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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Hydrogen applications in airport operations: a review using the Port Authority of New York and New Jersey as an illustrative airport system

Airports combine aircraft propulsion, ground operations, stationary power systems, and fuel logistics in ways that make emissions reduction technically and operationally complex. Existing studies often assess hydrogen applications in these areas separately, limiting understanding of the shared infrastructure, safety, and operational constraints that shape airport deployment. This review evaluates hydrogen across three airport-relevant operational domains: aviation propulsion, ground support equipment and vehicles, and stationary power systems. Within aviation propulsion, the review examines sustainable aviation fuel production and hydrogen-powered aircraft as two distinct hydrogen-relevant pathways. The Port Authority of New York and New Jersey is used as an illustrative airport system to relate the literature to a real operating context. Drawing on peer-reviewed studies, technical reports, demonstration projects, and public operational information, the review also includes screening-level calculations of hydrogen demand and potential CO 2 e reductions for selected applications. The findings show that hydrogen's role is highly application-specific. Near-term opportunities are strongest where hydrogen serves as a low-carbon process input, supports selected high-utilization ground equipment, or contributes to resilient stationary power-system configurations. Hydrogen-powered aircraft remain a longer-term option because storage, fueling infrastructure, certification, cost, and NO x management continue to constrain deployment. Across all domains, infrastructure readiness, fuel logistics, safety requirements, and leakage management emerge as recurring determinants of viability. Future research should focus on cross-domain infrastructure planning, comparative assessment of hydrogen against alternative pathways, improved treatment of leakage and non-CO 2 effects, and clearer safety and regulatory frameworks for airport deployment.

08 HYDROGEN↗
DOE OSTI2026

Wind and Temperature Consensus at Horn Point, HU-Beltsville, Piney Run (Maryland) in support of CoURAGE

The Maryland Department of the Environment (MDE) operates a ground-based atmospheric profiling network consisting of collocated radar wind profilers (RWP) and radio acoustic sounding systems (RASS) as part of its Ambient Air Monitoring Program. This network provides continuous observations of wind and temperature structure in the lower troposphere to support air quality forecasting, regulatory analysis, and atmospheric research. The network currently includes three fixed sites across Maryland: Horn Point (HP, lower eastern shore) [38.587525°,-76.141006°], Howard University-Beltsville (HUB, central Maryland) [39.055277°, -76.878632°], and Piney Run (PR, western Maryland) [39.705950°, -79.012000°] The network is designed to capture regional variability in atmospheric transport and boundary-layer processes. These systems measure vertical profiles of horizontal wind speed and direction using Doppler radar techniques, with observations typically spanning from ~100 m above ground level up to approximately 2.5–4 km. Measurements are derived from the Doppler shift of backscattered electromagnetic signals, enabling retrieval of wind vectors at multiple altitudes with high temporal resolution (e.g., 30-minute averages reported every 6 minutes). Each radar wind profiler is paired with a Radio Acoustic Sounding System (RASS) to provide profiles of virtual temperature in the lower atmosphere (~100–200 m AGL) by measuring the propagation speed of acoustic waves. Together, the RWP/RASS system yields a coupled data set of thermodynamic and kinematic atmospheric structure, including additional parameters such as vertical velocity, radial velocity, signal-to-noise ratio, and spectral width for advanced analysis. There are two types of files for each station: wind data (files with a "w" prefix) and virtual temperature RASS data (files with a "t" prefix). The wind data files are in the format wYYDDD.cns, where YY is the 2-digit year and DDD is the day of the year. The RASS virtual temperature data files are in the format tYYDDD.cns. Each record has the following header structure: Line 1 : Station Name RASS files Line 2 : RASS rev DeTect_2.0, WINDS files Line 2 : WINDS rev ATI 5.1 Line 3 : N latitude, W longitude, and site elevation (m) Line 4 : Date and begin time of consensus: yy mm dd hh mn ss plus # minutes to add to get UTC Line 5 : Consensus averaging time (minutes); number of beams; number of range gates Line 6 : Number of records required to make consensus (num) total number of records (tot) and the consensus window size (m/s) in the format: num:tot (window) RASS files Line 7 : no. of coded cells, no. of spec, pulse width (ns), and inter-pulse period (µs), WINDS files Line 7 : No. of coded cells, no. of spectra, pulse width (ns), and inter-pulse period (µs), each with a pair of values: first value is for oblique beams, second for vertical RASS files Line 8 : Full scale Doppler value (m/s) Delay to first gate (ns) Number of gates Spacing of gates (ns), WINDS files Line 8 : Full scale Doppler velocity (m/s), oblique and vertical Vertical correction applied to oblique beams? (0 = no, 1 = yes) Delay to first gate (ns), oblique and vertical Number of gates, oblique and vertical Spacing of gates (ns), oblique and vertical Line 9 : Azimuth and elevation (9s indicate vertical beam not used) RASS files Line 10, values : HT = Height above ground (km), T = Uncorrected virtual temperature consensus (deg C), Tc = Corrected virtual temperature consensus (deg C), W = Vertical wind consensus (9s indicate vertical beam not used, w-component, positive upward, m/s), CNT = Number of records that made consensus (for the 3 values in same order), SNR = Average signal to noise ratio (dB) of records in consensus (same order) WINDS files Line 10, values : HT = Height above ground (km), SPD = Wind speed (m/s), DIR = Wind direction (deg E of N from N), RAD = Radial velocities for each beam (m/s) in order given in azimuth and elevation line (positive toward radar; 9s indicate vertical beam not used, CNT = Number of records that made consensus, SNR = Average signal to noise ratio (dB) of records in consensus

{"wind speed and direction",temperature}↗
DOE OSTI2024

Modular Subsurface Sensors and Integrated Software for Advanced Subsurface Characterization and Monitoring using Unoccupied Vehicles

The advent and subsequent proliferation of autonomous airborne, waterborne, and groundbased vehicles (i.e., “drones”) promises to broadly transform the geosciences and associated industries, including fossil energy exploration and development, mineral resource exploration and development, water-resource management, and environmental remediation. For geophysical characterization and monitoring, the prospect of programming highly repeatable and low-cost drone missions for subsurface imaging will allow for deployments in hazardous and previously inaccessible areas. Coupled with autonomous workflows for data processing, management, and visualization, drone-based geophysical characterization and monitoring will enable unprecedented, real-time insight into diverse subsurface properties and processes of scientific and engineering importance. Toward this end, the objectives of this Lab Directed Research and Development (LDRD) project were to develop new (1) instrumentation for dronebased electromagnetic induction (EMI) geophysical imaging, including separated transmitter and receivers and associated electronics, (2) software for real-time data telemetry, processing, management, and visualization. Although EMI has been previously deployed using unoccupied aerial systems (UASs), these applications failed to capitalize on the game-changing capabilities of drone platforms. Whereas drone-based data acquisition allows for collection of rich, three-dimensional (3D) multi-offset/multi-angle configurations between transmitters and receivers, past efforts have relied on conventional instrumentation that was designed for ground-based data collection with the transmitter and a single receiver housed in the same unit; nor did these previous applications demonstrate real-time delivery of results to support rapid management decisions in the field. In this 1-year project, we (1) designed and constructed new lightweight independent transmitter and receiver antenna platforms that communicate with a laptop computer; (2) developed software to control data acquisition, manage/transfer data, and visualize data as its collected; and (3) demonstrated the operation of the new hardware and software systems in a ground-based field test. Our work entails major technological advances for EMI and established a foundation on which to build a new drone-based, real-time geophysical EMI imaging capability to support diverse challenges facing the nation.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Geothermal Heat Pump System Showcase: Short-Term Validation of Borehole Heat Exchanger Performance from Field Data to Numerical Modeling: Preprint

Since 2011, a geothermal heat pump (GHP) system has been operating to provide space heating and cooling for the Solar Radiation and Research Laboratory building at the National Laboratory of the Rockies (NLR) in Golden, Colorado. The system consists of 23 vertical boreholes, each extending to a depth of 300 ft (91 m), connected to 11 water-to-air heat pump units and four circulation pumps. Between fiscal years 2023 and 2025, additional power meters and temperature sensors were retrofitted to support detailed system performance assessment and model development. This study presents preliminary monitoring results and the development of an initial numerical model of the borehole heat exchanger field. The model incorporated site-specific geometry, ground thermal properties derived from thermal response tests, and ambient temperatures, and simulated system behavior over a representative operating day in September. Model predictions of outlet temperatures were compared against corresponding field measurements. Results showed that modeling initialized with a simplified linear subsurface temperature gradient presents systematic discrepancies in outlet temperature, whereas incorporating depth-resolved borehole temperature measurements for initialization yields substantially improved agreement with observations. The findings highlight the sensitivity of short-term predictive modeling to the representation of initial subsurface thermal conditions and underscore the value of high-resolution field measurements for model calibration and validation. These preliminary results inform ongoing efforts to extend the modeling framework to longer time horizons and to refine monitoring and modeling strategies that support the design guidance and operational optimization of GHP systems in research and commercial buildings.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Physical Modeling and Design of a Nonvolatile Optically Gated High‐Power Diamond Transistor

In this work, we present the theory and modeling framework of a diamond optically gated junction field‐effect transistor (DOGFET). The device utilizes nitrogen substitutional centers in type‐1b diamond to optically modulate a p‐ boron doped diamond channel. Using sub‐gap lasers with intensities as low as 100 W/cm 2 , electrons are optically excited from substitutional nitrogen sites to the conduction band of the diamond substrate, thus enabling the optical gate to exercise control on modulating the space‐charge region at the junction and therefore the channel conductivity. We show that the device can deliver a current of 7 μA/μm, or equivalently 1750 A/cm 2 , while switching at a frequency greater than 100 kHz, in a form factor of 5 μm 2 . The breakdown voltage is found to be greater than 1850 V, with a breakdown field strength of ~13 MV/cm. Moreover, the device supports nonvolatile operation with a “memory effect” enabling single transistor state retention. The presented simulation framework provides a physically grounded insight into the limits and opportunities of optoelectronic diamond systems.

Engineering - Electronic and electrical engineerin↗
DOE OSTI2025

Klickitat Valley Health Central Utility Plant Modernization

This project replaced legacy heating and cooling equipment at Klickitat Valley Health (KVH), a rural hospital in Goldendale, Washington. Work included constructing a new chiller building, installing modern hydronic piping systems, replacing the main Emergency Department air handler (Air Handlining Unit (AHU-1)), upgrading electrical systems, and converting the Emergency Department wing from electric reheat to hydronic heating and cooling. Commissioning, testing, adjusting, and balancing were completed under this grant. These upgrades improve energy efficiency, operational resilience, and system reliability while preparing the campus for future electrification and renewable energy integration. They also support growth in healthcare services for a rural population. The project directly reduces energy costs and provides a foundation for future decarbonization measures, including a FEMA-funded microgrid and a large-scale ground source heat pump (GSHP) project. These follow-on initiatives would not have been possible without the hydronic backbone built through this project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Nine Canyon Long-Duration Energy Storage: A Feasibility Study

The Nine Canyon Long Duration Energy Storage (LDES) Feasibility Study explores the technical and economic viability of deploying advanced energy storage technologies at Energy Northwest's (EN) Nine Canyon (9C) Wind Project site in Benton County, Washington. Supported by the Washington State Department of Commerce and the U.S. Department of Energy’s Office of Electricity under its LDES Voucher Program, the study represents a collaborative effort between EN, Pacific Northwest National Laboratory (PNNL), and ARES North America. At the core of this effort is the development of a generalized techno-economic modeling framework and evaluation tool designed to assess the value proposition of LDES projects across a variety of contexts. The modeling tool is technology-agnostic and accommodates user-defined parameters such as rated power, energy duration, round-trip efficiency, capital and operational costs, and dispatch constraints. It also integrates economic inputs, including market prices, energy revenue structures, and financing parameters to evaluate performance through key metrics. The tool provides utilities with a transparent, adaptable platform to support decision-making, investment prioritization, and portfolio planning for various storage technologies. To guide scenario design and interpretation, the study first surveyed the LDES technology landscape, including lithium-ion batteries, flow batteries, non-hydro gravity storage, and thermo-mechanical systems, comparing cost trajectories, technical performance, safety and hazards, materials sourcing and recyclability, and spatial/siting considerations. This literature-grounded review highlights technology trade-offs and reinforces the need to align technology choice with site characteristics, use cases, and project objectives. A companion chapter examines ownership structures (EN ownership, third-party ownership, shared models) and offtake options (energy marketing, capacity/energy PPAs, time-of-use PPAs, block-delivery PPAs, and tolling), where PPAs (power purchase agreements) represent contractual arrangements for buying and selling electricity. The chapter also highlights implications for risk allocation, capital access, operational control, and revenue certainty. The study also evaluates supervisory control and data acquisition (SCADA) and transmission interconnection pathways, options include upgrading the existing SCADA or deploying a dedicated LDES controller, with attention to protection schemes, data telemetry, cybersecurity, and regulatory coordination with BPA. In addition, an ARES-specific geotechnical and hydrology assessment presented in the appendix screens multiple corridors for slope stability, bearing capacity, cut-and-fill magnitude, and stormwater behavior.

25 ENERGY STORAGE↗
DOE OSTI2025

NGEE Arctic Tram: Radiation and Environmental Measurements over Low- and High-Centered Polygon Vegetation Communities, Barrow, Alaska, 2014-2017

These data were generated from an observational platform (Tram), an automated cart carrying a suite of radiation and remote sensing instruments running on 68 meters of elevated track 1 to 1.5 meters above the surface of the vegetation communities of low- and high-centered polygon ground. The Tram is in the footprint of the NGEE Arctic/AmeriFlux eddy covariance flux tower (US-NGB) at the Barrow Environmental Observatory, Barrow, Alaska, and operated before, during, and after the growing seasons of 2014-2017. Dataset includes one user guide (*.pdf) with final and raw *.csv data files. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Precursor Analysis Report: Blackmatter Ransomware Attack on New Cooperative 2021

The BlackMatter Ransomware Attack on New Cooperative 2021 Precursor Analysis Report leverages publicly available information about the New Cooperative cyber attack and catalogs anomalous observables for each technique employed in the attack. This analysis is based upon the methodology of the Cybersecurity for the Operational Technology Environment (CyOTE) program. The BlackMatter ransomware was first identified in July 2021 and is reported to have infected more than 50 corporations around the world. , The Iowa-based grain cooperative, New Cooperative, was impacted by the BlackMatter ransomware on or before 18 September 2021. The adversary likely resided on New Cooperative’s networks for 15 days prior to encrypting its network and demanding New Cooperative pay $5.9 million in ransom by 25 September to unlock systems and prevent 1 terabyte (TB) of sensitive data from being publicly released. It is not clear if New Cooperative paid the ransom. The full impact of the ransomware attack is not known; however, according to New Cooperative’s general manager, the attack caused the company’s automated processes to revert back to processes used in the 1970s. , As of 6 October, only 50 percent of New Cooperative’s operations were utilizing automated processes. The company took eight weeks to rebuild the entire network and information technology (IT) systems from the ground up, which puts the date of fully recovery around 13 November. Researchers and analysts identified 20 unique techniques utilized during the attack with a total of 404 observables using MITRE ATT&CK® for Industrial Control Systems. The CyOTE program assesses observables accompanying techniques used prior to the triggering event to identify opportunities to detect malicious activity. If observables accompanying the attack techniques are perceived and investigated prior to the triggering event, earlier comprehension of malicious activity can take place. Seventeen of the identified techniques used during the New Cooperative cyber attack were precursors to the triggering event. Analysis identified 360 observables associated with these precursor techniques, 284 of which were assessed to have an increased likelihood of being perceived in the 15 days preceding the triggering event. The response and comprehension time could have been reduced if the observables had been identified earlier. The information gathered in this report contributes to a library of observables tied to a repository of artifacts, data sources, and technique detection references for practitioners and developers to support the comprehension of indicators of attack. Asset owners and operators can use these products if they experience similar observables or to prepare for comparable scenarios.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2025

​​Hydrogen Transportation and Distributed Energy Systems Seismic Risk Assessment for Cascadia Subduction Zone Airport Facilities​

Portland International Airport in Oregon is exploring operating a fleet of 28 fuel cell electric buses to support airport operations and possibly provide backup power during outages. In this report, we evaluate the risk of hydrogen deployment at an airport considering the potential for seismic activity. We present a seismic risk assessment for pressurized piping for a generic hydrogen refueling station at the airport. Only pressurized piping was considered in the risk analysis because of its vulnerability and availability of fragility information unlike the other components. Seismic capacity of other components should be included as more information becomes available. To characterize the seismic hazard, we considered the initiating event frequency of a Cascadia Subduction Zone earthquake event based on data from the literature. Seismic stressors that translate to the site from an earthquake event are expressed as peak ground acceleration. Site disturbance is characterized as a function of soil conditions, represented by different shear wave velocity values, which affect how seismic waves propagate and impact structures. Pipe fragility curves as a function of seismic capacity were used to correlate ground acceleration and failure probability. These fragility characteristics were combined with site-specific soil conditions to calculate the probability of pipe rupture during a seismic event. It should be noted that multiple simultaneous failures due to an earthquake event as a common cause are not considered. Some epistemic uncertainties such as self-exciting shocks, aftershocks, and the time variant nature of the ground motion are not considered either.

08 HYDROGEN↗
DOE OSTI2024

SpecFIDLER User Manual (Software V.2.6.0)

The Spectroscopic Field Instrument for Detection of Low Energy Radiation (SpecFIDLER) allows response teams to detect and quantify plutonium contamination on the ground. Notional scenarios include dispersion from a weapon accident, or the launch failure of a space probe containing a radioisotope thermoelectric generator. Unlike other instruments, the thin-window sodium iodide detector is sensitive to the low-energy gamma rays emitted by plutonium isotopes. The system supports both mobile survey as well as stationary sampling. This manual provides information about installing, maintaining, and troubleshooting the SpecFIDLER. The scope of this document includes the physical hardware, software for data acquisition, and algorithms for data analysis. Recent changes to the software and algorithms aim to streamline the operation of the system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Adaptive spectra-to-exposure conversion using ridge regularized polynomial response models

Real-time gamma spectra-to-exposure conversion in aerial and ground monitoring commonly relies on calibration-derived, detector- or system-specific conversion coefficients that are assumed to generalize across operational environments. In practice, deployment specific differences in spectral composition and transport conditions can introduce systematic bias relative to reference instruments, motivating methods that adapt coefficients using minimal field supervision while explicitly limiting overfitting. In this work, we present a conservative coefficient adaptation framework that updates a baseline polynomial energy-weighting function using ridge-regularized regression, with leave-one-out cross-validation (LOOCV) used to select the regularization strength. The findings support ridge-constrained minimal-supervision adaptation as a practical mechanism to suppress site-specific bias without destabilizing a calibration-derived baseline.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2026

A U.S. scientific community review of carbon cycle science gaps and opportunities to better support earth system science and carbon management

Greenhouse gas (GHG) emissions continue to grow, while natural carbon reservoirs are becoming increasingly vulnerable to anthropogenic pressures, climate extremes, and disturbance. These changes are impacting humans, ecosystems, and natural resources worldwide. Tracking and mitigating GHG emissions require a pivot to operational monitoring of regional carbon flux and stock changes. The current GHG observing system is addressing needs at two distinct scales: 1) Local scale (< 1 km), related to anthropogenic point source emissions, and 2) global scales (> 1000 km), related to land and ocean carbon sinks. More focus on intermediate (10–1000 km) scales is needed to more effectively monitor progress in reducing carbon emissions, enhancing removals, and maintaining sinks. Representatives from carbon cycle biomass and flux communities across United States government agencies and academic institutions met in September 2024 to discuss the rationale and scientific context for more effectively implementing an operational system for GHG monitoring in support of urban and national carbon management needs. To guide development of this system, we propose a multi-tiered global spaceborne observing framework for carbon flux and stock, prioritizing: 1) frequent GHG partial columns for carbon emissions and removals; 2) continuous time series and data fusion of biomass from Lidar and Synthetic Aperture Radar (SAR) for carbon stocks, and 3) expanded coverage of tropical, high latitude, and oceanic regions to monitor carbon cycle tipping points and feedbacks. This system should be complemented by expanded surface and airborne networks for oceanic and terrestrial/aquatic ecosystems for calibration, ground truthing, and study of under-sampled regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Energy, power, and infrastructure demands from electrifying airport ground support equipment at United States airports

As the airline industry seeks to reduce costs and transition to clean energy, electric ground support equipment is emerging as a favorable option. The integration of electric ground support equipment into airport operations requires careful planning for vehicle deployment, charging infrastructure, and grid impacts. We develop a flexible, bottom-up modeling framework to assess energy and infrastructure needs across more than 300 U.S. airports. Our analysis estimates site-specific power and energy demand, equipment counts by type, charger requirements, and costs. Here we quantify the magnitude of new electrical loads created by the electrification of airport ground support equipment, finding that peak power demand at the largest airports can reach up to 20 megawatts, with annual electricity consumption approaching 51,000 megawatt-hours. We further show that behind-the-meter battery energy storage systems and solar photovoltaic systems can reduce peak load and lower total system costs by as much as 10 million dollars.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Electrifying Airport GSE: Monte Carlo Grid Impacts

Airports globally are shifting from ICE-powered to electric Ground Support Equipment (eGSE) to enhance efficiency, reduce operational costs, and improve operator health. Leveraging predictable routes, flat terrain, and low operational speeds, airports provide ideal conditions for electrification. This study evaluates freight GSE electrification at Dallas-Fort Worth International Airport (DFW), USA, using the Agile@ platform, which integrates three analytical methods: Freight Facility Model (FFM), Activity-Structure-Intensity-Fuel (ASIF), and Monte Carlo simulations. Results from 10,000 simulations indicate modest but critical increases in electricity demand and significant variability in GSE energy consumption. These insights emphasize the importance of data-driven scheduling, targeted maintenance, and strategic infrastructure planning. For high-uncertainty scenarios, airports are advised to deploy buffer energy storage systems (battery banks), implement demand-response charging strategies, schedule flexible workforce shifts, and prioritize proactive maintenance-particularly for equipment with higher operational uncertainty, such as tug tractors with trailers. Agile@ thus offers a robust, scalable, and data-driven framework to optimize long-term GSE planning and enhance reliability across diverse airport environments.

Bose, Ranjan [ORNL] (ORCID:0009000791026327)↗
DOE OSTI2025

Floating photovoltaic power plants: A review of energy yield, reliability, and operation and maintenance

Photovoltaic (PV) systems are essential for the transition to sustainable energy, reducing fossil fuel dependence and mitigating climate change. Although PV requires minimal land area — PV can meet the European Union's energy needs using only 0.26% of its land — space for deployment is often scarce in densely populated regions. Floating photovoltaics (FPV) offer an effective solution to land-use challenges by installing PV systems on floating structures in water bodies. FPV is a growing niche within PV with a cumulative installed capacity reaching 7.7 GW globally by 2023. Almost 90% of the installed FPV capacity is in Asia, with close to 50% of in China alone, while the Netherlands and France are the largest markets outside Asia. FPV shows strong potential to support climate targets, but still faces challenges like regulatory barriers, cost competitiveness compared to ground-based PV (GPV), and uncertainties about environmental impacts and system reliability. FPV systems are currently installed mainly on sheltered inland waters, such as quarry lakes, irrigation ponds and reservoirs. FPV technical standards are still being developed. Guidelines have been published by the World Bank, DNV, and Solar Power Europe, and emerging national standards from South Korea, China, and Singapore address design, components, and safety. The International Electrotechnical Commission (IEC) is working on formal standards for floats, mooring systems, and electrical connectors. However, the published best practices lack quantitative guidance for yield modelling and reliability, which this report aims to address. It provides data-driven insights, models, and parameters essential for accurate energy yield, reliability, and maintenance predictions over FPV systems' lifetimes.

14 SOLAR ENERGY↗
DOE OSTI2026

Artificial Intelligence in Nuclear Safeguards; Evaluating Safeguards and Security Risks and Benefits for Advanced and Small Modular Reactor Deployments

Rapidly growing interest in advanced and small modular reactor (A/SMR) technologies presents challenges as well as opportunities for implementing international safeguards and security. A/SMR deployments are expected to be more numerous, more geographically dispersed, and more varied in their designs, placing new demands on the data systems and analytical tools used to support oversight (Alberti et al., 2023; Canadian Nuclear Safety Commission et al., 2024). Because of this variability, the importance and reliance on data systems for A/SMR deployments is expected to be higher than for previous reactor generations. Artificial Intelligence and Machine Learning (AI/ML) offer potential capabilities to address the high variability inherent in A/SMR technology. The beneficiaries of AI-assisted tools include facility operators, government regulators, IAEA inspectors, and A/SMR vendors. This report analyzes how AI/ML-assisted technologies can strengthen the implementation of IAEA safeguards and security measures. It also identifies AI-assisted tools to strengthen operator, facility, and regulator knowledge management practices and examines the potential risks AI/ML-based tools may introduce to IAEA safeguards and security efforts. It concludes with a set of hypothetical, standards-style requirements for AI/ML systems used in safeguards contexts, grounded in an inspector-centric view of system verification. Despite the potential benefits of AI/ML systems, understanding potential intentional and unintentional failure modes is critical for ensuring adequate protection of nuclear materials and facilities. Unique features of A/SMRs including sealed cores, remote and novel paradigms of operation, off-site reactor fabrication, novel fuel forms, and varied refueling requirements, introduce challenges for traditional safeguards technological approaches (Pensado et al., 2024; Federation of American Scientists, 2025). AI/ML systems deployed to address these challenges may introduce new risks requiring systematic evaluation rooted in both AI-specific risk frameworks, such as the NIST AI Risk Management Framework (NIST AI RMF), and established cyber risk management standards such as NIST SP 800-30 (National Institute of Standards and Technology [NIST], 2023; NIST, 2012).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

An Open-source Llm Enhanced-tool Specialized In Helping Moose Related Problems And Tasks

MOOSEenger is an open-source, terminal-first chat application for the MOOSE ecosystem that couples specialized parsing of MOOSE documentation and “.i” input files with retrieval-augmented generation to deliver grounded answers about multiphysics modeling and workflows. It includes dedicated readers for MOOSE-style HTML and a pyhit-based parser that uses the MOOSE syntax tree to preserve block structure and attach retrieval metadata. A data-ingestion pipeline performs semantic chunking into atomic facts and stores them hierarchically in a local Chroma vector database that maintains parent–child relationships across documents; the system can ingest directories, individual files, and single-page web content, and it provides CRUD operations (insert, update, delete) to manage the corpus. At query time, relevant chunks are embedded, retrieved, and fused into the model context, with interactive features such as token streaming, persistent chat history, and dynamic RAG (retrieval triggered by user input or intermediate model output). Deployment is flexible: MOOSEenger runs with local Ollama models or remote Hugging Face/OpenAI backends—typically coordinating generation, lightweight tagging/summarization, and embeddings across three models—and it also supports a server mode and integration with the VS Code Continue interface.

Li, Mengnan [Idaho National Laboratory (INL), Idah↗