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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Deep learning methods for event verification and image re-purposing detection

Systems and methods herein describe accessing an image, generating a resized image, generating an image feature vector by applying an image classification neural network to the resized image, generating analysis of the image by processing the image feature vector using a machine-learning classifier trained to analyze the image feature vector, and based on the analysis, determining an event that is attributed to the image.

Nataraj, Lakshmanan↗
DOE OSTI2023

Allosterically coupled conformational dynamics in solution prepare the sterol transfer protein StarD4 to release its cargo upon interaction with target membranes

Complex mechanisms regulate the cellular distribution of cholesterol, a critical component of eukaryote membranes involved in regulation of membrane protein functions directly and through the physiochemical properties of membranes. StarD4, a member of the steroidogenic acute regulator-related lipid-transfer (StART) domain (StARD)-containing protein family, is a highly efficient sterol-specific transfer protein involved in cholesterol homeostasis. Its mechanism of cargo loading and release remains unknown despite recent insights into the key role of phosphatidylinositol phosphates in modulating its interactions with target membranes. We have used large-scale atomistic Molecular dynamics (MD) simulations to study how the dynamics of cholesterol bound to the StarD4 protein can affect interaction with target membranes, and cargo delivery. We identify the two major cholesterol (CHL) binding modes in the hydrophobic pocket of StarD4, one near S136&S147 (the Ser-mode), and another closer to the putative release gate located near W171, R92&Y117 (the Trp-mode). We show that conformational changes of StarD4 associated directly with the transition between these binding modes facilitate the opening of the gate. To understand the dynamics of this connection we apply a machine-learning algorithm for the detection of rare events in MD trajectories (RED), which reveals the structural motifs involved in the opening of a front gate and a back corridor in the StarD4 structure occurring together with the spontaneous transition of CHL from the Ser-mode of binding to the Trp-mode. Further analysis of MD trajectory data with the information-theory based NbIT method reveals the allosteric network connecting the CHL binding site to the functionally important structural components of the gate and corridor. Mutations of residues in the allosteric network are shown to affect the performance of the allosteric connection. These findings outline an allosteric mechanism which prepares the CHL-bound StarD4 to release and deliver the cargo when it is bound to the target membrane.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A digital twin approach to system-level fault detection and diagnosis for improved equipment health monitoring

Automating the task of fault detection and diagnosis is crucial in the effort to reduce the operation and maintenance cost in the nuclear industry. This paper describes a physics-based approach for system-level diagnosis in thermal-hydraulic systems in nuclear power plants. The inclusion of physics information allows for the creation of virtual sensors, which provide improved fault diagnosis capability. The physics information also serves to better constrain diagnostic solutions to the physical domain. As a demonstration, various test cases for fault diagnosis in a high-pressure feedwater system were considered. The use of virtual sensors allows constructing performance models for two first-point feedwater heaters which would not have been possible otherwise due to the limited sensor set. Real-time plant data provided by a utility partner were used to assess the diagnostic approach. The detection of an abnormal event immediate after a plant startup pointed to faulty behaviors in the two first-point feedwater heaters. Further, this double-blind fault diagnosis was subsequently confirmed by the plant operator. In addition, several simulated sensor fault events demonstrated the capability of our algorithms in detecting and discriminating sensor faults.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

The 2024 July 16 solar event: a challenge to the coronal mass ejection origin of long-duration gamma-ray flares

We present a multi-spacecraft analysis of the 2024 July 16 long-duration gamma-ray flare (LDGRF) detected by the Large Area Telescope on the Fermi satellite. The measured > 100 MeV γ-ray emission persisted for over seven hours after the flare impulsive phase, and was characterized by photon energies exceeding 1 GeV and a remarkably hard parent-proton spectrum. In contrast, the phenomena related to the coronal mass ejection (CME)-driven shock linked to this eruption were modest, suggesting an inefficient proton acceleration unlikely to achieve energies well above the 300 MeV pion-production threshold to account for the observed γ-ray emission. Specifically, the CME was relatively slow (∼600 km/s) and the accompanying interplanetary type-II/III radio bursts were faint and short-lived, unlike those typically detected during large events. In particular, the type-II emission did not extend to kilohertz frequencies and disappeared ∼5.5 hours prior to the LDGRF end time. Furthermore, the associated solar energetic particle (SEP) event was very weak, short-duration, and limited to a few tens of MeV, even at magnetically well-connected spacecraft. These findings demonstrate that a very fast CME resulting in a high-energy SEP event is not a necessary condition for the occurrence of LDGRFs, challenging the idea that the high-energy γ-ray emission is produced by the back-precipitation of shock-accelerated ions into the solar surface. The alternative origin scenario based on local particle trapping and acceleration in large-scale coronal loops is instead favored by the observation of giant arch-like structures of hot plasma over the source region that persisted for the entire duration of this LDGRF.

Sun: UV radiation↗
DOE OSTI2021

The Broadband Counterpart of the Short GRB 200522A at z = 0.5536: A Luminous Kilonova or a Collimated Outflow with a Reverse Shock?

In this work, we present the discovery of the radio afterglow and near-infrared (NIR) counterpart of the Swift short gamma-ray burst (GRB) GRB 200522A, located at a small projected offset of ≈1 kpc from the center of a young, star-forming host galaxy at z = 0.5536. The radio and X-ray luminosities of the afterglow are consistent with those of on-axis cosmological short GRBs. The NIR counterpart, revealed by our Hubble Space Telescope observations at a rest-frame time of ≈2.3 days, has a luminosity of ≈(1.3–1.7) × 10 42 erg s -1 . This is substantially lower than on-axis short GRB afterglow detections but is a factor of ≈8–17 more luminous than the kilonova of GW170817 and significantly more luminous than any kilonova candidate for which comparable observations exist. The combination of the counterpart's color (i - y = -0.08 ± 0.21; rest frame) and luminosity cannot be explained by standard radioactive heating alone. Additionally, we present two scenarios to interpret the broadband behavior of GRB 200522A: a synchrotron forward shock with a luminous kilonova (potentially boosted by magnetar energy deposition), or forward and reverse shocks from a ≈14°, relativistic (Γ0 ≳ 10) jet. Models that include a combination of enhanced radioactive heating rates, low-lanthanide mass fractions, or additional sources of heating from late-time central engine activity may provide viable alternate explanations. If a stable magnetar was indeed produced in GRB 200522A, we predict that late-time radio emission will be detectable starting ≈0.3–6 yr after the burst for a deposited energy of ≈10 53 erg. Counterparts of similar luminosity to GRB 200522A associated with gravitational wave events will be detectable with current optical searches to ≈250 Mpc.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Phase effects from strong gravitational lensing of gravitational waves

Assessing the probability that two or more gravitational wave (GW) events are lensed images of the same source requires an understanding of the properties of the lensed images. For short enough wavelengths where wave effects can be neglected, lensed images will generically have a fixed relative phase shift that needs to be taken into account in the lensing hypothesis. For non-precessing, circular binaries dominated by quadrupole radiation these lensing phase shifts are degenerate with either a shift in the coalescence phase or a detector and inclination dependent shift in the orientation angle. This degeneracy is broken by the presence of higher harmonic modes with |m| ≠ 2 in the former and |m| ≠ l in the latter. The presence of precession or eccentricity will also break this degeneracy. This implies that a lensed GW image will not necessarily be consistent with (unlensed) predictions from general relativity (GR). Therefore, unlike the conventional scenario of electromagnetic waves, strong lensing of GWs can lead to images with a modified phase evolution that can be observed. However, we find that templates with a shifted orientation angle remain a good approximation, with signal-to-noise ratio differences of less than 1% for mass ratios up to 1/18, and less than 5% for precession parameters up to 0.5 and eccentricities up to 0.4. We conclude that an optimal strong lensing search strategy would incorporate phase information in all stages of the identification of strong-lensing, with an exact treatment in the final assessment of the probability of multiple lensed events. Here, this work clarifies the role that strong lensing plays in the phase evolution of GWs: how it can lead to apparent deviations from GR, how it can affect the detectability of GW events, and how it can be exploited to help identify cases of strong gravitational lensing of gravitational wave sources.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Dark Photon Search at the Short-Baseline Near Detector

Neutrino physics has long been a key field in elementary particle physics, both enhancing our understanding of the Standard Model (SM) and raising new questions. Among these are the so-called "Short-Baseline Anomalies" observed by neutrino experiments, particularly the MiniBooNE experiment at Fermilab, which detected an excess of low-energy electron-like events. In recent years, beyond Standard Model (BSM) explanations have been proposed to address this anomaly, with a focus on neutrino beam-related processes. A novel interpretation involving a dark-sector explanation was recently suggested, introducing a vector portal that connects the SM and dark sectors through a new interaction mediated by a bosonic particle, the Dark Photon. This work investigates the production of dark-sector particles, specifically Dark Photons, in the Booster Neutrino Beam (BNB) at Fermilab and their potential detection at the Short-Baseline Near Detector (SBND). The BNB produces mesons which decay into Dark Photons, detectable via their decay into electron-positron pairs at SBND. By exploiting the temporal structure of the neutrino beam, we propose a method to isolate Dark Photon signals from neutrino backgrounds using time-delayed event detection. In this thesis, the sensitivity of SBND to Dark Photons is assessed using a three-year exposure, demonstrating that SBND has the potential to significantly improve current experimental constraints on Dark Photons. This analysis provides a promising avenue for future dark sector searches in neutrino experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather

Abstract. Identifying, detecting, and localizing extreme weather events is a crucial first step in understanding how they may vary under different climate change scenarios. Pattern recognition tasks such as classification, object detection, and segmentation (i.e., pixel-level classification) have remained challenging problems in the weather and climate sciences. While there exist many empirical heuristics for detecting extreme events, the disparities between the output of these different methods even for a single event are large and often difficult to reconcile. Given the success of deep learning (DL) in tackling similar problems in computer vision, we advocate a DL-based approach. DL, however, works best in the context of supervised learning – when labeled datasets are readily available. Reliable labeled training data for extreme weather and climate events is scarce. We create “ClimateNet” – an open, community-sourced human-expert-labeled curated dataset that captures tropical cyclones (TCs) and atmospheric rivers (ARs) in high-resolution climate model output from a simulation of a recent historical period. We use the curated ClimateNet dataset to train a state-of-the-art DL model for pixel-level identification – i.e., segmentation – of TCs and ARs. We then apply the trained DL model to historical and climate change scenarios simulated by the Community Atmospheric Model (CAM5.1) and show that the DL model accurately segments the data into TCs, ARs, or “the background” at a pixel level. Further, we show how the segmentation results can be used to conduct spatially and temporally precise analytics by quantifying distributions of extreme precipitation conditioned on event types (TC or AR) at regional scales. The key contribution of this work is that it paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data using a curated expert-labeled dataset – ClimateNet. ClimateNet and the DL-based segmentation method provide several unique capabilities: (i) they can be used to calculate a variety of TC and AR statistics at a fine-grained level; (ii) they can be applied to different climate scenarios and different datasets without tuning as they do not rely on threshold conditions; and (iii) the proposed DL method is suitable for rapidly analyzing large amounts of climate model output. While our study has been conducted for two important extreme weather patterns (TCs and ARs) in simulation datasets, we believe that this methodology can be applied to a much broader class of patterns and applied to observational and reanalysis data products via transfer learning.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

In Situ Machine Learning for Intelligent Data Capture on Exascale Platforms. Final Report

In many dynamic systems, interesting events occur locally in time and space. Examples of such systems include ignition events in combustion simulations, material fractures in mechanics simulations, and extreme weather events in climate simulations. Due to memory constraints and data I/O costs, current simulation workflows save data at regularly spaced time-steps, at a fixed rate determined before the start of the simulation. Often this mode of operation results in missed events of interest, necessitating a simulation restart from before an event occurred with more frequent data saves. This data saving workflow is grossly inefficient and is already a bottleneck in the computing process. We propose to develop machine learning algorithms that can detect when interesting dynamical events are occurring, triggering data saves. These machine learning algorithms will perform in situ anomaly detection to flag regions with different dynamical properties than those previously recorded. The adaptive data saves would be local in time and space to match the event of interest, thereby enabling a much more efficient workflow that will reduce data I/O costs and data storage memory requirements. The algorithms will be tested on two applications: auto-ignition simulations and climate simulations. A critical component of this project will be developing machine learning algorithms that can be deployed efficiently in situ on HPC platforms with out-of-the-box functionality. The development of in situ machine learning methods to detect anomalous events would enable a more efficient and effective workflow, in which all the relevant data are saved in a single simulation run, without re-starts or scientist intervention.

42 ENGINEERING↗
DOE OSTI2024

Single phonon detection for dark matter via quantum evaporation and sensing of He 3

Dark matter is five times more abundant than ordinary visible matter in our Universe. While laboratory searches hunting for dark matter have traditionally focused on the electroweak scale, theories of low mass hidden sectors motivate new detection techniques. Extending these searches to lower mass ranges, well below 1 GeV / c 2 , poses new challenges as rare interactions with standard model matter transfer progressively less energy to electrons and nuclei in detectors. Here, we propose an approach based on phonon-assisted quantum evaporation combined with quantum sensors for detection of desorption events via tracking of spin coherence. The intent of our proposed dark matter sensors is to extend the parameter space to energy transfers in rare interactions to as low as a few meV for detection of dark matter particles in the keV / c 2 mass range. Published by the American Physical Society 2024

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Streaming Analytics for Anomaly Detection in Large-Scale Data

Anomalous behavior poses serious risks to assured performance and reliability of complex, high-consequence systems. For spaceborne assets and their state-of-health (SOH) telemetry, the challenges of high-dimensional data of varying data types are compounded by computational limitations from size, weight, and power (SWaP) constraints as well as data availability. Automated anomaly detection methods tend to perform poorly under these constraints, while current operational approaches can introduce delays in response time due to the manual, retrospective processes for understanding system failures. As a result, presently deployed space systems, and those deployed in the near future, face situations where mission operations might be delayed or only be able to operate under degraded capabilities. Here, we examine a near-term lightweight solution that provides real-time detection capabilities for rare events and assess state-of-the-art anomaly detection techniques against real SOH telemetry from space platforms. This report describes our methodology and research, which could support more automated capabilities for comprehensive space operations as well as for other resource-constrained edge applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Unsupervised acoustic detection of fatigue-induced damage modes from wind turbine blades

This paper proposes a new in-situ damage detection approach for wind turbine blades, which leverages blade-internal non-stationary acoustic pressure fluctuations caused by the mechanical loading as the main source of excitation. This acoustic excitation was leveraged for the detection of fatigue-related damage modes on a full-scale wind turbine blade undergoing edgewise fatigue testing. An unsupervised, data-driven structural health monitoring strategy was developed to learn the normal cavity-internal acoustic sequences generated by the blade’s load cycles and to detect damage-related anomalies in the context of those sequences. A linear cepstral-coefficient based feature set was used to characterize the cavity-internal acoustics and LSTM-autoencoders were trained to accurately reconstruct healthy-case sequences. The reconstruction error was then used to characterize anomalous acoustic patterns within the blade cavity. The technique was able to detect a damage event earlier than a strain-based system by 120,000 load cycles.

17 WIND ENERGY↗
DOE OSTI2021

TESS Hunt for Young and Maturing Exoplanets (THYME). V. A Sub-Neptune Transiting a Young Star in a Newly Discovered 250 Myr Association

The detection and characterization of young planetary systems offer a direct path to study the processes that shape planet evolution. We report on the discovery of a sub-Neptune-sized planet orbiting the young star HD 110082 (TOI-1098). Transit events we initially detected during TESS Cycle 1 are validated with time-series photometry from Spitzer. High-contrast imaging and high-resolution, optical spectra are also obtained to characterize the stellar host and confirm the planetary nature of the transits. The host star is a late-F dwarf (M {sub ⋆} = 1.2M {sub ⊙}) with a low-mass, M dwarf binary companion (M {sub ⋆} = 0.26M {sub ⊙}) separated by nearly one arcminute (∼6200 au). Based on its rapid rotation and Lithium absorption, HD 110082 is young, but is not a member of any known group of young stars (despite proximity to the Octans association). To measure the age of the system, we search for coeval, phase-space neighbors and compile a sample of candidate siblings to compare with the empirical sequences of young clusters and to apply quantitative age-dating techniques. In doing so, we find that HD 110082 resides in a new young stellar association we designate MELANGE-1, with an age of 250{sub −70}{sup +50} Myr. Jointly modeling the TESS and Spitzer light curves, we measure a planetary orbital period of 10.1827 days and radius of R {sub p} = 3.2 ± 0.1R {sub ⊕}. HD 110082 b’s radius falls in the largest 12% of field-age systems with similar host-star mass and orbital period. This finding supports previous studies indicating that young planets have larger radii than their field-age counterparts.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Disk Failure Dataset from the Campaign Storage System

This dataset consists of 1,389 disk (HDD) failure events collected from the Campaign storage system at LANL. The Campaign system supported various compute platforms throughout its lifespan, including Cielo, Fire, Ice, and notably, the Trinity supercomputer. Each recorded event includes its detection timestamp (in ISO 8601 format) and details such as its location within the storage system—rack, enclosure, and drive slot number. The data, spanning from May 4, 2021, to July 25, 2023 (2 years, 2 months, and 22 days), represents failure events from the terminal years of Campaign's operational period, accounting for 26% of its total operational time.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Condition-Based Maintenance of a Circulating Water System of a Canadian Nuclear Power Plant using Machine Learning and Statistical Tools

Canada Deuterium Uranium pressurized-heavy-water reactors (PHWR) are a type of nuclear power plant that generate clean and reliable energy. The scope of this work is to automate data analysis methodologies to inform a condition-based maintenance strategy of a circulating water system (CWS) of a PHWR. The multiunit CWS provides a continuous supply of water to cool steam condensers, even during transient scenarios, thereby improving the thermal efficiency. This work aims to develop a machine learning (ML) based approach to detect anomalies in heterogeneous data of a CWS in a PHWR to help inform a predictive maintenance strategy. The heterogeneous data include textual and numeric time series data for a PHWR. Natural-language-processing (NLP)-based models are used to analyze textual data contained in work orders and operator logs and an event-timeseries correlation detection method is applied to assist anomalies diagnoses for CWS. An ML model Robust Linear Model (RLM) is also used to remove the seasonal variations in the system variable distributions based on distributions of environmental variables. A machine learning model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), trained on both original data and data without any seasonal variations will then be used to detect if an anomaly exists. Thus, by moving to an automated methodology to detect, classify, and forecast anomalies, the maintenance strategy would be based on component condition instead of a time-based schedule.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Combinatorial Evaluation of Physical Feature Engineering, Classical Machine Learning, and Deep Learning Models for Synchrophasor Data at Scale

A major objective of the project was to train and evaluate the effectiveness of multiple event and anomaly detection, identification and classification deep temporal learning models for processing of real-time phasor measurement unit (PMU) data streams. A vast dataset, consisting of two years of phasor measurements from all three U.S. Interconnections, was curated and released by the Department of Energy (DOE) through Pacific Northwest National Laboratory (PNNL). The dataset also included an event log that provided event times and types (e.g. generator trips, line trips, planned service events, transformer operations, etc.). Our analysis of this dataset addressed six (6) of the eleven (11) research priorities identified in Funding Opportunity Announcement (FOA) DE-FOA-0001861 “Big Data Analysis of Synchrophasor Data” (FOA 1861). Rather than being limited to pre-determined specific algorithms, this project relied on the uniquely structured, highly performant underlying time series database capabilities of the PredictiveGrid platform to assess the vast dataset utilizing a wide variety of algorithms.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

A Full-length Quench Antenna for MQXFA Production Series Quadrupole Magnet Testing

The MQXFA production series quadrupoles being built for the Hi-Lumi (HL) LHC upgrade by the US Accelerator Upgrade Project (US-HL-LHC AUP) will have very limited instrumentation for characterizing quench events that occur during magnet training and performance validation testing. In order to understand the origin of the quenches, and whether they have some implication for ongoing magnet fabrication, it was decided to build a full-length Quench Antenna (QA) with axial resolution of 50mm to be employed during cold testing in the anticryostat used for magnetic measurements. The goal is to have fineresolution, full-length coverage detection of quench events axially, as well as to have azimuthal resolution on the order of the cable width (about 1 degree for the cross-section), in a device that can be used both for vertical and horizontal testing. To achieve this, a 5m long QA with 128 channels of high-speed data acquisition has been designed and fabricated. The cylindrical antenna features an array of 12 full-length, dipole- and quadrupole-bucked coils positioned radially in the QA interior for azimuthal localization, and over 100 individual channels of short, high-sensitivity, antennas etched as flexible PCB circuits (which also buck dipole and quadrupole fields) wrapped circumferentially on the cylinder exterior in steps of 50mm along the magnet length for axial detection. This report discusses the design, construction and analysis of the MQXFA QA, and first results from using the antenna during quench testing in the production magnets.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗