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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Impact of High Energy νe (ν¯e) Events on NOvA Oscillation Sensitivities

NOvA is a two detector, long-baseline neutrino oscillation experiment located at Fermilab, Batavia, IL, USA. It is primarily designed to constraint neutrino oscillation parameters using muon neutrino (anti-neutrino) disappearance data and electron neutrino (anti-neutrino) appearance data. NOvA detects neutrinos from Fermilab’s Neutrinos at Main Injector (NuMI) beam-line. The un-oscillated muon neutrino and beam $\nu_e$ events are observed by the NOvA Near Detector (ND), which is 100m underground and at a distance of 1km from the beam source. The Far Detector (FD), situated 809 km away from the ND, is in Ash River, MN, USA, and observes $\nu_\mu$ and $\nu_e$ events after oscillations. Traditionally, NOvA has used $\nu_e$ events in the energy range 1 < E < 4 GeV for 3-flavor neutrino oscillation analyses to constraint the neutrino oscillation parameters. In this study we looked at the impact of including high-energy neutrino events with energies up to 12 GeV in the analysis with the aim of constraining the beam electron neutrino/antineutrino background events. Event count predictions after adding this high-energy sample events and latest three flavor oscillation results of the NOvA experiment are presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2018

Single Event Effect Testing of the Analog Devices ADXL354 3-Axis MEMS Accelerometer

The Analog Devices ADXL354 3-AXIS MEMS accelerometer was tested for single-event effects, both destructive and non-destructive. The device was characterized for single-event upset (SEU) sensitivity and evaluated for any possibility of single-event latchup (SEL), single-event dielectric rupture (SEDR) and single-event functional interrupts (SEFI). Single-event transient (SET) response was also monitored.

Single-Event Effect (SEE)↗
DOE OSTI2024

DECA: Discrete Event inspired Cellular Automata for grain structure prediction in additive manufacturing

Microstructure largely dictates macroscopic material properties and is strongly affected by processing. Therefore, the simulation of microstructure evolution in response to thermal fields during processing is of significant interest within the computational materials science community. Additive manufacturing (AM) has emerged as a technique for producing complex geometries and unique microstructures. Yet, complex and rapid thermal cycles in AM pose computational challenges for existing microstructure models. This work proposes a discrete event inspired cellular automata (CA) approach, titled DECA, to accelerate simulation of grain structure evolution in AM. In contrast to conventional time-stepped CA models, this model directly solves the times capture events would take place allowing for stepping in events rather than time (a technique also found in the field of discrete-event simulation). In comparison to purely serial discrete-event models, DECA allows for temporary violation of the causality constraint, but detects and corrects these violations, leading to an emergent phenomenon dubbed causality rippling, in which previously calculated capture events are overwritten. The amount of repeated calculations, defined by the capture ratio, is taken as a measure of computational inefficiency, and the model parameters that affect this ratio are evaluated. The new DECA approach was found to be more computationally efficient than conventional time-stepped CA models while guaranteeing an accurate solution, which can only be achieved in the conventional models for vanishingly small time steps. Finally, opportunities for parallelization and scaling of the new approach are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Evaluating precipitation, streamflow, and inundation forecasting skills during extreme weather events: A case study for an urban watershed

Integrated forecasting systems for precipitation, streamflow, and floodplain inundation are of critical importance in mitigating the impacts of destructive floods caused by extreme weather events. However, the skills of streamflow and floodplain inundation forecasts derived from various Quantitative Precipitation Forecasts (QPF) require a greater level of understanding. In this paper, a set of QPF developed by the National Weather Service (NWS) were used to drive a flood modeling system obtained utilizing offline coupling of a physics-based distributed hydrological model, the Distributed Hydrology Soil and Vegetation Model (DHSVM), and a hydrodynamic model, the Two-dimensional Runoff Inundation Toolkit for Operational Needs (TRITON). This flood modeling system was used to produce forecasts of streamflow and floodplain inundation maps during three major flood events in the Brays Bayou Watershed (Houston, Texas, USA) for a range of QPF durations (6–72 h). Then, to investigate the effects of increasing QPF durations on the forecasts, the forecasting skills of precipitation, streamflow, and floodplain inundation were quantified. The results show that: 1) QPF skills for more intense and sustained events such as hurricanes and tropical storms are higher than for shorter, less intense events; 2) while QPF and streamflow forecasting skills decrease as QPF durations increase, inundation forecasts under longer QPF durations (24 or 72 h) show higher skills; 3) extending the maximum QPF duration in operational hydrologic modeling from 24 h (under normal circumstances) to 72 h (for extreme events) may increase the skills of long lead time forecasts for large-scale events like Hurricane Harvey.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Increased frequency of planetary wave resonance events over the past half-century

We demonstrate a tripling in the frequency of planetary wave resonance events over the past halfcentury, coinciding with the rise in persistent boreal summer weather extremes. This increase aligns with changes in the underlying climate conditions favoring these events, including amplified Arctic warming and land-sea thermal contrast. We also observe increased prevalence of resonant amplification events following the mature phase of strong El Niño events, suggesting that such events may precondition the mean state conditions in ways that favor large-scale quasi-stationary wave patterns and quasi-resonant wave amplification. Since the impact of anthropogenic warming on quasi-resonant amplification is not well captured by current-generation climate models, it is likely that models are underpredicting the potential increase, indicating even greater risk of persistent extreme summer weather events with ongoing warming.

Arctic amplification↗
DOE OSTI2025

A Twin Circuit Theory-Based Framework for Oscillation Event Analysis in Inverter-Dominated Power Systems With Case Study for Kaua‘i System

Here, this paper proposes a real-world oscillation event analysis framework for power systems that include inverter-based resources together with synchronous generators. Specifically, the proposed framework combines both measurement-and model-based techniques to readily identify potential oscillation sources, replay the oscillation event with numerical simulation, unveil the underlying oscillation mechanism, and suggest mitigation methods for a wide range of oscillation events. To strengthen the theoretical foundation of our analysis framework, this paper proposes a twin circuit theory that provides theoretical support for one key utilized but not well-proven measurement-based oscillation source identification method-Dissipating Energy Flow. Our twin circuit theory also shows that adopting well-tuned grid-forming inverters can be a potential mitigation method for oscillation events. Finally, the effectiveness of our proposed oscillation event analysis framework is demonstrated by addressing a real-world 18-20 Hz oscillation event in Kaua‘i's power system on November 21, 2021.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Hierarchical Convolutional Neural Networks for Event Classification on PMU Measurements

Event classification is one of the central components of automated disturbance analysis based on PMU measurements. Obtaining high-quality event labels remains a challenge for supervised learning-based classification of local and system-wide events in power grids due to its labor-intensive requirement. We present a sensitivity study considering rapidly refined, partially and fully inspected event labels that leads to evidence that hierarchical convolutional neural networks (HCNNs) outperform traditional classification models regardless of the quality of the available event labels. Furthermore, it is demonstrated that performance similar to the one obtained using entirely domain-driven labeling can be achieved as long as the involved expert does not mislabel more than ~5% of the event data captured by PMU measurements.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Learning Latent Interactions for Event Identification via Graph Neural Networks and PMU Data

Phasor measurement units (PMUs) are being widely installed on power systems, providing a unique opportunity to enhance wide-area situational awareness. One essential application is the use of PMU data for real-time event identification. However, how to take full advantage of all PMU data in event identification is still an open problem. Thus, we propose a novel method that performs event identification by mining interaction graphs among different PMUs. The proposed interaction graph inference method follows an entirely data-driven manner without knowing the physical topology. Moreover, unlike previous works that treat interactive learning and event identification as two different stages, our method learns interactions jointly with the identification task, thereby improving the accuracy of graph learning and ensuring seamless integration between the two stages. Moreover, to capture multi-scale event patterns, a dilated inception-based method is investigated to perform feature extraction of PMU data. To test the proposed data-driven approach, a large real-world dataset from tens of PMU sources and the corresponding event logs have been utilized in this work. We report numerical results validate that our method has higher classification accuracy compared to previous methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Data-Driven Event Detection of Power Systems Based on Unequal-Interval Reduction of PMU Data and Local Outlier Factor

With the deployment of phasor measurement units (PMU) and wide area measurement system (WAMS), it is feasible to have an insight into the events occurred in power systems based on measured data. Thus, a novel data-driven algorithm based on local outlier factor (LOF) is proposed in this work to detect and locate events in power systems using reduced PMU data. First, the unequal-interval reduction method is presented to reduce the scale of PMU data in sub-stations and reconstruct it in master station of WAMS, which can relieve the burden of communication systems. Then, principle component analysis (PCA)-based similarity search method is proposed to measure the differences of operation state between any two buses. Next, LOF is presented to detect the abnormal events in power systems, and employed to determine the region of the event source. Finally, six cases from the Western electricity coordinating council (WECC) 179-bus power system, a case from the South China power system (SCPS), and a case from the Guangdong power system (GDPS) are utilized to demonstrate the effectiveness of the proposed algorithm. Overall, the results show that proposed algorithm is effective and can be applied to event detection, event location, and online monitoring, which can enhance the situation awareness ability of power system operators.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Improvements to Contributions from Neutron Inelastic Scattering for Next-Event Estimators in MCNP ® Software

An improvement to the handling of contributions from neutron inelastic scattering to next-event estimators has been implemented in the MCNP6 ® software for release with version 6.3.1. The kinematic equations that govern the outgoing energy of inelastic neutron scattering contributions to next-event estimators have two roots. Historically, the implementation in the MCNP coding has only used the upper root to the quadratic equation. This includes all versions predating version 6.3.1 including all versions of MCNP5 and MCNPX software. However, a review of the neutron next-event estimator physics has shown that this does not reproduce the track-length estimator results at low energies. Several examples are presented that test single neutron inelastic scattering reaction types, Level Scattering (Law 3), Tabulated Energy Angle (Law 61), and Kalbach-Mann distribution (Law 44). The test problems compare the track-length estimator (f4 tally) in the MCNP software, with the existing implementation of the neutron next-event estimator (f5 tally), and the modified changes to the neutron next-event estimator implementation. As the MCNP implementation ignores the lower root, the unmodified neutron next-event estimators will generally underestimate the lower energy contribution. However, a second issue with the Kalbach-Mann distribution (Law 44) implementation allows contributions to backward scattering in the center-of-mass frame that is not kinematically possible, thus overestimating backward scattering contributions. A third issue with the way the MCNP implementation handles floating point comparison for scattering directly ahead or directly backward in the center-of-mass frame generally leads to underestimation (except for backwards scattering for Law 44).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2001

Analysis and visualization of single-trial event-related potentials

In this study, a linear decomposition technique, independent component analysis (ICA), is applied to single-trial multichannel EEG data from event-related potential (ERP) experiments. Spatial filters derived by ICA blindly separate the input data into a sum of temporally independent and spatially fixed components arising from distinct or overlapping brain or extra-brain sources. Both the data and their decomposition are displayed using a new visualization tool, the "ERP image," that can clearly characterize single-trial variations in the amplitudes and latencies of evoked responses, particularly when sorted by a relevant behavioral or physiological variable. These tools were used to analyze data from a visual selective attention experiment on 28 control subjects plus 22 neurological patients whose EEG records were heavily contaminated with blink and other eye-movement artifacts. Results show that ICA can separate artifactual, stimulus-locked, response-locked, and non-event-related background EEG activities into separate components, a taxonomy not obtained from conventional signal averaging approaches. This method allows: (1) removal of pervasive artifacts of all types from single-trial EEG records, (2) identification and segregation of stimulus- and response-locked EEG components, (3) examination of differences in single-trial responses, and (4) separation of temporally distinct but spatially overlapping EEG oscillatory activities with distinct relationships to task events. The proposed methods also allow the interaction between ERPs and the ongoing EEG to be investigated directly. We studied the between-subject component stability of ICA decomposition of single-trial EEG epochs by clustering components with similar scalp maps and activation power spectra. Components accounting for blinks, eye movements, temporal muscle activity, event-related potentials, and event-modulated alpha activities were largely replicated across subjects. Applying ICA and ERP image visualization to the analysis of sets of single trials from event-related EEG (or MEG) experiments can increase the information available from ERP (or ERF) data. Copyright 2001 Wiley-Liss, Inc.

Non-NASA Center↗
NASA NTRS2015

Compendium of Current Single Event Effects for Candidate Spacecraft Electronics for NASA

NASA spacecraft are subjected to a harsh space environment that includes exposure to various types of ionizing radiation. The performance of electronic devices in a space radiation environment are often limited by their susceptibility to single event effects (SEE). Ground-based testing is used to evaluate candidate spacecraft electronics to determine risk to spaceflight applications. Interpreting the results of radiation testing of complex devices is and adequate understanding of the test condition is critical. Studies discussed herein were undertaken to establish the application-specific sensitivities of candidate spacecraft and emerging electronic devices to single-event upset (SEU), single-event latchup (SEL), single-event gate rupture (SEGR), single-event burnout (SEB), and single-event transient (SET). For total ionizing dose (TID) and displacement damage dose (DDD) results, see a companion paper submitted to the 2015 Institute of Electrical and Electronics Engineers (IEEE) Nuclear and Space Radiation Effects Conference (NSREC) Radiation Effects Data Workshop (REDW) entitled "compendium of Current Total Ionizing Dose and Displacement Damage for Candidate Spacecraft Electronics for NASA by M. Campola, et al.

spacecraft electronics↗
DOE OSTI2023

Production of $ {\textrm{K}}_{\textrm{S}}^0 $, Λ ($ \overline{\Lambda}$), Ξ ± , and Ω ± in jets and in the underlying event in pp and p–Pb collisions

The production of strange hadrons ($K^{0}_{S}$, Λ, Ξ ± , and Ω ± ), baryon-to-meson ratios (Λ/$K^{0}_{S}$, Ξ/$K^{0}_{S}$, and Ω/$K^{0}_{S}$), and baryon-to-baryon ratios (Ξ/Λ, Ω/Λ, and Ω/Ξ) associated with jets and the underlying event were measured as a function of transverse momentum (p T ) in pp collisions at $\sqrt{s}$ = 13 TeV and p Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV with the ALICE detector at the LHC. The inclusive production of the same particle species and the corresponding ratios are also reported. The production of multi-strange hadrons, Ξ ± and Ω ± , and their associated particle ratios in jets and in the underlying event are measured for the first time. In both pp and p–Pb collisions, the baryon-to-meson and baryon-to-baryon yield ratios measured in jets differ from the inclusive particle production for low and intermediate hadron p T (0.6–6 GeV/c). Ratios measured in the underlying event are in turn similar to those measured for inclusive particle production. In pp collisions, the particle production in jets is compared with P YTHIA 8 predictions with three colour-reconnection implementation modes. None of them fully reproduces the data in the measured hadron p T region. The maximum deviation is observed for Ξ ± and Ω ± which reaches a factor of about six. The event multiplicity dependence is further investigated in p–Pb collisions. In contrast to what is observed in the underlying event, there is no significant event-multiplicity dependence for particle production in jets. The presented measurements provide novel constraints on hadronisation and its Monte Carlo description. In particular, they demonstrate that the fragmentation of jets alone is insufficient to describe the strange and multi-strange particle production in hadronic collisions at LHC energies.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

A study of events with photoelectric emission in the DarkSide-50 liquid argon Time Projection Chamber

Finding unequivocal evidence of dark matter interactions in a particle detector is a major goal of research in physics. Liquid argon time projection chambers offer a path to probe Weakly Interacting Massive Particles scattering cross sections on nuclei down to the so-called neutrino floor, in a mass range from a few GeV to hundreds of TeV. Based on the successful operation of the DarkSide-50 detector at LNGS, a new and more sensitive experiment, DarkSide-20k, has been designed and is now under construction. A thorough understanding of the DarkSide-50 detector response and, therefore, of all types of events observed in the detector, is essential for the optimal design of the new experiment. In this article, we report on a specific set of events, namely, standard two-pulse scintillation–ionization signals with a third small amplitude pulse, occurring within the 440 μ s data acquisition window of standard events. Some of these events are due to the photoionization of the TPC cathode. We compare our results with those published by collaborations using liquid xenon time projection chambers, which observed a similar phenomenon, and, in particular, with a recent paper by the LUX Collaboration (D.S. Akerib et al. Phys.Rev.D 102, 092004 (2020)) From the measured rate of these events, we estimate for the first time the quantum efficiency of the tetraphenyl butadiene deposited on the DarkSide-50 cathode at wavelengths of around 128 nm, in liquid argon. Also, both experiments observe events likely related to the photoionization of impurities in the liquid. The probability of photoelectron emission per unit length turns out to be an order of magnitude lower in DarkSide-50 than in LUX.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Unsupervised learning for identifying events in active target experiments

This article presents novel applications of unsupervised machine learning methods to the problem of event separation in an active target detector, the Active-Target Time Projection Chamber (AT-TPC). The overarching goal is to group similar events in the early stages of the data analysis, thereby improving efficiency by limiting the computationally expensive processing of unnecessary events. The application of unsupervised clustering algorithms to the analysis of two-dimensional projections of particle tracks from a resonant proton scattering experiment on 46 Ar is introduced. We explore the performance of autoencoder neural networks and a pre-trained VGG16 Simonyan and Zisserman (2015) convolutional neural network. We study clustering performance on both data from a simulated 46 Ar experiment, and real events from the AT-TPC detector. We find that a -means algorithm applied to simulated data in the VGG16 latent space forms almost perfect clusters. Additionally, the VGG16+-means approach finds high purity clusters of proton events for real experimental data. Here, we also explore the application of clustering the latent space of autoencoder neural networks for event separation. While these networks show strong performance, they suffer from high variability in their results.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Clash of the titans: ultra-high energy KM3NeT event versus IceCube data

KM3NeT has reported the detection of a remarkably high-energy through-going muon. Lighting up about a third of the detector, this muon likely originated from a neutrino exceeding 10 PeV in energy. The crucial question we need to answer is where this event comes from and what its source is. Intriguingly, IceCube has been operating with a much larger effective area for a considerably longer time, yet it has not reported neutrinos above 10 PeV. We quantify the tension between the KM3NeT event and the absence of similar high-energy events in IceCube under various assumptions of the origin for the neutrino including the isotropic diffuse flux, cosmogenic flux, and a steady or transient point source. Through a detailed analysis, we determine the most likely neutrino energy to be in the range of 23 – 2400 PeV, and find a tension between the experiments ranging from 3.6 to 3.1 σ for diffuse origins and 2.9 to 2.0 σ for point sources. The lack of observation of high-energy events in IceCube seriously challenges the explanation of this event coming from any known diffuse fluxes. Our results indicate the KM3NeT event is likely the first observation of a new astrophysical source.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Multiple Sources of Riparian Wetland Suspended Solids during Episodic Rain Events: Influence on Uranium Transport

Suspended solids can be the primary vector for transporting contaminants in streams. The objective of this study was to determine whether changes in the properties of suspended solids during rain events impacted contaminant transport. Stream water was collected during five episodic events downstream from a U-contaminated wetland located in South Carolina, USA. The suspended particles were initially composed of Fe-flocs (particles formed in situ prior to the rain event) that had significantly greater Fe, Mn, organic-C, and U content than particles collected later during a sampling rain event. XANES and EXAFS revealed that U in the Fe-flocs was U(VI) and that it was not incorporated in a mineral structure but existed as inner- or outer-sphere adsorbed uranyl species associated with organic matter and Fe-oxides. The uranyl had an extraordinarily high affinity for the suspended solids, with solid to liquid U ratios of >72,000 (μg/kg)/(μg/L). After the initial flush of Fe-flocs, a greater fraction of the suspended solids had lower organic-C, Fe, Mn, and amorphous phases and were composed of more quartz, kaolinite, and gibbsite, resulting in lower U concentrations than those in the solids collected earlier in the rain event. This study highlights the importance of understanding suspended solids as transport vectors and their potential dynamic nature during rain events.

computer simulations↗
DOE OSTI2022

Event‐Based Training in Label‐Limited Regimes

Abstract The distribution of attributes assigned using data on independent sensors for a specific source, for example, magnitude, can be richly descriptive for final event characterization and associated uncertainty. Attribute distributions can also provide powerful context for event characterization in the absence of comprehensive annotation. This work develops a way to leverage distributional information across a set of sensors in the absence of comprehensive annotation as a domain‐informed regularization term applied during gradient‐based learning. The regularization term is the basis of event‐based training which I show can be a powerful semi‐supervised learning (SSL) approach. I first use a simple feed forward neural network and a toy data set to outline how data set structure interacts with the assumptions inherent to many semi‐supervised learning approaches. I then demonstrate the effectiveness of event‐based training using a deep convolutional neural network for seismic event classification in Utah, which increases SSL accuracy from 92% to 97% on event classification with a limited number of training labels.

Linville, Lisa M.↗