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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Synchro-Waveform-Based Event Identification Using Multi-Task Time-Frequency Transform Networks

Influenced by the transient dynamics and reduced inertia characteristics of high-penetration renewable energy systems, power system events frequently exhibit distinct characteristics such as high-frequency components including wide-band oscillations and hyper-harmonics. This makes standard systems face challenges including significant latency and reduced accuracy due to limited data resolution. However, current methods face significant limitations, including insufficient pattern capture ability, low noise immunity, limited feature learning, and restricted localization capabilities, thereby hindering real-time performance. To tackle this issue, this paper proposed a novel synchro-waveform-based event identification approach via a Multi-task Time-frequency Transform Network (MTTNet). Initially, a Time-frequency Transform Block (TTB) is developed to extract both local and global information. The TTB leverages both Fourier and S-transforms to derive comprehensive time-frequency information from synchro-waveforms. Subsequently, a multi-task learning strategy is employed to identify the type and distinguish localization of events. Integrating the TTB and multi-task learning, the MTTNet is designed for synchro-waveform-based event identification, incorporating an adaptive weighting strategy and simplified computation for the S-transform. Two different datasets, comprising simulated and actual synchro-waveforms, are collected from the IEEE 123 bus system and a real-world high-penetration renewable energy system using a universal grid analyzer. Extensive experiments on various conditions are carried out. In conclusion, results demonstrated that the MTTNet consistently surpasses both basic and advanced baselines, with maximum improvements of 13.24% and 9.86%, respectively, while reducing the calculation burden by 15-19 times to achieve real-time event identification.

Event identification↗
DOE OSTI2025

First Event-by-Event Identification of Cherenkov Radiation from Sub-Mev Particles in Liquid Argon

This Letter reports the event-by-event observation of Cherenkov light from sub-MeV electrons in a high scintillation light-yield liquid argon detector by the coherent CAPTAIN-Mills (CCM) experiment. The CCM200 detector, located at Los Alamos National Laboratory, instruments seven tons (fiducial volume) of liquid argon with 200 eight-inch photomultiplier tubes, 80% of which are coated in a wavelength-shifting material and the remaining 20% are uncoated. In the prompt time region of an event, defined as - 6 ≤ t < 0 ns relative to the event start time t = 0 , the uncoated photomultiplier tubes are primarily sensitive to visible Cherenkov photons. Using gamma rays from a Na 22 source for production of sub-MeV electrons, we isolated prompt Cherenkov light with > 5 σ confidence and developed a selection to obtain a low-background electromagnetic sample. This is the first event-by-event observation of Cherenkov photons from sub-MeV electrons in a high-yield scintillator detector and represents a milestone in low-energy particle detector development.

Aguilar-Arevalo, A. A. [UNAM, Mexico]↗
DOE OSTI2026

Post-Event Fault Identification with Machine Learning for Protection System Validation

Power system protection devices have transitioned over the past few decades from mechanical to analog devices, then to solid state and finally digital. Relays and their associated critical network of equipment have significantly increased in complexity. Even internally, relays have gained significant intricacy, with relatively simple overcurrent or differential functions now being assisted by a myriad of other functions. This is necessary as the grid becomes more complex, but it brings increased difficulty in monitoring and upkeep. Misoperation caused by accidental improper relay settings or deliberate malicious actions is a constant challenge faced by all utilities. These improper settings can be difficult to identify and may require exhaustive post-mortem analysis, typically after a major outage event has already occurred. A mechanism is needed for monitoring the behavior of protection systems to validate that their performance falls within expectations. Relays that fail to isolate a fault or trip when there is no system disturbance can be flagged for settings review in situations where this behavior may not have been noticed due to manual restoration or backup protection operations. This work presents a concept for a machine learning (ML) system capable of validating the performance of protection systems by identifying fault events and characterizing protection system responses based solely on available current and voltage measurements. As a first step in its development, an experimental dataset is generated, and a random forest model is implemented with high accuracy in distinguishing four power system scenarios.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Seismic Event Characterization Using Full Moment Tensors on the Hypersphere

Moment tensor solutions provide insights into the deformation that has occurred in the source region of a seismic event and are therefore of great value in identifying different types of seismic sources, such as when monitoring for underground nuclear tests. Despite this utility, inversion of waveforms recorded by seismometers for their full seismic moment tensor is not yet routine, and development of robust methods to classify events based on this information is in its infancy. Here, we assemble an inventory of 1405 full moment tensor solutions that include explosive, earthquake, and collapse events, and investigate the use of anisotropic probability distribution functions on the 5D hypersphere to discriminate between these sources. Using a Bayesian classifier, we obtain optimal success rates of 98.4% across all events and demonstrate that modification of the prior probabilities provides a natural way to alter the balance between not missing desirable events (such as explosions) versus misclassifying large numbers of undesired events (such as earthquakes). The approach is specifically designed to progress from traditional, bipolar event screening metrics to more generalized event identification across multiple types of seismic sources. Despite current databases containing insufficient numbers of events to definitively demonstrate at present, we also find intriguing evidence of subgroupings within individual source populations on the hypersphere, for example, between chemical and nuclear explosions, raising the potential possibility of discriminating between these event types in the future.

Geosciences↗
NASA NTRS1974

Development of a Korotkov sound processor for automatic identification of auscultatory events. I - Specification of preprocessing bandpass filters

Frequency bands that best discriminate the Korotkov sounds at systole and at diastole from the sounds immediately preceding these events are defined. Korotkov sound data were recorded from five normotensive subjects during orthostatic stress (lower body negative pressure) and bicycle ergometry. A spectral analysis of the seven Korotkov sounds centered about the systolic and diastolic auscultatory events revealed that a maximum increase in amplitude at the systolic transition occurred in the 18-26-Hz band, while a maximum decrease in amplitude at the diastolic transition occurred in the 40-60-Hz band. These findings were remarkably consistent across subjects and test conditions. These passbands are included in the design specifications for an automatic blood pressure measuring system used in conjuction with medical experiments during NASA's Skylab program.

Golden, D. P., Jr.↗
NASA NTRS1974

Evidence for an episode of crater obliteration intermediate in Martian history

It is shown that Mars experienced a brief episode of an increased obliteration rate contemporaneous with the formation of the cratered plains units. The obliteration rate during this episode was at least an order of magnitude greater than the preexisting rate. The obliteration rates prior to and after the event were identical within a factor of 3. Regional variations in the observed densities of degraded craters on the cratered terrain are shown to result from differences in the intensity of the obliteration rate during the event. The identification and characterization of the event are accomplished by using crater diameter-frequency distributions, not only for the total crater population but also for four morphological classes. Distributions are selected from 38 regions within the Martian equatorial latitudes.

Jones, K. L.↗
DOE OSTI2025

High Energy Physics Network Requirements Review: Final Report, July 2024–December 2024

The world-class research infrastructure at the US Department of Energy (DOE) Office of Science (SC) provides the research community with premier observational, experimental, computational, and network capabilities. Each user facility is designed to provide unique capabilities to advance the core DOE mission in science and technology for its SC program to stimulate rich scientific discoveries and enhance its innovation ecosystem. Research communities gather and flourish around each user facility, bringing together new and enhanced perspectives. The continual reinvention of the practice of science — as users and staff forge novel approaches expressed in research workflows — unlocks new discoveries and propels scientific progress. Within this research ecosystem, the high-performance computing (HPC) and networking user facilities stewarded by the SC’s Advanced Scientific Computing Research (ASCR) program play a dynamic cross-cutting role, enabling complex workflows demanding high-performance data, networking, and computing solutions. The ASCR facilities enterprise seeks to understand and meet the needs and requirements across SC and DOE domain science programs and priority efforts, highlighted by the formal requirements review methodology. Between July and December 2024, the Energy Sciences Network (ESnet) and the Office of High Energy Physics (HEP) of the DOE-SC organized an ESnet requirements review of HEP-supported program activities. Preparation for these events included identification of key stakeholders: program and facility management, research groups, and technology providers. Each stakeholder group was asked to prepare formal case study documents about its relationship to the HEP program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Neutrino Physics with Deep Learning on NOvA

The NOvA experiment has made both νμ \nu_\mu disappearance and νe \nu_e appearance measurements in Fermilab's NuMI beam, and is working on cross section measurements using near detector data. At the core of NOvA's measurements is the use of deep learning algorithms for identification and reconstruction of the neutrino flavor and energy. These algorithms, used for the first time on NOvA in 2016, yielded large improvements in selection efficiency, and will be applied to our first anti-neutrino results to be released this year. Presented here is the extension of our deep learning efforts for identification of neutrino signal events, final state identification, single particle tagging, and reconstruction using instance segmentation techniques. We will describe the new implementations of modified Convolutional Neural Networks for anti-neutrino events, single particles and their performance for analysis final states selection, standard candle measurements, and reconstruction.

Psihas, Fernanda [Indiana U.]↗
DOE OSTI2026

Simultaneous Probe of the Charm and Bottom Quark Yukawa Couplings Using $t\bar{t}$𝐻 Events

A search for the standard model Higgs boson decaying to a charm quark-antiquark pair, 𝐻→$c\bar{c}$, produced in association with a top quark-antiquark pair ($t\bar{t}$𝐻) is presented. The search is performed with data from proton-proton collisions at √𝑠 =13 TeV, corresponding to an integrated luminosity of 138 fb−1. Advanced machine learning techniques are employed for jet flavor identification and event classification. The Higgs boson decay to a bottom quark-antiquark pair is measured simultaneously and the observed $t\bar{t}$𝐻(𝐻→$b\bar{b}$) event rate relative to the standard model expectation is 0.91$^{+0.26}_{−0.22}$. The observed (expected) upper limit on the product of production cross section and branching fraction 𝜎⁡($t\bar{t}$𝐻)⁢ℬ⁡(𝐻→$c\bar{c}$) is 0.11 (0.13) pb at 95% confidence level, corresponding to 7.8 (8.7) times the standard model prediction. When combined with the previous search for 𝐻 →$c\bar{c}$ via associated production with a 𝑊 or 𝑍 boson, the observed (expected) 95% confidence interval on the Higgs-charm Yukawa coupling modifier, 𝜅 𝑐 , is |𝜅 𝑐 | < 3.5 (2.7), the most stringent constraint to date.

Bottom quark↗
DOE OSTI2025

Discovery of new isotopes in the fragmentation of 82 Se and insights into their production

The production cross sections of neutron-rich nuclei for elements just above 60 Ca were measured at the Facility for Rare Isotope Beams (FRIB). Four previously unobserved isotopes ( 63 Sc, 65,66 Ti, and 68 V) were produced, separated, and identified for the first time using the Advanced Rare Isotope Separator (ARIS). One event was found to be consistent with 61 Ca . The new isotopes were created through the interaction of an 82 Se beam with a carbon target at an energy of 228 MeV/𝑢 and a primary beam current of 1.07 p⁢µ⁢A. The event-by-event particle identification of the mass number (𝐴), atomic number (𝑍), and ionic charge state (𝑞) for the reaction products was achieved by combining measurements of energy loss, time of flight, magnetic rigidity, and total kinetic energy. This successful search for new isotopes, conducted at the beginning of FRIB's third year of operation, highlights the facility's discovery potential, which will continue to grow as the beam current increases. The production cross sections were analyzed in the context of the newly developed Δ⁢𝐵⁢𝐸 systematics that provides trends in neutron-rich regions and increased sensitivity to nuclear binding, an important nuclear structure observable. Furthermore, the new systematics addresses gaps in 𝑄 𝑔 -based methods commonly used to interpret the production cross sections of light neutron-rich elements, and provides an efficient empirical approach to describe cross-section trends across isotopic chains. Good agreement with the measured data was obtained when the Δ⁢𝐵⁢𝐸 systematics and abrasion-ablation calculations were performed using the Hartree-Fock-Bogoliubov (HFB-22) mass table.

59 ≤ A ≤ 89↗
NASA NTRS1979

Alsep data processing: How we processed Apollo Lunar Seismic Data

The Apollo lunar seismic station network gathered data continuously at a rate of 3 x 10 to the 8th power bits per day for nearly eight years until the termination in September, 1977. The data were processed and analyzed using a PDP-15 minicomputer. On the average, 1500 long-period seismic events were detected yearly. Automatic event detection and identification schemes proved unsuccessful because of occasional high noise levels and, above all, the risk of overlooking unusual natural events. The processing procedures finally settled on consist of first plotting all the data on a compressed time scale, visually picking events from the plots, transferring event data to separate sets of tapes and performing detailed analyses using the latter. Many problems remain especially for automatically processing extraterrestrial seismic signals.

Latham, G. V.↗
NASA NTRS1980

How we processed Apollo lunar seismic data

The Apollo lunar seismic station network gathered data continuously at a rate of 3 x 10 to the 8th bits per day for nearly eight years until termination in September, 1977. The data were processed and analyzed using a PDP-15 minicomputer. On average, 1500 long-period seismic events were detected yearly. Automatic event detection and identification schemes proved unsuccessful because of occasional high noise levels and, above all, the risk of overlooking unusual natural events. The processing procedures which were finally chosen consist of plotting all the data on a compressed time scale, visually picking events from the plots, transferring event data to separate sets of tapes and performing detailed analyses using the latter. Many problems remain, especially in the automatic processing of extraterrestrial seismic signals.

Nakamura, Y.↗
DOE OSTI2026

A search for microscopic black holes, string balls, and sphalerons in proton-proton collisions at $\sqrt{s}=13$ TeV

A search for microscopic black holes, string balls, and electroweak sphalerons using proton-proton collisions at $\sqrt{s}=13$ TeV recorded with the CMS detector at the CERN LHC during the 2016–2018 data taking, and corresponding to an integrated luminosity of 138 fb −1 , is presented. Two search strategies based on control samples in data are used. Model-independent limits on the cross section of physics phenomena with multiple energetic jets, leptons, and photons are set using a method that relies on the shape invariance of the scalar sum of the transverse momenta of all objects in the event. Model-dependent limits on black hole and sphaleron production are set using a newly introduced method that has been developed for the identification of collider events with distinct kinematic features by separating them into classes based on phase space proximity. In the context of models with large extra dimensions, semiclassical black holes and string balls with masses below 8.4–11.4 TeV and 9.0–10.7 TeV, respectively, are excluded at 95% confidence level, significantly extending the reach beyond previous searches. Results of a dedicated search for electroweak sphalerons are used to derive an upper limit of 0.0034 at 95% confidence level on the fraction of quark-quark interactions, with a center-of-mass energy above the nominal sphaleron transition energy threshold of 9 TeV, that result in a sphaleron transition.

Beyond Standard Model↗
DOE OSTI2026

Pandora-based muon neutrino disappearance search in the SBN program with 1muonNp quasi-elastic like channel

The Short-Baseline Neutrino (SBN) program at Fermilab aims to perform a definitive search for light sterile neutrinos using multiple liquid argon time projection chamber detectors. We present the status of an analysis of muon neutrino charged-current interactions in two SBN detectors (SBND and ICARUS), selecting fully contained events with one muon and at least one proton in the final state, inspired by the recent ICARUS standalone results. This topology-driven selection reduces dependence on neutrino interaction cross-section modeling while preserving high statistics and good neutrino energy resolution, enabled by robust reconstruction and particle identification. Fully contained events enable precise kinematic reconstruction and support relative measurements between detectors, directly addressing the core goals of the SBN program. A detailed evaluation of systematic uncertainties is currently underway, with the objective of reducing the dominant systematics to the percent level. The current status of this analysis, with a particular focus on the event selection performance, will be presented in this poster.

Artero Pons, Maria [Padua U.; INFN, Padua]↗
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 OSTI2024

Near-Field Passive Wireless Sensor for High-Temperature Metal Corrosion Monitoring

This work focuses on the fabrication and evaluation of a passive wireless sensor for the monitoring of the temperature and corrosion of a metal material at high temperatures. An inductor–capacitor (LC) resonator sensor was fabricated through the screen printing of Ag-based inks on dense polycrystalline Al 2 O 3 substrates. The LC design was modeled using the ANSYS HFSS modeling package, with the LC passive wireless sensors operating at frequencies from 70 to 100 MHz. The wireless response of the LC was interrogated and received by a radio frequency signal generator and spectrum analyzer at temperatures from 50 to 800 °C in real time. The corrosion kinetics of the Cu 110 was characterized through thermogravimetric (TGA) analysis and microscopy images, and the oxide thickness growth was then correlated to the wireless sensor signal under isothermal conditions at 800 °C. The results showed that the wireless signal was consistent with the corrosion kinetics and temperature, indicating that these two characteristics can be further deconvoluted in the future. In addition, the sensor also showed a magnitude- and frequency-dependent response to crack/spallation events in the oxide corrosion layer, permitting the in situ wireless identification of these catastrophic events on the metal surface at high temperatures.

36 MATERIALS SCIENCE↗