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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Fault systems monitoring using machine learning

Traditional seismology methods are designed to build catalogs of seismic events that are obviously different from the remaining seismic data, even to the naked eye, and discard more than 99% of the data. Los Alamos National Lab researchers developed machine learning methods to examine massive amounts of raw seismic data. The scientists found that the continuous data discarded by cataloging methods are informative of the physical state of faults in theoretical models, in laboratory experiments, and in the field. This information may enable the detection of predictive markers of seismic events and the provide insights into the underlying physics to support earthquake hazard assessment.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Intermittent Criticality Multi‐Scale Processes Leading to Large Slip Events on Rough Laboratory Faults

Abstract We discuss data of three laboratory stick‐slip experiments on Westerly Granite samples performed at elevated confining pressure and constant displacement rate on rough fracture surfaces. The experiments produced complex slip patterns including fast and slow ruptures with large and small fault slips, as well as failure events on the fault surface producing acoustic emission bursts without externally‐detectable stress drop. Preparatory processes leading to large slips were tracked with an ensemble of ten seismo‐mechanical and statistical parameters characterizing local and global damage and stress evolution, localization and clustering processes, as well as event interactions. We decompose complex spatio‐temporal trends in the lab‐quake characteristics and identify persistent effects of evolving fault roughness and damage at different length scales, and local stress evolution approaching large events. The observed trends highlight labquake localization processes on different spatial and temporal scales. The preparatory process of large slip events includes smaller events marked by confined bursts of acoustic emission activity that collectively prepare the fault surface for a system‐wide failure by conditioning the large‐scale stress field. Our results are consistent overall with an evolving process of intermittent criticality leading to large failure events, and may contribute to improved forecasting of large natural earthquakes.

Geochemistry & Geophysics↗
DOE OSTI2021

Physics-Based Feature Extraction from Bulk Time-Series PMU Datasets for Event Detection

In this work, two physics-based feature extraction techniques are developed for bulk time-series phasor measurement unit (PMU) datasets collected from the field to train the machine learning model for anomaly detection. Two approaches have been developed to extract useful features for different types of events. An admittance-based feature extraction technique is developed to detect events that involve line outages and system topology variations. The developed algorithm extracts the system equivalent admittance variation. Additionally, Fielder’s Theory is utilized to further reduce the potential computation burden by sectionalizing large-scale grids and datasets into smaller areas. Second, an oscillation-based feature extraction technique is developed to detect low-frequency oscillations in power grids. The dominant oscillation modes in the grids are extracted using energy-sorted Prony analysis. The extracted dominant oscillation modes by the developed work exhibit a high fitting resolution. Finally, the developed techniques have been validated using large-scale and real-world datasets.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Deep Analysis Net with Causal Embedding for Coal-fired Power Plant Fault Detection and Diagnosis (DANCE4CFDD)

Fault detection and diagnosis is critical to power plant operation to ensure attaining high reliability while reducing operation cost. As more renewable power is introduced to the power grid, traditional fossil power plants take on the extra burden of excessive load cycling to compensate the generation variability from renewable power. Such load cycling will pose more reliability challenges to power plant operation. There are a number of challenges faced by today’s asset health management system in coal- fired (or gas) power plants: 1) high-dimensional nonlinear interaction among multiple time series measurements; 2) high measurement variance induced by operational conditions/modes; 3) variation among asset types and plant configurations; and 4) a small number of faulty events to learn from. To cope with these challenges, today’s fielded asset health management systems rely heavily on manual efforts from domain experts and hand-crafted features or rules based on domain knowledge. Despite its role in plant reliability, such a practice is costly and hinders its scalability and sustainability, particularly when a plant undergoes modifications. The objective of this project is to develop a novel end-to-end AI learning system that is trainable (i.e., the AI representation of a complex system behavior can be directly learned from properly labeled data) for accurate fault detection and root cause analysis. The ability to create a fault detection model directly from time series could alleviate the efforts associated with today’s asset management solution development. In the course of this project, we have achieved the following: Created an AI model development environment incorporating state-of-the-art neural network architectures for rapid model development and evaluation; Developed novel learning strategies for training of fault detection model; Developed special-purpose neural network architecture embedded with variable association graph aiming for better interpretability; Developed a learning strategy to leverage a small number of faulty events for enhanced fault detection capability; Conducted detailed experimental study based on public benchmark datasets and demonstrated the effectiveness of the proposed solution; and Validated the developed system with data from both a coal-fired plant boiler dynamic simulation model and real-world coal-fired power plant covering multiple asset and fault types. Overall, the project attained a technology readiness level of TRL 5 from TRL 2 at the beginning of the project.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

(Un)Explained EMIC Waves: Understanding Quiet Time EMIC Wave Drivers

Electromagnetic ion cyclotron (EMIC) waves are known to be generated through cyclotron resonance with the local ion particle population and grow when there is a large enough temperature anisotropy. In general, these temperature anisotropies necessary for wave growth are found to be associated with either solar wind pressure pulses or particle injections during geomagnetic storms or substorms. However, some EMIC events do not show any clear association with these known drivers and appear unexplained. In our analysis of high-amplitude (>1 nT) non-storm time EMIC waves, we find that 24 out of the 223 (~11%) EMIC events with peak amplitude greater than 1 nT were not found to be associated with any clear EMIC wave driver. This raises a compelling question: What magnetospheric or solar wind driver provides the free energy to grow these quiet-time EMIC waves? Here, we examine two EMIC events on 13 April 2017 and 13 February 2014, which were excited during extremely quiet solar wind and geomagnetic conditions. Furthermore, an in-depth analysis of field and particle measurements from multiple data sets, including ground and in situ data for these two events, indicates that extremely weak and otherwise insignificant pressure values and/or very weak substorm injections occurring multiple hours before the event play a significant role in quiet time wave generation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Heliostat Consortium: Gap Analysis on Wind Load for Achieving a Fully Competitive Heliostat Industry

The Heliostat Consortium (HelioCon) Wind Load Subtask was initiated with the aim of bringing research work pertaining to wind load measurement, characterization, and prediction taking place across several tasks, including Advanced Manufacturing, Components and Controls, and Field Deployment. The cross-cutting wind load subtopic in the HelioCon roadmap report [1] highlighted standardized methods and tools that are needed for a more detailed understanding of the static and dynamic loads on a heliostat. This will enable cost reduction of wind-dependent heliostat components to avoid unnecessarily conservative, overly constrained designs and increase field efficiency/reliability, to reduce the risk of component failures due to high-wind events (>15 m/s). Gaps related to heliostat wind load include site characterization for wind measurements, critical load cases for heliostat design, turbulence impacts on heliostat tracking error, testing of heliostat array configurations, understanding spatial variation of maximum loads across the solar field, and heliostat field layout and operating strategies. The recommended highest-priority pathway as first steps taken by HelioCon to address these gaps are to develop site characterization guidelines for heliostat design and field load measurements.

Emes, Matthew (ORCID:0000000341474387)↗
DOE OSTI2023

Frost prediction using machine learning and deep neural network models

This study describes accurate, computationally efficient models that can be implemented for practical use in predicting frost events for point-scale agricultural applications. Frost damage in agriculture is a costly burden to farmers and global food security alike. Timely prediction of frost events is important to reduce the cost of agricultural frost damage and traditional numerical weather forecasts are often inaccurate at the field-scale in complex terrain. In this paper, we developed machine learning (ML) algorithms for the prediction of such frost events near Alcalde, NM at the point-scale. ML algorithms investigated include deep neural network, convolution neural networks, and random forest models at lead-times of 6–48 h. Our results show promising accuracy (6-h prediction RMSE = 1.53–1.72°C) for use in frost and minimum temperature prediction applications. Seasonal differences in model predictions resulted in a slight negative bias during Spring and Summer months and a positive bias in Fall and Winter months. Additionally, we tested the model transferability by continuing training and testing using data from sensors at a nearby farm. We calculated the feature importance of the random forest models and were able to determine which parameters provided the models with the most useful information for predictions. We determined that soil temperature is a key parameter in longer term predictions (>24 h), while other temperature related parameters provide the majority of information for shorter term predictions. The model error compared favorable to previous ML based frost studies and outperformed the physically based High Resolution Rapid Refresh forecasting system making our ML-models attractive for deployment toward real-time monitoring of frost events and damage at commercial farming operations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Status of the MicroBooNE Inclusive Single Photon Selection Using Wire-Cell Reconstruction

MicroBooNE is a Liquid Argon Time Project Chamber (LArTPC) detector whose primary design goal is to understand the “low-energy-excess” anomaly seen by MiniBooNE. MicroBooNE’s currently published results see no excess consistent with the MiniBooNE observation, therefore creating a need for searches in more channels. This note summarizes MicroBooNE’s inclusive single photon selection using Wire-Cell reconstruction and pattern recognition, which will be used to search for a low-energy-excess (LEE) anomaly in the inclusive single photon channel. The selection is similar to the Wire-Cell inclusive electron neutrino selection, but with a different signal definition and some modifications and additions to the pattern recognition tools. A selection with 15.2% efficiency and 18.5% purity is achieved for simulated Standard Model single photon events.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Status of the MicroBooNE inclusive single photon selection using Wire-Cell reconstruction

MicroBooNE is a Liquid Argon Time Project Chamber (LArTPC) detector whose primary design goal is to understand the “low-energy-excess” anomaly seen by MiniBooNE. MicroBooNE’s currently published results see no excess consistent with the MiniBooNE observation, therefore creating a need for searches in more channels. This note summarizes MicroBooNE’s inclusive single photon selection using Wire-Cell reconstruction and pattern recognition, which will be used to search for a low-energy-excess (LEE) anomaly in the inclusive single photon channel. The selection is similar to the Wire-Cell inclusive electron neutrino selection, but with a different signal definition and some modifications and additions to the pattern recognition tools. A selection with 15.2% efficiency and 18.5% purity is achieved for simulated Standard Model single photon events.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Measurement of the ionization response of amorphous selenium with 122 keV γ rays

In this work, we performed a measurement of the ionization response of 200 μm-thick amorphous selenium (aSe) layers under drift electric fields of up to 50 V/μm. The aSe target was exposed to ionizing radiation from a 57 Co radioactive source and the ionization pulses were recorded with high resolution. Using the spectral line from the photoabsorption of 122 keV γ rays, we measured the charge yield in aSe and the line width as a function of drift electric field. From a detailed microphysics simulation of charge generation and recombination in aSe, we conclude that the strong dependence of recombination on the ionization track density provides the dominant contribution to the energy resolution in aSe. These results provide valuable input to estimate the sensitivity of a proposed next-generation search for the neutrinoless ββ decay of 82 Se that aims to employ imaging sensors with an active layer of aSe. We estimate the RMS line width of the integrated ionization signal from neutrinoless ββ decay events (of deposited energy 3 MeV) to be 2.0% for a drift field of 50 V/μm. The energy resolution can be improved to 1% by correcting for the charge yield as a function of ionization density along the imaged electron tracks.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

TEMPEST3 surface runoff water chemistry and organic matter composition

Coastal flooding, driven by storm surges and sea level rise, can mobilize organic matter (OM) via runoff, while introducing compositionally distinct OM (e.g., estuarine OM) into the system. To understand event-scale OM dynamics, we monitored source waters and surface runoff during an ecosystem-scale field manipulation experiment, TEMPEST (Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments), in June 2024. The TEMPEST experiment is part of the COMPASS-FME (Coastal Observations, Mechanisms, and Predictions Across Systems and Scales – Field, Measurements, and Experiments) project and designed to investigate biogeochemical and ecological impacts of freshwater and seawater flooding on coastal terrestrial-aquatic interface ecosystems by simulating freshwater and seawater storm events in two 2000m2 coastal upland forest plots (freshwater and brackish seawater plots). The temporal coverage of this dataset is during the TEMPESTⅢ event (June 11-13, 2024). This dataset contains: - Surface runoff discharge measured by flumes - Sensor data (specific conductivity, salinity, dissolved oxygen, and temperature) - Particle size distribution - Total suspended sediment concentrations (TSS), particulate and dissolved organic carbon (POC, DOC) concentrations, total nitrogen and total dissolved nitrogen (TN, TDN) concentrations - Bulk particulate and dissolved OM compositions (stable C and N isotopes of particulates and optical measurements of chromophoric dissolved OM) - High resolution mass spectrometry analysis data - Water isotope data All data files are plain-text CSV (comma-separated value), and no special software is required to read them.

COMPASS-FME↗
DOE OSTI2021

Mechanism of Microseismic Generation During Hydraulic Fracturing - With Evidence From HFTS 2 Observations

The objective of this study is to understand how microseismic events are generated during hydraulic fracturing, as well as the role of geomechanical conditions (i.e., stress and mechanical stratigraphy) in this process. In the industry, microseismic event clouds have been generally used as an “outer-boundary” of the “stimulated reservoir volume” (SRV). However, by comparing with other surveillance data (Low frequency Distributed Acoustic Sensing, or LF-DAS strain) in the Hydraulic Fracturing Test Site (HFTS) 2 experiment, we show that this assumption is fundamentally flawed. The HFTS 2 data has three unique observations that have not been commonly observed in other datasets: 1. Due to influence of offset pad depletion, microseismic data shows that hydraulic fractures from the child well can propagate over 3000 feet into the depleted low stress zone. 2. By comparing microseismic and horizontal fiber LF-DAS strain data, we observe that microseismic event cloud does not necessarily reflect the created hydraulic fracture volume. Particularly, the extent of microseismic event clouds near heel stages are much shorter than what is shown with LF-DAS strain data. 3. Microseismic event magnitudes are larger in the depleted regions. Through geomechanical analysis, we demonstrate that the “bedding-plane-slip” model is likely the mechanism for microseismic generation during hydraulic fracturing. Furthermore, this model successfully explains the above field observations from HFTS 2 experiment. We also provide a quantitative relationship connecting the microseismic event magnitude with fracture width increment and layer mechanical property contrast.

58 GEOSCIENCES↗
DOE OSTI2021

Evidence of Alfvenic Poynting Flux as the Primary Driver of Auroral Motion During a Geomagnetic Substorm

Geomagnetic substorms are major energy transfer events where energy stored in the Earth's magnetotail is released into the ionosphere. Substorm phenomena, including auroral activities, earthward Poynting flux, magnetic field dipolarization, etc, have been extensively studied. However, the complex interplay among them is not fully understood. In a fortuitous event on June 7, 2013, the twin Van Allen Probes (separated by 0.4 h in local time) observed bursts of earthward Alfvenic Poynting flux in the vicinity of the plasma sheet boundary layer (PSBL). The Poynting flux bursts correlate with enhancements of auroral brightness around the footpoints of both spacecraft. This indicates a temporal and spatial correlation between the auroral brightening and Poynting flux bursts, and that the auroral motion is directly linked to the perpendicular expansion of the Alfven wave. These observations suggest that the Alfvenic Poynting flux is a primary driver for the auroral electron acceleration. Furthermore, around the time of auroral brightening, a dipolarization was seen to propagate more than 4 h in local time during a 20 min period. The azimuthal phase speed of this dipolarization (2 deg/min) is too small to explain the azimuthal motion of the aurora (13.6 deg/min), but the dipolarization could be related to the generation of the Alfvenic Poynting flux through phase mixing at strong density gradients like those in the PSBL.

58 GEOSCIENCES↗
DOE OSTI2019

EFCOG, IOSC Best Practices Guide

Members of the Energy Facility Contractors Group (EFCOG), Safeguards and Security Working Group, Information Security Sub-Working Group Incidents of Security Concern (IOSC) Team prepared this best practice guide. For the purposes of this guide, “best practices” are “positive examples of work processes, procedures, good ideas, or effective solutions. The team made up of IOSC Subject Matter Experts (SME) identified these best practices as a result of actual operational experience and training. The guide describes best practices for categorizing incidents and managing inquiries. These practices may serve as guidelines for developing program plans, policy and procedures. These practices are suggestions for Department sites to consider while working in the IOSC subject area. The authors acknowledge that there may be alternatives to the practices identified in this guide. Subject matter Experts from the IOSC program across the Department developed this guide. Extensive discussion and document reviews were conducted to identify best practices relating to security inquiries. It was not within the scope of this study to assess individual site performance or evaluate compliance. Department of Energy’s (DOE), Order 470.4B, Chg.2, Safeguards and Security Program, July-21-2011, Chg 2, January-17-2017, Attachment 5, Incidents of Security Concern, contains the requirements for the IOSC program. In accordance with DOE directives and requirements established by DOE/National Nuclear Security Administration (NNSA) oversight, the working group evaluated elements of the IOSC program at each of the working group member’s sites such as categorizing events, conducting inquiries, and reporting events. Inconsistencies in categorization across the sites may be due to various factors, including each local field office having different reporting expectations, subjectivity in making determinations, and potential inherent deficiencies in the categorization tables and category descriptions. Many sites have adopted a sub reportable category for a security anomaly event that it believes does not meet the criteria as a reportable IOSC. The sites typically establish a local standardized process for reporting, analyzing, and trending sub-reportable events. Security incident program managers may need to discuss inconsistent categorization with other Inquiry Officials (IO) and suggest standardized solutions (ie…through policy changes or a forum attended by incident program managers and inquiry officials to discuss standardization solutions. At least one EFCOG IOSC team member’s site is currently using the best practices identified in this guide.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

Searches for violation of Lorentz invariance in top quark pair production using dilepton events in 13 TeV proton-proton collisions

A search for violation of Lorentz invariance in the production of top quark pairs ($t\bar{t}$) is presented. The measured normalized differential $t\bar{t}$production cross section, as a function of the sidereal time, is examined for potential modulations induced by Lorentz-invariance breaking operators in an effective field theory extension of the standard model (SM). The cross section is measured from collision events collected by the CMS detector at a center-of-mass-energy of 13 TeV, corresponding to an integrated luminosity of 77.8 fb −1 , and containing one electron and one muon. The results are found to be compatible with zero, in agreement with the SM, and are used to place upper limits at 68% confidence level on the magnitude of the Lorentz-violating couplings ranging from 1–8 × 10 −3 . This is the first precision test of the isotropy in special relativity with top quarks at the LHC, restricting further the bounds on such couplings by up to two orders of magnitude with respect to previous searches conducted at the Tevatron.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Global Simulation of Electron Cyclotron Harmonic Wave Instability in a Storm-Time Magnetosphere

Electron cyclotron harmonic (ECH) waves are electrostatic emissions between the ECHs and play a dominant role for precipitating energetic electrons in the magnetotail. Statistically, the ECH wave intensity is stronger at nightside and dawnside than at dayside and duskside. In this study, we, for the first time, simulate the global ECH wave evolution during a geomagnetic storm event using Ring current Atmosphere interactions Model with Self-Consistent Magnetic field (RAM-SCB) combined with a linear growth rate solver.We find that the simulation results are generally consistent with the statistical and real-time observations. The ECH wave instability is much stronger at nightside and dawnside, compared to the instability at dayside and duskside. Before a geomagnetic storm (quiet time), the unstable regions of the ECH waves lie beyond $5R_E$ with a weak instability level. During the main phase of a geomagnetic storm, the unstable regions can extend to a lower altitude $(3R_E)$ with a strong instability level. Finally, during the recovery phase, the unstable regions return to $5$ ~ $6R_E$. We also find that the inner boundary of unstable ECH wave regions is coincident with the plasmapause location during the whole geomagnetic storm event.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Modeling assessment of storm surge in the Salish Sea

Here we present a modeling assessment of storm surge in the Salish Sea—a large and complex estuarine system in the Pacific Northwest that is exposed to extratropical cyclones. A method was developed to systematically identify storm surge events in the Salish Sea based on water level records at four representative tidal gauges in the Salish Sea. The maximum storm surge distribution in the Salish Sea was calculated based on model simulations of 34 major historical storm events from 1980 to 2016. The model simulations were validated by field observations of water level and velocity using a suite of model performance metrics. Model results suggest that the maximum storm surge in the Salish Sea is generally in the range of 0.8 m–1.03 m and is heterogeneous in space. The model results also show a strong north-south gradient with larger surge magnitudes occurring in the north that is primarily caused by the southerly wind in winter months. The sensitivity analysis of wind forcing suggested that storm surge in the Salish Sea is dominated by the remote surge from the Pacific Ocean, and the local wind contributes up to approximately 20% of the surge variability. This paper systematically elucidates storm surge characteristics and potential risk in the Salish Sea.

54 ENVIRONMENTAL SCIENCES↗