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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Galactic Rain: Cool Gas Inflows in Red Geyser Galaxies and Their Connection to Active Galactic Nuclei Activity and Interactions

Red geysers are a population of massive (logM/M ⊙ ~ 10.5), quiescent galaxies with large-scale but weak, bisymmetric ionized gas outflows, interpreted as signatures of ongoing, low-level active galactic nucleus (AGN) feedback. We investigate the kinematics and prevalence of cool (T ∼ 100–1000 K) neutral gas traced by Na I D absorption, and its connection to galaxy environment and AGN activity. Using 140 red geyser galaxies from the Sloan Digital Sky Survey-IV Mapping Nearby Galaxies at Apache Point Observatory (or MaNGA), we measure spatially resolved velocities and dispersions via double-Gaussian fits to the Na I D doublet. We find that ∼70% of the cool gas is inflowing, with a median velocity of ∼47 km s −1 (∼10% of the expected freefall velocity), and that it also exhibits kinematically ordered motions with σ NaD /σ* ∼ 0.4. Additionally, the Na I D absorption is more prevalent in red geysers than in a matched control sample, with a higher detection fraction (63% versus 40%) and reservoir areas ∼1.6 times larger. Acceleration (∼1 Myr) and accretion (∼20 Myr) timescales indicate that the Na I D-absorbing clouds are likely young and short-lived. Another intriguing result is that radio-detected red geysers (30% of the sample) show inflowing gas reservoirs ∼7 times larger than in non-radio systems. Similarly, galaxies subject to environmental effects host inflowing gas reservoirs ∼2.7 times larger than isolated red geysers. We take this as evidence that galaxy environments play a key role in replenishing the cool gas reservoirs of red geysers, fueling central AGN activity, sustaining radio emission, and regulating long-term quiescence. These findings reveal that quiescent systems are governed by cycles of inflow, feedback, and regulation.

Moghni, Arian [Univ. of California, Santa Cruz, CA↗
DOE OSTI2025

Mesoscale Cellular Convection Detection and Classification Using Convolutional Neural Networks: Insights From Long-Term Observations at ARM Eastern North Atlantic Site

Marine boundary layer clouds are crucial in Earth's climate system. They frequently manifest as closed or open cell mesoscale cellular convection (MCC). MCC clouds are challenging to represent accurately in current climate models, highlighting the need for detailed observational data sets and in-depth analyses. This study utilizes over 8 years of observations from the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) User Facility Eastern North Atlantic (ENA) site at Graciosa Island, Azores, to investigate these clouds. We first apply a convolutional neural network with a U-Net architecture to classify open and closed cells, marking the first application of such an approach for automatically detecting MCC patterns from ground-based radar measurements. This method addresses some observational gaps in satellite data related to low temporal resolution, nighttime challenges, and limited vertical structure capture. The analysis of the MCC cases shows clear differences between closed and open MCCs: Closed MCC clouds are characterized by lower cloud tops and bases, shallower cloud geometrical depth, weaker horizontal wind speeds, stronger atmospheric stability, and a more homogeneous liquid water path than open MCCs. Finally, we demonstrate two potential applications of our radar-based MCC classifications: (a) facilitating the investigation of aerosol-cloud interactions and (b) exploring meteorological factors along with MCC's evolution by integrating satellite imagery and back-trajectory analysis. The identified MCC cases offer a valuable resource for the scientific community to study MCC processes further and improve climate model accuracy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Measurement and applications: Exploring the challenges and opportunities of hierarchical federated learning in sensor applications

Sensor applications have become ubiquitous in modern society as the digital age continues to advance. AI-based techniques (e.g., machine learning) are effective at extracting actionable information from large amounts of data. An example would be an automated water irrigation system that uses AI-based techniques on soil quality data to decide how to best distribute water. However, these AI-based techniques are costly in terms of hardware resources, and Internet-of-Things (IoT) sensors are resource-constrained with respect to processing power, energy, and storage capacity. These limitations can compromise the security, performance, and reliability of sensor-driven applications. To address these concerns, cloud computing services can be used by sensor applications for data storage and processing. Unfortunately, cloud-based sensor applications that require real-time processing, such as medical applications (e.g., fall detection and stroke prediction), are vulnerable to issues such as network latency due to the sparse and unreliable networks between the sensor nodes and the cloud server [1]. As users approach the edge of the communications network, latency issues become more severe and frequent. A promising alternative is edge computing, which provides cloud-like capabilities at the edge of the network by pushing storage and processing capabilities from centralized nodes to edge devices that are closer to where the data are gathered, resulting in reduced network delays [2], [3].

Po-Leen Ooi, Melanie↗
DOE OSTI2023

Electron pulse-dilation diagnostic instruments

During the past decade, a number of diagnostic instruments have been developed that utilize electron pulse-dilation to achieve temporal resolution in the 5–30 ps range. These development efforts were motivated by the need for advanced diagnostics for high-energy density physics experiments around the world. The new instruments include single- and multi-frame gated imagers and non-imaging detectors that record continuous data streams. Electron pulse-dilation provides high-speed detection capability by converting incoming signals into a free electron cloud and manipulating the electron signal with electric and magnetic fields. In this paper we discuss design details and applications of these instruments along with issues and challenges associated with employing the electron pulse-dilation technique. Additionally, methods to characterize instrument performance and improve tolerance to gamma and neutron background radiation are discussed.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

“Catastrophic Hail Storm & Lightning Characteristics From It” for NWSFO Amarillo June 19, 2020

The storm developed along an outflow boundary. Daytime heating helped break two capping inversions noted on the morning weather balloon launch from KAMA. The storm appears to have reached a 35-40 dbz threshold quickly around 15:20. Pantex’s LMA detects “source densities” at 15:20. The first intra-cloud (CC) strikes occur simultaneously at 15:26 just west of 34th & Soncy. The first cloud-to-ground (CG) lightning strike occurred 7 minutes later at15:33 just west of Jason’s Deli (34th and Coulter Shopping Center).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A Simple Model of the Energy Threshold for Snowball Chambers

Cloud and bubble chambers have historically been used for particle detection, capitalizing on supersaturation and superheating, respectively. Here, we present new results from a prototype snowball chamber, in which an incoming particle triggers the crystallization of a purified, supercooled liquid. We demonstrate, for the first time, simulation agreement with our first results from 5 years ago: the higher temperature of the freezing of water and significantly shorter time spent supercooled compared to the control in the presence of a Cf-252 fission neutron source. This is accomplished by combining Geant4 modeling of neutron interactions with the Seitz nucleation model used in superheated bubble chambers, including those seeking dark matter. We explore the possible implications of using this new technology for GeV-scale WIMP searches, especially in terms of spin-dependent proton coupling, and report the first supercooling of WbLS (water-based liquid scintillator).

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

A Hydro-based MCMC Analysis of SNR 0509–67.5: Revealing the Explosion Properties from Fluid Discontinuities Alone

Using Hubble Space Telescope/ACS Hα images of SNR 0509–67.5 taken ~10 yr apart, we measure the forward shock (FS) proper motions (PMs) at 231 rim locations. The average shock radius and velocity are 3.66 ± 0.036 pc and 6315 ± 310 km s –1 . Hydrodynamic simulations, recast as similarity solutions, provide models for the supernova remnant’s expansion into a uniform ambient medium. These are coupled to a Markov chain Monte Carlo (MCMC) analysis to determine explosion parameters, constrained by the FS measurements. For our baseline model, the MCMC posteriors yield an age of 315.5 ± 1.8 yr, a dynamical explosion center at 5 h 09 m 31. s 16, $-67^\circ 31^{\prime} 17\buildrel{\prime\prime}\over{.} 1$ and ambient medium densities at each azimuth ranging over 3.7–8.0 × 10 –25 g cm –3 . The age uncertainty can be an order of magnitude larger when considering other models, or subsets of the data. We detect stellar PMs corresponding to speeds in the Large Magellanic Cloud ≥ 770 km s –1 . Five stars in the remnant show measurable PMs but none are moving radially from the dynamical center. There are four stars 1."4 from the center, including three faint, previously unidentified ones. Using coronal [Fe XIV ] λ5303 emission as a proxy for the reverse shock location, we constrain the explosion energy (for a compression factor of 4) to a value of E = (1.30 ± 0.41) × 10 51 erg for the first time from shock kinematics alone. Higher compression factors (7 or more) are strongly disfavored based on multiple criteria, arguing for inefficient particle acceleration in the Balmer shocks of SNR 0509–67.5.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Enhancing Cluster Identification in Atom Probe Tomography Data Using Transfer Learning

Atom Probe Tomography (APT) is a powerful technique for visualizing the atomic-scale distribution of solutes in materials, but quantitative cluster analysis of APT datasets remains a challenge due to the need for subjective parameter selection in clustering algorithms. While distance-based and density-based methods such as HDBSCAN are widely used, their performance is highly sensitive to user-defined parameters, which undermines reproducibility and accuracy. This study proposes an image-based, deep learning-aided workflow for automating parameter selection and cluster detection in APT data analysis. By projecting 3D APT point clouds onto 2D planes, we leverage pretrained convolutional neural networks (ConvNeXt-Tiny and ResNet-50) through transfer learning to predict the number of clusters present in synthetic datasets. The output is used to guide K-means clustering and estimate HDBSCAN parameters, specifically minimum cluster size and minimum sample points. This approach reduces reliance on manual parameter tuning, improving consistency and scalability. The methodology demonstrates the feasibility of using image-based deep learning for interpreting complex spatial patterns in APT data, enabling faster and more objective analysis. The complete workflow and code are made publicly available to support reproducibility and future research.

Density-based clustering↗
DOE OSTI2022

Time Sequence Machine Learning-Based Data Intrusion Detection for Smart Voltage Source Converter-Enabled Power Grid

Smart inverters of distributed energy resources can enable cloud computing, condition monitoring, result visualization, remote control, and peer-to-peer energy trading in advanced power systems. However, the advent of data injection attacks in the communication architecture can alter measurement characteristics of power grids and have devastating consequences. In this article, we propose a time sequence machine learning-based anomaly detection methodology for detecting cyber intrusion into control signal setpoints and dc voltage signal measurement bias of the voltage source converter (VSC) in wind generators. We first investigated the effects of four types of denial of service, tampering signal, and stealthy-type data intrusion attacks on smart VSCs and overall wind farms. We then proposed a novel time sequence machine learning-based intrusion detection framework that can be implemented to detect different cyberattacks in the VSCs. The performance of the proposed framework has been compared with that of autoencoder and clustering-based intrusion detection framework. The proposed framework was validated by using the IEEE 39 bus power system in the presence of four wind farms in different locations. Using several metrics for intrusion detection performance, we validated the effectiveness of the proposed framework.

42 ENGINEERING↗
DOE OSTI2020

Massive Stars in Molecular Clouds Rich in High-energy Sources: The Bridge of G332.809–0.132 and CS 78 in NGC 6334

Detections of massive stars in the direction of the H ii region CS 78 in NGC 6334 and of G332.809−0.132 are here presented. The region covered by the G332.809−0.132 complex coincides with the RCW 103 stellar association. In its core (40′ in radius), approximately 110 OB candidate stars (K {sub s} < 10 mag and 0.4 < A{sub K{sub s}} < 1.6 mag) were identified using 2MASS, DENIS, and GLIMPSE data. This number of OB stars accounts for more than 50% of the observed number of Lyman continuum photons from this region. Medium-resolution K-band spectra were obtained for seven early types, including one WN 8 star and one Ofpe/WN 9 star; the latter is located near the RCW 103 remnant and its luminosity is consistent with a distance of ≈3 kpc. The area analyzed encloses 9 of the 34 OB stars previously known in RCW 103, as well as IRAS 16115−5044, which we reclassify as a candidate luminous blue variable. The line of sight is particularly interesting, crossing three spiral arms; a molecular cloud at −50 (with RCW 103 in the Scutum-Crux arm) and another at −90 km s{sup −1} (in the Norma arm) are detected, both rich in massive stars and supernova remnants. We also report the detection of a B supergiant as the main ionizing source of CS 78, 2MASS J17213513−3532415. Medium-resolution H- and K-band spectra display H i and He i lines, as well as Fe ii lines. By assuming a distance of 1.35 kpc, we estimate a bolometric magnitude of −6.16, which is typical of supergiants.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Machine Learning for Synchrophasor Analysis

The report presents results from the development of a cloud-based, Big Data analysis framework for power systems. The computational pipeline uses the Apache Spark framework running in an OpenStack cloud infrastructure. A real-world phasor measurement unit (PMU) dataset has been used to carry out the analysis. Several Machine Learning (ML) methods have been developed and implemented for event and anomaly detection and classification. Actual examples of power system events detection and analysis using synchrophasor data are presented. It has been shown that applications of the cloud-based computing environment and the Apache Spark framework enable a significant increase in the computational efficiency of large-scale PMU data analysis.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2020

A Search for Polarized Thermal Emission from Directly Imaged Exoplanets and Brown Dwarf Companions to Nearby Stars

Aerosols in the atmospheres of cloudy gas giant exoplanets and brown dwarfs scatter and polarize these objects’ thermal emission. If such an object has an oblate shape or nonuniform cloud distribution, the net degree of linear polarization can show an increase ranging from several tenths of a percent to a few percent. Modern high-contrast imaging polarimeters are now poised to detect such low-polarization signals, opening up a new window into the rotational velocities and cloud properties of substellar companions to nearby stars. In this paper, we present the results of a near-IR survey searching for linearly polarized thermal emission from a sample of two planetary-mass companions and five brown dwarf companions using GPI and SPHERE-IRDIS. We probe the subpercent linear polarization regime that typifies polarized free-floating brown dwarfs and place limits on each object’s degree of linear polarization. We relate our upper limits on each target’s degree of linear polarization to its rotation rate, and place our results in the context of rotation rates measured using high-resolution spectroscopy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Energetics of surface melt in West Antarctica

We use reanalysis data and satellite remote sensing of cloud properties to examine how meteorological conditions alter the surface energy balance to cause surface melt that is detectable in satellite passive microwave imagery over West Antarctica. This analysis can detect each of the three primary mechanisms for inducing surface melt at a specific location: thermal blanketing involving sensible heat flux and/or longwave heating by optically thick cloud cover, all-wave radiative enhancement by optically thin cloud cover, and föhn winds. We examine case studies over Pine Island and Thwaites glaciers, which are of interest for ice shelf and ice sheet stability, and over Siple Dome, which is more readily accessible for field work. During January 2015 over Siple Dome we identified a melt event whose origin is an all-wave radiative enhancement by optically thin clouds. During December 2011 over Pine Island and Thwaites glaciers, we identified a melt event caused mainly by thermal blanketing from optically thick clouds. Over Siple Dome, those same 2011 synoptic conditions yielded a thermal-blanketing-driven melt event that was initiated by an impulse of sensible heat flux and then prolonged by cloud longwave heating. The December 2011 synoptic conditions also generated föhn winds at a location on the Ross Ice Shelf adjacent to the Transantarctic Mountains, and we analyze this case with additional support from automatic weather station data. In contrast, a late-summer thermal blanketing period over Pine Island and Thwaites glaciers during February 2013 showed surface melt initiated by cloud longwave heating and then prolonged by enhanced sensible heat flux. One limitation thus far with this type of analysis involves uncertainties in the cloud optical properties. Nevertheless, with improvements this type of analysis can enable quantitative prediction of atmospheric stress on the vulnerable Antarctic ice shelves in a steadily warming climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Cloud Particle Imager (CPI) and 3-View Cloud Particle Imager (3V-CPI) Instrument Handbook

The cloud particle imager (CPI) is a U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility instrument for measuring the concentration and size of cloud droplet and ice crystals. Hydrometeors entering the tube will be detected by a system of two continuous wave lasers that are perpendicular to each other, monitoring the sample volume. When a hydrometeor obscures both laser beams it is inside the sample volume and a third laser is triggered to illuminate the hydrometeor and a camera records a two-dimensional image. The CPI records images with a resolution of 2.3 μm. Since the camera has 1024 x 1024 pixels, the maximum detectable size is 2.3 mm x 2.3 mm.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Design and implementation of a real-time underwater acoustic telemetry system for fish behavior study and environmental sensing

Underwater acoustic telemetry is an important tool for studying the behavior of aquatic animals and assessing the environmental impact of man-made structures such as water power turbines. However, current design of the acoustic telemetry system lacks the ability to transmit collected data continuously in real-time and requires manual retrieval of the recorded measurements, leading to a long delay of data retrieval, high operational cost associated with manpower as well as safety risk for the operators. In addition, there is no efficient way to monitor the status of the acoustic telemetry system, including acoustic transmitters and receivers. For example, if multiple receivers in an array fail, the detection efficiency could be significantly reduced. Herein, we developed a cloud-based, real-time underwater acoustic telemetry system that can be used to estimate behavior or survival of tagged aquatic animals and monitor environmental parameters in near-real-time (< 2 hours). The system incorporates microcontrollers and integrated circuits to create real-time data communication and processing units with a cloud-based service that processes the transmitted data stream to derive behavior and survival information of the tagged animals.

Yang, Yang↗
DOE OSTI2020

Neutrinos from the Galactic Center Hosting a Hypernova Remnant

Similar to star-forming galaxies or starburst galaxies, star-forming regions in our Galaxy can host cosmic-ray (CR) accelerators and rich gas as targets of hadronuclear interaction. By our estimations, the IceCube neutrino observatory might detect muon neutrinos from a CR accelerator associated with a molecular cloud complex in our Galaxy. The associated high-energy gamma-ray emission might be observed by the Cherenkov Telescope Array (CTA), High-Altitude Water Cherenkov Gamma-Ray Observatory (HAWC), and Large High Altitude Air Shower Observatory (LHAASO). Furthermore, taking the Galactic Center (GC) region as an example, we assume that a hypernova exploded in the past in the GC. We simulate the acceleration of CRs in the hypernova remnant (HNR) as well as their confinement and escape. The high-energy protons escape from the HNR, diffuse around the GC, interact with molecular clouds, and then produce gamma-rays and neutrinos. In the optimal cases, the GC would be a promising 100 TeV gamma-ray source for LHAASO’s one-month observation. We propose that neutrino-induced searching for starting track-like and high-energy starting events (HESEs) observed by IceCube, from the GC region with a radius of 1°.8, would help us discover the particle accelerator in the GC or constrain our models. Under the constraint from high-energy gamma-ray observations by the H.E.S.S. telescope, we estimate the exposure time needed to make a significant discovery for the optimal cases. The analysis combining observations of IceCube and ANTARES, starting track-like events and HESEs, future observations by neutrino detectors IceCube-Gen2 and KM3net, and gamma-ray telescopes CTA, HAWC, and LHAASO would help to constrain our models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Collisional Excitation and Non-LTE Modeling of Interstellar Chiral Propylene Oxide

Abstract The first set of theoretical rotational cross sections for propylene oxide (CH 3 CHCH 2 O) colliding with cold He atoms has been obtained at the full quantum level using a high-accuracy potential energy surface. By scaling the collision reduced mass, rotational rate coefficients for collisions with para-H 2 are deduced in the temperature range 5–30 K. These collisional coefficients are combined with radiative data in a non-LTE radiative transfer model in order to reproduce observations of propylene oxide made toward the Sagittarius B2(N) molecular cloud with the Green Bank and Parkes radio telescopes. The three detected absorption lines are found to probe the cold (∼10 K) and translucent ( n H ∼ 2000 cm −3 ) gas in the outer edges of the extended Sgr B2(N) envelope. The derived column density for propylene oxide is N tot ∼ 3 × 10 12 cm −2 , corresponding to a fractional abundance relative to total hydrogen of ∼2.5 × 10 −11 . The present results are expected to help our understanding of the chemistry of propylene oxide, including a potential enantiomeric excess, in the cold interstellar medium.

79 ASTRONOMY AND ASTROPHYSICS↗