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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Distributed strain sensing using Bi-metallic coated fiber optic sensors embedded in stainless steel

Silica fiber optic sensors are resistant to corrosive environments and high temperatures, making them attractive candidates for harsh conditions found in nuclear and aerospace industries. Moreover, fibers can be deployed remotely for continuous measuring of spatially distributed temperatures and strains. Here, this study investigated embedding a Ni/Cu bi-metallic coated fiber in a stainless-steel 316 (SS316) matrix using laser powder bed fusion towards functionalizing metal components for site-specific health monitoring. The embedded fiber was continuously interrogated during controlled heating to 1000°C. The measured fiber strains were similar to the expected differential thermal strains between the fiber and the SS316 matrix, until divergent behavior was observed at temperatures >500°C. No debonding at the matrix–coating–fiber interfaces was observed during microscopy, but significant interactions between the coatings and matrix resulted in diffusion-driven chemistry variations and Kirkendall void formation. Applying the strain-lag theory revealed plastic behavior in the Ni coating at temperatures >500°C, limiting the strain transfer to the fiber at higher temperatures. It was estimated that the elastic modulus in the Ni coating had decreased from ~200 GPa at room temperature to below 40 GPa, starting at 600°C. The low elastic modulus above 600°C is within the margin of what the tangent modulus would be in the case of bilinear isotropic hardening. Regardless of the divergent strain transfer at higher temperatures, the fiber was exposed to the equivalent of 1.9 % engineering strain at 1000°C, but measured only a 0.7 % engineering strain due to the poor strain transfer. Although compensating for the plastic behavior of Ni proved challenging, the bonding of a brittle silica fiber to a metal matrix surviving to 1000°C invites potential iterations on coating material for future application. For example, the embedded fiber is sufficient for acoustic energy transfer, realizing high temperature distributed acoustic sensing.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Deep learning models map rapid plant species changes from citizen science and remote sensing data

Anthropogenic habitat destruction and climate change are reshaping the geographic distribution of plants worldwide. However, we are still unable to map species shifts at high spatial, temporal, and taxonomic resolution. Here, we develop a deep learning model trained using remote sensing images from California paired with half a million citizen science observations that can map the distribution of over 2,000 plant species. Our model— Deepbiosphere— not only outperforms many common species distribution modeling approaches (AUC 0.95 vs. 0.88) but can map species at up to a few meters resolution and finely delineate plant communities with high accuracy, including the pristine and clear-cut forests of Redwood National Park. These fine-scale predictions can further be used to map the intensity of habitat fragmentation and sharp ecosystem transitions across human-altered landscapes. In addition, from frequent collections of remote sensing data, Deepbiosphere can detect the rapid effects of severe wildfire on plant community composition across a 2-y time period. These findings demonstrate that integrating public earth observations and citizen science with deep learning can pave the way toward automated systems for monitoring biodiversity change in real-time worldwide.

Gillespie, Lauren E.↗
DOE OSTI2026

SPARTAN and IMPROVE Comparison Experiment (SPICE) Interim Campaign Report

SPICE (the SPARTAN and IMPROVE Comparison Experiment) aims to obtain and quantify comparisons between aerosol PM 2.5 mass concentration measurements from the University of Oklahoma (OU) Surface Particulate Matter Network (SPARTAN) station and the U.S. Environmental Protection Agency (EPA) Interagency Monitoring of Protected Visual Environments (IMPROVE) station hosted by the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility at ARM’s Southern Great Plains (SGP) observatory in Oklahoma. Aerosols—both natural and anthropogenic—affect human populations in multiple ways. Much of ARM’s research focuses on how aerosols influence weather and climate through their optical and radiative effects, as well as their impacts on clouds and precipitation. However, aerosols also pose direct risks to humans and other organisms through inhalation, with the severity of health impacts depending on particle size, chemical composition, and duration of exposure. The IMPROVE network was established by the U.S. EPA to monitor air quality, including visible clarity as well as total and chemically speciated aerosol mass concentrations. The ARM SGP site hosts the IMPROVE SOGP station. Separately, the SPARTAN network operates a globally distributed set of stations similar to IMPROVE but with an emphasis on remote deployment and semi-autonomous operation for use beyond the borders of the United States (IMPROVE only operates within the U.S.). The University of Oklahoma operates a SPARTAN station. To establish confidence in the OU SPARTAN instrumentation and measurement protocol relative to the EPA-certified IMPROVE station, the OU SPARTAN station is currently deployed at SGP in close proximity to the IMPROVE SOGP station. The SPICE campaign was envisioned as a contiguous calendar-year effort for 2025 to capture seasonal variation in mass loading as well as composition. However, independent of the SPICE campaign, the SPARTAN network adopted a new filter construction part-way through the year, interrupting our contiguous data set. Thus, to obtain a contiguous data set with a uniform consistent configuration, SPICE desires an extension through 2026.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Development and preliminary results of 270 GHz microwave forward scattering diagnostic system on the experimental advanced superconducting tokamak (EAST)

To measure localized (intermediate, high) poloidal wavenumber electron density fluctuations, a tangential millimeter-wave collective scattering system has been designed and successfully developed on the experimental advanced superconducting tokamak (EAST). This innovative system employs a 270 GHz mm-wave probe beam, emitted from the mid-plane of Port K and directed towards Port P after passing through a combination of two in-vessel mirrors. Here these two ports are located on the EAST device’s midplane and are 110° apart. The downward scattered signals pass through two in-vessel mirrors, and exit through the P window. The received multi-channel separated scattering signals are arranged along the poloidal direction. This optical arrangement makes the monitored turbulent wave number is poloidal dominant and up to 40 cm −1 . The tangential microwave scattering scheme enables this diagnostic to monitor local density fluctuations, with typically a radial spatial resolution △ R = 5 cm, and a wavenumber resolution of △ k = 0.4 cm −1 . The scattering system underwent comprehensive laboratory testing in 2023, with installation finalized in 2024. The system features steerable launch and receiver optics, allowing remote control to position the scattering volume from normalized radius ρ –0 to the pedestal region on a shot-by-shot basis. Effective experimental data, characterized by distinct off-center spectral peaks, have been successfully obtained in neutral beam-heated plasmas on EAST. The beam tracing forward modeling has been employed for experimental data interpretation. The advanced millimeter-wave scattering system provides fluctuation measurement capability from ion temperature gradient to electron temperature gradient scale on low field side. Preliminary experimental density fluctuation data have been successfully obtained in neutral beam-heated plasmas on EAST. The localized measurement will be used for kinetic turbulence transport numerical simulation validation.

high-k collective scattering↗
DOE OSTI2024

Inferring building height from footprint morphology data

As cities continue to grow globally, characterizing the built environment is essential to understanding human populations, projecting energy usage, monitoring urban heat island impacts, preventing environmental degradation, and planning for urban development. Buildings are a key component of the built environment and there is currently a lack of data on building height at the global level. Current methodologies for developing building height models that utilize remote sensing are limited in scale due to the high cost of data acquisition. Other approaches that leverage 2D features are restricted based on the volume of ancillary data necessary to infer height. Here, we find, through a series of experiments covering 74.55 million buildings from the United States, France, and Germany, it is possible, with 95% accuracy, to infer building height within 3 m of the true height using footprint morphology data. Our results show that leveraging individual building footprints can lead to accurate building height predictions while not requiring ancillary data, thus making this method applicable wherever building footprints are available. The finding that it is possible to infer building height from footprint data alone provides researchers a new method to leverage in relation to various applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Creating Accurate Methane Emission Inventories through Data-Driven Airborne Survey Strategies

Because natural gas emits less carbon than other fossil fuels, it holds promise as a green energy transition fuel. However, the overall carbon footprint of natural gas is significantly elevated by methane emissions that occur during its production and transmission (Cusworth et al. 2022). Methane “super-emitters,” while comprising only about 1% of sites, are responsible for the majority of oil- and gas-sourced methane emissions, making their detection and mitigation critical in reducing the climate impact of natural gas and in meeting national and global sustainability goals (Sherwin et al. 2024). Yet, despite advancements in detection, significant uncertainties remain regarding the size, frequency, and duration distributions of methane emissions (e.g., Frankenberg et al. 2016, Cusworth et al. 2022, Chen, Sherwin et al. 2022, Conrad et al. 2023, Johnson et al. 2023, Sherwin et al. 2024) underscoring the need for comprehensive emissions inventories segmented by basin across the US. Airborne surveys are well-suited for collecting data to build these comprehensive, basin-level inventories because they allow for extensive spatial coverage, and have the spatial resolution, and the sensitivity to pinpoint individual methane sources. As remote sensing technologies enable rapid basin-scale surveys, it is imperative to establish scientifically and statistically robust standards to generate reliable and actionable emissions inventories. Recent work has shown that differences in airborne sampling strategies, detection technologies, and analysis can lead to large differences between survey conclusions if not correctly accounted for (Chen et al. 2024). This elevates the importance of incorporating proper sampling and analysis techniques when designing a methane emissions monitoring campaign to produce accurate results and facilitate cross-study comparisons. In this paper, we describe a survey strategy designed using the latest conclusions from the literature to align results from different aerial surveys. We identify several sampling and analysis principles, including large sample sizes, balanced sampling across oil and gas production, careful survey area definition, and a unified protocol for analysis, to be vital to producing an unbiased estimate of basin-scale emissions. We present results from a Department of Energy-funded project that deployed this survey strategy in two understudied oil and gas- producing regions in the United States: the Haynesville Basin in Texas and Louisiana, and the Woodford Shale in the Anadarko Basin in Oklahoma.

03 NATURAL GAS↗
DOE OSTI2026

Integrating very-high-resolution imagery, Sentinel-2 time-series data, and machine learning to map shrub fractional abundance across arid and semi-arid ecosystems in China

Shrub fractional abundance (SFA), the proportion of shrub cover per unit area, serves as a critical indicator of environmental aridity and ecosystem health in arid and semi-arid regions, particularly across the Mongolian steppe. However, large-scale SFA mapping in Mongolian steppe ecosystems remains challenging due to the small crown size of shrubs, their sparse distribution, and spectral overlap with coexisting low vegetation (e.g., grasses and herbs), which hinders accurate detection using coarser-resolution satellite data or traditional field surveys. To address these challenges, we developed a two-step approach that integrates very-high-resolution (VHR) imagery, time-series Sentinel-2 data, and deep learning techniques. First, we generated high-accuracy benchmark maps of individual shrub crowns from 0.5 m VHR imagery by combining manual segmentation with a hybrid deep learning framework (Dino V2 and convolutional neural networks). Second, we used these shrub crown maps as training data to build an XGBoost model for predicting SFA from 20 m Sentinel-2 time-series data, leveraging phenological information to improve estimation. We validated our approach across 70 sites (1km 2 each) in the Inner Mongolia Autonomous Region, which is representative of Mongolian steppe ecosystems. From VHR imagery, we mapped 1.31 million shrub crowns with an accuracy of R 2 = 0.92. Scaling up with Sentinel-2 data yielded regional SFA maps with an R 2 = 0.60. Further SHAP (SHapley Additive exPlanations) analysis on the developed XGBoost model revealed that phenological metrics (particularly observations in early-May, mid-July, and late-September), which distinguish shrub phenology from that of other land cover types (e.g., grasses and bare soil), were the most influential predictors of SFA. Finally, our regional SFA maps uncovered unimodal relationships between shrub distribution and climate variables, peaking at mean annual minimum temperatures near 0 °C and annual precipitation around 200 mm. Collectively, these findings demonstrate how the integration of multi-source remote sensing and machine learning can overcome historical limitations in SFA mapping, enabling accurate, spatially continuous assessments across vast Inner-Mongolian steppe ecosystems. Our framework has the potential to be applied to other steppe ecosystems and dryland ecosystems across the Mongolian steppe and beyond, offering a foundation for improved monitoring and ecological impact assessments in the face of global climate changes.

Arid and semi-arid landscapes↗
DOE OSTI2025

WRPS Hanford Update [Slides]

The purpose of this report is to coordinate with the staff from WRPS, the current contract holder for operations at the Hanford site to better inform design decisions for the Autonomous systems for Hanford waste tank handling project. The project goals are to support risk reduction associated with monitoring, inspection and mapping of Hanford tank underground pits and includes: 1. A pit mock-up demonstration of Idaho National Laboratory’s (INL) Autonomous Pit Exploration System (APES) technology, which has two configurations: A. The Autonomous Robotic Arm, which is a proposed collaboration with Florida International University (FIU) and has capabilities to visually inspect, monitor, map and conduct simple tool manipulation tasks in the tank pits, and B. The robotic crawler configuration, which has capabilities to deploy to the bottom of the pits and conduct closer and bottom-up visual inspections. 2. An Implementation Plan that identifies Hanford site-wide application of the INL remote robotics technologies to enhance the performance of the tank farm systems and the characterization of the waste they contain to mitigate risk and optimize the overall waste mission. 3. A first-of-a-kind environmental digital twin will be produced. Digital twins to date require permanently installed sensors in order to produce asset specific predictions. This project will use the intermittent signals during inspections from deployed sensors on robotic systems to produce the data sets used by AI/ML to enable predictions of issues on tanks.

42 - ENGINEERING↗
DOE OSTI2026

Human Supervision of Autonomous Vehicle Fleet Operations and Associated Passenger Communications: Preprint

Advances in automated vehicle (AV) technology and expanded operations are rapidly emerging with Automated Mobility District (AMD) deployments in global cities. NLR's AMD research addresses critical elements of human supervision of AV fleet operations and associated passenger communications for vehicles in which no driver or safety attendant is present. Although sufficiently advanced AVs no longer have direct oversight by a driver, fleet management remains staffed with operations personnel at the operations command and control (OCC) facility. This paper examines the functionality of the OCC, drawing comparisons of how automated train control and automated people mover OCCs operate. Within an AMD, the OCC manages various vehicle types, sizes, and operational modes, including on-demand and fixed route service, to facilitate a 'network of networks' for transport within a metropolitan area. The OCC serves as oversight for multiple AV fleets assisting AVs via remote operation of vehicles, communication, and dispatching personnel to resolve problems. The OCC also coordinates system operation, geographically staging vehicles, and managing weather, police, and emergency events. Informed by traffic management center (TMC) strategies using highly integrated software and communications, OCCs facilitate seamless information flows. OCC personnel remotely assist passengers and oversee multi-party operation to ensure safety and security. Although social norms mitigate large-capacity unattended vehicle operations, social interaction in multi-party automated small vehicles has little precedent. This poses a new frontier for society and requires research to effectively understand and manage. Future research will monitor OCC implementations, passenger interfaces, and deployment scaling of initial AMD systems.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Center for Alternate Synchronization and Timing (CAST) PTP Network Monitoring Report

The Oak Ridge National Laboratory Center for Alternative Synchronization and Timing (CAST) performs research, development, testing, and evaluation of alternative terrestrial-based timing and synchronization infrastructure for the US power grid and other critical infrastructures. Alternative timing options reduce reliance on GPS and enhance the overall resilience of critical infrastructures. CAST infrastructure uses Precision Time Protocol (PTP) as the primary conduit for delivery of synchronization packets. CAST deploys PTP over long terrestrial links to synchronize a multitude of remote boundary clocks and downstream power grid components with the authoritative grand master clocks. Network traffic issues can severely degrade PTP accuracy. This report focuses on examining network traffic anomalies and their effects on PTP operation as well as the potential implications to CAST’s high-precision remote synchronization operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Utilization of Unmanned Aircraft Systems for Environmental Purposes at the Savannah River Site – 26578

Born in the 1950s, the Savannah River Plant was constructed as a plutonium and tritium production plant. As the Cold War era came and went, a change of direction was signaled when the name of the facility changed from Savannah River Plant to Savannah River Site (1989) – the main mission at Savannah River shifted from “production” to “cleanup.” The Department of Energy is responsible for managing the 310 square miles of land that is the Savannah River Site and the cleanup/remediation activities that occur. The Savannah River National Laboratory utilizes unmanned aircraft systems to assist with some of those environmental monitoring and remediation activities. One function of unmanned aircraft systems at the Savannah River National Laboratory is conducting aerial photography and videography. Various infrastructure evaluations have been performed with unmanned aircraft – facility rooftop inspections, water tower lock verifications, earthen dam surveys, etc. An unmanned aircraft system has been used for progress footage of remediation projects – Dunbarton Bay remediation, Saltstone Disposal Unit construction, etc. Thermal cameras on an unmanned aircraft system are used to help identify cracks/leaks in structures from vantage points not readily accessible by personnel. Virtual tours of different waste units are conducted with unmanned aircraft systems for Site groups and federal/state regulators to save costs and travel time. Additionally, advanced remote sensing technologies are used on unmanned aircraft systems at the Savannah River Site. Light Detection and Ranging uses laser pulses to measure distances to the Earth's surface or other targets to create highly detailed topographic maps that accurately portray terrain (e.g., elevation changes, slopes, and contours). Data collection with a Light Detection and Ranging unit mounted on an unmanned aircraft system is quick and efficient – large-area surveys are conducted in shorter time frames. Because Light Detection and Ranging can penetrate through foliage and vegetation to ground level, it is being used in conjunction with a watershed model to study the General Separations Area of the Site to determine stormwater flow volume and direction, monitor stream stability, and forecast river flow behavior. A hyperspectral camera captures a wide spectrum of electromagnetic wavelengths across numerous narrow bands, beyond what the human eye can see. It provides detailed spectral information about the objects or surfaces in an image, which can be used to identify and analyze materials based on their spectral signatures. A hyperspectral camera mounted on an unmanned aircraft system has been used at the Savannah River Site for non-destructive evaluation of concrete and concrete structures. Unmanned aircraft systems at the Savannah River Site are also used for the application of herbicide on identified rooftops. P-Reactor and R-Reactor are entombed nuclear reactors at the Savannah River Site. Unwanted vegetation is often present on these rooftops – over time roots can penetrate seams, cracks, and joints of the general roof and concrete caps, leading to water ingress and deterioration of the structural components. For time and cost efficiencies as well as in the interest of personnel safety, an unmanned aircraft is used to dispense herbicide on rooftop areas where vegetation is noticed to help alleviate the issues/hazards.

Lorier, Troy [Savannah River National Laboratory (↗
DOE OSTI2024

Two decades of aerosol trends over India: seasonal characteristics and urban-rural dynamics

Abstract India faces significant air quality challenges, with one of the highest air pollution levels of any country in the world. Here, we examine two decades (2001–2019) of both particulate matter (PM 2.5 ) concentration and aerosol optical depth (AOD) over the country. Increases are seen between the two decadal averages, for 2001–2010 and 2011–2019, in western India, particularly in the Indo-Gangetic Plain (IGP). IGP region, including Bihar, West Bengal, Jharkhand, and Uttar Pradesh, shows the highest increases in AOD (+0.03, 13%) and PM 2.5, s (+8 µ g m −3 ). Seasonal AOD patterns fluctuate, with the IGP experiencing the highest wintertime increase, especially in Bihar (+0.07). In summer, there are increases in AOD along the southern and eastern coastal areas. Monsoons cause a slight rise in AOD, except in Rajasthan. In the post-monsoon season, the IGP experiences a notable increase in AOD (+0.057, 25%), potentially driven by biomass burning in Bihar (+0.11) and Uttar Pradesh (+0.075). Dividing our study area into urban and peri-urban clusters ( n = 2791), AOD is found to be similar, possibly due to advective mixing. However, the differences between urban and rural areas become more noticeable, especially in the second decade. Correlations between AOD and PM 2.5, g vary across locations, with the highest found in Kanpur ( R 2 = 0.61) and weaker in Delhi ( R 2 = 0.42), highlighting the need for more ground monitoring. However, it suggests that satellite-derived AOD can generally be used to examine trends in PM 2.5 over longer time frames.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Co-Firing Switchgrass and Waste Coal in A Power Plant: A Techno-Economic and Life Cycle Evaluation for The Ohio River Valley (SWITCH) (Final Technical Report for Ohio State/FE0032204)

Abandoned coal mine lands (AMLs) represent one of the most persistent environmental challenges in the United States. Prior to the enactment of the Surface Mining Control and Reclamation Act (SMCRA) in 1977, coal mining operations were not legally required to reclaim disturbed lands, leaving behind approximately 500,000 AML sites nationwide. These sites pose severe environmental and health risks, including acid mine drainage, soil and water contamination, and spontaneous combustion of waste coal piles. Millions of Americans live within one mile of these AMLs, underscoring the urgency of remediation. Traditional reclamation practices, such as planting cool-season grasses, often fail to fully restore ecological function or leverage the economic potential of these lands. This project addressed these challenges by developing integrated strategies for resource recovery, land reclamation, and sustainable energy production. This project evaluated an integrated strategy to convert this liability into an opportunity by recovering waste coal and co-firing it with switchgrass (Panicum virgatum L.) cultivated on reclaimed or marginal AML areas in existing coal-fired power plants. Switchgrass not only provides a renewable feedstock but also aids in land reclamation and carbon sequestration. 1) Remote Sensing and Machine Learning for Waste Coal Identification Using Sentinel-2 satellite imagery and supervised classification, we applied four machine learning models to detect historical waste coal piles. Random Forest achieved the highest accuracy (precision: 86%, recall: 77%). Time-series analysis revealed gradual vegetation recovery since 1986, indicating natural reclamation processes in historical sites, while active mining areas showed ongoing disturbance. This workflow enables scalable monitoring and prioritization of reclamation efforts. 2) UAS-Based Stockpile Volume Estimation To quantify recoverable waste coal, we evaluated Unmanned Aerial Systems (UAS) equipped with Light Detection and Ranging (LiDAR) and multispectral sensors. Structure-from-Motion (SfM) photogrammetry combined with interpolated Digital Terrain Models (DTMs) achieved strong agreement with LiDAR reference volumes (Root Mean Square Error (RMSE) ≈147 m 3 , Mean Absolute Percentage Error (MAPE) ≈2%). Sensitivity analysis confirmed that spatial resolution significantly influences accuracy, emphasizing the need for high-resolution data for precise volume estimation. This approach offers a scalable, cost-effective, and accurate alternative to conventional ground-based surveys. 3) Switchgrass Cultivation for Bioenergy and Water Quality Improvement We assessed the hydrological and water quality impacts of converting AMLs to switchgrass production areas using the Soil and Water Assessment Tool (SWAT). Results showed that converting 10% of the watershed area into the switchgrass production zone reduced streamflow by 3.1%, total suspended solids by 18.1%, total nitrogen by 7.6%, and total phosphorus by 6.2%, while achieving biomass yields of 8.6–9.2 metric tons per hectare. These findings highlight switchgrass as a dual-benefit strategy for land reclamation and bioenergy feedstock production. 4) Integrated Co-Firing and CCS for Carbon-Negative Power Generation We modeled co-firing scenarios using the Power Plant Flexible Model (PPFM) to evaluate plant efficiency, greenhouse gas (GHG) emissions, and levelized cost of electricity (LCOE). Without carbon capture and storage (CCS), increasing switchgrass co-firing ratios reduced LCOE from $\$$150/MWh at 0% biomass to $\$$110/MWh at full substitution. Under CCS, costs remained higher (~$\$$250/MWh at 0% biomass) but decreased to $\$$200/MWh at 100% biomass, while enabling net-zero or carbon-negative electricity due to switchgrass sequestration benefits. Although CCS introduced efficiency penalties, pairing it with biomass co-firing offset these impacts and maximized climate benefits. Overall, optimizing co-firing ratios between 60-100%, supported by reliable logistics and storage strategies, emerged as a practical pathway to balance affordability, sustainability, and net-zero or negative GHG emissions while promoting productive reuse of AMLs.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

Editorial: Applications of spectroscopy and chemometrics in nuclear materials analysis

Optical analysis techniques, including spectroscopy and image analysis, have many advantages when applied to the study of nuclear materials. They require small sample sizes, can be performed remotely, and can be proceduralized through consistent practice. Most importantly, they provide a wealth of information by generating multivariate data. For example, ultraviolet–visible–near-infrared absorbance spectroscopy of actinides in aqueous and organic solutions is dependent on the oxidation state, anionic complexation, and temperature. These variables are important for solution-based separation processes, and sensitivity to these factors, combined with online monitoring, can drive the efficiency and control of these processes. The morphology and chemical composition of actinide particles can also provide a vital clue to the mechanisms by which the particles were formed, providing forensic information on the origins of the particles.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Low Yield Nuclear Monitoring Physics Experiment 1 – Integrated Data Acquisition System Design and Initial Observations

The report documents the design of the Integrated Data AcQuisition (IDAQ) system and observations recorded during the first in a series of underground chemical explosions conducted on the Nevada National Security Site (NNSS) in southern Nevada. Experiments are funded as part of Low Yield Nuclear Monitoring (LYNM) research and development within the United States National Nuclear Security Administration NA-22 nuclear non-proliferation program. The series is part of the broader Physical Experiment 1 (PE1) being conducted in and around the P-tunnel facility on the NNSS. Each explosive experiment utilizes several tons of comp-B to generate signals recorded by a broad suite of instrumentation. The IDAQ serves as the backbone for all subsurface instrumentation providing precise time synchronization, remote control, data exfiltration and backup, along with recording several sensing modalities throughout the underground complex that includes ground motion, environmental conditions, and electromagnetic signals.

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

Continuous thermostat setpoint monitoring and correction (Thermostat setpoint correction) v1.0

The Continuous Thermostat Setpoint Monitoring and Correction software is a set of fault detection and correction algorithms that can be implemented in thermostats with two-way OpenAPIs. It is written in the Python language. The algorithms aim to detect the most common and impactful efficiency problems associated with thermostat setpoints - overly aggressive heating or cooling setpoints, incorrect schedules/setbacks, and overly narrow deadbands. These algorithms can automatically detect faults, and implement associated corrective actions to bring the system back to a state of efficient operation. The algorithms can run remotely in the cloud, and directly implemented by connected thermostat manufacturers, or by third party service providers. The software enables a lightweight cost-effective energy management strategy for HVAC systems. The solution is specially viable for small and medium sized commercial buildings, where a full scale building automation system and fault detection and diagnostic tools are often unavailable.

Granderson, Jessica↗
DOE OSTI2025

Distributed Acoustic Sensing to Estimate the Permeability

Optical fiber in a borehole can be interrogated with distributed acoustic sensors (DAS) to capture fracture displacements with the potential to map surrounding fracture networks. We designed a laboratory experiment to test the capability of DAS to determine borehole flow characteristics, and we show that for the first time DAS can be used to remotely estimate permeability. Optical fiber was wrapped around a bead filled pipe and the pressure drop and flow velocity were measured to directly calculate permeability. A machine learning model using statistical features from continuous DAS estimated the bulk permeability. Fluid interactions with the permeable material demonstrate insufficient resolution using DAS amplitude-based measurements for estimating pressure drop to infer permeability. Variations in the spectral domain relate DAS measurements to the pressure drop and provide consistent permeability estimates. Resolution with DAS is sufficient to estimate permeability and provides a reliable method to monitor at depth in borehole conditions.

58 GEOSCIENCES↗
DOE OSTI2025

Remotely Sensed High‐Resolution Soil Moisture and Evapotranspiration: Bridging the Gap Between Science and Society

This paper reviews the current state of high‐resolution remotely sensed soil moisture (SM) and evapotranspiration (ET) products and modeling, and the coupling relationship between SM and ET. SM downscaling approaches for satellite passive microwave products leverage advances in artificial intelligence and high‐resolution remote sensing using visible, near‐infrared, thermal‐infrared, and synthetic aperture radar sensors. Remotely sensed ET continues to advance in spatiotemporal resolutions from MODIS to ECOSTRESS to Hydrosat and beyond. These advances enable a new understanding of bio‐geo‐physical controls and coupled feedback mechanisms between SM and ET reflecting the land cover and land use at field scale (3–30 m, daily). Still, the state‐of‐the‐science products have their challenges and limitations, which we detail across data, retrieval algorithms, and applications. We describe the roles of these data in advancing 10 application areas: drought assessment, food security, precision agriculture, soil salinization, wildfire modeling, dust monitoring, flood forecasting, urban water, energy, and ecosystem management, ecohydrology, and biodiversity conservation. We discuss that future scientific advancement should focus on developing open‐access, high‐resolution (3–30 m), sub‐daily SM and ET products, enabling the evaluation of hydrological processes at finer scales and revolutionizing the societal applications in data‐limited regions of the world, especially the Global South for socio‐economic development.

54 ENVIRONMENTAL SCIENCES↗