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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Number of J/ψ events at BESIII*

Using inclusive decays of $J/\psi $, a precise determination of the number of $J/\psi $ events collected with the BESIII detector was performed. For the two data sets taken in 2009 and 2012, the numbers of $J/\psi $ events were recalculated to be $ (224.0 \pm 1.3)\times10^6 $ and $ (1088.5 \pm 4.4)\times10^6 $, respectively; these numbers are in good agreement with the previous measurements. For the $J/\psi $ sample taken in 2017–2019, the number of events was determined to be $ (8774.0 \pm 39.4)\times10^{6} $. The total number of $J/\psi $ events collected with the BESIII detector was determined to be $ (10087 \pm 44)\times10^{6} $, where the uncertainty is dominated by systematic effects, and the statistical uncertainty is negligible.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Adversarial Attacks on Deep Neural Network-based Power System Event Classification Models

Online event classification is essential to strengthening the reliability of the power transmission system. Recently, deep learning based methods have achieved great success in numerous domains such as computer vision and natural language processing. Researchers began to adopt deep learning based methods to solve the power system event identification problem and achieved effective results. However, these previous works do not consider that deep learning models are vulnerable to adversarial attacks, potentially influencing real-world applications' reliability. In this paper, we adopt several adversarial attack mechanisms by adding tailored noise signal to the input Phasor Measurement Units (PMU) time series and make the deep learning model misclassify the power system event. This numerical study discloses that current state-of-the-art deep learning based power system event classifiers are extremely vulnerable to adversarial attacks, which may jeopardize the reliability of the power transmission system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Rare Events via Cross-Entropy Population Monte Carlo

Rare events are events that happen with very low frequency. Estimating rare event probabilities using Monte Carlo techniques is computationally expensive, often to the point of intractability, and special methods are required. Importance sampling (IS) is a well known technique that uses a proposal distribution in place of a target distribution to lower the variance of the estimator. Key to the success of IS methods is the choice of a proposal distribution, or the parameters governing the distribution. Adaptive importance sampling improves the parameters of a family or population of proposal distributions iteratively through trials. We present a novel cross-entropy population Monte Carlo algorithm, which adapts the parameters of proposals through the cross-entropy method. The proposed method stands apart from previous work in that we are not optimizing a mixture distribution. Instead, we leverage deterministic mixture weights and optimize the distributions individually through a reinterpretation of the typical derivation of the cross-entropy method. Demonstrations on rare event examples show that the algorithm can outperform existing resampling based population Monte Carlo methods, especially for higher-dimensional problems. Finally, we also demonstrate efficacy on a conjunction analysis problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Event Detection and Classification Using Machine Learning Applied to PMU Data for the Western US Power System

Smart grid technology enhances our comprehension and reliability of the power grid, leveraging Phasor Measurement Unit (PMU) data—time-synchronized, high-frequency measurements gathered across the US power grid. This paper employs machine learning techniques to effectively analyze the vast PMU data in Wide Area Monitoring Systems (WAMS) for power grid event detection and classification. Analyzing several months of real-world PMU data, the paper focuses on machine learning for fast, precise event detection and classification, corroborated by utility event logs. Practical challenges like feature extraction, dimensionality reduction, and model selection are addressed. A novel feature yielding improved results is discovered, and a supplementary algorithm for detecting small power grid faults is developed. The final algorithm is validated using a month-long real PMU data set, demonstrating its capability in accurately identifying power grid events in near real-time.

machine learning, event detection, PMU↗
DOE OSTI2022

Practical Event Location Estimation Algorithm for Power Transmission System Based on Triangulation and Oscillation Intensity

Event location in power systems is quite essential information for system operators to enhance control-room situational awareness capability. Therefore, it is of great importance to develop an event location estimation algorithm for transmission systems with high accuracy. With the development of wide-area measurement system (WAMS) such as FNET/GridEye, and the synchrophasor measurement devices (SMDs) such as frequency disturbance recorders (FDRs), the synchronous measurement data including frequency, voltage amplitude and phase angle can be collected and used for event location estimation. First, the phase angle and rate of change of frequency (RoCoF) trajectories are respectively used for determining two sets of wave arrival time associated with each FDR. Then, a convolutional neural network (CNN) is utilized to determine the wave arrival order to select the more suitable set of wave arrival times for a given case and to perform corresponding modifications. Next, the oscillation intensity associated with each FDR is determined based on phase angle trajectories in the center of inertia (COI) coordinate system. Finally, the multiple criteria for event location estimation are represented. In conclusion, case studies and comparisons between the proposed and previous algorithms using actual and confirmed cases in U.S. power systems are performed to demonstrate the effectiveness and improvement of the proposed algorithm in practical applications.

frequency disturbance recorder (FDR)↗
DOE OSTI2021

Online Voltage Event Detection Using Synchrophasor Data with Structured Sparsity-Inducing Norms

This paper develops an accurate and computationally efficient data-driven framework to detect voltage events from PMU data streams. It develops an innovative Proximal Bilateral Random Projection (PBRP) algorithm to quickly decompose the PMU data matrix into a low-rank matrix, a row-sparse event-pattern matrix and a noise matrix. Here, the row-sparse pattern matrix significantly distinguishes events from normal behavior. These matrices are then fed into a clustering algorithm to separate voltage events from normal operating conditions. Large-scale numerical study results on real-world PMU data show that the proposed algorithm is computationally more efficient and achieves higher F scores than state-of-the-art benchmarks.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Risk Assessment of Rare Events in Probabilistic Power Flow via Hybrid Multi-Surrogate Method

The risks associated with rare events threatening the security of power system operation are of paramount importance to power system planners and operators. To analyze the risks caused by high-impact, low-frequency rare events, an immensely large number of samples are typically required for the Monte-Carlo (MC) method on the high-fidelity power system model to achieve a sufficient accuracy, thereby rendering this approach computationally prohibitive. To handle this problem efficiently, it is desirable to construct a surrogate model for the power system response. However, the straightforward MC sampling of the low-fidelity surrogate can lead to biased results in the low-probability tail regions that are vital to risk assessment. Moreover, a single surrogate is unable to handle the topology uncertainties caused by random branch outages. To overcome these issues, we propose a hybrid multi-surrogate (HMS) method based on the polynomial chaos expansion (PCE) with low-probability tail events reevaluated by the high-fidelity model through a probabilistic analysis. This method improves the computational efficiency of the MC method for rare-event risk assessment by leveraging multi-fidelity models while retaining the desired accuracy. Simulations conducted in three test systems verify the excellent performances of the HMS method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

WHOLESCALE: Microseismic Event Catalog for San Emidio, Nevada 2022

This submission includes a high-precision seismic event catalog estimated from seismic data collected at San Emidio, Nevada from April to May 2022. The catalog lists the precise time, location, and magnitude of microseismic events recorded during this period. Both the seismic data and microseismic event catalog were produced as part of the Water & Hole Observations Leverage Effective Stress Calculations and Lessen Expenses (WHOLESCALE) project. Attached here are the microseismic event catalog, a link to the raw seismic data, and a detailed description of methods used to create the catalog.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

SIDIS-RC EvGen: A Monte-Carlo event generator of semi-inclusive deep inelastic scattering with the lowest-order QED radiative corrections

SIDIS-RC EvGen is a C++ standalone Monte-Carlo event generator for studies of semi-inclusive deep inelastic scattering (SIDIS) processes at medium to high lepton beam energies. In particular, the generator contains binary and library components for generating SIDIS events and calculating cross sections for unpolarized or longitudinally polarized beam and unpolarized, longitudinally or transversely polarized target. The structure of the generator incorporates transverse momentum-dependent parton distribution and fragmentation functions, whereby we obtain multi-dimensional binned simulation results, which will facilitate the extraction of important information about the three-dimensional nucleon structure from SIDIS measurements. In order to build this software, we have used recent elaborate QED calculations of the lowest-order radiative effects, applied to the leading order Born cross section in SIDIS. Here, in this paper, we provide details on the theoretical formalism as well as the construction and operation of SIDIS-RC EvGen, e.g., how we handle the event generation process and perform multi-dimensional integration. We also provide example programs, flowcharts, and numerical results on azimuthal transverse single-spin asymmetries.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Measurement of hadronic event shapes in high-p T multijet final states at $ \sqrt{s} $ = 13 TeV with the ATLAS detector

A measurement of event-shape variables in proton-proton collisions at large momentum transfer is presented using data collected at $ \sqrt{s} $ = 13 TeV with the ATLAS detector at the Large Hadron Collider. Six event-shape variables calculated using hadronic jets are studied in inclusive multijet events using data corresponding to an integrated luminosity of 139 fb -1 . Measurements are performed in bins of jet multiplicity and in different ranges of the scalar sum of the transverse momenta of the two leading jets, reaching scales beyond 2 TeV. These measurements are compared with predictions from Monte Carlo event generators containing leading-order or next-to-leading order matrix elements matched to parton showers simulated to leading-logarithm accuracy. At low jet multiplicities, shape discrepancies between the measurements and the Monte Carlo predictions are observed. At high jet multiplicities, the shapes are better described but discrepancies in the normalisation are observed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Machine learning-based jet and event classification at the Electron-Ion Collider with applications to hadron structure and spin physics

We explore machine learning-based jet and event identification at the future Electron-Ion Collider (EIC). We study the effectiveness of machine learning-based classifiers at relatively low EIC energies, focusing on (i) identifying the flavor of the jet and (ii) identifying the underlying hard process of the event. We propose applications of our machine learning-based jet identification in the key research areas at the future EIC and current Relativistic Heavy Ion Collider program, including enhancing constraints on (transverse momentum dependent) parton distribution functions, improving experimental access to transverse spin asymmetries, studying photon structure, and quantifying the modification of hadrons and jets in the cold nuclear matter environment in electron-nucleus collisions. We establish first benchmarks and contrast the estimated performance of flavor tagging at the EIC with that at the Large Hadron Collider. We perform studies relevant to aspects of detector design including particle identification, charge information, and minimum transverse momentum capabilities. Additionally, we study the impact of using full event information instead of using only information associated with the identified jet. These methods can be deployed either on suitably accurate Monte Carlo event generators, or, for several applications, directly on experimental data. We provide an outlook for ultimately connecting these machine learning-based methods with first principles calculations in quantum chromodynamics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Long- and short-range correlations and their event-scale dependence in high-multiplicity pp collisions at $ \sqrt{s} $ = 13 TeV

Two-particle angular correlations are measured in high-multiplicity proton-proton collisions at √s = 13 TeV by the ALICE Collaboration. The yields of particle pairs at short-(Δη ~ 0) and long-range (1.6 < |Δη| < 1.8) in pseudorapidity are extracted on the near-side (Δ~ 0). They are reported as a function of transverse momentum (p T ) in the range 1 < p T < 4 GeV/c. Furthermore, the event-scale dependence is studied for the first time by requiring the presence of high-p T leading particles or jets for varying p T thresholds. The results demonstrate that the long-range “ridge” yield, possibly related to the collective behavior of the system, is present in events with high-p T processes as well. The magnitudes of the short- and long-range yields are found to grow with the event scale. The results are compared to EPOS LHC and PYTHIA 8 calculations, with and without string-shoving interactions. It is found that while both models describe the qualitative trends in the data, calculations from EPOS LHC show a better quantitative agreement for the p T dependency, while overestimating the event-scale dependency.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Parameter inference from event ensembles and the top-quark mass

One of the key tasks of any particle collider is measurement. In practice, this is often done by fitting data to a simulation, which depends on many parameters. Sometimes, when the effects of varying different parameters are highly correlated, a large ensemble of data may be needed to resolve parameter-space degeneracies. An important example is measuring the top-quark mass, where other physical and unphysical parameters in the simulation must be profiled when fitting the top-quark mass parameter. We compare four different methodologies for top-quark mass measurement: a classical histogram fit similar to one commonly used in experiment augmented by soft-drop jet grooming; a 2D profile likelihood fit with a nuisance parameter; a machine-learning method called DCTR; and a linear regression approach, either using a least-squares fit or with a dense linearly-activated neural network. Despite the fact that individual events are totally uncorrelated, we find that the linear regression methods work most effectively when we input an ensemble of events sorted by mass, rather than training them on individual events. Although all methods provide robust extraction of the top-quark mass parameter, the linear network does marginally best and is remarkably simple. For the top study, we conclude that the Monte-Carlo-based uncertainty on current extractions of the top-quark mass from LHC data can be reduced significantly (by perhaps a factor of 2) using networks trained on sorted event ensembles. More generally, machine learning from ensembles for parameter estimation has broad potential for collider physics measurements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Search for new particles in events with energetic jets and large missing transverse momentum in proton-proton collisions at $ \sqrt{s} $ = 13 TeV

A search is presented for new particles produced at the LHC in proton-proton collisions at s√ = 13 TeV, using events with energetic jets and large missing transverse momentum. The analysis is based on a data sample corresponding to an integrated luminosity of 101 fb -1 , collected in 2017–2018 with the CMS detector. Machine learning techniques are used to define separate categories for events with narrow jets from initial-state radiation and events with large-radius jets consistent with a hadronic decay of a W or Z boson. A statistical combination is made with an earlier search based on a data sample of 36 fb -1 , collected in 2016. No significant excess of events is observed with respect to the standard model background expectation determined from control samples in data. The results are interpreted in terms of limits on the branching fraction of an invisible decay of the Higgs boson, as well as constraints on simplified models of dark matter, on first-generation scalar leptoquarks decaying to quarks and neutrinos, and on models with large extra dimensions. Several of the new limits, specifically for spin-1 dark matter mediators, pseudoscalar mediators, colored mediators, and leptoquarks, are the most restrictive to date.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Measurement of the top quark mass using events with a single reconstructed top quark in pp collisions at $$ \sqrt{s} $$ = 13 TeV

A measurement of the top quark mass is performed using a data sample enriched with single top quark events produced in the t channel. The study is based on proton- proton collision data, corresponding to an integrated luminosity of 35.9 fb -1 , recorded at √s = 13 TeV by the CMS experiment at the LHC in 2016. Candidate events are selected by requiring an isolated high-momentum lepton (muon or electron) and exactly two jets, of which one is identified as originating from a bottom quark. Multivariate discriminants are designed to separate the signal from the background. Optimized thresholds are placed on the discriminant outputs to obtain an event sample with high signal purity. The top quark mass is found to be $172.13^{+0.76}_{-0.77}$ GeV, where the uncertainty includes both the statistical and systematic components, reaching sub-GeV precision for the first time in this event topology. The masses of the top quark and antiquark are also determined separately using the lepton charge in the final state, from which the mass ratio and difference are determined to be $0.9952^{+0.0079}_{-0.0104}$ and $0.83^{+1.79}_{-1.35}$ GeV, respectively. The results are consistent with CPT invariance.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Formation, cooling history and age of impact events on the IIE iron parent body: Evidence from the Miles meteorite

Most iron meteorites formed in planetary cores during differentiation, but the IIE iron meteorites have chemical and physical features that are inconsistent with this origin. By combining mineral chemistry, mineral modes and three-dimensional petrography, we reconstruct the bulk chemistry of the felsic silicate-bearing Miles IIE iron meteorite and demonstrate that the silicate inclusion compositions are similar to partial melts produced experimentally from an H chondrite composition. We use the reconstructed bulk composition, mineralogy and thermodynamic modelling to show that melting above ~ 1200°C under reducing conditions formed metal (Fe-Ni alloy) and felsic silicate partial melts. Upon cooling, the melts crystallized Mg-rich pyroxenes, Na- and K-rich feldspars, and tridymite. Importantly, this mechanism enriches cosmochemically volatile elements (i.e., those with a 50% condensation temperature of ~430–830°C, like Na and K) to the level found in the felsic silicate inclusions. The presence of crystallographically disordered srilankite (only stable above 1160°C) and an absence of Widmanstätten texture require both high peak temperatures and rapid cooling, which cannot be explained by core formation. Instead, they point to small melt volumes, a transient heat pulse, and small thermal mass, and imply efficient physical segregation of silicate and metallic melts through buoyancy separation followed by rapid cooling that arrested the separation of metal and silicate liquid phases. In situ 207 Pb/ 206 Pb age of 4542.3 ± 4.0 Ma in Zr-oxide and phosphate minerals dates the melting event that formed the silicate inclusions. This age aligns with the earliest ages found in other IIE iron meteorite silicates and requires a heating event ~25 million years after the solar system formed. We found 39 Ar/ 40 Ar ages of 3495 ± 52 Ma (low-T) and 4303 ± 7 Ma (high-T) in a K-feldspar grain, with the 3495 Ma age aligning with later thermal events recorded in other IIE iron meteorites. Dating reveals the complex petrogenetic and thermal history of Miles and the IIE iron meteorites. This is the first IIE iron meteorite found to record evidence of heating at 4.5 and 3.5 Ga likely from impact events. We propose that high-velocity impact(s) into an iron-rich, porous chondritic parent body at ~4.54 Ga produced immiscible metal and silicate melts that cooled rapidly and trapped low density silicate inclusions within high density metal. Other IIE irons that formed at lower peak temperatures (900–1000°C) contain chondritic silicate inclusions and relict chondrules, supporting this conceptual model. In conclusion, this petrogenesis is consistent with thermodynamic modelling, experimental data and the wide range of peak temperatures and cooling rates observed in the IIE iron meteorites.

58 GEOSCIENCES↗
DOE OSTI2025

A sequential Attacker-Defender game for distribution systems resilience enhancement against extreme weather events

Improving distribution system resilience against frequent extreme weather events is important for reliable power system operations. Especially when dealing with events such as hurricanes that have short-term predictions, proactive pre-event preparedness plays a vital role in system resilience performance. In this paper, we propose a novel approach to construct pre-event resource allocation plans for system operators to cope with upcoming threats through a sequential attacker-defender game framework. The sequential attacker-defender game is designed to model the interaction between the extreme weather and the system operator. In each round of the game, the attacker and the defender sequentially update their current strategies by accounting for the opponent’s action set. The attacker model is formulated as a bi-level problem to identify the severe outage scenarios, and the defender model is formulated as a two-stage optimization problem to determine the allocation of restoration resources including mobile responsive resources and repair crews. Two scale-reduction strategies are proposed to ensure the scalability of the game scheme. Finally, case studies on the IEEE 33-bus and a 7149-node practical utility system validate the effectiveness of the proposed sequential game and the efficiency of the scale-reduction strategies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Trends and meteorological drivers of extreme daily reservoir evaporation events in the western United States

Extreme daily evaporation from reservoir surfaces can lead to significant short-term water losses, affecting water quality, water supply, and reservoir operation strategies. Historical trends in daily reservoir evaporation events have eluded the scientific and operational communities, largely due to a lack of long-term, consistent data record. This study quantifies trends in extreme daily reservoir evaporation events at 165 major reservoirs located in the western U.S. Here, we use the place-based energy balance and aerodynamic Daily Lake Evaporation Model (DLEM) driven by multiple meteorological data products (RTMA, gridMET, Daymet) to estimate daily evaporation rates at these reservoirs from 1981 to 2022. The results—while are based on different meteorological forcing datasets—consistently indicate that the California, Lower Colorado, and Rio Grande hydrologic regions are more prone to higher daily evaporation extremes. Compared to the rest of western U.S, these three regions also experience a more pronounced increasing trend in the annual maximum daily evaporation rate, at about 0.3 mm day -1 decade -1 during 1981-2022. The results show that heat and dryness are the main drivers to the increasing trend of extreme evaporation, while extreme wind speed is the primary contributor to exceptionally high daily evaporation events across all regions. This phenomenon is particularly prominent in the arid Lower Colorado region, but less significant in the cold and humid Pacific Northwest region. We also find that the correlation between extreme wind speed and extreme evaporation degrades as the time scale increases from daily, to monthly and seasonal. Our findings have strong implications for the pattern and distribution of extreme evaporation events at the western U.S. reservoirs, and illustrate how various drivers influence extreme evaporation across different time scales.

13 HYDRO ENERGY↗