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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

No warm-phase invigoration of convection detected during GoAmazon

It has been proposed that air pollution increases the updraft speeds of warm-phase convective clouds by reducing their supersaturation and, thereby, enhancing their buoyancy. Observations from the GoAmazon field campaign, sampled using subjective criteria, have been offered as evidence for this warm-phase invigoration. Here, we reexamine those GoAmazon observations using objective criteria and find no indication that air pollution increases warm-phase updraft speeds. Additionally, the observations yield no statistically significant relationship between aerosol concentrations and either moist-convective vertical velocity or reflectivity in either the lower or upper troposphere.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Fast Cloud Droplet Probe (FCDP) Instrument Handbook

The fast cloud droplet probe (FCDP) is a U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility instrument for measuring the concentration and size of cloud droplets in the diameter size range 1.5-50 μm. It is an open-path instrument mounted on the outside of the aircraft (typically below the wing). When a droplet passes through the sample volume, light from a laser source is scattered, which is used to detect and size the droplet. The sample volume is a focused area of the laser beam that is continuously monitored along the flight path. If a droplet crosses the laser beam but is outside the sample volume, a separate detector will reject the droplet. Only single droplets at a time will be detected and sizes measured over one second have to be averaged to obtain a size distribution. When measuring ice clouds, the FCDP will not yield useful information, since the measurement method, which assumes spherical particles to obtain size and scattering, is very different for ice crystals.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Microcam: A Low Power and Privacy Preserving Multi-modal Platform for Occupancy Detection (Final Report)

Heating, ventilation, and air conditioning (HVAC) consumes a significant portion of the energy used in buildings. Much of this is wasted energy, used when buildings are either not occupied at all, or occupied well under their maximum design conditions. This project has focused on residential occupancy detection to autonomously control HVAC systems and save energy. Limitations of existing occupancy sensors include one or more of the following: (i) they employ sensors or algorithms that are not able to detect stationary occupants; (ii) they cannot classify the source of the motion (such as a pet); (iii) depending on the camera resolution and employed algorithms, they do not allow for embedded or onboard computation, and require external or cloud-based processing; (iv) many algorithms developed for camera-based systems are sensitive to lighting changes, and thus prone to missed detections or false alarms; (v) Most existing systems depend on adjustment of settings for different scenarios, complicating self-commissioning; (vi) they cannot provide high enough accuracy; (vii) they are costly; (viii) they are not battery-powered, thus limiting ease of use and installation. In this project, Syracuse University and its partner SRI have developed a low-cost, high accuracy, standalone residential occupancy sensing platform, referred to as the MicroCam, to address all of the aforementioned challenges. MicroCam can operate on typical alkaline batteries without relying on the “cloud” or external computing resources, and consists of low-power, Artificial Intelligence (AI)-based, IoT platforms. Each platform has multi-modal sensors and can process motion, audio and video data, and send binary occupancy result to a lead platform. All sensor data is processed locally on platforms, and the only transmitted data is the binary occupancy state. In addition, preliminary work has been done on images wherein occupants are not discernable. Thus, MicroCam is a standalone solution preserving privacy of the occupants.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Cloud-Based Demonstration of the Eastern Interconnection Situational Awareness Monitoring System (ESAMS)

This report describes a cloud-based implementation and field demonstration of the Eastern Interconnection Situational Awareness and Monitoring System (ESAMS). ESAMS was developed to support the detection and source localization of forced oscillations using synchrophasor measurements from tie-lines connecting areas served by different reliability coordinators (RCs), so that RCs could better coordinate their response to wide-area events. A previous effort had identified deployment barriers associated with hosting shared situational awareness tools at a single RC. To address these barriers, ESAMS was migrated to Amazon Web Services and evaluated in a six-month field demonstration. ISO New England (ISO-NE) and PJM streamed data to the platform using AWS Direct Connect and a site-to-site VPN, respectively. The resulting multi-utility measurement footprint enabled regional source localization across major portions of the U.S. Eastern Interconnection and supported routine identification of oscillation events. During the final three months of the trial, 24 events above 2 MW/MVAR were detected. The largest detected oscillation approached a 25 MW peak-to-peak amplitude, and the longest persisted intermittently for more than 11 hours. The demonstration also assessed operational considerations—including data transfer volumes, end-to-end latency, and cloud computing costs—and found that network and compute requirements were modest relative to typical cloud capabilities while providing performance comparable to prior on-premises deployments. Overall, the results indicate that cloud hosting can provide a practical path to shared interconnection-wide oscillation monitoring. The cloud ESAMS demonstration establishes a foundation for broader utility participation and for building future wide-area analytics that leverage measurements across organizational boundaries.

Follum, James D.↗
DOE OSTI2026

Deep Learning and Photogrammetric Reconstruction for Automated Crack Detection and Dimensional Measurement in Mining Operations

Surface crack detection and dimensional measurement at active mining sites present significant safety and operational challenges. Manual inspection methods are labor-intensive, spatially incomplete, and expose personnel to hazardous environments, while existing automated approaches have been developed primarily for concrete civil infrastructure and have not been validated on the complex, variable surfaces characteristic of mining environments. This dissertation presents an automated pipeline that integrates deep learning semantic segmentation with Structure-from-Motion photogrammetry to detect surface cracks and measure their aperture, length, and vertical displacement from standard RGB imagery acquired during routine Uncrewed Aerial Vehicle (UAV) survey operations, without requiring additional sensor hardware or manual measurement. The pipeline combines a U-Net architecture with an EfficientNet-B0 encoder, pretrained on the SDNET2018 concrete crack dataset and fine-tuned on a mining-specific dataset spanning laboratory concrete specimens, coal refuse impoundment embankments, and post-blast limestone quarry benches. Photogrammetric reconstruction is performed using COLMAP Structure-from-Motion and Multi-View Stereo, with crack segmentation masks projected into the reconstructed point cloud to enable three-dimensional vertical displacement measurement through local plane fitting and bimodal surface detection. The pipeline was validated across 36 controlled laboratory specimens at three imaging distances and four vertical displacement levels, achieving aperture measurement RMSE of 0.047 cm and R² of 0.954, and vertical displacement RMSE of 0.140 cm and R² of 0.966, against independent caliper measurements. Field application at a coal refuse impoundment in southwestern Pennsylvania detected 71 crack components across the embankment crest, with a dominant longitudinal crack exhibiting aperture values reaching 28 cm and a 95th percentile vertical displacement of 35.53 cm, consistent in magnitude and spatial distribution with simultaneously acquired LiDAR-derived estimates. Application across four post-blast limestone quarry bench datasets in California successfully characterized blast-induced fracture networks at ground sampling distances ranging from 0.59 to 1.23 cm/pixel, with detected crack geometries physically consistent with observable surface conditions at each site. The results demonstrate that deep learning-based crack detection and photogrammetric measurement can be integrated into routine UAV inspection workflows at mining sites, providing repeatable, scalable, and quantitative crack characterization across surface types, crack scales, and displacement magnitudes not previously addressed in the literature. The pipeline requires no dedicated surveying equipment beyond the UAV platforms already deployed at mine sites for survey and monitoring purposes, supporting practical adoption within existing operational workflows.

Crack detection, Dimensional Measurement↗
DOE OSTI2020

Sub-critical assembly die-away analysis with organic scintillators

Determining the reactivity of systems containing fissile material is essential for verifying safety, and possession and enrichment levels. The goal of this experiment was to characterize a subcritical nuclear fuel assembly irradiated by a pulsed neutron generator through differential die-away analysis. Similar characterizations are commonly performed with thermal neutron detectors, but these detectors are bulky and cumbersome, the materials are not widely available, and timing can be clouded by the required thermalization time. In this work, an organic scintillator capable of detecting both neutrons and photons was used to measure the die-away profiles of both particles. These time profiles were then used to estimate the reactivity of the assembly. Here, the calculated reactivity estimates compare favorably to simulated estimates, and therefore provide a useful tool for fissile material characterization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Cepheids with giant companions: II. Spectroscopic confirmation of nine new double-lined binary systems composed of two Cepheids

Context.Binary Cepheids with giant companions are crucial for studying the physical properties of Cepheid variables, in particular providing the best means to measure their masses. Systems composed of two Cepheids are even more important, but to date, only one such system has been identified, in the Large Magellanic Cloud (LMC). Aims.Our current aim is to increase the number of these systems known tenfold and to provide their basic characteristics. The final goal is to obtain the physical properties of the component Cepheids, including their masses and radii, and to learn about their evolution in the multiple systems, also revealing their origin. Methods.We started a spectroscopic monitoring campaign of nine unresolved pairs of Cepheids from the OGLE catalog to check if they are gravitationally bound. Two of these so-called double Cepheids are located in the LMC, five are in the Small Magellanic Cloud (SMC), and two are in the Milky Way (MW). Results.We report a spectroscopic detection of the binarity of all nine of these double Cepheids with orbital periods ranging from 2 to 18 years. This increases the number of known binary double (BIND) Cepheids from 1 to 10 and triples the number of all confirmed double-lined binary (SB2) Cepheids. For five BIND Cepheids, the disentangled pulsational light curves of the components show anti-correlated phase shifts due to orbital motion. We show the first empirical evidence that typical period–luminosity relations (PLRs) are rather binary Cepheid PLRs, as they include light of the companion. Conclusions.The statistics of pulsation period ratios of BIND Cepheids do not agree with those expected for pairs of Cepheids of the same age. These ratios together with the determined mass ratios far from unity suggest a merger origin of at least one component for about half of the systems. The SMC and MW objects are the first found in SB2 systems composed of giants in their host galaxies. The Milky Way BIND Cepheids are also the closest such systems, being located at about 11 and 26 kpc.

Astronomy & Astrophysics↗
DOE OSTI2021

Signatures of Inflowing Gas in Red Geyser Galaxies Hosting Radio Active Galactic Nuclei

We study cool neutral gas traced by NaD absorption in 140 local ($z$ < 0.1) early-type red geyser galaxies. These galaxies show unique signatures in spatially resolved strong line emission maps that have been interpreted as largescale active galactic nucleus–driven ionized winds. To investigate the possible fuel source for these winds, we examine the abundance and kinematics of cool gas ($T$ ~ 100–1000 K) inferred from Na I D absorption in red geysers and matched control samples drawn from the Sloan Digital Sky Survey IV Mapping Nearby Galaxies at Apache Point Observatory survey. We find that red geysers host greater amounts of NaD-associated material. Substantial cool gas components are detected in more than 50% of red geysers (compared to 25% of the control sample) going up to 78% for radio-detected red geysers. Our key result is that cool gas in red geysers is predominantly infalling. Among our 30 radio-detected red geysers, 86% show receding NaD absorption velocities (with respect to the systemic velocity) between 40 and 50 km s-1 . We verify this result by stacking NaD profiles across each sample, which confirms the presence of infalling NaD velocities within red geysers (~40 km s -1 ) with no velocity offsets detected in the control samples. Interpreting our observations as signatures of inflowing cool neutral clouds, we derive an approximate mass inflow rate of $\dot{M}$ in ~ 0.1$M$ ⊙ yr -1 , similar to that expected from minor merging and internal recycling. In conclusion, some red geysers show much higher rates ($\dot{M}$ in ~ 5$M$ ⊙ yr -1 ), which may indicate an ongoing accretion event.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Real-time Data Analytics for Condition Monitoring of Complex Industrial Systems

Modern industrial systems are now fitted with several sensors for condition monitoring. This is advantageous because these sensors can provide mass amounts of data that have the potential for aiding in tasks such as fault detection, diagnosis, and prognostics. However, the information valuable for performing these tasks is often clouded in noise and must be mined from high-dimensional data structures. Therefore, this dissertation presents a data analytics framework for performing these condition monitoring tasks using high-dimensional data. Demonstrations of this framework are detailed for challenges related to power generation systems in automobiles, power plants, and aircraft engines. These implementations leverage data collected from state-of-the-art, industry class test-rigs. Results indicate the ability of this framework to develop effective methodologies for condition monitoring of complex systems.

Peters, Benjamin↗
DOE OSTI2021

Blockchain-Based Man-in-the-Middle (MITM) Attack Detection for Photovoltaic Systems

Cybersecurity of photovoltaic (PV) systems entails a much larger scope than just encryption and firewall of communications. For instance, integrity of data in transit between inverters and a cloud server can be compromised by authorized third-party, devices, and internal network within security perimeter (i.e., man-in-the-middle (MITM) attack). To address this challenge, this paper proposes a blockchain-based MITM attack detection method for a PV system. A breakthrough method includes screening network data, network intrusion detection, and hash comparison of in-transit data using distributed ledgers. Furthermore, the proposed method is implemented in Internet-of-Thing (IoT) security modules as clients of a blockchain network and validated by experiments.

blockchain↗
DOE OSTI2020

ACCESS: Confirmation of No Potassium in the Atmosphere of WASP-31b

We present a new optical (400–950 nm) transmission spectrum of the hot Jupiter WASP-31b (M = 0.48 M {sub J}; R = 1.54 R {sub J}; P = 3.41 days), obtained by combining four transit observations. These transits were observed with IMACS on the Magellan Baade Telescope at Las Campanas Observatory as part of the ACCESS project. We investigate the presence of clouds/hazes in the upper atmosphere of this planet, as well as the contribution of stellar activity on the observed features. In addition, we search for absorption features of the alkali elements Na i and K i, with particular focus on K i, for which there have been two previously published disagreeing results. Observations with Hubble Space Telescope (HST)/STIS detected K i, whereas ground-based low- and high-resolution observations did not. We use equilibrium and nonequilibrium chemistry retrievals to explore the planetary and stellar parameter space of the system with our optical data combined with existing near-IR observations. Our best-fit model is that with a scattering slope consistent with a Rayleigh slope (α=5.3{sub −3.1}{sup +2.9}), high-altitude clouds at a log cloud top pressure of −3.6{sub −2.1}{sup +2.7} bars, and possible muted H{sub 2}O features. We find that our observations support other ground-based claims of no K i. Clouds are likely why signals like H{sub 2}O are extremely muted and Na or K cannot be detected. We then juxtapose our Magellan/IMACS transmission spectrum with existing VLT/FORS2, HST/WFC3, HST/STIS, and Spitzer observations to further constrain the optical-to-infrared atmospheric features of the planet. We find that a steeper scattering slope (α = 8.3 ± 1.5) is anchored by STIS wavelengths blueward of 400 nm and only the original STIS observations show significant potassium signal.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Development of an end state vision to implement digital monitoring in nuclear plants

Transitioning from an onsite Maintenance & Diagnostics Center to cloud-based services offers many new opportunities with computing power and storage, but also new challenges in terms of networking and security. This report will cover everything required for that transition including data processing and uploading to cloud services, feature selection, model creation, and result visualization for decision making. Although there are several other cloud-based services (e.g. Amazon Web Services and Google Cloud), this report explores Microsoft Azure to simplify nomenclature and maintain a consistent focus. Many of the services offered by Microsoft Azure are also available in the other cloud-based services, and their differences have been recorded in other literature. The Azure services most important to a nuclear power plant including networking & security, storage & databases, and Artificial Intelligence (AI) are reviewed here. Networking covers all aspects related to communication to Azure resources including security, privacy, and redundancy. Storage & databases includes data storage, upgrading, patching, backups, and monitoring. The AI services allows the user access to the machine learning (ML) techniques developed with Azure including automated ML, anomaly detection, computer vision, and natural language processing. This report summaries the features, capabilities, and challenges when using cloud-based services in a user-friendly manner.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Quantification of the Radiative Effect of Aerosol–Cloud Interactions in Shallow Continental Cumulus Clouds

The indirect radiative effect of aerosol variability on shallow cumulus clouds is realized in nature with considerable concurrent meteorological variability. Large-eddy simulations constrained by observations at a continental site in Oklahoma are performed to represent the variability of different meteorological states on days with different aerosol conditions. The total radiative effect of this natural covariation between aerosol and other meteorological drivers of total cloud amount and albedo is quantified. The changes to these bulk quantities are used to understand the response of the cloud radiative effect to aerosol–cloud interactions (ACI) in the context of concurrent processes, as opposed to attempting to untangle the effect of individual processes on a case-by-case basis. Mutual information (MI) analysis suggests that meteorological variability masks the strength of the relationship between cloud drop number concentration and the cloud radiative effect. This is shown to be mostly due to variation in solar zenith angle and cloud field horizontal heterogeneity masking the relationship between cloud drop number and cloud albedo. By combining MI and more traditional differential analyses, a framework to identify important modes of covariation between aerosol, clouds, and meteorological conditions is developed. This shows that accounting for solar zenith angle variation and implementing an albedo bias correction increases the detectability of the radiative effects of ACI in simulations of shallow cumulus.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Fast and sensitive measurements of sub-3 nm particles using Condensation Particle Counters For Atmospheric Rapid Measurements (CPC FARM)

New particle formation (NPF) is the atmospheric process whereby gas molecules react and nucleate to form detectable particles. NPF has a strong impact on Earth's radiative balance as it produces roughly half of global cloud condensation nuclei. However, the time resolution and sensitivity of current instrumentation are inadequate in measuring the size distribution of sub-3 nm particles, the particles critical for understanding NPF. Here we present the Condensation Particle Counters For Atmospheric Rapid Measurements (CPC FARM), a method to measure the concentrations of freshly nucleated particles. The CPC FARM consists of five CPCs operating in parallel, each configured to operate at different detectable particle sizes between 1–3 nm. This study explores two methods to calculate the size distribution from the differential measurements across the CPC channels. The performance of both inversion methods was tested against the size distribution measured by a pair of stepping particle mobility sizers (SMPSs) during an ambient air sampling study in Pittsburgh, PA. Observational results indicate that the CPC FARM is more accurate with higher time resolution and sensitivity in the sub-3 nm range compared to the SMPS.

Cheng, Darren [Carnegie Mellon Univ., Pittsburgh, ↗
DOE OSTI2020

TOI-824 b: A New Planet on the Lower Edge of the Hot Neptune Desert

We report the detection of a transiting hot Neptune exoplanet orbiting TOI-824 (SCR J1448-5735), a nearby (d = 64 pc) K4V star, using data from the Transiting Exoplanet Survey Satellite. The newly discovered planet has a radius R {sub p} = 2.93 ± 0.20 R{sub ⊕} and an orbital period of 1.393 days. Radial velocity measurements using the Planet Finder Spectrograph and the High Accuracy Radial velocity Planet Searcher spectrograph confirm the existence of the planet, and we estimate its mass to be 18.47 ± 1.84 M{sub ⊕}. The planet’s mean density is ρ{sub p} = 4.03{sub −0.78}{sup +0.98} g cm{sup −3}, making it more than twice as dense as Neptune. TOI-824 b's high equilibrium temperature makes the planet likely to have a cloud-free atmosphere, and thus it is an excellent candidate for follow-up atmospheric studies. The detectability of TOI-824 b’s atmosphere from both ground and space is promising and could lead to the detailed characterization of the most irradiated small planet at the edge of the hot Neptune desert that has retained its atmosphere to date.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Allometric and Mobile Terrestrial LiDAR Modeling of Aboveground Woody Biomass of Populus in Coppice Production

Poplars ( Populus spp.) and their hybrids are increasingly being grown in coppice production to generate bioenergy feedstocks at frequent intervals. Allometric equations are re-quired to predict aboveground biomass (AGB) of coppiced individuals with minimal field measurements. Likewise, remote sensing tools like LiDAR (light detection and ranging) can be used if models are available to predict AGB from point cloud data. Therefore, this study sought to develop equations to predict dry woody AGB from field measurements and LiDAR data from coppiced poplar field trials containing eastern cottonwood ( P. del-toides ) and hybrid poplar taxa. We found that taxa-specific allometric models containing the summed basal area of the three largest stems in the coppice provided the best predictive model, with stem height and stem count failing to provide additional explanatory power. The best predictive LiDAR-based model was independent of taxa but had slightly lower adjusted R 2 and higher RMSE than the allometric model. It contained four parameters including crown volume, leaf area index, variance of height returns, and the top point density (i.e., density metric 9 or the proportion of points in the highest point interval when the point cloud is evenly divided into ten vertical intervals). In total, these models can be used to quickly and efficiently estimate dry woody AGB of Populus coppice systems for bioenergy feedstock production.

AGB↗
DOE OSTI2023

Rapid Spaceborne Mapping of Wildfire Retardant Drops for Active Wildfire Management

Aerial application of fire retardant is a critical tool for managing wildland fire spread. Retardant applications are carefully planned to maximize fire line effectiveness, improve firefighter safety, protect high-value resources and assets, and limit environmental impact. However, topography, wind, visibility, and aircraft orientation can lead to differences between planned drop locations and the actual placement of the retardant. Information on the precise placement and areal extent of the dropped retardant can provide wildland fire managers with key information to (1) adaptively manage event resources, (2) assess the effectiveness of retardant slowing or stopping fire spread, (3) document location in relation to ecologically sensitive areas; and perform or validate cost-accounting for drop services. This study uses Sentinel-2 satellite data and commonly used machine learning classifiers to test an automated approach for detecting and mapping retardant application. We show that a multiclass model (retardant, burned, unburned, and cloud artifact classes) outperforms a single-class retardant model and that image differencing (post-application minus pre-application) outperforms single-image models. Compared to the random forest and support vector machine, the gradient boosting model performed the best with an overall accuracy of 0.88 and an F1 Score of 0.76 for fire retardant, though results were comparable for all three models. Our approach maps the full areal extent of the dropped retardant within minutes of image availability, rather than linear representations currently mapped by aerial GPS surveys. The development of this capability allows for the rapid assessment of retardant effectiveness and documentation of placement in relation to sensitive environments.

54 ENVIRONMENTAL SCIENCES↗