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Lidar and UAS Measurements of Winds for the NASA Advanced Air Mobility Mission

The envisioned future of Advanced Air Mobility involves low-altitude operation of a new class of air vehicles. The low altitudes of the flight paths would have these vehicles spending most of their operation in conditions where unpredictable wind and turbulence effects may occur. Wind measurements will hence be critical to ensure safe and efficient operations for AAM. Such wind measurement could be used as a monitoring system for warning of hazardous wind events, as data input to forecasting models, or as a research tool to understand wind effects in complex environments. To meet this need NASA is evaluating wind sensing technologies with a capability to probe the atmospheric boundary layer. Doppler lidar is a leading candidate ground-based sensor, as shown by the decades-long history of being an effective tool for many wind studies. However, there is a need to re-assess the Doppler wind lidars for the AAM application which involves evaluating wind effects as vehicles operate in and out of vertiports, notably regarding spatial resolution. Meeting the needs for spatial resolution may involve the use of dual-Doppler techniques, rather than just single Doppler lidar. The study was furthermore motivated by looking toward the future of AAM, in which the air vehicles involved can also provide wind measurements. Airborne wind measurements, obtained directly from vehicle-mounted anemometers or indirectly from vehicle navigation data, offer a means to compare remotely sensed wind lidar with in-situ measurements. The following sections report on results of wind measurements obtained using lidar and the small uninhabited aircraft systems (sUAS’s) operating in the same volume of air.

Adam Medina↗
DOE OSTI2025

A universal implementation of radiative effects in neutrino event generators

Due to the similarities between electron-nucleus (eA) and neutrino-nucleus scattering (νA), eA data can contribute key information to improve cross-section modeling in eA and hence in νA event generators. However, to compare data and generated events, either the data must be radiatively corrected or radiative effects need to be included in the event generators. We implemented a universal radiative corrections program that can be used with all reaction mechanisms and any eA event generator. Our program includes real photon radiation by the incident and scattered electrons, and virtual photon exchange and photon vacuum polarization diagrams. It uses the “extended peaking” approximation for electron radiation and neglects charged hadron radiation. This method, validated with GENIE, can also be extended to simulate νA radiative effects. This work facilitates data-event-generator comparisons used to improve νA event generators for the next-generation of neutrino experiments. Program Title: emMCRadCorr CPC Library link to program files:https://doi.org/10.17632/hmsxg82vnf.1 Developer's repository link:https://github.com/e4nu/emMCRadCorr Licensing provisions: AGPLv3 Programming language:C++ Nature of problem: Radiative effects can significantly modify the event kinematics and the resulting cross-sections. Such effects must be accounted for when comparing event generators to eA data. Existing radiative correction codes are tailored to specific processes and topologies, and are limited to a restricted phase space defined by the spectrometer acceptance. Therefore, a more general approach is required to apply radiative corrections to semi-inclusive and exclusive eA measurements. Solution method: Our program incorporates real photon radiation from both the incident and scattered electrons, as well as virtual photon exchange and photon vacuum polarization effects. It employs the “extended peaking” approximation for electron radiation while neglecting contributions from charged hadron radiation. The code is fully decoupled from event generator codes and can be used for all event generators in the market.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Big Data Synchrophasor Monitoring and Analytics for Resiliency Tracking (BDSMART)

This report contains key findings from a project titled Big Data Synchrophasor Monitoring and Analytics for Resiliency Tracking (BDSMART), which was carried out through a collaborative effort of a team of researchers from Texas A&M Engineering Experiment Station, Temple University, and Quanta Technology, LLC. The in-kind support came from OSIsoft (acquired by AVEVA), which provided their PI Historian software to demonstrate the use case of streaming PMU data. The first section of the report describes the project goals and objectives related to the development of Machine Learning (ML) models capable of detecting and classifying events by processing phasor measurements captured in the field by Phasor Measurement Units (PMUs). The data for this study was contributed by the utilities/ISOs from the Western and Eastern interconnects and ERCOT, further referred to as Interconnect B (IC B), Interconnect A (IC A), and Interconnect C (IC C), respectively. The approach that the BDSMART Research Team proposed and the key research tasks defined by the team are outlined in this section. The next section describes the technical approach. We first discuss the data constraints related to the PMU measurements and data interpretation constraints imposed by the data contributors. They provided neither the topological information of the grid nor PMU placement locations and captured recorded data at very few locations in the system with the reporting rate of either 30 or 60 fps. The recordings are mostly positive sequence voltage, frequency, and ROCOF, and in some limited cases, three-phase voltages and currents. We then reflect on the bad data issues that stem from poor recording practices and vague definitions of the PMU status bits to supposedly be used for bad data identification. Finally, the data discovery points to imprecise time stamps with incomplete event start/end time, as well as inconsistent and incomplete event labeling, which combined make the implementation of the data models using supervising learning quite challenging. Following the data discovery study, we hypothesize that because the IC B data has the most complete labels, we should focus our model development on that data and then test it on data from other interconnects. We also define the common metrics used to evaluate the results from the ML algorithm tests. We concluded this section by summarizing the common ML models we used and explaining how we implemented and tested them. The issues from this section are expanded in the Training Dataset Report from this project. The final section of this report deals with the accomplishments and conclusions. As the accomplishments, we formulate the problem we are solving and what is achieved by solving the problem. We then reflect on each of the analytics tools we developed and point out the performance of each tool when applied to solving the mentioned problems. We reference this work for further details to the papers we published on each tool. In the conclusions, we give recommendations on how to improve future PMU recording practices to facilitate the ML algorithm implementation and guidance for the future standardization work aimed at clarifying the ambiguities associated with the PMU status bits. We finally list future tasks that can bring about further improvements in the proposed algorithms. The issues from this section are expanded in the Training, and Test Dataset Report filed at the project completion date.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Quantitative resilience evaluation on recovery from emergency situations in nuclear power plants

Here, this paper mainly introduces how to develop a resilience evaluation model by quantifying the relationship between resilience and resilience components in regard to recovering from emergency situations in nuclear power plants (NPPs). It is an extension of the author’s previous researches. To develop the model, the first step was to analyze event reports published in Republic of Korea in terms of resilience factors from different safety perspectives. Second, statistical methods such as factor analysis, principal component analysis, and multivariate regression analysis were applied to the dataset to identify relations among resilience factors and develop a quantitative resilience evaluation model for recovery from emergency situations in NPPs. Third, the model was validated using the recent event report data released in Korea. Lastly, we present a discussion on how to apply the quantitative resilience evaluation model to results from stress tests performed in Korea.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Weakly supervised anomaly detection for resonant new physics in the dijet final state using proton-proton collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector

An anomaly detection search for narrow-width resonances beyond the Standard Model that decay into a pair of jets is presented. The search is based on 139 fb −1 of proton-proton collisions at $\sqrt{s}$ = 13 TeV recorded during 2015–2018 with the ATLAS detector at the Large Hadron Collider. The analysis is optimized without a particular signal model and aims to be sensitive to a broad range of new physics. It uses two different machine learning strategies to estimate the background in different signal regions. In each region, a weakly supervised classifier is trained to distinguish this background model from data. The analysis focuses on events with high transverse momentum jets reconstructed as large-radius jets. The mass and substructure of these jets are used as inputs to the classifiers. After a classifier-based selection, the distribution of the invariant mass of the two jets is used to search for potential local excesses. The model-independent results of both the anomaly detection methods show no signs of significant local excesses. In addition to model-independent results, a representative set of signal models is injected into the data, and the sensitivity of the methods to these scenarios is reported.

Aad, G. [Aix-Marseille Université] (ORCID:00000002↗
DOE OSTI2025

Advances in geophysical forensic event monitoring

Forensic analysis of man-made, non-nuclear events (such as industrial accidents, explosion experiments and mine collapses) has become more frequent and detailed owing to advancements in geophysical monitoring. Here, in this Technical Review, we demonstrate how geophysical forensic monitoring using seismic, infrasound and hydroacoustic recordings provides insights on events in the solid earth, atmosphere and underwater. Advanced techniques, including machine-learning-based models, have been developed to detect, identify and investigate these events, providing information on location, subevents, sources and explosive yield. The increase in data availability, application of advanced methods and computation and the growth of multitechnology approaches have increased the accuracy of forensic event analysis and enabled more realistic characterization of uncertainties. For example, the 2020 Beirut explosion in Lebanon demonstrated that various seismic, acoustic and other methods could be used to estimate explosive yield (and yield uncertainties) of about 1 ktonne, providing confidence in the application of these methods to smaller events where data are available. However, forensic investigations remain largely limited to known events with identified sources. Increased access to data, sophisticated analysis methods and high-resolution earth models will improve forensic event analysis further, enabling civil and scientific applications, such as localization in the search for the lost ARA San Juan submarine.

geophysics↗
NASA NTRS2000

Observed On-Orbit Background of the ACIS Detector on the Chandra X-Ray Observatory

We have analyzed calibration data acquired during the Orbital Activation and Checkout (OAC) phase of the Chandra X-ray Observatory (CXO) mission in order to characterize the background of the Advanced CCD Imaging Spectrometer (ACIS) produced by charged particles and non-cosmic X-rays. The ACIS instrument contains 8 Front-Illuminated (FI) CCDs and 2 Back-Illuminated (BI) CCDs. The FI and BI CCD)s exhibit dramatically different responses to enhancements in the particle flux. The F1 CCDs show relatively little increase in the overall count rate, typical increases are 1 - 3 counts/s; the BI CCDs show large excursions to as high as 100 counts/s. The duration of these intervals of enhanced background are highly variable ranging from 100 s to 5000 s. The spatial distribution of these background events is relatively flat across the power-law. The events produce morphologies which are similar to cosmic X-ray events, so that morphology alone cannot be used as a rejection criterion. We explore the correlation of these times of high background with the data from Chandra's on-board radiation monitor, the EPHIN (Electron, Proton, Helium Instrument particle detector) instrument and archival data from the Advanced Composition Explorer (ACE) satellite. We discuss strategies for observers to identify and exclude times of high background and to model and subtract the background events from their data.

Plucinsky, P. P.↗
NASA NTRS2000

The Observed On-Orbit Background of the ACIS Instrument of the Chandra X-Ray Observatory

We have analyzed calibration data acquired during the Orbital Activation and Checkout (OAC) phase of the Chandra X-ray Observatory (CXO) mission in order to characterize the background of the Advanced CCD Imaging Spectrometer (ACIS) produced by charged particles and non-cosmic x-rays. The ACIS instrument contains eight Front-Illuminated (FI) CCDs and two Back-Illuminated (BI) CCDs. The FI and BI CCDs exhibit dramatically different responses to enhancements in the particle flux. The FI CCDs show relatively little increase in the overall count rate, typical increases are 1-3 counts/s; the BI CCDs show large excursions to as high as 100 counts/s. The directions of these intervals of enhanced background are highly variable ranging from 100 s to 5000 s. The spatial distribution of these background events is relatively flat across the detectors. The spectral distribution can be characterized by a simple power law. The events produce morphologies which are similar to cosmic x-ray events, so that morphology alone cannot be used as a rejection criterion. We explore the correlation of these times of high background with the data from Chandra's on-board radiation monitor, the EPHIN (Electron, Proton, Helium Instrument particle detector) instrument and archival data from the Advanced Composition Explorer (ACE) satellite. We discuss strategies for observers to identify and exclude times of high background and to model and subtract the background events from their data.

Plucinsky, Paul P.↗
NASA NTRS2013

On the Lack of Stratospheric Dynamical Variability in Low-top Versions of the CMIP5 Models

We describe the main differences in simulations of stratospheric climate and variability by models within the fifth Coupled Model Intercomparison Project (CMIP5) that have a model top above the stratopause and relatively fine stratospheric vertical resolution (high-top), and those that have a model top below the stratopause (low-top). Although the simulation of mean stratospheric climate by the two model ensembles is similar, the low-top model ensemble has very weak stratospheric variability on daily and interannual time scales. The frequency of major sudden stratospheric warming events is strongly underestimated by the low-top models with less than half the frequency of events observed in the reanalysis data and high-top models. The lack of stratospheric variability in the low-top models affects their stratosphere-troposphere coupling, resulting in short-lived anomalies in the Northern Annular Mode, which do not produce long-lasting tropospheric impacts, as seen in observations. The lack of stratospheric variability, however, does not appear to have any impact on the ability of the low-top models to reproduce past stratospheric temperature trends. We find little improvement in the simulation of decadal variability for the high-top models compared to the low-top, which is likely related to the fact that neither ensemble produces a realistic dynamical response to volcanic eruptions.

troposphere↗
NASA NTRS2007

Application of Multi-Satellite Precipitation Analysis to Floods and Landslides

Satellite data acquired and processed in real time now have the potential to provide the spacetime information on rainfall needed to monitor flood and landslide events around the world. This can be achieved by integrating the satellite-derived forcing data with hydrological models and landslide algorithms. Progress in using the TRMM Multi-satellite Precipitation Analysis (TMPA) as input to flood and landslide forecasts is outlined, with a focus on understanding limitations of the rainfall data and impacts of those limitations on flood/landslide analyses. Case studies of both successes and failures will be shown, as well as comparison with ground comparison data sets both in terms of rainfall and in terms of flood/landslide events. In addition to potential uses in real-time, the nearly ten years of TMPA data allow retrospective running of the models to examine variations in extreme events. The flood determination algorithm consists of four major components: 1) multi-satellite precipitation estimation; 2) characterization of land surface including digital elevation from NASA SRTM (Shuttle Radar Terrain Mission), topography-derived hydrologic parameters such as flow direction, flow accumulation, basin, and river network etc.; 3) a hydrological model to infiltrate rainfall and route overland runoff; and 4) an implementation interface to relay the input data to the models and display the flood inundation results to potential users and decision-makers. In terms of landslides, the satellite rainfall information is combined with a global landslide susceptibility map, derived from a combination of global surface characteristics (digital elevation topography, slope, soil types, soil texture, and land cover classification etc.) using a weighted linear combination approach. In those areas identified as "susceptible" (based on the surface characteristics), landslides are forecast where and when a rainfall intensity/duration threshold is exceeded. Results are described indicating general agreement with landslide occurrences. However, difficulties in comparing landslide event information (mostly from news reports) with the satellite-based forecasts are analyzed.

Adler, Robert↗
NASA NTRS1986

Time-dependent coronal shock acceleration of energetic solar flare particles

A global time-dependent model is presented for the coronal and interplanetary shock acceleration and propagation of energetic solar flare particles. The calculations are carried out to help prove that coronal shock acceleration of solar flare particles is responsible for energetic solar flare event data gathered in interplanetary space. The model is based on the theory of diffusive shock acceleration, and requires particle speeds to be much greater than bulk velocities. Also, sufficient scattering must occur upstream and downstream of the shock for the particle scattering mean free path to be smaller than the characteristic scale lengths, which causes the same particles to encounter the shock repeatedly. A spherically symmetric shock wave is assumed, which leads to the same emission configuration for impulsively and monoenergetically emitted particles. Consideration is given to acceleration by compression at the shock front, adiabatic deceleration in the divergent downstream flow, the temporal evolution of the shock and the three-dimensional geometry of the corona. The model is used to generate normalized proton omnidirectional distributions at 1 AU and at the shock front. The spectral exhibit trends similar to those in observational data, especially proton acceleration times and the proton distribution profiles at 1 AU.

Lee, M. A.↗
DOE OSTI2021

Modeling Framework for Bulk Electric Grid Impacts from HEMP E1 and E3 Effects (Tasks 3.1 Final Report)

This report presents a framework to evaluate the impact of a high-altitude electromagnetic pulse (HEMP) event on a bulk electric power grid. This report limits itself to modeling the impact of EMP E1 and E3 components. The co-simulation of E1 and E3 is presented in detail, and the focus of the paper is on the framework rather than actual results. This approach is highly conservative as E1 and E3 are not maximized with the same event characteristics and may only slightly overlap. The actual results shown in this report are based on a synthetic grid with synthetic data and a limited exemplary EMP model. The framework presented can be leveraged and used to analyze the impact of other threat scenarios, both manmade and natural disasters. This report d escribes a Monte-Carlo based methodology to probabilistically quantify the transient response of the power grid to a HEMP event. The approach uses multiple fundamental steps to characterize the system response to HEMP events, focused on the E1 and E3 components of the event. 1) Obtain component failure data related to HEMP events testing of components and creating component failure models. Use the component failure model to create component failure conditional probability density function (PDF) that is a function of the HEMP induced terminal voltage. 2) Model HEMP scenarios and calculate the E1 coupled voltage profiles seen by all system components. Model the same HEMP scenarios and calculate the transformer reactive power consumption profiles due to E3. 3) Sample each component failure PDF to determine which grid components will fail, due to the E1 voltage spike, for each scenario. 4) Perform dynamic simulations that incorporate the predicted component failures from E1 and reactive power consumption at each transformer affected by E3. These simulations allow for secondary transients to affect the relays/protection remaining in service which can lead to cascading outages. 5) Identify the locations and amount of load lost for each scenario through grid dynamic simulation. This can be an indication of the immediate grid impacts from a HEMP event. In addition, perform more detailed analysis to determine critical nodes and system trends. 6) To help realize the longer-term impacts, a security constrained alternating current optimal power flow (ACOPF) is run to maximize critical load served. This report describes a modeling framework to assess the systemic grid impacts due to a HEMP event. This stochastic simulation framework generates a large amount of data for each Monte Carlo replication, including HEMP location and characteristics, relay and component failures, E3 GIC profiles, cascading dynamics including voltage and frequency over time, and final system state. This data can then be analyzed to identify trends, e.g., unique system behavior modes or critical components whose failure is more likely to cause serious systemic effects. The proposed analysis process is demonstrated on a representative system. In order to draw realistic conclusions of the impact of a HEMP event on the grid, a significant amount of work remains with respect to modeling the impact on various grid components.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

A Single-Feature Machine Learning Method for Detecting Multiple Types of Events from PMU Data

This paper describes simple and efficient machine learning (ML) methods for efficiently detecting multiple types of power system events captured by PMUs scarcely placed in a large power grid. It uses a single feature from each PMU based on a rectangle area enclosing the event in a given data window. This single feature is sufficient to enable commonly used ML models to detect different types of events quickly and accurately. The feature is used by five ML models on four different data-window sizes. The results indicated a tradeoff between the execution speed and detection accuracy in variety of data-window size choices. The proposed method is insensitive to most data quality issues typical for data from field PMUs, and thus it does not require major data cleansing efforts prior to feature extraction.

Dokic, Tatjana↗
NASA NTRS2007

Real-Time Application of Multi-Satellite Precipitation Analysis for Floods and Landslides

Satellite data acquired and processed in real time now have the potential to provide the spacetime information on rainfall needed to monitor flood and landslide events around the world. This can be achieved by integrating the satellite-derived forcing data with hydrological models and landslide algorithms. Progress in using the TRMM Multi-satellite Precipitation Analysis (TMPA) as input to flood and landslide forecasts is outlined, with a focus on understanding limitations of the rainfall data and impacts of those limitations on flood/landslide analyses. Case studies of both successes and failures will be shown, as well as comparison with ground comparison data sets-- both in terms of rainfall and in terms of flood/landslide events. In addition to potential uses in real-time, the nearly ten years of TMPA data allow retrospective running of the models to examine variations in extreme events. The flood determination algorithm consists of four major components: 1) multi-satellite precipitation estimation; 2) characterization of land surface including digital elevation from NASA SRTM (Shuttle Radar Terrain Mission), topography-derived hydrologic parameters such as flow direction, flow accumulation, basin, and river network etc.; 3) a hydrological model to infiltrate rainfall and route overland runoff; and 4) an implementation interface to relay the input data to the models and display the flood inundation results to potential users and decision-makers, In terms of landslides, the satellite rainfall information is combined with a global landslide susceptibility map, derived from a combination of global surface characteristics (digital elevation topography, slope, soil types, soil texture, and land cover classification etc.) using a weighted linear combination approach. In those areas identified as "susceptible" (based on the surface characteristics), landslides are forecast where and when a rainfall intensity/duration threshold is exceeded. Results are described indicating general agreement with landslide occurrences.

Adler, Robert↗
DOE OSTI2023

CMS PYTHIA 8 colour reconnection tunes based on underlying-event data

New sets of parameter tunes for two of the colour reconnection models, quantum chromodynamics-inspired and gluon-move, implemented in the PYTHIA 8 event generator, are obtained based on the default CMS PYTHIA 8 underlying-event tune, CP5. Measurements sensitive to the underlying event performed by the CMS experiment at centre-of-mass energies $\sqrt{s}=$7 and 13 TeV, and by the CDF experiment at 1.96 TeV are used to constrain the parameters of colour reconnection models and multiple-parton interactions simultaneously. The new colour reconnection tunes are compared with various measurements at 1.96, 7, 8, and 13 TeV including measurements of the underlying-event, strange-particle multiplicities, jet substructure observables, jet shapes, and colour flow in top quark pair ($t\bar{t}$) events. The new tunes are also used to estimate the uncertainty related to colour reconnection modelling in the top quark mass measurement using the decay products of $t\bar{t}$ events in the semileptonic channel at 13 TeV .

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1988

Research at Palomar Observatory in planetary astronomy

A wide range of observational studies are carried out to improve our understanding of the bodies of the outer solar system. Using the 200-inch Hale telescope, near-infrared observations are made of Uranus, Neptune, and the Pluto-Charon system. High time resolution occultation observations of the Uranus Ring system are used to study in detail the dynamics of this system. Occultation studies of Neptune are probing this intriguing ring-arc system. Occulation observations of the Pluto-Charon system probe the surface properties of these distant bodies. In addition, the plate material of the PSSII servey is being used to search for new comets and asteroids. Researchers observed one Neptune stellar occultation in July 1987 and completed the analysis of a series of seven separate Neptune occultation observations in conjunction with Nicholson et al., of Cornell. The analysis has shown that minimum of three ring arcs, at radii ranging from 54,000 km - 67,000 km are required to account for the high quality ring events. Current theoretical models can account for these data. Of two observations scheduled of Pluto-Charon mutual occulations scheduled for the 200-inch, the Charon eclipse event was successfully observed (the other was clouded out).

Soifer, B. T.↗
DOE OSTI2022

Real-time Event Detection Using Rank Signatures of Real-world PMU Data

Timely detection of power system events is a crucial task, which can facilitate the implementation of remedial actions to improve reliability, resiliency, and security of the system. Meanwhile, the widespread deployment of phasor measurement units (PMUs) makes it possible to develop data-driven event detection techniques. However, relying purely on data without incorporating domain knowledge for the event detection task in power systems poses substantial security and stability risks due to issues associated with data misinterpretation and model accuracy. In this regard, we propose a real-time event detection method using real-world PMU data by incorporating domain knowledge to adequately capture the event signatures. Specifically, we track the change in rank signatures of PMU data to accurately localize the events. To optimize the detection process, we incorporate an offline Bayesian optimization algorithm to tune the parameters by efficiently searching for the best values. The experiments using the real-world PMU dataset from a U.S. interconnection show that the proposed event detection approach can efficiently detect the events from PMU data streams with high accuracy.

Ghasemkhani, Amir↗