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NASA NTRS2010

Coordinating Multiple Spacecraft Assets for Joint Science Campaigns

This paper describes technology to support a new paradigm of space science campaigns. These campaigns enable opportunistic science observations to be autonomously coordinated between multiple spacecraft. Coordinated spacecraft can consist of multiple orbiters, landers, rovers, or other in-situ vehicles (such as an aerobot). In this paradigm, opportunistic science detections can be cued by any of these assets where additional spacecraft are requested to take further observations characterizing the identified event or surface feature. Such coordination will enable a number of science campaigns not possible with present spacecraft technology. Examples from Mars include enabling rapid data collection from multiple craft on dynamic events such as new Mars dark slope streaks, dust-devils or trace gases. Technology to support the identification of opportunistic science events and/or the re-tasking of a spacecraft to take new measurements of the event is already in place on several individual missions such as the Mars Exploration Rover (MER) Mission and the Earth Observing One (EO1) Mission. This technology includes onboard data analysis techniques as well as capabilities for planning and scheduling. This paper describes how these techniques can be cue and coordinate multiple spacecraft in observing the same science event from their different vantage points.

automated commanding↗

NASA NTRS2001

Evaporation/SST Sensitivity Over the Tropical Oceans During ENSO Events as Estimated from the da Silva, Young, Levitus Surface Marine Data Set

The da Silva, Young and Levitus Surface Marine Atlas, based on observations from the Comprehensive Ocean Atmosphere Data Set (COADS) Release 1, has been used to investigate the relationship between evaporation and sea-surface temperature (SST) over the global oceans. For the period 1950 to 1987 SST, surface latent heat flux, and other related variables have been filtered to minimize data uncertainties and to focus upon interannual variations associated with warm (El Nino) and cold (La Nina) ENSO events. Compositing procedures have enabled identification of systematic variations in latent heat fluxes accompanying these events and the relationship to spatial anomalies in ocean surface wind speed and humidity. The evaporation response associated with ENSO sea surface temperature (SST) variability is systematic in nature and composed of offsetting contributions from the surface wind and humidity variations. During warm events exceeding 1.0 S.D. delta SST, increases in the surface humidity deficit, delta(qs-qa), between the surface and 2m height dominate regions of positive SST anomalies and lead to increases in evaporation of almost 2 Wm (exp -2) at deltaSST = 0.23 K. Despite the increases in specific humidity, relative humidity decreases slightly in regions of elevated SSTs. For the most part, variations in wind speed are consistent with previous investigations. Weakening of the equatorial easterlies (and generation of westerlies) between 160 degrees E and 140 degrees W dominates during the early phases of warm events. Elevated wind speeds in adjacent subtropical regions and in the eastern equatorial Pacific subsequently develop too. The net contribution of these winds, which reflect adjustments in Hadley and Walker circulation components is toward reduced evaporation. Results for cold periods are approximately similar, but opposite in sign to warm events, though evidence of different temporal evolution is noted.

Robertson, F. R.↗

NASA NTRS1994

Quantitative numerical modeling of a coronal mass ejection

The results of an investigation on how the initial state (pre-event corona) quantitatively affects the magnetohydrodynamic model output of a coronal mass ejection (CME) are presented. The extension of the study on the identification of pre-event streamer properties which are crucial in determining the observed characteristics of CMEs, to more realistic configurations, also taking into account the coronal hole regions, adjacent to quiet coronal streamers, whose physical conditions are not adequately reproduced by global coronal models, is addressed. Results from the present model show three distinct types of CME's (loop, plasmoid and spike) which correspond to the classes of CME's identified in observations made with white light coronographs. The aim is a better understanding of the intercorrelations between streamers and adjacent coronal hole properties.

Wang, A. H.↗

DOE OSTI2022

Inferring safety critical events from vehicle kinematics in naturalistic driving environment: Application of deep learning Algorithms

Advances in sensing technology has enabled the collection of countless terabytes of second-by-second kinematics data. Such data provides opportunities for real-time monitoring of driving behavior and identification of safety critical events (SCEs) including crashes and near crashes. The concept of volatility is relevant in this context, which identifies instability and erratic variations in driving behavior prior to involvement in SCEs. This study utilized vehicle kinematics from a large-scale naturalistic driving data to develop a deep learning approach based on 1D convolutional neural networks (CNN) for inferring SCEs. The data are unique in the sense that such accurate pre-crash data at high fidelity are not available in traditional crash repositories. This study contributes to the literature by providing a first attempt at predicting responses to SCEs by developing deep learning-based CNN architectures using novel driving volatility based kinematic thresholds for a sample of 9553 events. The key contribution lies in developing a volatility-based CNN input layout that is acceptable to CNN schemes and represents the motion kinematics such as speed, acceleration and volatility measures. Several 1D-CNN architectures were developed using layers, numbers of convolutions, layer patterns, and kernels. Shallow and deep architectures were tested, revealing higher accuracy of shallow architectures in detecting SCEs. The optimal number of epochs were identified using an early stopping method while the CNN performance was improved by increasing the number of epochs. The ensemble CNN had the highest predictive accuracy of 95.6% for detection of crashes and near crashes, which was 2.5% higher than the optimal CNN using 20% hold out test data. The ensemble CNN also outperformed classical machine learning models and model performance reported in past studies on detection of SCEs. Finally, these results have implications for identification of safety hotspots and providing real-time alerts and warnings in connected and highly automated vehicle environment including society of automotive engineers levels 3–5.

42 ENGINEERING↗

DOE OSTI2020

Enhanced dynamic equivalent identification method of large-scale power systems using multiple events

The increasing complexity of the interconnected power system makes high-fidelity dynamic simulation models computationally more intensive. To improve computation efficiency, model reduction techniques have been investigated to only preserve the dynamics in a limited area of interest (study area), while deriving equivalent representation for the external area. For this purpose, a measurement-based reduction approach using system identification techniques has been previously proposed. Accordingly, external areas are represented by dynamic equivalent loads using transfer function estimation. In this paper, in order to enhance the accuracy of the reduced model in preserving dynamics of the study area, multiple “grid” events of different types and at different locations are considered for identifying the parameters of the equivalent loads. Additionally, case studies are carried out in the NPCC 140-bus system. Comparisons are made between multiple events training and single event training, highlighting the advantages of the proposed method in providing a better representation of the grid dynamics under different operating conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Identification of Worst Impact Zones for Power Grids During Extreme Weather Events Using Q-Learning: Preprint

Both the frequency and intensity of extreme weather events have been trending higher in recent years, leading to significant infrastructure loss in the electric grid. The impact of these extreme weather events is desired to be analyzed and quantified in order to help transmission and distribution system operators to prepare and prevent significant losses. In this paper, we developed an approach that models the impact of extreme weather on the power grid and identifies the worst impact zone using Q-learning (a reinforcement learning approach). The identification results reveal grid vulnerability to weather events and provide insights for system operators to help achieve optimal resource allocation and crew dispatch in order to minimize the adverse impact of extreme weather. Simulation studies are conducted on the IEEE 123-node system to demonstrate the performance of the proposed approach.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Identification of Worst Impact Zones for Power Grids During Extreme Weather Events Using Q-learning

Both the frequency and intensity of extreme weather events have been trending higher in recent years, leading to significant infrastructure damage in the electric grid. The impact of these extreme weather events is desired to be analyzed and quantified to help transmission and distribution system operators prepare for and prevent significant damage and subsequent loss of power. In this paper, we develop an approach that models the impact of extreme weather on the grid and identifies the worst impact zone using Q-learning (a reinforcement learning approach). The identification results reveal grid vulnerability to weather events and provide insights for system operators to help achieve optimal resource allocation and crew dispatch to minimize the adverse impacts of extreme weather. Simulation studies are conducted on the IEEE 123-node system to demonstrate the performance of the proposed approach.

distribution system↗

DOE OSTI2021

Identification of Worst Impact Zones for Power Grids During Extreme Weather Events Using Q-Learning

extreme weather↗

DOE OSTI2020

Enhanced Dynamic Equivalent Identification Method of Large-Scale Power Systems Using Multiple Event

The increasing complexity of the interconnected power system makes high-fidelity dynamic simulation models computationally more intensive. To improve computation efficiency, model reduction techniques have been investigated to only preserve the dynamics in a limited area of interest (study area), while deriving equivalent representation for the external area. For this purpose, a measurement-based reduction approach using system identification techniques has been previously proposed. In that case, external areas are represented by dynamic equivalent loads using transfer function estimation. In this paper, in order to enhance the accuracy of the reduced model in preserving dynamics of the study area, multiple “grid” events of different types and at different locations are considered for identifying the parameters of the equivalent loads. Case studies are carried out in the NPCC 140-bus system. Comparison are made between multiple events training and single event training, highlighting the advantages of the proposed method in providing a better representation of the grid dynamics under different operating conditions.

Xue, Yaosuo↗

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↗

NASA NTRS2015

Mass Wasting In Planetary Environments: Implications For Seismicity

On Earth, mass wasting events such as rock falls and landslides are well known conse-quences of seismic activity. Through a variety of re-mote sensing techniques, tectonic faults have been pos-itively identified on all four of the inner planets, Earth's Moon, several outer planet satellites, and aster-oids. High-resolution imaging has furthermore ena-bled positive identification of mass wasting events on many of these bodies. On Mars, it has been suggested that fallen boulders may be indicative of pale-omarsquakes. On the Moon, meteor impacts and moonquakes have likewise been suggested as potential triggering mechanisms for mass wasting. Indeed, we know from the Apollo era that the Moon experienc-es a wide variety of seismicity. Seismicity estimates play an important role in creat-ing regional geological characterizations, which are useful not only for understanding a planet's formation and evolution, but also of key importance to site selec-tion for landed missions. Here we investigate the re-gional effects of seismicity in planetary environments with the goal of determining whether surface features such as landslides and boulder trails on the Moon, Mars, and Mercury could be triggered by fault motion. We attempt to quantify the amount of near-source ground shaking necessary to mobilize the mate-rial observed in various instances of mass wasting.

seismicity↗

NASA NTRS1974

A catalogue of solar cosmic ray events: IMPS 4 and 5, May 1967 - December 1972

This catalogue of solar cosmic ray events has been prepared for the use of solar physicists and other interested scientists. It contains some 185 solar particle events detected by the Goddard Space Flight Center Cosmic Ray Experiments on IMP's IV and V (Explorer 34 and 41) for the period May 1967 - December 1972. The data is presented in the form of hourly averages for three proton energy intervals - 0.9 - 1.6 MeV; 6 - 20 MeV and 20 - 80 MeV. In addition the time histories of .5 - 1.1 MeV electrons are shown on a separate scale. To assist in the identification of related solar events, the onset time of the electron event is indicated. The details of the instrumentation and detector techniques are described. Further descriptions of data reduction procedure and on the time-history plots are given.

Vanhollebeke, M. A.↗

DOE OSTI2020

Lowering the Energy Threshold of the CUORE Experiment: Benefits in the Surface Alpha Events Reconstruction

CUORE is a tonne-scale cryogenic experiment located at the Laboratori Nazionali del Gran Sasso that exploits bolometric technique to search for neutrinoless double beta decay of 130 Te. Thanks to its very low background and large mass, CUORE is also a powerful tool to study a broad class of phenomena, such as solar axions and WIMP scattering. The ability to conduct such sensitive searches crucially depends on the energy threshold, which has to be kept as low as possible. Here, we show how the trigger algorithm affects the sensitivity to low-energy phenomena and the interpretation of the energy spectrum. In particular, we focus on the impact that the trigger algorithm has on the identification of the coincidence events among different crystals and, consequently, on the reconstruction of the background.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Identifying and tracking bubbles and drops in simulations: A toolbox for obtaining sizes, lineages, and breakup and coalescence statistics

Knowledge of bubble and drop size distributions in two-phase flows is important for characterizing a wide range of phenomena, including combustor ignition, sonar communication, and cloud formation. The physical mechanisms driving the background flow also drive the time evolution of these distributions. Accurate and robust identification and tracking algorithms for the dispersed phase are necessary to reliably measure this evolution and thereby quantify the underlying mechanisms in interface-resolving flow simulations. The identification of individual bubbles and drops traditionally relies on an algorithm used to identify connected regions. This traditional algorithm can be sensitive to the presence of spurious structures. A cost-effective refinement is proposed to maximize volume accuracy while minimizing the identification of spurious bubbles and drops. An accurate identification scheme is crucial for distinguishing bubble and drop pairs with large size ratios. The identified bubbles and drops need to be tracked in time to obtain breakup and coalescence statistics that characterize the evolution of the size distribution, including breakup and coalescence frequencies, and the probability distributions of parent and child bubble and drop sizes. An algorithm based on mass conservation is proposed to construct bubble and drop lineages using simulation snapshots that are not necessarily from consecutive time steps. These lineages are then used to detect breakup and coalescence events, and obtain the desired statistics. Accurate identification of large-size-ratio bubble and drop pairs enables accurate detection of breakup and coalescence events over a large size range. Accurate detection of successive breakup and coalescence events requires that the snapshot interval be an order of magnitude smaller than the characteristic breakup and coalescence times to capture these successive events while minimizing the identification of repeated confounding events. Together, these algorithms serve as a toolbox for detailed analysis of two-phase simulations, and enable insights into the mechanisms behind bubble and drop formation and evolution in flows of practical importance.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2026

SRF cavity instability detection with machine learning at CEBAF

During the operation of the Continuous Electron Beam Accelerator Facility (CEBAF), one or more unstable superconducting radio-frequency (SRF) cavities often cause beam loss trips while the unstable cavities themselves do not necessarily trip off. The present RF controls for the legacy cavities report at only 1 Hz, which is too slow to detectfast transient instabilities during these trip events. These challenges make the identification of an unstable cavity out of the hundreds installed at CEBAF a difficult and time-consuming task. To tackle these issues, a fast data acquisition system (DAQ) for the legacy SRF cavities has been developed, which records the sample at 5 kHz. An unsupervised learning framework has been developed to identify anomalous SRF cavity behavior. We will discuss the present status of the DAQ system and our framework, along with recent successes in detecting anomalous cavity behavior. Overall, our method offers a practical solution for identifying unstable SRF cavities, contributing to increased beam availability and machine reliability.

Accelerator Physics↗

DOE OSTI2022

The scintillation light detection system of ICARUS-T600: Hardware implementation and early results

The ICARUS-T600 Liquid Argon (LAr) Time Projection Chamber (TPC) is taking data with the Fermilab Booster Neutrino Beam-line (BNB) in the Short Baseline Neutrino (SBN) program to search for a possible LSND-like sterile neutrino signal. A light detection system, based on 360 Hamamatsu R5912-MOD Photo-Multiplier Tubes (PMTs) deployed behind the TPC wire chambers, has been realized to detect vacuum ultraviolet (VUV) photons produced by ionizing particles in LAr. This system is fundamental for the detector operation, providing an efficient trigger and contributing to the 3D reconstruction of events. Moreover, since the TPC is exposed to a huge flux of cosmic rays due to its shallow depths operations, the light detection system allows for the time reconstruction of events, contributing to the identification and to the selection of neutrino interactions within the beam spill gates.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

DeFault: DEep‐Learning‐Based FAULT Delineation Using the IBDP Passive Seismic Data at the Decatur CO2 Storage Site

Abstract The carbon capture, utilization, and storage (CCUS) framework is an essential component in reducing greenhouse gas emissions, with its success hinging on the comprehensive knowledge of subsurface geology and geomechanics. Passive seismic event relocation and fault detection offer vital insights into subsurface structures and the ability to monitor fluid migration pathways. Accurate identification and localization of seismic events, however, face significant challenges, including the necessity for high‐quality seismic data and advanced computational methods. To address these challenges, we introduce a novel deep learning method, , specifically designed for passive seismic source relocation and fault delineating for passive seismic monitoring projects. By leveraging data domain‐adaptation, allows us to train a neural network with labeled synthetic data and apply it directly to field data. Using , the passive seismic sources are automatically clustered based on their recording time and spatial locations, and subsequently, faults and fractures are delineated accordingly. We demonstrate the efficacy of on a field case study involving injection related microseismic data from Decatur, Illinois area. Our approach accurately and efficiently relocated passive seismic events, identified faults and could aid in potential damage induced by seismicity. Our results highlight the potential of as a valuable tool for passive seismic monitoring, emphasizing its role in ensuring CCUS project safety. This research bolsters the understanding of subsurface characterization in CCUS, illustrating machine learning’s capacity to refine these methods. Ultimately, our work has significant implications for CCUS technology deployment, an essential strategy in combating climate change. Plain Language Summary In our quest to tackle climate change, we use a strategy known as carbon capture, utilization, and storage (CCUS) to keep greenhouse gases out of the atmosphere. This strategy relies heavily on our ability to understand what's happening deep under the earth's surface. To make sure we store super critical safely, we need to accurately map out the geological structure, especially faults, but this is tough without high‐quality data and complex computer programs. We've developed a new tool called “DeFault,” which uses advanced machine learning to improve how we find and map these underground features. “DeFault” is smart enough to learn from numerically simulated data and then apply what it’s learned to real‐world situations. It groups together seismic activity—tiny tremors and shifts in the earth—based on when and where they happen, which helps us spot where there might be cracks or faults. We tested “DeFault” in Illinois, where CO 2 is injected underground, and it successfully pinpointed where these tremors occurred and mapped out the faults, helping to prevent accidents accurately in the future. Our study shows that “DeFault” will be a powerful ally in making CCUS safer and more effective, especially for the Illinois Basin Decatur Project. Key Points Faults and fractures introduced by carbon storage can be monitored by passive seismicity DeFault algorithm enables an automatic process for accurate and efficient passive seismic event locating and clustering

58 GEOSCIENCES↗

DOE OSTI2020

Design and implementation of the new scintillation light detection system of ICARUS T600

ICARUS T600 is the far detector of the Short Baseline Neutrino program at Fermilab (U.S.A.), which foresees three Liquid Argon Time Projection Chambers along the Booster Neutrino Beam line to search for LSND-like sterile neutrino signal. The T600 detector underwent a significant overhauling process at CERN, introducing new technological developments while maintaining the already achieved performances. The realization of a new liquid argon scintillation light detection system is a primary task of the detector overhaul. As the detector will be subject to a huge flux of cosmic rays, the light detection system should allow the 3D reconstruction of events contributing to the identification of neutrino interactions in the beam spill gate. The design and implementation of the new scintillation light detection system of ICARUS T600 is described.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗