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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Tracking the Hunga Tonga-Hunga Ha’apai Eruption Stratospheric Aerosol and Trace Gas Plumes Using Machine Learning

On January 15, 2022, the Hunga Tonga-Hunga Ha’apai (hereafter, Hunga Tonga) submarine volcano had an explosive eruption that thrusted ash, gases, and water vapor through the troposphere into the stratosphere and mesosphere. Previous studies manually tracked the aerosol and trace gas plumes over time across different positions in the southern hemisphere. Using data retrieved from low earth orbiting satellite instruments (e.g., OMPS, OMI, and CALIPSO), this research demonstrates how open-source machine learning (ML) models, like Meta’s Segment Anything Model (SAM), with prompt engineering can perform automatic plume tracking following the Hunga Tonga eruption. This extensible methodology, and modular data processing and modeling pipeline using NASA Earthdata and Openscapes, establishes a framework for systematically and rapidly studying extreme events, including volcanic eruptions and large-scale wildfires. By combining advanced machine learning techniques, such as SAM’s zero-shot learning, with large volumes of remote sensing data, this work demonstrates how AI and open science can accelerate research and generate actionable results. The tools and technologies presented here can help translate earth science to action from NASA’s current and future Earth observing satellite missions (e.g., the Atmosphere Observing System (AOS)), and assist researchers and stakeholders in understanding, mapping, and responding to natural disasters and extreme events in a changing world.

David M. Giles↗
NASA NTRSDate not supplied

Satellite Based Precipitation Estimation in Orographic Regions within the Southwestern United States

Predicting precipitation-induced landslides requires accurate estimation of orographic precipitation. Many research studies have been done to estimate orographic precipitation using measurement/estimation methods that include rain gauges, ground-based radar, satellite-based estimates, and modeling. Each method has strengths and weaknesses, but none have been able to fully resolve orographic precipitation. The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Mission (IMERG) early run product provides precipitation estimates at a 0.1° spatial resolution, at half-hour time scales, with only a 4-hour latency. This product is currently used for the global landslide hazard assessment, but there are known issues in mountainous terrain that the IMERG algorithm has not been able to fully resolve; and the sparse gauge density in these regions makes it even more difficult. In this study, precipitation events were identified using high temporal resolution (5-15 minute) precipitation observations from rain gauges in mountainous terrain in the southwestern United States. The brightness temperature from several infrared (IR) bands from the Geostationary Operational Environmental Satellite (GOES) 16 satellite were used to estimate precipitation with a k-nearest neighbor machine learning model. Compared to IMERG, the IR estimates from GOES-16 performed better at predicting gauge-identified precipitation events. Additionally, the IR-only based estimates were able to estimate precipitation when IMERG failed to detect precipitation, false negative events. While additional analysis is needed, results indicate the need for better integration of IR observations for more accurate precipitation estimation in mountainous regions.

Jessica Sutton↗
NASA NTRSDate not supplied

Space Charge Analysis Capabilities for the Evaluation of High Voltage Insulation Materials

Materials development is crucial for the advancement of electric aviation. Current research focusing on novel multifunctional materials is underway to solve the needs for aerospace systems; however, additional testing of these materials to understand their failure modes is equally critical. This project will utilize pulsed electroacoustic (PEA) instrumentation to investigate space charge accumulation of multifunctional composites and other insulation materials critical to robust high power transmission systems. Evaluating the space charge phenomena in dielectric materials will provide unique and fundamentally necessary data to determine the feasibility and lifetime of any high voltage insulation system. When electrical charge is injected into a solid dielectric, charge can accumulate and become trapped, leading to the failure of the insulation via breakdown or arcing events. This study will discuss the pulsed electroacoustic methodology and its use on evaluating various dielectric polymer and composite films.

pulsed electroacoustic↗
DOE OSTI2025

Expanding the physics reach of DUNE in the near and far detectors

The Deep Underground Neutrino Experiment (DUNE) is a next-generation long-baseline neutrino oscillation experiment. Its primary goal is the determination of the neutrino mass hierarchy and the CP-violating phase. The DUNE physics programme also includes the detection of astrophysical neutrinos and the search for beyond the Standard Model (BSM) phenomena. DUNE will consist of a near detector (ND) complex placed at Fermilab, and a modular Liquid Argon Time Projection Chamber (LArTPC) far detector (FD) to be built in the Sanford Underground Research Facility (SURF), approximately 1300 km away from the neutrino production point. This thesis describes three different projects within DUNE. First, a novel strategy to improve the triggering capabilities of the DUNE FD is proposed. It uses matched filters to enhance the production of online hits across all charge collection planes. Next, the possibility of detecting neutrinos coming from dark matter (DM) annihilations in the Sun with the FD is explored. The complementarity of DUNE to this kind of DM searches is shown. Finally, the simulation and reconstruction framework of ND-GAr, the gas argon ND proposed for Phase II of DUNE, is presented. A number of additions to this are described, particularly focused on the development of the particle identification (PID) capabilities of the detector. These are then used to perform the first event selection studies with an end-to-end simulation in ND-GAr, in particular the selection of pion exclusive samples in $\nu_{\mu}$ CC interactions. All three of these projects share the common goal of enhancing the physics programme of DUNE.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Initial Development of Fusion Magnet Simulation Capabilities for Performance and Safety Evaluation Using the MOOSE Framework

Fusion energy holds the promise of being a transformative technology as a carbon-neutral, sustainable source of energy. Whole device modeling and the development of fusion digital twins will be increasingly important for emerging fusion device concepts at both national laboratories and within the commercial fusion industry. However, meeting the challenge of whole device modeling of fusion energy devices requires robust, multiphysics, multiscale modeling and simulation technologies capable of running on large-scale supercomputers. Detailed analysis of individual systems at-scale is also required to ensure safe and efficient operation as well as provide the safety basis for future device designs and licensing activities. In a tokamak, toroidal and poloidal magnets confine and shape the fusion plasma to promote the fusion reaction. High plasma temperatures and high magnetic field requirements in modern design concepts (leading to high amounts of energy stored within each magnet) impose electrical, thermal, and mechanical loads on the magnet components, which in turn impacts the safety considerations of the magnet and their supporting systems. Idaho National Laboratory (INL) has a history of working in this space, including development and benchmarking of the Magnetic System Circuitry Analysis Program (MSCAP) and Magnet Arcing (MAGARC) codes to study magnet quench events; notably, MAGARC was used to study quenching during the ITER Engineering Design Activity. However, these legacy codes and capabilities are not parallel and scalable, and new tools are required for future advances in this area, which leads to the INL-developed Multiphysics Object-Oriented Simulation Environment (MOOSE) framework. Developed originally for fission reactor systems under United States Department of Energy, Office of Nuclear Energy modeling and simulation programs, the MOOSE framework has been well-suited to multiscale, multiphysics modeling and simulation needs for nuclear systems. The framework is open-source, well-tested, under continuous development and deployment, and developed to a Nuclear Quality Assurance, Level 1 software quality standard. MOOSE has also been used in the fusion space previously in several projects: INL’s Tritium Migration Analysis Program, Version 8 (TMAP8) for tritium migration, UK Atomic Energy Authority’s A Unified Resource for OpenMC (fusion) Reactor Applications (AURORA) code for fusion thermo-mechanical and neutronics analysis, and Argonne National Laboratory’s Cardinal for high-fidelity computational fluid dynamics and neutronics. However, to model superconducting magnets, several MOOSE enhancements are required: additions to the current MOOSE electromagnetic capabilities, new material libraries for superconductors of interest (such as YBCO), as well as fusion-specific models for thermo-mechanics. This talk will discuss initial development activities to build these capabilities in MOOSE, focusing on initial validation and benchmarking activities. Proposed coupling workflows and future work to support the simulation of fusion magnets and magnet structural assemblies for performance and safety evaluation in MOOSE will also be discussed.

70 - PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1975

Consortium studies of matrix of light gray breccia 73215

A description is presented of the preliminary results of interdisciplinary studies of matrix samples. The significance of the 73215 studies is considered and the relationship of 73215 to other Apollo 17 highland breccias is discussed. According to a tentative hypothesis 73215 is an aggregate of fragments plus melt generated in a very large impact, possibly the Serenitatis basin-forming event. If this is correct, studies of this sample will make it possible to date the Serenitatis event and will provide an insight into the breccia-forming processes associated with such events. Studies of the clasts in the breccia will provide an opportunity to formulate a partial characterization of the preimpact source terrain. An investigation of the deep lunar crust might perhaps also be possible.

James, O. B.↗
DOE OSTI2023

Evidence of pair production of longitudinally polarised vector bosons and study of CP properties in ZZ → 4ℓ events with the ATLAS detector at $ \sqrt{s} $ = 13 TeV

A study of the polarisation and CP properties in ZZ production is presented. The used data set corresponds to an integrated luminosity of 140 fb –1 of proton-proton collisions at a centre-of-mass energy of 13 TeV recorded by the ATLAS detector at the Large Hadron Collider. The ZZ candidate events are reconstructed using two same-flavour opposite-charge electron or muon pairs. The production of two longitudinally polarised Z bosons is measured with a significance of 4.3 standard deviations, and its cross-section is measured in a fiducial phase space to be 2.45 ± 0.60 fb, consistent with the next-to-leading-order Standard Model prediction. The inclusive differential cross-section as a function of a CP-sensitive angular observable is also measured. The results are used to constrain anomalous CP-odd neutral triple gauge couplings.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Study of NuMI Dirt Monte Carlo Events in the ICARUS T600 Detector (Fermilab Internship Final Report)

The moving of the Icarus detector from Laboratori Nazionali del Gran Sasso to Fermilab in order to pursue the physics goals of the Short-Baseline Neutrino (SBN) program has introduced the necessity of a more careful study of backgrounds. In this work some developments of the validation of the Monte Carlo backgrounds for the Icarus run 1 data taking are presented, focusing on the study of the so called “Dirt” events from the NuMI beamline. In particular the analysis hints at a possible underestimation of the muon background. Hopefully this issue will be solved at a later stage of the analysis with the inclusion of components that were not considered in the present work.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Air-sea interactions yielding rapid Beaufort Sea ice losses during the 2021 ONR THINICE Pilot Field Campaign

Seasonal sea ice coverage has declined in the Pacific Arctic region. Losses of the ice cover have modified the region’s surface heat and momentum fluxes providing weather systems more direct interaction with the upper ocean. For example, recent cyclones that have passed over these waters have initiated multiple air-sea interactions and have impacted mixed layer depth, wave properties, and sea ice mass budgets due to diabatic and/or advective processes. As a United States Office of Naval Research (ONR) Departmental Research initiative, one key component of the ONR THINICE program was to advance knowledge on Arctic cyclone coupling with the ocean surface, its ice cover, and understand how such coupling can lead to rapid ice loss. Leading up to the primary field campaign in August 2022, a pilot field campaign from 19 August to 13 September 2021 focused in Utqiagvik, Alaska and Longyearbyen, Svalbard was carried out. This period was marked by frequent Arctic cyclones, periods of strong low-level winds, and stints of extreme short-term Beaufort Sea ice area losses. Cyclones traversed these waters much more frequently than usual during this 26-day period, but did not exhibit extreme central pressures indicative of high intensity and impactful storms. Using ERA5 atmospheric reanalysis fields, CERES and AIRS surface flux retrievals, and in situ sensor data collected from ice-tethered profilers and ice mass balance buoys, we highlight air-sea-ice interactions in the Beaufort Sea during and between these cyclones leading up to extreme ice loss events. Our case study analysis is aimed at understanding how Arctic cyclones, including air-sea interactions prompted by their passage over the marginal ice zone, influence the end-of-summer ice conditions and ice loss events. This study provides an example where frequent, yet weak storms drive rather large marginal sea-scale losses albeit with heterogenous sea ice effects at local scales.

Ballinger, Thomas [International Arctic Research C↗
DOE OSTI2022

Forced Oscillation Grid Vulnerability Analysis and Mitigation Using Inverter-Based Resources: Texas Grid Case Study

Forced oscillation events have become a challenging problem with the increasing penetration of renewable and other inverter-based resources (IBRs), especially when the forced oscillation frequency coincides with the dominant natural oscillation frequency. A severe forced oscillation event can deteriorate power system dynamic stability, damage equipment, and limit power transfer capability. This paper proposes a two-dimension scanning forced oscillation grid vulnerability analysis method to identify areas/zones in the system that are critical to forced oscillation. These critical areas/zones can be further considered as effective actuator locations for the deployment of forced oscillation damping controllers. Additionally, active power modulation control through IBRs is also proposed to reduce the forced oscillation impact on the entire grid. The proposed methods are demonstrated through a case study on a synthetic Texas power system model. The simulation results demonstrate that the critical areas/zones of forced oscillation are related to the areas that highly participate in the natural oscillations and the proposed oscillation damping controller through IBRs can effectively reduce the forced oscillation impact in the entire system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Multi-Level Impacts of Extreme Climate-Related Events on Food Supply Chain: Idaho Potato's Case Study

Background: The world is experiencing frequent extreme weather events like droughts, floods, snowstorms, and shifting of seasons due to climate change. Increasing frequencies and severity of these extreme weather events threaten food security because agriculture is extremely vulnerable to climate change. Higher temperature reduces overall yields of the desirable crops but proliferates weeds and harmful pests. Current data shows that every year U.S. farmers spend more than 11 billion USD to fight weeds and this number is expected to rise in the coming years due to climate change. Due to droughts, the water required for irrigation is becoming scarcer. Since many areas are becoming climatically unusable for crop production, the amount of farmland area is shrinking rapidly. In addition, changes in precipitation patterns are causing crop failures and negatively impacting expected yields. In this study, we investigated how different extreme climate events impact different stages of a food supply chain. Approach: As a case study, we chose Idaho’s potato supply chain (SC). Idaho is the top potato producing state in the U.S. and contributes around 30% of the nation’s potato production. Using the agent-based modeling (ABM) approach, we developed a multi-echelon potato supply chain computer simulation model with six types of agents – seed producers, farmers, shippers, processors, retailers, and logistics companies. In addition to the baseline scenario, we developed two additional climate-related disruption events – drought and snowstorm. We investigated how each of these disruption events impacts different stakeholders across the supply chain in terms of revenue and some other key performance metrics such as lead time, food waste, number of delayed orders, and order fulfillment rate. Results: Extreme climate events not only impact the growers but there is also cascading ripple effects downstream of the supply chain. The methodology developed in this study could be applied to other food and agricultural SC to understand the vulnerabilities of the different links. The findings would potentially help to adopt new policies for the well-being of the overall SC.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1979

Characteristics of the spectra of protons and alpha particles in recurrent events at 1 AU

A study of 31 individual recurrent events from three long-lived corotating particle streams is presented. The spectra of protons and alpha particles during these streams are fit well by an exponential in particle rigidity. Although the spectra show considerable temporal variation, the proton and alpha particle spectra are correlated so that the e-folding rigidities Po(alpha) and Po(p) of the two spectra are in the ratio Po(alpha)/Po(p) = 1.5 plus or minus 0.1. The consistency of this ratio may be a characteristic of the interplanetary acceleration process.

Mewaldt, R. A.↗
NASA NTRS1982

Interplanetary gas. XXVIII - A study of the three-dimensional properties of interplanetary sector boundaries using disconnection events in cometary plasma tails

Studies of the solar wind on the basis of cometary plasma tail observations are considered. Niedner and Brandt (1978, 1979) have concluded that the plasma tail frequently disconnects from the cometary head, and that these disconnection events (DEs) are produced by magnetic reconnection at sector boundary passages. They proposed that the disconnections are a natural combination of Alfven's model and the solar-wind sector structure first discovered by Wilcox and Ness (1965). The DEs can be utilized as probes of interplanetary sector structure. Correlations between DEs and sector boundaries observed at earth are considered, and sector boundary properties deduced from DEs are discussed. Attention is given to a review of the warped sheet model, the latitude extent of sector structure, the sector boundary tilt, and specific sources of error in the tilt angles derived from DEs.

Niedner, M. B., Jr.↗
DOE OSTI2024

Measured Long-Term Solar Irradiance for Climate Studies

Solar radiation is primarily measured using high-quality radiometers (e.g., pyranometers and pyrheliometers). These instruments need to be calibrated regularly (every two years at a minimum). They are also susceptible to various sources of uncertainties. Therefore, rigorous data quality assessment is required to obtain high-confidence data from these radiometers. This is especially true when the data is used to understand climatic trends and/or extreme weather events. In this study, we attempted to create a continuous and reliable dataset by correcting the underlying data which we believe contains bias due to reference pyranometers swap procedures. These biases can be significant which can be up to two percentage points thereby influencing the interpretation of climate changes and/or extreme events. This study elaborates on the biases and methods to correct those biases.

data integrity↗
NASA NTRS1983

Residents' annoyance responses to aircraft noise events

In a study conducted in the vicinity of Salt Lake City International Airport, community residents reported their annoyance with individual aircraft flyovers during rating sessions conducted in their homes. Annoyance ratings were obtained at different times of the day. Aircraft noise levels were measured, and other characteristics of the aircraft were noted by trained observers. Metrics commonly used for assessing aircraft noise were compared, but none performed significantly better than A-weighted sound pressure level. A significant difference was found between the ratings of commercial jet aircraft and general aviation propeller aircraft, with the latter being judged less annoying. After the effects of noise level were accounted for, no significant differences were found between the ratings of landings and takeoffs. Aircraft noise annoyance reactions are stronger in lowered ambient noise conditions. This is consistent with the theory that reduced nighttime and evening ambient levels could create different reactions at different times of day. After controlling for ambient noise in a multiple regression analysis, no significant differences were found between the ratings of single events obtained during the three time periods: morning, afternoon, and evenings.

Dempsey, T. K.↗
DOE OSTI2021

Reconstructing the kinematics of deep inelastic scattering with deep learning

We introduce a method to reconstruct the kinematics of neutral-current deep inelastic scattering (DIS) using a deep neural network (DNN). Unlike traditional methods, it exploits the full kinematic information of both the scattered electron and the hadronic-final state, and it accounts for QED radiation by identifying events with radiated photons and event-level momentum imbalance. The method is studied with simulated events at HERA and the future Electron–Ion Collider (EIC). We show that the DNN method outperforms all the traditional methods over the full phase space, improving resolution and reducing bias. Our method has the potential to extend the kinematic reach of future experiments at the EIC, and thus their discovery potential in polarized and nuclear DIS.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Normalizing flows for domain adaptation when identifying Λ hyperon events

Here this study focuses on the application of a normalizing flow as a method of domain adaptation when classifying physics data. Normalizing flows offer a way to transform data points between two different distributions. The present study investigates a novel method of transforming latent representations of physics data to a normal distribution and then to a physics distribution again. The final distribution models a simulated distribution. After being transformed, the data can be classified by a neural network trained on labeled simulation data. The present study succeeds in training two normalizing flows that can transform between data (or simulation) and a Gaussian distribution.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Algorithmically detected rain-on-snow flood events in different climate datasets: a case study of the Susquehanna River basin

Abstract. Rain-on-snow (RoS) events in regions of ephemeral snowpack – such as the northeastern United States – can be key drivers of cool-season flooding. We describe an automated algorithm for detecting basin-scale RoS events in gridded climate data by generating an area-averaged time series and then searching for periods of concurrent precipitation, surface runoff, and snowmelt exceeding predefined thresholds. When evaluated using historical data over the Susquehanna River basin (SRB), the technique credibly finds RoS events in published literature and flags events that are followed by anomalously high streamflow as measured by gauge data along the river. When comparing four different datasets representing the same 21-year period, we find large differences in RoS event magnitude and frequency, primarily driven by differences in estimated surface runoff and snowmelt. Using dataset-specific thresholds improves agreement between datasets but does not account for all discrepancies. We show that factors such as meteorological forcing and coupling frequency, as well as choice of land surface model, play roles in how data products capture these compound extremes and suggest care is to be taken when climate datasets are used by stakeholders for operational decision-making.

54 ENVIRONMENTAL SCIENCES↗