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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Lightning radiometry in visible and infrared bands

Calibrated measurements of lightning optical emissions are critical for both quantifying the impacts of lightning in our atmosphere and devising detection instruments with sufficient dynamic range capable of yielding close to 100% detection efficiency. However, to date, there is only a limited number of investigations that have attempted to take such calibrated measurements. Here, in this work, we report the power radiated by lightning in both visible and infrared bands, assuming isotropic emission, and accounting for atmospheric absorption. More precisely, we report peak radiated power and total radiated energy in the combined visible plus near-infrared range (VNIR, 0.34–1.1 μm), around the H α line (652–667 nm), and for the 2–2.5 μm infrared band. The estimated peak power and total energy radiated by negative cloud-to-ground return strokes in the VNIR range is 130 MW and 20 kJ, respectively. Additionally, we detected peak radiated powers of 12 and 0.19 MW in the H α and infrared bands, respectively. We cross-reference the optical data set with peak current reported by a lightning detection network. The resulting trend is that optical power emitted around the H α line scales with peak return stroke current according to a power law with exponent equal to 1.25. This trend, which should be approximately true across the entire visible spectrum, can be attributed to the plasma negative differential resistance of the lightning return stroke channel. We conclude by discussing the challenges in performing calibrated measurements of lightning optical power in different bands and comparing the results with previously-collected data with different experimental setups, observation conditions, and calibration methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Surface and Buried Thermal, and RGB Unexploded Ordnance Data Collection

This document provides a description of a data collection campaign of unexploded ordnance (UXOI) set. The dataset captures a controlled UAV imaging campaign designed to support detection of UXO across varied environmental conditions. Data were collected during three campaigns in Norris and Northeast Knoxville, Tennessee, using RGB, and thermal sensors mounted on Parrot UKR. In total, the dataset contains 9925 images, 26 full-motion video, and approximately 81.99 GB of data, collected across late spring/summer conditions, every hour during sunlight, and multiple surface contexts, including tall grass, short grass, gravel, as well as buried in sand, and other gravel mixtures. The collection was designed to capture thermal and visual variability relevant to UXO detection in agricultural land, bare earth, and subsurface. Review of the imagery showed that ordnance was most detectable during periods of changing solar input, especially approximately 10-60 minutes after sunrise, approximately 20-60 minutes after sunset, and 2-3 min after cloud cover interrupted prolonged solar heating. These conditions increased thermal contrast because many ordnance items retained or released heat differently than the surrounding vegetation and ground surface. This dataset provides a useful resource for developing and evaluating airborne UXO detection methods under realistic field conditions. All ordnance used in the study was inert, and thermal behavior may differ from that of live ordnance. In addition, variation in ordnance type, composition, and placement introduced differences in thermal response that should be considered when interpreting results.

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

Reference Shapefiles and Pre-trained Random Forest Classification Models for Detecting Aufeis on the North Slope of Alaska in Landsat Imagery

This dataset provides shapefiles and trained machine learning models used for aufeis detection at four sites on the North Slope of Alaska. It includes reference data for evaluating Landsat-based detection methods, supporting research on remote sensing approaches for identifying aufeis. The ReferenceData folder contains ArcGIS shapefiles of semi-automated land cover classifications for 217 Landsat Collection 2 images, categorizing pixels into six classes: aufeis, snow, ground, none, water, and cloud. The SiteBuffers.zip file includes 10-kilometer buffer shapefiles defining regions of interest around four aufeis fields (Canning21, FH1, Firth, and Kuparuk), used to test three detection techniques. Additionally, the TrainedRFModels folder contains six pre-trained Scikit-Learn Random Forest classifiers (100 trees, max depth = 30) designed to predict aufeis presence in Landsat Collection 2 Surface Reflectance images using Red, Blue, SWIR2, NDVI, and NDWI bands. This dataset supports the development and validation of remote sensing methods for mapping aufeis in Arctic environments.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 OSTI2024

GRB 180128A: A second magnetar giant flare candidate from the Sculptor Galaxy

Magnetars are slowly rotating neutron stars that possess the strongest magnetic fields known in the cosmos (10 14 − 10 15 G). They display a range of transient high-energy electromagnetic activity. The brightest and most energetic of these events are the gamma-ray bursts (GRBs) known as magnetar giant flares (MGFs), with isotropic energies E iso ≈ 10 44 − 10 46 erg. Only seven MGF detections have been made to date: three unambiguous events occurred in our Galaxy and the Magellanic Clouds, and the other four MGF candidates are associated with nearby star-forming galaxies. As all seven identified MGFs are bright at Earth, additional weaker events likely remain unidentified in archival data. We conducted a search of the Fermi Gamma-ray Burst Monitor database for candidate extragalactic MGFs and, when possible, collected localization data from the Interplanetary Network (IPN) satellites. Our search yielded one convincing event, GRB 180128A. IPN localizes this burst within NGC 253, commonly known as the Sculptor Galaxy. The event is the second MGF in modern astronomy to be associated with this galaxy and the first time two bursts have been associated with a single galaxy outside our own. Here we detail the archival search criteria that uncovered this event and its spectral and temporal properties, which are consistent with expectations for a MGF. We also discuss the theoretical implications and finer burst structures resolved from various binning methods. Our analysis provides observational evidence of an eighth identified MGF.

Trigg, Aaron C. (ORCID:000900068598728X)↗
DOE OSTI2023

Detecting Dwarf Galaxies Around NGC 55

Lambda-CDM predicts the existence of faint dwarf galaxies orbiting both large host galaxies, like the Milky Way, and smaller hosts like the Magellanic Clouds. However, the satellite populations around these smaller host galaxies are poorly understood from both a theoretical and observational perspective. In order to improve our understanding of the satellite populations of low-mass galaxies, the DELVE-DEEP Survey is performing a search for satellites around the nearby, low-mass galaxy, NGC 55. As part of this search, we aim to characterize our detection sensitivity by injecting artificial dwarf galaxies into our data set and attempting to recover them. In this poster, I present our methods of injection and recovery, as well as our preliminary results showing our measured detection rates for artificial dwarf galaxies of varying size and luminosity. Additionally, we explore the blending of resolved stellar fluxes that results as a consequence of source recovery and how this affects the recovered magnitudes of injected dwarf galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Cognitive IoT and Edge Computing for Intrusion Detection with Federated TinyML

Internet of Things (IoT) and Edge Computing (EC) are rapidly becoming an integral part of the modern society. By 2030, there is estimated to be over 40 billion active and connected IoT devices [1]. This rapid progress also comes with a significant implication on cybersecurity. Back-end infrastructure and systems have a much broader attack than they did previously due to vulnerable IoT/EC devices being connected to wireless networks. This expanding attack surface is a growing concern because IoT/EC are increasingly being used in critical systems such as power grids, health care, and smart homes. To effectively address a problem of this scale, cognitive cyber methods—which can autonomously detect and react to cyber attacks as they develop—are needed. To address this, we bring Artificial Intelligence (AI) and Machine Learning (ML) to IoT/EC devices, using tinyML to monitor voluminous IoT data against cyber threats, and using Federated Learning (FL) to share local detection knowledge across the system while preserving privacy. We propose a novel three-layer architecture: (1) an IoT layer for tinyML-based inference, (2) an edge layer for ML model training, and (3) a cloud layer for FL operations. Using the publicly available 11-class N-BaIoT dataset [2], we demonstrate that this architecture mitigates resource constraints at the IoT layer while improving detection accuracy over standard two-layer designs. An outlier-resistant scaler, feature reduction, and quantization enable the tinyML model to maintain detection accuracy with a reduced model size. Additionally, federated learning that only utilizes the intersection (across heterogenous devices) of the reduced feature set achieves superior detection accuracy compared to locally trained models.

Li, Mingyan [ORNL] (ORCID:0009000569532640)↗
DOE OSTI2025

Zero Trust Strategies for Chemical, Biological, Radiological, and Nuclear Detection Systems: D.1 Cyber Scenarios

The evolving landscape of cybersecurity necessitates a paradigm shift to a Zero Trust (ZT) model, which assumes breaches and continuously verifies trust. This approach reshapes how trust boundaries are established, focusing on identities, devices, networks, applications, and data, rather than solely relying on perimeter defenses such as firewalls. Central to this transformation is the National Institute of Standards and Technology's (NIST) Special Publication 800-207, outlining the Zero Trust Architecture (ZTA), along with Executive Order 14028, which mandates federal agencies to adopt ZT principles. Complementary to these efforts, the Cybersecurity and Infrastructure Security Agency (CISA) developed the Zero Trust Maturity Model (ZTMM), providing a framework with five pillars and three cross-cutting capabilities to guide agencies toward enhanced cybersecurity maturity. In support of these initiatives, the DHS Countering Weapons of Mass Destruction Office (CWMD) is applying ZT principles to secure Chemical, Biological, Radiological, and Nuclear (CBRN) detection systems. Recognizing the diverse deployment models and network connectivity of these systems—from stationary, non-networked units to mobile, cloud-connected devices—the Pacific Northwest National Laboratory (PNNL) is developing cybersecurity scenarios specifically for CBRN environments. These scenarios examine various configurations and technological capabilities, offering insights into the application of ZTMM pillars in enhancing the security postures of CBRN devices. The cybersecurity scenarios presented by PNNL are hypothetical, crafted to explore theoretical situations and stimulate discussion on the potential use or compromise of CBRN detection systems in varied contexts. These narratives are illustrative and do not reference any real events or actual networks. Instead, they employ generalized reference models to highlight concepts and potential issues within CBRN security, focusing on how Zero Trust strategies can be adapted to address these challenges effectively.

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 OSTI2023

Collisional excitation of C2H− by H2: New interaction potential and scattering calculations

Interstellar anions play an important role in astrochemistry as being tracers of the physical and chemical conditions in cold molecular clouds and circumstellar gas. The local thermodynamic equilibrium is generally not fulfilled in media where anions are detected and radiative and collisional data are required to model the observed lines. The C2H− anion has not yet been detected in the interstellar medium; however, collisional data could be used for non-LTE models that would help in identifying the most intense lines. For this purpose, we have computed the first 4D potential energy surface (PES) of the C2H−–H2 complex using an explicitly correlated coupled-cluster approach. The PES is characterized by a single deep minimum with a well-depth of 924.96 cm−1. From this interaction potential, we derived excitation cross sections and rate coefficients of C2H− induced by collisions with para- and ortho-H2. The results obtained for collisions with para-H2 are compared to previous calculations performed using a 2D-PES obtained from an average over H2 rotations.

Chemistry↗
DOE OSTI2023

Sensitivity of the Cherenkov Telescope Array to TeV photon emission from the Large Magellanic Cloud

ABSTRACT A deep survey of the Large Magellanic Cloud at ∼0.1–100 TeV photon energies with the Cherenkov Telescope Array is planned. We assess the detection prospects based on a model for the emission of the galaxy, comprising the four known TeV emitters, mock populations of sources, and interstellar emission on galactic scales. We also assess the detectability of 30 Doradus and SN 1987A, and the constraints that can be derived on the nature of dark matter. The survey will allow for fine spectral studies of N 157B, N 132D, LMC P3, and 30 Doradus C, and half a dozen other sources should be revealed, mainly pulsar-powered objects. The remnant from SN 1987A could be detected if it produces cosmic-ray nuclei with a flat power-law spectrum at high energies, or with a steeper index 2.3–2.4 pending a flux increase by a factor of >3–4 over ∼2015–2035. Large-scale interstellar emission remains mostly out of reach of the survey if its >10 GeV spectrum has a soft photon index ∼2.7, but degree-scale 0.1–10 TeV pion-decay emission could be detected if the cosmic-ray spectrum hardens above >100 GeV. The 30 Doradus star-forming region is detectable if acceleration efficiency is on the order of 1−10 per cent of the mechanical luminosity and diffusion is suppressed by two orders of magnitude within <100 pc. Finally, the survey could probe the canonical velocity-averaged cross-section for self-annihilation of weakly interacting massive particles for cuspy Navarro–Frenk–White profiles.

Astronomy & Astrophysics↗
DOE OSTI2020

CEEP (Cyber-Energy Emulation Platform) [SWR-20-102]

NREL's Cyber-Energy Emulation Platform (CEEP) provides the capability to realize cyber-energy security and resilience through automation and orchestration of virtualized systems and software defined networks for the electric grid. CEEP enables testing and validation of grid-security and -control methodologies as the grid evolves to include smart technologies/systems, such as virtualization and containerization of grid components, software defined networking, simulation and co-simulation frameworks, and hardware in the loop. CEEP is a modular system that can be distributed and deployed across different hardware infrastructure sizes and network architectures. For example, CEEP can visualize, emulate, and/or coordinate the Smart-Grid Network Visualization, Intrusion Detection, and Network Healing system. Using CEEP, intrusion-detection and network-self-healing solutions can be deployed at grid control centers, within secure private clouds, and in cyber-energy appliances.

Vaughan, Evan↗
DOE OSTI2022

Cyber Energy Emulation Platform (CEEP) [SWR-20-102]

NREL's Cyber-Energy Emulation Platform (CEEP) provides the capability to realize cyber-energy security and resilience through automation and orchestration of virtualized systems and software defined networks for the electric grid. CEEP enables testing and validation of grid-security and -control methodologies as the grid evolves to include smart technologies/systems, such as virtualization and containerization of grid components, software defined networking, simulation and co-simulation frameworks, and hardware in the loop. CEEP is a modular system that can be distributed and deployed across different hardware infrastructure sizes and network architectures. For example, CEEP can visualize, emulate, and/or coordinate the Smart-Grid Network Visualization, Intrusion Detection, and Network Healing system. Using CEEP, intrusion-detection and network-self-healing solutions can be deployed at grid control centers, within secure private clouds, and in cyber-energy appliances.

Rivera, Joshua↗
DOE OSTI2024

Full phase space resonant anomaly detection

Physics beyond the Standard Model that is resonant in one or more dimensions has been a longstanding focus of countless searches at colliders and beyond. Recently, many new strategies for have been developed, where sideband information can be used in conjunction with modern machine learning, in order to generate synthetic datasets representing the Standard Model background. Until now, this approach was only able to accommodate a relatively small number of dimensions, limiting the breadth of the search sensitivity. Using recent innovations in point cloud generative models, we show that this strategy can also be applied to the full phase space, using all relevant particles for the anomaly detection. As a proof of principle, we show that the signal from the R&D dataset from the LHC Olympics is findable with this method, opening up the door to future studies that explore the interplay between depth and breadth in the representation of the data for anomaly detection. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Seismology in the Cloud: A New Streaming Workflow

Data-intensive research in seismology is experiencing a recent boom, driven in part by large volumes of available data and advances in the growing field of data science. However, there are significant barriers to processing large data volumes, such as long retrieval times from data repositories, complex data management, and limited computational resources. New tools and platforms have reduced the barriers to entry for scientific cluster computing, including the maturation of the commercial cloud as an accessible instrument for research. Here, we build a customized research cluster in the cloud to test a new workflow for large-scale seismic analysis, in which data are processed as a stream (retrieved on-the-fly and acted upon without storing), with data from the Incorporated Research Institutions for Seismology Data Management Center. We use this workflow to deploy a spectral peak detection algorithm over 5.6 TB of compressed continuous seismic data from 2074 stations of the USArray Transportable Array EarthScope network. Using a 50-node cluster in the cloud, we completed the noise survey in 80 hr, with an average data throughput of 1.7 GB per minute. By varying cluster sizes, we find the scaling of our analysis to be sublinear, due to a combination of algorithmic limitations and data center response times. The cloud-based streaming workflow represents an order-of-magnitude increase in acquisition and processing speed compared to a traditional download-store-process workflow, and offers the additional benefits of employing a flexible, accessible, and widely used computing architecture. It is limited, however, due to its reliance on Internet transfer speeds and data center service capacity, and may not work well for repeated analyses or those for which even higher data throughputs are needed. These research applications will require a new class of cloud-native approaches in which both data and analysis are in the cloud.

58 GEOSCIENCES↗
DOE OSTI2023

Rapid Growth of Galactic Supermassive Black Holes through Accreting Giant Molecular Clouds during Major Mergers of Their Host Galaxies

Understanding the formation of the supermassive black holes (SMBHs) present in the centers of galaxies is a crucial topic in modern astrophysics. Observations have detected SMBHs with masses of 10 9 M ⊙ in the high-redshift galaxies with z ~ 7. However, how SMBHs grew to such huge masses within the first billion years after the Big Bang remains elusive. One possible explanation is that SMBHs grow quickly through the frequent mergers of galaxies, which provides sustainable gas to maintain rapid growth. This study presents the hydrodynamics simulations of the SMBHs’ growth with their host galaxies using the GIZMO code. In contrast to previous simulations, we have developed a giant molecular cloud (GMC) model by separating molecular gas particles from atomic gas particles and then evolving them independently. During major mergers, we show that the more massive molecular gas particles’ cloud bears stronger dynamical friction. Consequently, GMCs are substantially accreted onto the galactic centers that grow SMBHs from ~10 7 to ~10 9 M ⊙ within 300 Myr, which explains the rapid growth of SMBHs, and this accretion also triggers a violent starburst at the galactic center. Furthermore, we examine the impact of minor mergers on the bulge of a Milky Way–like galaxy and find that the size and mass of the bulge can increase from 0.92 to 1.9 kpc and from 4.7 × 10 10 to 7 × 10 10 M ⊙ .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Automatic Segmentation of Building Envelope Point Cloud Data Using Machine Learning

About 50% of buildings in the US were constructed before energy codes were introduced. Modular overclad panel retrofits, in which a new envelope is constructed over the existing building, are a promising solution given that it minimizes occupant disruption and shortens construction time at the jobsite. Current state-of-the-art retrofit panel layout and dimensioning consists of three steps: 1) 3D point cloud data generation of the building envelope using commonly available surveying equipment, 2) manual segmentation of 3D point cloud data by a trained professional to identify and dimension window openings, door openings, and other architectural features, and 3) modular panel layout optimization and dimensioning by an architect or engineer. Among these steps, the second one remains the most difficult and costly because it is very labor-intensive. We propose a methodology to automatically label 3D point cloud data to reduce the time and expense spent in manual segmentation. Machine learning methods were employed to classify the point cloud data into distinct groups, each of which corresponds to different features of the building envelope. After classification, a segmentation algorithm was developed to perform boundary detection and separate the components of the façade. Finally, the algorithm returns the relative positions and dimensions of the features in the building envelope. The measurements obtained with the proposed automated method were compared against the actual dimensions to determine the overall algorithm accuracy. The proposed algorithm can then be used to reduce manual efforts for 3D point cloud labeling before modular panel layout optimization is performed.

Maldonado Puente, Bryan↗
DOE OSTI2020

Observational Evidence of State‐Dependent Positive and Negative Land Surface Feedback on Afternoon Deep Convection Over the Southern Great Plains

Abstract The detection of soil moisture‐precipitation feedback is complicated by the dependence of feedback sign on atmospheric state and cloud types. We evaluated responses of convective clouds to changes in surface evaporative fraction (EF) from satellite and ground‐based measurements over the Southern Great Plains in summer months. Strong responses to EF were inferred only after accounting for tropospheric states supporting systematically positive or negative responses. State dependence (inferred from controlled model experiments) explains the weak response found in previous studies. The probability of transitions from shallow to deep cumulus, and subsequent precipitation onset, increases by 58% over drier surfaces in weakly stratified environments having deeper boundary layers. Boundary layer dynamics play a more important role than low‐level relative humidity in these environments. Wetter surfaces yield increased deep convection in moist and thermally stratified environments. The distinct pathways of convective initiation over dry and wet land surfaces can play important roles in drought onset and termination.

Qiu, Shaoyue↗
DOE OSTI2022

The population of hot subdwarf stars studied with Gaia: IV. Catalogues of hot subluminous stars based on Gaia EDR3

In light of substantial new discoveries of hot subdwarfs by ongoing spectroscopic surveys and the availability of the Gaia mission Early Data Release 3 (EDR3), we compiled new releases of two catalogues of hot subluminous stars: The data release 3 (DR3) catalogue of the known hot subdwarf stars contains 6616 unique sources and provides multi-band photometry, and astrometry from Gaia EDR3 as well as classifications based on spectroscopy and colours. This is an increase of 742 objects over the DR2 catalogue. This new catalogue provides atmospheric parameters for 3087 stars and radial velocities for 2791 stars from the literature. In addition, we have updated the Gaia Data Release 2 (DR2) catalogue of hot subluminous stars using the improved accuracy of the Gaia EDR3 data set together with updated quality and selection criteria to produce the Gaia EDR3 catalogue of 61 585 hot subluminous stars, representing an increase of 21 785 objects. Furthermore, the improvements in Gaia EDR3 astrometry and photometry compared to Gaia DR2 have enabled us to define more sophisticated selection functions. In particular, we improved hot subluminous star detection in the crowded regions of the Galactic plane as well as in the direction of the Magellanic Clouds by including sources with close apparent neighbours but with flux levels that dominate the neighbourhood.

79 ASTRONOMY AND ASTROPHYSICS↗