization of unknowns. We successfully demonstrate the feasibility of broad human contaminant GVP detection in proteomics data, develop a better understanding of GVP detectability, characterize the sample-to-sample variability in GVP detection, and identify a core set of GVPs that can potentially be used as markers indicative of the human contaminant traces portion of the “dark” proteome. Further, we developed and evaluated a combined pipeline, MARLOWE-GVP, that enables both untargeted source organism characterization and GVP detection. We show high accuracy of correct source organism characterization and high degree of similarity of human contaminant GVP detection compared to the conventional approach. Success on both these efforts have allowed us to advance our understanding and characterization of the “dark” proteome.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Countering Weapons of Mass Destruction (CWMD) Device Cybersecurity Characterization Process and Profile

Countering Weapons of Mass Destruction (CWMD) recognizes that threats in the cyberspace domain continue to grow, which requires CWMD devices and supporting systems to be both cybersecure (ability to protect or defend from cyber-attacks) and resilient (ability to maintain required capability in the face of adversity) to cyber threats. The CWMD cybersecurity characterization approach in this document supports existing cyber resilience activities within the Acquisition Lifecycle Framework. Similarly, this process supports existing Department of Homeland Security Cyber Resilience Test and Evaluation activities, which consist of iterative processes, starting at the initiation of system acquisition and continuing throughout the entire device and system life cycle. Cyber resilience is the ability of an information system to continue to operate while under attack, even if in a degraded or debilitated state,1 and to rapidly recover operational capabilities for essential functions after a successful attack.2 The goal of the security characterization task for CWMD is to support the development of a CBRN device-dependent profile that aligns with device network capabilities and maps to recommended security controls to create a characterization security profile impact levels. The impact levels for CWMD devices should be characterized as Low (L), Moderate (M), High (H) to align with the low, moderate, high control baselines. To estimate the impact levels, the device’s security-related attributes are translated into the security objectives: Confidentiality (C), Integrity (I), and Availability (A), known as the CIA triad. The potential impact for each device can be L, M, H, for devices that connect and transmit different types of data and may have different impact levels. National Institute of Standards and Technology Federal Information Processing Standards Publication 199 states, “the potential impact values assigned to the respective security objectives shall be the highest value from among those security categories that have been determined for each type of information resident on the information system.”3 As CWMD is determining the cybersecurity impact levels of CBRN devices based on network connections and data transfers, the impact levels are aligned with the associated attributes of network connections and communications. For example, if the device system is connected to a wireless network and transmits different data types based on the confidentiality of the data, the highest impact value for each security objective should represent the device’s CIA impact level. This document is intended to be used by test managers, test team, and program managers.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

CO2-Locate: A Dynamic Database and Tool for Accessing National Oil and Gas Well Data to Inform Carbon Storage Projects

The CO2-Locate Database is a growing compilation of publicly available wellbore resources that have been merged based on common attributes across data sources with an attribute schema developed to be consistent across disparate resources, reduce data gaps, and eliminate record redundancy. The first version of CO2-Locate has been published to Energy Data eXchange (EDX) and includes the integrated public wells dataset as well as additional geospatial summary layers of key wellbore characteristics to protect proprietary resources. Additionally, the CO2-Locate database has been deployed into a web application, enabling easy access, data filtering capabilities, and visualization of U.S. wellbore infrastructure by stakeholders to inform injection site selection and risk assessments.

Dyer, Alec S. [NETL Site Support Contractor, Natio↗
DOE OSTI2024

Energy Services Interface: Architecture, Requirements, and Agreements

This report continues and extends the development of the Energy Services Interface (ESI) that recently has been led by the U.S. Department of Energy’s Grid Modernization Laboratory Consortium. The ESI is intended to facilitate the coordination of multiple, flexible energy resources that work in tandem to satisfy grid objectives by using performance attributes encoded within an ESI Service Template. The ESI relies on a pair of interfaces, representing the service requestor (the consumer of a service) and a service provider (who manages its resources per the agreed performance attributes) to achieve what is commonly known as a “grid service”. Due to its performance-driven approach, the ESI is hypothesized to be able to satisfy all common grid needs via only six common ESI service types. This report first reviews the status of ESI development, including its fundamental tenets. The report makes three important contributions to ESI development: First, it recognizes the similarity between service level agreements and the contract-like agreements that would be needed between energy service requestors and providers and recommends that ESI service agreements be modeled after the web services agreement specification. Second, whereas prior development efforts had focused on a flat, five-stage lifecycle, this report asserts that the ESI should have three behavioral layers in its architecture—the discovery, agreement, and service layers. Finally, this report offers concrete requirements that should be useful toward the development of the service requestors’ and service providers’ respective communications interfaces.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Dual-Comb Spectroscopy to Tethered Balloon System (DCS to TBS) Field Campaign Report

There is a critical need for the ability to detect, quantify, and attribute sources and sinks of greenhouse gases (GHGs) with high temporal and spatial resolution. Current methods are typically based on continuously operating near-surface point sensors or intermittent aerial/satellite observations. Dual optical frequency comb spectroscopy (“dual-comb spectroscopy”; DCS) is a laser-based technique that can bridge this sensing gap by providing high-precision, high-accuracy, and high-temporal-resolution detection and quantification of trace gases over kilometer-length scales, connecting local and regional observations. A single DCS system can also provide spatial resolution for source/sink attribution with the addition of a few minor optical elements, enabling cross-validation of other observations (e.g., Total Carbon Column Observing Network [TCCON], Orbiting Carbon Observatory [OCO-2/3], Greenhouse Gases Observing Satellite [GOSAT-1/2][).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Initial Mobility Analysis for ORNL VA-EDH Synthetic Populations

Travel burdens are a major barrier to healthcare access among US Veteran patient populations, particularly those residing in rural areas. Spatial accessibility to points of care for US Veteran populations is commonly assessed in two ways. The first approach uses open data from the US Census to represent collective travel burdens, for example the distance between population-weighted census tract centroids and VHA points of care. The second approach uses restricted-access VHA patient data to measure travel costs (e.g., distance, time) for accessing points of care with respect to geolocated patient addresses and real or approximated transportation networks. While the advantage of the open data approach lies in its reproducibility, it has notable limitations in its tendency to infer individual travel behavior from aggregate population characteristics, a problem known as ecological fallacy. Conversely, while the patient data approach is able to account for individual travel behavior, its ability to account for localized access disparities (e.g., a neighborhood with exceptionally high transportation costs) and patient demographics is limited as protecting individual patient data requires their storage in closed systems with limited capacity for adequately modeling real-world travel patterns or for supplementing patient attributes. Additionally, the patient data approach cannot account for veterans who are not enrolled in the VHA system but who may be eligible for care. These challenges limit the ability to perform “what if” analyses on the effects of place-specific interventions on veteran populations with high access barriers to healthcare. To address these challenges, we explore the application of realistic synthetic populations to examine travel burdens and spatial accessibility issues among veteran patient populations. Synthetic populations provide a virtual, individually-resolved and cross-sectional representation of the veteran patient population that enables investigation of spatial access to points of care in ways in which aggregate data and patient data do not. First, synthetic populations allow one to directly assess how individuals access points of care, from synthesized residential locations to outpatient facilities on real-world transportation networks. Modeling access to points of care at the individual scale addresses the ecological fallacy problem associated with using aggregated census data to represent veteran populations and patterns of movement. Second, synthetic populations provide a means of completely representing an area’s veteran population using only publicly available, anonymized census microdata from the American Community Survey (ACS) to ensure the privacy of real-world individuals. Generating synthetic populations from the ACS also expands descriptive characteristics beyond what patient data typically offers to include socio-demographic, economic, housing, and mobility attributes. More detailed profiles of both VHA patient populations and veterans not enrolled in the VA system will provide a comprehensive picture of groups that may benefit from interventions or outreach. As an initial exercise for using synthetic populations to measure veteran travel burdens to VA care, we apply Oak Ridge National Laboratory’s (ORNL) UrbanPop capability to generate a series of synthetic VHA patient populations for 9 Veterans Integrated Services Networks (VISN) market areas in 9 Census Divisions across the continental United States, which are listed in Table 1. We use UrbanPop to produce synthetic populations for the VISN markets selected for each US Census Division, then assign VA outpatient clinic destinations to synthetic VHA patients based on travel about each VISN market’s road network. To demonstrate using the synthetic populations to evaluate healthcare travel burdens, we compare the time-based impedance between simulated home locations and VA outpatient clinics in each VISN market. We then perform validation exercises on the synthetic populations with respect to neighborhood (block group) demographic composition as well as patient mobility, comparing aggregate origin-destination statistics for the synthetic population to outpatient visits available in restricted patient data from the VA’s Corporate Data Warehouse (CDW) database.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Automated Air Sealing Demonstration: Denver Federal Center Building 40

This project aimed to demonstrate building airtightness achieved by automated air sealing in a commercial building setting. The automated air sealing demonstrated in this project uses a modified blower door to pressurize and distribute the sealing aerosol to achieve the required building airtightness. To demonstrate this technology, Building 40 at the Denver Federal Center, a federally owned campus under the jurisdiction, custody and control of the U.S. General Services Administration (GSA), was selected for testing (Figure 2). This building is currently undergoing several retrofit projects, including increasing insulation, installing high-performance windows, and adding advanced equipment, control systems, and automated air sealing. This demonstration project involved installing automated air sealing and measuring the improvements in the building's airtightness. The automated air sealing was installed over two days by AeroBarrier, the vendor. An external blower door test contractor measured the airtightness of the demonstration space before and after air sealing. The new airtightness value and the percentage increase in airtightness were used to evaluate the energy savings potential of automated air sealing. These airtightness values were used to compute the energy savings and CO 2 emissions reduction for different climate zones, building types, and initial airtightness conditions. In addition, the heating, ventilation and air conditioning (HVAC) load reduction attributable to the reduced building air leakage was studied. This study included research to determine the cost and time reduction of automated air sealing. Finally, the automated air sealing performance was evaluated qualitatively using a focus group discussion that included GSA and Bristol, the general contractor. The installation has shown the demonstration site, with a floor size of 4,462 ft 2 , air leakage has reduced by more than 50% in less than 7 hours, including preparation, site sealing, and cleanup. The performance objectives were classified as quantitative or qualitative based on the evaluation metrics used to assess the project’s success. The key performance objectives for this project were the level of airtightness achieved, the time and cost required to perform the sealing, and the HVAC load reduction attributable to air sealing. Table 1 shows the quantitative performance objectives.

42 ENGINEERING↗
DOE OSTI2025

129 I, 99 Tc, and U Distribution Coefficients of Subsurface Sediments Collected from the Proposed Site of the Environmental Management Disposal Facility

Performance Assessment calculations were completed in 2020 to evaluate the Environmental Management Disposal Facility (EMDF), a proposed new low-level radioactive waste (LLW) disposal facility on the U.S. Department of Energy’s Oak Ridge Reservation (ORR). Among the large number of input parameters needed for such calculations, are distribution coefficients (K d values; radionuclide concentration solid: liquid ratio) that provide a measure of the tendency of radionuclides to bind to sediments. The objective of this study was to measure K d values of three radionuclides that may pose a disproportionately large amount of risk, U, iodine-129 ( 129 I) and technetium-99 ( 99 Tc). The average 129 I K d value for the 14 geological materials recovered from the proposed EMDF site was 37.8 mL/g and ranged from 0.45 to 140.9 mL/g. These values were consistent, but somewhat larger than previous measurements made with ORR sediments and were about an order of magnitude greater than those used in previous EMDF PA calculations. The median 99 Tc K d value was 365.7 mL/g, much greater than previously reported using ORR geological materials. Five of the 14 tested geological materials sorbed large quantities of 99 Tc, suggesting that the weakly sorbing 99 Tc(VII) species had been reduced to the sparingly soluble 99 Tc(IV) species. The five strongly sorbing sediments had apparent 99 Tc solubility values of approximately <10 -8 mol/L. The median U K d value was 5,726 mL/g. All of the tested geological materials had large K d values, ranging from 625 to >10,208 mL/g. Among the sediment samples that exhibited strong U binding, the apparent solubility value was approximately <10 -9 mol/L. Based on sediment properties and general ORR geological considerations, it was proposed that much of the 129 I and 99 Tc retention could be attributed to the site materials exhibiting low pH (average pH = 4.94), and/or the elevated levels of iron oxides, manganese oxides, and natural organic matter. Similarly, the extremely high U binding measured in these sediments may also be attributed to the low conditions of carbonates, which can complex and therefore solubilize uranyl in these tests due to the low pH, and also the relatively high concentrations of iron and organic coatings on these samples. An implication of this study is that the areas of the EMDF subsurface environment may have natural properties for attenuating 129 I, 99 Tc, and U movement, and potentially other radionuclides, thereby possibly reducing risk posed by burial of LLW at this site.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Summary of Expansions and Updates in R&D GREET® 2025

The research and development (R&D) version of Greenhouse gases, Regulated Emissions, and Energy use in Technologies (GREET®) model, or R&D GREET, is developed by Argonne National Laboratory (Argonne) with the support of the U.S. Department of Energy (DOE) and other federal agencies. R&D GREET is a life cycle analysis (LCA) model, structured to systematically examine the energy and environmental effects of a wide variety of transportation fuels and vehicle technologies in all transportation sub-sectors (i.e., road, air, marine, and rail) and other end-use sectors, and energy technology systems. Argonne has expanded and updated the model in many areas in R&D GREET 2025. This report provides a summary of the expansions and updates. R&D GREET is designed as an attributional LCA model, but specific consequential effects for certain pathways are included by default when they are scientifically important and/or highly policy relevant. For example, two major consequential elements – induced land use change (ILUC) for biofuels and avoided emissions for certain RNG-based pathways – are implemented by default in R&D GREET because they are highly relevant to our user base and crucial for a comprehensive evaluation of the environmental effects of these technologies. In a future version of R&D GREET, these consequential elements for biofuels and RNG could be more clearly distinguished from core attributional results. We will consider adding a highly visible toggle that would allow users to turn off or adjust these consequential impacts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Differential Privacy in Grid Kitchen: Implementation & Software Documentation

Sharing of power grid feeder models faces significant challenges due to the potential risk of exposing sensitive operational information. Traditional anonymization techniques have shown notable limitations in other sensitive domains, as evidenced by documented re-identification attacks that combine supposedly anonymized datasets with auxiliary information, raising concerns that similar vulnerabilities could affect power grid data. Consequently, there is a pressing need for a more rigorous privacy protection strategy that not only delivers formal mathematical guarantees but also preserves the analytical value of the shared models. To address this challenge, we have enhanced the Grid Kitchen framework by implementing differential privacy mechanisms within the distribution model dehydration pipeline. This implementation carefully calibrates and applies noise to sensitive attributes in feeder models according to configurable privacy levels—low, moderate, and high—each offering different balances between data utility and privacy protection. Our approach uses established noise functions (Gaussian for continuous data and Discrete Laplace for integer values) with parameters carefully calibrated so that the impact of individual data points is effectively masked in the final output. The integration leverages our Noise Catalog, which we developed to categorize feeder model properties by component type, data type, and sensitivity. This catalog guides the application of appropriate noise functions and privacy parameters ($\varepsilon$ and $\delta$) to each attribute, ensuring consistent privacy protection across the model while maintaining its structural integrity and analytical usefulness. This implementation also includes evaluation tools that allow model owners to assess the impact of privacy-preserving transformations before sharing data with external parties. This report provides documentation for the differential privacy capabilities added to the Grid Kitchen project. It includes a primer on differential privacy concepts and their importance in modern data sharing, details the architecture of our implementation, explains the privacy modes and parameter configurations, and offers practical guidance on using the code for applying differential privacy to grid feeder models. Through examples and code snippets, we demonstrate the effective application of these privacy-enhancing technologies, enabling utility operators and researchers to confidently share grid data while protecting sensitive information.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Solar Photovoltaics Resilient Fasteners Levelized Cost of Energy (LCOE) Tool

Solar photovoltaics (PV) module fasteners are one of the most common structural failure points on PV systems, particularly in high winds and coastal areas with ocean spray. Some fastener types have been shown to survive these conditions at higher rates than others. The fastener type, material, quantity, and placement all impact performance. Fasteners that fail less often typically have a higher upfront cost, but this investment can pay off in savings from less frequent torque audits (which reduces O&M costs), reduced system damage, and decreased system downtime. We developed an Excel-based tool to evaluate different module fasteners for a PV system - either a new or retrofit project - and compare differences in upfront and outyear costs to determine the expected life cycle costs and simple payback periods of different fastener options. The tool is site-specific, with inputs including system attributes (such as system size, location, price of power) and fastener attributes (such as design, washer type, nut type, use of locking hardware, materials, installation time, and torque audit requirements). A baseline fastener scenario can be compared to up to four proposed fastener scenarios. In addition to presenting expected life cycle cost implications of the different fastener options, the tool produces results showing the reductions in outyear costs needed to offset any initial cost premiums for more reliable fasteners across four categories: preventative O&M, avoided damage, reduced downtime, and reduced insurance premiums. These numbers can serve as decision aids for users when considering fastener options on new or existing projects. This poster will present the tool, methodology, and scenarios using example sites to highlight the tool capabilities and how it can inform different fastener decisions on different projects. Future work includes incorporating lifetime expected damage costs by embedding damage function curves that the authors are developing from field data.

14 SOLAR ENERGY↗
DOE OSTI2026

Fuel-Cladding Eutectic Study of Legacy Fast Flux Test Facility (FFTF) MFF HT9/U-10Zr Metallic Fuel

This report presents the first systematic investigation of fuel-cladding eutectic interaction (FCEI) in irradiated HT9/U-10Zr metallic fuel from the Fast Flux Test Facility (FFTF) Materials Fuels Form (MFF) program, using differential scanning calorimetry (DSC) coupled with scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS). Two irradiated fuel cross-sections, MNT07H (9.5 at% burnup, x/L = 0.78) and MNT08H (7.0 at% burnup, x/L = 0.93), were subjected to three successive isothermal annealing rounds (R1–R3) at 820°C for 20 minutes each, yielding a cumulative transient duration of one hour. This study directly addresses a recognized gap in the existing FCEI database, which previously lacked irradiated HT9/U-10Zr data at burnup levels above 8 at%. Two principal findings emerge from the study. First, for both samples, FCEI remained spatially confined within the pre-existing fuel-cladding chemical interaction (FCCI) zone boundaries after R3, with no measurable eutectic penetration into unaffected cladding beyond the original FCCI layer. This self-limiting behavior is consistent with historical Fuel Behavior Test Apparatus (FBTA) results and is attributed to the near-eutectic phase composition of the FCCI zone, which rapidly absorb the available eutectic-forming constituents and then stall penetration once the FCCI zone is consumed. A comparison with unirradiated surrogate data further supports this mechanism: whereas a U–34 at.% Fe sample would be expected to show ~176 µm of iron penetration under comparable conditions, the irradiated samples exhibited only ~20 µm, a discrepancy attributed to irradiation-induced interfacial porosity and pre-existing FCCI composition gradients. Second, for MNT08H, FCEI was observed exclusively on the half of the cladding circumference where pre-existing steady-state FCCI was present, with no detectable FCEI on the opposite half. Three hypotheses are proposed to explain this asymmetry: the inhibiting role of a zirconium-rich rind at the fuel-cladding interface; the chemical sequestration of iron by redistributed zirconium within the fuel matrix; and the persistence of fuel-cladding gaps on the FCEI-free half that preclude direct contact. All three hypotheses require further experimental investigation. The results extend the empirical FCEI database into higher-burnup territory and demonstrate the viability of DSC-based testing as a substitute for the no-longer-available FBTA apparatus. Future work will include additional cross-section testing, compilation of the full FCEI dataset, model evaluation, and DSC testing of ternary fuel compositions to broaden the experimental basis for safety assessment of sodium-cooled fast reactor systems.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Real-Time Inference For MI/RR Deblending

The Fermilab Main Injector (MI) and Recycler Ring (RR) share a common beam loss monitor (BLM) system, making loss events difficult to attribute to their source machine when beam is present in both simultaneously. The Real-time Edge AI for Distributed Systems (READS) project addresses this by deblending BLM readings in real time using machine learning (ML). The current FPGA based implementation meets the sub-3 ms latency requirement but carries a resource intensive hls4ml development cycle, motivating exploration of GPU based deployment. This paper characterizes inference latency on an NVIDIA Jetson Orin Nano and introduces a packet organization scheme for assembling synchronized event frames from seven distributed BLM DAQ streams. Using a Python based DAQ simulation with injected timing jitter in place of unavailable live beam data, the pipeline achieved an average end to end latency of 0.456 ms (σ = 0.122 ms) across 167,000 test frames, comfortably meeting the timing constraint. Early outliers were attributed to TensorRT warm-up rather than steady state limitations, suggesting GPU based inference is a viable alternative to the existing FPGA implementation.

Yu, Kellen [Cornell U.]↗