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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Nanometer scale structural and compositional inhomogeneities of half-Heusler CoTi 1-x Fe x Sb thin films

The correlative use of X-ray diffraction (XRD), scanning transmission electron microscopy (STEM), and atom probe tomography (APT) allows the structure of substitutionally alloyed half-Heusler compound CoTi 1-x Fe x Sb to be characterized at the nanometer scale. For x = 0.2, 0.3, and 0.5, XRD patterns indicate an epitaxial cube-on-cube growth with no detectable secondary phases and are suggestive of high crystalline quality for all three of the films. This is supported using atomic resolution STEM images, which revealed the absence of secondary phases or polycrystalline regions, thus confirming the half-Heusler structure of the CoTi 1-x Fe x Sb films. For all three samples, the APT reconstructions were optimized to resolve the atomic planes in the [001] growth direction allowing the exploitation of reliable datasets. We note the presence of Fe-rich phases in the samples with Fe atoms sitting on Ti crystallographic sites is revealed. A strong phase separation is observed for x = 0.2 and x = 0.3 and is almost no longer observed for x = 0.5 in good agreement with the magnetic properties of the films.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Spatial turnover of soil viral populations and genotypes overlain by cohesive responses to moisture in grasslands

Viruses shape microbial communities, food web dynamics, and carbon and nutrient cycling in diverse ecosystems. However, little is known about the patterns and drivers of viral community composition, particularly in soil, precluding a predictive understanding of viral impacts on terrestrial habitats. To investigate soil viral community assembly processes, here we analyzed 43 soil viromes from a rainfall manipulation experiment in a Mediterranean grassland in California. We identified 5,315 viral populations (viral operational taxonomic units [vOTUs] with a representative sequence ≥10 kbp) and found that viral community composition exhibited a highly significant distance–decay relationship within the 200-m 2 field site. This pattern was recapitulated by the intrapopulation microheterogeneity trends of prevalent vOTUs (detected in ≥90% of the viromes), which tended to exhibit negative correlations between spatial distance and the genomic similarity of their predominant allelic variants. Although significant spatial structuring was also observed in the bacterial and archaeal communities, the signal was dampened relative to the viromes, suggesting differences in local assembly drivers for viruses and prokaryotes and/or differences in the temporal scales captured by viromes and total DNA. Despite the overwhelming spatial signal, evidence for environmental filtering was revealed in a protein-sharing network analysis, wherein a group of related vOTUs predicted to infect actinobacteria was shown to be significantly enriched in low-moisture samples distributed throughout the field. Overall, our results indicate a highly diverse, dynamic, active, and spatially structured soil virosphere capable of rapid responses to changing environmental conditions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Anomaly Detection in Electronic Health Records Across Hospital Networks: Integrating Machine Learning With Graph Algorithms

In a large hospital system, a network of hospitals relies on electronic health records (EHRs) to make informed decisions regarding their patients in various clinical domains. Consequently, the dependability of the health information technology (HIT) systems responsible for collecting EHR data is of utmost importance for patient safety. Recently, novel methods and tools aimed at identifying anomalies in EHR data to bolster the reliability of HIT systems have been introduced. However, these existing methods and tools primarily concentrate on individual hospitals, which limits our understanding of system-wide anomalous events and their potential impact on patient safety across multiple hospitals. In this article, we introduce a new approach to detecting anomalies in EHR data within a network of hospitals. This is achieved by combining advanced machine learning techniques with graph algorithms to create a tool capable of swiftly identifying and responding to deviations. Our proposed approach employs a combination of five machine learning models, harnessing the unique strengths of each model to provide a more robust detection system. The detected anomalies are then represented as graphs, allowing us to recognize patterns across the hospital network. This aids in identifying anomalies that span multiple medical facilities, potentially indicating broader system-level risks. Extensive real-world testing of our approach demonstrated its ability to offer actionable insights compared to existing methods. Additionally, its scalable design ensures seamless integration into existing HIT infrastructures.

Niu, Haoran [Oak Ridge National Laboratory (ORNL),↗
DOE OSTI2015

CHERENCUBE: Concept definition and implementation challenges of a Cherenkov-based detector block for PET

Purpose: A new concept for a depth-of-interaction (DOI) capable time-of-flight (TOF) PET detector is defined, based only on the detection of Cherenkov photons. The proposed “CHERENCUBE” consists of a cubic Cherenkov radiator with position-sensitive photodetectors covering each crystal face. By means of the spatial distribution of the detected photons and their time of arrival, the point of interaction of the gamma-ray in the crystal can be determined. This study analyzes through theoretical calculations and Monte Carlo simulations the potential advantages of the concept toward reaching a Cherenkov-only detector for TOF-PET with DOI capability. Furthermore, an algorithm for the DOI estimation is presented and the requirements for a practical implementation of the proposed concept are defined. Methods: The Monte Carlo simulations consisted of a cubic crystal with one photodetector coupled to each one of the faces of the cube. The sensitive area of the detector matched exactly the crystal size, which was varied in 1 mm steps between 1 × 1 × 1 mm{sup 3} and 10 × 10 × 10 mm{sup 3}. For each size, five independent simulations of ten thousand 511 keV gamma-rays were triggered at a fixed distance of 10 mm. The crystal chosen was PbWO{sub 4}. Its scintillation properties were simulated, but only Cherenkov photons were analyzed. Photodetectors were simulated having perfect photodetection efficiency and infinite time resolution. For every generated particle, the analysis considered its creation process, parent and daughter particles, energy, origin coordinates, trajectory, and time and position of detection. The DOI determination is based on the distribution of the emission time of all photons per event. These values are calculated as a function of the coordinates of detection and origin for every photon. The common origin is estimated by finding the distribution with the most similar emission time-points. Results: Detection efficiency increases with crystal size from 8.2% (1 × 1 × 1 mm{sup 3}) to 58.6% (10 × 10 × 10 mm{sup 3}) and decreases applying a photon detection threshold of 5/10/20 photons to 6.3%/4.3%/0.7% and 49.3%/30.4%/2.8%, respectively. The detection rate in the six photodetectors is uniform due to the nearly isotropic cone emission. Most cones originated after a photoelectric effect interaction, with two dominating peaks for the kinetic energy of the electron at 422.99 and 441.47 keV. The detection distance between same-event photons defines the spatial resolution of the detector required for individual photon recognition, with 20% of the detected photons having their closest neighbor within a distance of 5% of the length of the cube. Same-event photons are detected within a time window whose width is determined by the crystal size, with values of 30 and 150 ps for a 1 × 1 × 1 mm{sup 3} and a 10 × 10 × 10 mm{sup 3} cube, respectively. The DOI reconstruction has an accuracy of approximately 23% of the length of the cube, with an average value of 2.2 mm for a 10 × 10 × 10 mm{sup 3} CHERENCUBE. Conclusions: The proposed concept requires a detector with high photodetection efficiency. The structure of the sensitive surface of the detector should be a two dimensional array of microcells, able to provide individual detection coordinates and time stamps. The microcell size determines the ability to recognize individual photons, influencing detection efficiency. The 3D DOI recognition relies on the accuracy of the time stamps and detection coordinates, without the need for a recognition of the projected patterns of photons. The refractive index of the material defines a detector intrinsic energy-based rejection of scattered PET events at the cost of reduced sensitivity.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

TRACER Perspectives on Gulf-Breeze and Bay-Breeze Circulations and Coastal Convection

Abstract This study explores gulf-breeze circulations (GBCs) and bay-breeze circulations (BBCs) in Houston–Galveston, investigating their characteristics, large-scale weather influences, and impacts on surface properties, boundary layer updrafts, and convective clouds. The results are derived from a combination of datasets, including satellite observations, ground-based measurements, and reanalysis datasets, using machine learning, changepoint detection method, and Lagrangian cell tracking. We find that anticyclonic synoptic patterns during the summer months (June–September) favor GBC/BBC formation and the associated convective cloud development, representing 74% of cases. The main Tracking Aerosol Convection Interactions Experiment (TRACER) site located close to the Galveston Bay is influenced by both GBC and BBC, with nearly half of the cases showing evident BBC features. The site experiences early frontal passages ranging from 1040 to 1630 local time (LT), with 1300 LT being the most frequent. These fronts are stronger than those observed at the ancillary site which is located further inland from the Galveston Bay, including larger changes in surface temperature, moisture, and wind speed. Furthermore, these fronts trigger boundary layer updrafts, likely promoting isolated convective precipitating cores that are short lived (average convective lifetime of 63 min) and slow moving (average propagation speed of 5 m s −1 ), primarily within 20–40 km from the coast.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Biomimetic Ultrasonic Whistle for Use as a Bat Deterrent on Wind Turbines

Our project goal was to design a novel mechanical ultrasonic deterrent for use on wind turbines in order to reduce bat fatalities around these structures. Our proposed deterrent design would be composed of a series of ultrasonic pulse generators, or whistles, affixed to a wind turbine blade, which produce ultrasound through mechanical means. The whistles would produce sounds mimicking the spectrotemporal patterns of bat echolocation pulses, thereby enhancing the bats’ ability to detect, localize and avoid the moving blades. These whistles are intended to be operated passively, blown by the wind, and to be positioned at intervals along a turbine blade. Because the source of sound generation will be mechanical in nature, these devices will require no external power source, should require little maintenance, and will be small and cost-effective.

17 WIND ENERGY↗
DOE OSTI2025

Tactical Analysis for Calculating Contextual Risk at Boundaries: Summary of Laboratory Directed Research & Development Effort

The Tactical Analysis for Calculating Contextual Risk at Boundaries (TACCRAB) tool is an innovative digital twin (DT) platform and automated risk algorithm designed to transform operational decision-making in structured screening environments, with an initial focus on Southern Border Land Ports of Entry (POEs). The invention provides integration points for advanced artificial intelligence, predictive modeling, and real-time data analysis to produce a comprehensive risk management tool that enables proactive, data-informed security strategies. The core inventive features of TACCRAB center on its unique risk algorithm, which dynamically calculates contextual risk by synthesizing historical data, near real-time streaming data from the checkpoints themselves, and AI-generated predictions. Unlike traditional risk assessment methods, TACCRAB utilizes a DT to provide comprehensive operational insights, allowing stakeholders to visualize, simulate, and optimize checkpoint configurations with unprecedented speed and contextual awareness. TACCRAB's key innovation lies in its ability to combine multiple complex inputs - including technology detection probabilities, resource availability, screening pathway characteristics, and threat actor behavioral patterns - into a unified risk calculation and update these inputs based on changing operational and environmental conditions. By leveraging a DT that continuously updates and learns from linked data, TACCRAB can suggest adaptive mitigation strategies that minimize risk while maintaining operational efficiency. Particularly novel is the platform's approach to decision support, which goes beyond static risk assessment. The DT provides dynamic metrics such as wait times, resource allocation effectiveness, and potential emerging threat scenarios, enabling users to view sophisticated, relevant what-if simulations and optimize checkpoint operations in near real-time. The system's architecture allows for generalized application across different screening environments, such as secure facilities, ports of entry, and soft targets, making it a versatile tool for security and operational management. The invention distinguishes itself through its comprehensive integration of predictive modeling, AI-driven pattern discovery, and user-friendly interface design. By combining these elements, TACCRAB transforms complex risk data into actionable insights, supporting decision-makers at various organizational levels - from booth agents making split-second screening decisions to checkpoint managers optimizing the day's resource allocation to strategic planners managing long-term investments.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2016

EFFECT OF A SAUSAGE OSCILLATION ON RADIO ZEBRA-PATTERN STRUCTURES IN A SOLAR FLARE

Sausage modes that are axisymmetric fast magnetoacoustic oscillations of solar coronal loops are characterized by variation of the plasma density and magnetic field, and hence cause time variations of the electron plasma frequency and cyclotron frequency. The latter parameters determine the condition for the double plasma resonance (DPR), which is responsible for the appearance of zebra-pattern (ZP) structures in time spectra of solar type IV radio bursts. We perform numerical simulations of standing and propagating sausage oscillations in a coronal loop modeled as a straight, field-aligned plasma slab, and determine the time variation of the DPR layer locations. Instant values of the plasma density and magnetic field at the DPR layers allowed us to construct skeletons of the time variation of ZP stripes in radio spectra. In the presence of a sausage oscillation, the ZP structures are shown to have characteristic wiggles with the time period prescribed by the sausage oscillation. Standing and propagating sausage oscillations are found to have different signatures in ZP patterns. We conclude that ZP wiggles can be used for the detection of short-period sausage oscillations and the exploitation of their seismological potential.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Ubiquitous Traffic Volume Estimation through Machine-Learning Procedure

Traffic volume data is one of the most important metrics for accurate assessment of the performance of a transportation system. Quality volume data is required to effectively assess extent of delay and congestion, detect real-time perturbations to the network, and understand traffic patterns during major weather events. Traffic volume on freeways are typically collected through continuous count stations installed by state DOTs, while there is lack of traffic volume observability on off-freeway roads. The National Renewable Energy Laboratory (NREL), in Collaboration with the I-95 Corridor Coalition and the University of Maryland, extended its research into estimating volumes anywhere anytime from industry probe based data for off-freeway roads. NREL combined vehicle probe count data with several other data sets (speed, whether, roadway geometry, time-of-day, day-of-week, etc.) to estimate hourly volumes as well as AADTs. The research validated and demonstrated the machine learning model, namely XGBoost, using data collected from Pennsylvania, North Carolina, and Tennessee.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Quantifying the influence of natural climate variability on in situ measurements of seasonal total and extreme daily precipitation

Abstract While various studies explore the relationship between individual sources of climate variability and extreme precipitation, there is a need for improved understanding of how these physical phenomena simultaneously influence precipitation in the observational record across the contiguous United States. In this work, we introduce a single framework for characterizing the historical signal (anthropogenic forcing) and noise (natural variability) in seasonal mean and extreme precipitation. An important aspect of our analysis is that we simultaneously isolate the individual effects of seven modes of variability while explicitly controlling for joint inter-mode relationships. Our method utilizes a spatial statistical component that uses in situ measurements to resolve relationships to their native scales; furthermore, we use a data-driven procedure to robustly determine statistical significance. In Part I of this work we focus on natural climate variability: detection is mostly limited to DJF and SON for the modes of variability considered, with the El Niño/Southern Oscillation, the Pacific–North American pattern, and the North Atlantic Oscillation exhibiting the largest influence. Across all climate indices considered, the signals are larger and can be detected more clearly for seasonal total versus extreme precipitation. We are able to detect at least some significant relationships in all seasons in spite of extremely large (> 95%) background variability in both mean and extreme precipitation. Furthermore, we specifically quantify how the spatial aspect of our analysis reduces uncertainty and increases detection of statistical significance while also discovering results that quantify the complex interconnected relationships between climate drivers and seasonal precipitation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Heliostat optical error inspection with polarimetric imaging drone

On a Concentrated Solar Power (CSP) field, optical errors have significant impacts on the collection efficiency of heliostats. Fast, cost-effective, labor-efficient, and non-intrusive autonomous field inspection remains a challenge. Approaches using imaging drone, i.e., Unmanned Aerial Vehicle (UAV) system integrated with high resolution visible imaging sensors, have been developed to address these challenges; however, these approaches are often limited by insufficient imaging contrast. Here, in this study, we report a polarimetry-based method with a polarization imaging system integrated on UAV to enhance imaging contrast for in-situ detection of heliostat mirrors without interrupting field operation. We developed an optical model for skylight polarization pattern to simulate the polarization images of heliostat mirrors and obtained optimized waypoints for polarimetric imaging drone flight path to capture images with enhanced contrast. The polarimetric imaging-based method improved the success rate of edge detections in scenarios which were challenging for mirror edge detection with conventional imaging sensors. We have performed field tests to achieve significantly enhanced heliostat edge detection success rate and investigate the feasibility of integrating polarimetric imaging method with existing imaging-based heliostat inspection methods, i.e., Polarimetric Imaging Heliostat Inspection Method (PIHIM). Our preliminary field test results suggest that the PIHIM hold the promise to enable sufficient imaging contrast for real-time autonomous imaging and detection of heliostat field, thus suitable for non-interruptive fast CSP field inspection during its operation.

CSP Field↗
DOE OSTI2017

Boost breaking in the EFT of inflation

If time-translations are spontaneously broken, so are boosts. This symmetry breaking pattern can be non-linearly realized by either just the Goldstone boson of time translations, or by four Goldstone bosons associated with time translations and boosts. Here in this paper we extend the Effective Field Theory of Multifield Inflation to consider the case in which the additional Goldstone bosons associated with boosts are light and coupled to the Goldstone boson of time translations. The symmetry breaking pattern forces a coupling to curvature so that the mass of the additional Goldstone bosons is predicted to be equal to √2H in the vast majority of the parameter space where they are light. This pattern therefore offers a natural way of generating self-interacting particles with Hubble mass during inflation. After constructing the general effective Lagrangian, we study how these particles mix and interact with the curvature fluctuations, generating potentially detectable non-Gaussian signals.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

ObstacleSense: Low-Power Neuromorphic Vision for Corridor Obstacle Awareness in Low-Level ADAS

The automotive industry’s pursuit of Level 5 autonomy is constrained by substantial perception-compute power requirements, often reaching 1, 000 + watts in full autonomy stacks. Reducing this energy burden requires rethinking perception not only at the high-end autonomy level, but also at the foundational Advanced Driver Assistance Systems (ADAS) level where low-power, safety-critical sensing can have broad impact. Neuromorphic vision provides a promising starting point: HD Dynamic Vision Sensors (DVS) can operate below 100 mW at the sensor level by reporting only asynchronous brightness changes. However, low-power sensing alone is insufficient if downstream perception reintroduces dense, energy-intensive computation. In particular, many event-driven object-detection pipelines still rely on CNN backbones, while purely spiking alternatives often trade away accuracy or ignore deployment constraints. We introduce ObstacleSense, a highly compact, CNN-free hybrid ANN–SNN framework for Level 0–1 forward-corridor obstacle awareness. Instead of performing full-scene object detection with a convolutional feature backbone, ObstacleSense targets the safety-critical question of whether the ego corridor is occupied and how far the nearest obstacle is. The architecture combines polarity-conditioned event encoding, lightweight temporal spiking dynamics, axial spatial mixing, and coarse-to-fine range estimation within a regular fixed-grid compute pattern. This design avoids the dense CNN backbone commonly used in event-based detection while maintaining a small state footprint suitable for eventual small-FPGA deployment. Before hardware mapping, we evaluate the software implementation using a model-side power proxy derived from MACs, weight and activation traffic, and spiking state updates under shared FP16 assumptions. On simulated CARLA event corpora, the deployment-oriented model achieves 0.9464 objectness F1, 0.9978 grid-level mAP, and 0.8987 m distance Mean Absolute Error at an estimated 1.92 mW proxy cost, while maintaining performance on unseen generalization test sequences.

Johnson-Scott, Zac [ORNL]↗
DOE OSTI2017

Twenty-First-Century Changes in U.S. Regional Heavy Precipitation Frequency Based on Resolved Atmospheric Patterns

Precipitation-gauge observations and atmospheric reanalysis are combined to develop an analogue method for detecting heavy precipitation events based on prevailing large-scale atmospheric conditions. Combinations of atmospheric variables for circulation (geopotential height and wind vector) and moisture (surface specific humidity, column and up to 500-hPa precipitable water) are examined to construct analogue schemes for the winter [December–February (DJF)] of the “Pacific Coast California” (PCCA) region and the summer [June–August (JJA)] of the Midwestern United States (MWST). The detection diagnostics of analogue schemes are calibrated with 1979–2005 and validated with 2006–14 NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA). All analogue schemes are found to significantly improve upon MERRA precipitation in characterizing the occurrence and interannual variations of observed heavy precipitation events in the MWST. When evaluated with the late twentieth-century climate model simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5), all analogue schemes produce model medians of heavy precipitation frequency that are more consistent with observations and have smaller intermodel discrepancies than model-based precipitation. Under the representative concentration pathways (RCP) 4.5 and 8.5 scenarios, the CMIP5-based analogue schemes produce trends in heavy precipitation occurrence through the twenty-first century that are consistent with model-based precipitation, but with smaller intermodel disparity. The median trends in heavy precipitation frequency are positive for DJF over PCCA but are slightly negative for JJA over MWST. Overall, the analyses highlight the potential of the analogue as a powerful diagnostic tool for model deficiencies and its complementarity to an evaluation of heavy precipitation frequency based on model precipitation alone.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Performance Validation of Pulsed Thermal Imaging System for In-Service Applications

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of complex topology nuclear reactor parts from high-strength corrosion resistance alloys, such as stainless steel and Inconel. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process, which has the capability of melting metallic powder and net shaping the structures with relatively high precision. Some of the challenges with using LPBF method for nuclear manufacturing include the possibility of introducing pores into metallic structures. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures in high temperature nuclear reactor environment. Currently, there exist limited capabilities to evaluate actual AM structures non-destructively. Pulsed Thermography Imaging (PTI) provides a capability for non-destructive evaluation (NDE) of subsurface defects in arbitrary size structures. The PTI method is based on recording material surface temperature transients with infrared (IR) camera following thermal pulse delivered on material surface with flash light. The PTI method has advantages for NDE of actual AM structures because the method involves one-sided non-contact measurements and fast processing of large sample areas captured in one image. Following initial qualification of an AM component for deployment in a nuclear reactor, a PTI system can also be used for in-service nondestructive evaluation (NDE) applications. In this report, we describe recent progress in enhancing PTI capabilities in detecting microscopic defects in metallic specimens. SS316 and IN718 specimens were developed with a pattern of subsurface calibrated flat bottom hole (FBH) defects with diameters from 500µm to 200µm. FBH’s were created with EDM (electron discharge machining) drill. PTI imaging data was processed Spatial Temporal Denoised Thermal Source Separation (STDTSS) unsupervised machine learning (ML) algorithm. We show that defects as small as 200µm in SS316 and IN718 can be detected with STDTSS algorithm. To the best of our knowledge, these are the smallest detected defects which are reported in literature.

42 ENGINEERING↗
DOE OSTI2022

A Methodological Overview of Seismic Analysis for Nuclear Event Detection

Underground explosions generate potentially detectable signatures, including energy waves that travel through the Earth’s subsurface (i.e., seismic waves), low-frequency sound waves (i.e., infrasound and hydroacoustic waves), and radioactive gases and/or particles that might leak from the test cavity (if the event was nuclear). There can also be intelligence indicators of a test, such as observations of modified patterns of life and activity at a suspected test site. If all of these detectable signatures and intelligence indicators are present and self-consistent, then analysts have high confidence in classifying a signature generating event as an explosion. However, because only partial information about an event is likely to be available, determining whether an event was natural (e.g., an earthquake or landslide) or manmade (e.g., a chemical or nuclear explosion) is much more challenging. This primer describes how one category of event signatures—seismic signatures—can augment event analyses. While universities and government organizations have generated detailed technical descriptions of seismic analytic techniques, we seek to translate seismic event analysis for a broad, non-technical audience. When the geologic conditions near an event are well-characterized, seismic data can be used to calculate critical information, such as event location and depth, with relatively high accuracy. Moreover, specific features within seismic datasets can help determine whether an event was an explosion. However, a key challenge in seismic analysis is that geologic site conditions are often poorly characterized, complicating the ability to discern the true nature of the event. To overcome this challenge, geologists answer a series of questions (discussed in section 1) to guide seismic event analysis and determine the most probable nature of an event. As more information is gathered during each analytic step, confidence grows regarding the nature of the event. Section 2 addresses uncertainties in seismic analysis and the vital nature of high-fidelity geologic data for accurate seismic event analysis.

58 GEOSCIENCES↗
DOE OSTI2017

Diversity of Abundance Patterns of Light Neutron-capture Elements in Very-metal-poor Stars

We determine the abundances of neutron-capture elements from Sr to Eu for five very-metal-poor stars (−3 < [Fe/H] < −2) in the Milky Way halo to reveal the origin of light neutron-capture elements. Previous spectroscopic studies have shown evidence of at least two components in the r-process; one referred to as the “main r-process” and the other as the “weak r-process,” which is mainly responsible for producing heavy and light neutron-capture elements, respectively. Observational studies of metal-poor stars suggest that there is a universal pattern in the main r-process, similar to the abundance pattern of the r-process component of solar-system material. Still, it is uncertain whether the abundance pattern of the weak r-process shows universality or diversity, due to the sparseness of measured light neutron-capture elements. We have detected the key elements, Mo, Ru, and Pd, in five target stars to give an answer to this question. The abundance patterns of light neutron-capture elements from Sr to Pd suggest a diversity in the weak r-process. In particular, scatter in the abundance ratio between Ru and Pd is significant when the abundance patterns are normalized at Zr. Our results are compared with the elemental abundances predicted by nucleosynthesis models of supernovae with parameters such as electron fraction or proto-neutron-star mass, to investigate sources of such diversity in the abundance patterns of light neutron-capture elements. This paper presents that the variation in the abundances of observed stars can be explained with a small range of parameters, which can serve as constraints on future modeling of supernova models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Spatiotemporal Pattern Recognition in the PMU Signals in the WECC system

Phasor measurement unit (PMU) data has been used by multiple power system applications, including state estimation, post event analysis, oscillation detection, model validation, and many others. Still, due to its big data nature and availability to general research institutions, comprehensive understanding of the spatiotemporal patterns and underlying mechanisms are incomplete. This study applies a set of signal processing and machine learning approaches aiming at deciphering the characteristic behaviors of multiple phasor measurement units (PMUs) attributes (e.g., voltage, frequency, rate of change of frequency, phase angle), including their auto-correlation, cross-dependence, similarities and discrepancies across units and temporal scales, and distributions of anomalies and their linkages to potential external factors such as weather events. Data analytics are applied to PMUs from the U.S. Western Electricity Coordinating Council (WECC) system. The PMU measurements, recorded events, outages, and weather extremes are all from real world datasets. The findings from the study and mechanistic understanding of the PMU dynamics help provide guidance on system control or preventing blackouts. The derived metrics can be directly used for adjusting or filtering simulated PMU data used for advanced algorithm development.

Hou, Zhangshuan↗