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At least 577 records · Page 32

Tethered Balloon Observations of Vertically Resolved Aerosol Size Distributions during the U.S. DOE ARM CoURAGE and BNF Campaigns

This dataset contains vertically resolved aerosol particle number size distributions measured using a miniature Scanning Electrical Mobility Spectrometer (mSEMS) deployed aboard the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Tethered Balloon System (TBS) during the 2025 Coast-Urban-Rural Atmospheric Gradient Experiment (CoURAGE) and Bankhead National Forest (BNF) field campaigns. The mSEMS measured aerosol size distributions over approximately 10–300 nm during repeated TBS vertical profiles through the atmospheric boundary layer. The dataset provides measurements of particle number size distributions as a function of particle diameter, altitude, and time and supports investigation of aerosol vertical structure, elevated ultrafine and Aitken-mode particle layers, boundary-layer evolution, and residual-layer processes.

ARM↗

DIGITAL APPLICATIONS USING REAL-TIME VEHICLE EXHAUST INFORMATION

Vehicle emission is a major source of air pollution that causes a significant number of deaths globally. It has a profound impact on energy and the environment as well. The existing vehicle emission monitoring system is unable to help mitigating the pollution properly and therefore, requires precise real-time pollution measurement. The purpose of this paper is to discuss novel applications using the real-time measurement of pollutants from a vehicle tailpipe where exhaust gases enter the environment. Today, it is possible to measure such emission due to the emergence of affordable digital technologies such as the Internet of Things (IoT), wireless connectivity, cloud platform, and artificial intelligence. This paper discusses how digital technologies can be used for real-time monitoring of NOx gas as a measure of vehicle emission and predictive analytics applications. A description of data collection and pre-processing methodologies, actual collected data, and an approach to identify patterns between inputs such as vehicle speed and altitude and output such as NOx emission are included. Applying a simple neural network has produced promising results and is a first step towards developing predictive applications.

Digital, Vehicle Exhaust, IoT, AI, 1.4.2, Predicti↗

Million Worker Study

Radiation health issues have been an important aspect of DOE's Worker Safety and Health programs. The goal is to ensure that workers are adequately protected from the various radiological hazards associated with DOE sites and operations. Since the early 1940’s DOE has supported the conduct of epidemiologic studies of practically every DOE (AEC) facility and collected these data, now managed by the Oak Ridge Associated Universities (ORAU), in remarkable detail. In the early 2000s, the Office of Health, Safety and Security authorized access to specific DOE worker datasets. Shortly thereafter, the DOE Office of Science provided funds for a pilot study that confirmed the feasibility of the Million Worker Study (MWS), and then additional resources were provided (with other agencies) to extend the follow-up of many populations, not just DOE workers, but also atomic veterans, industrial radiographers, nuclear power plant workers and medical radiation workers (the Million Persons Study (MPS)). This proposal was a continuation of support provided by DOE, specifically to extend the follow-up of the worker populations at the Mallinckrodt Chemical Works (MCW) and Los Alamos National Laboratory (LANL). The work addresses DOE’s interest in clarifying the health risks of their workers as well as contributing knowledge on radiation risks that is relevant today with regard to compensation schemes. The findings are also important for the US public in light of the increased population exposure to medical imaging, environmental circumstances such as hydraulic fracturing, increased exposures during high altitude flights, and with regard to nuclear accidents such as Fukushima and possible terrorist events. Furthermore, it is important to consider reducing the uncertainty in current risk estimates by developing risk coefficients based on healthy American workers who are more representative of U.S. workers and the general public than 1945 Japanese survivors of the atomic bombs living in a war-torn country which experienced deprivation, malnourishment, and increased rates of infections and other diseases. But more importantly, it is important to learn whether radiation exposures received gradually over time (e.g., years) are more or less effective in causing health effects, cancer in particular, than if the radiation dose is received all at once in a fraction of a second as experienced by the Japanese atomic bomb survivors. Finally, the ability to evaluate and combine large populations with intakes of radionuclides such as uranium, radium, plutonium, americium and polonium will provide new quantitative knowledge on human health effects that hitherto has not been possible. This cost-efficient study has built on the investments made and foundations laid by investigators and government agencies, including DOE, over the past 30-40 years, which have established early worker cohorts that can now provide answers to questions on the lifetime human health risks associated with low-level radiation exposures. Collaborating institutions included: International Epidemiology Institute, Oak Ridge Associated Universities, Oak Ridge National Laboratory, Los Alamos National Laboratory, Landauer, Inc., and Vanderbilt University. Follow-up of these two populations and the integration of them with the many other cohorts (now a total of 31) in the MPS continues under a separate DOE grant (DE-AU0000046).

61 RADIATION PROTECTION AND DOSIMETRY↗

Mesoscale Modeling to Characterize Eagle Soaring Habitat

Uncovering drivers of risk is crucial to understanding interactions between wildlife and wind turbines, and identifying options for impact minimization. These drivers tie to co-variates linked to behavior and movement patterns that allow us to estimate locations and periods of risk. For volant species, atmospheric flow can have significant influence on flight patterns. For obligate soaring birds, like golden eagles, updraft velocities can inform where eagles are likely to travel, at what altitude, and where conditions are not likely sufficient to sustain soaring flight. This has been an active area of study in recent years, using relatively coarse atmospheric data generally at the 20km x 20km scale or larger. Leveraging a 20-year dataset from the Weather Research and Forecasting Model (WRF) (https://www.mmm.ucar.edu/weather-research-and-forecasting-model), we are quantifying vertical velocities across the continental United States at a 2km x 2km resolution. Wind resource data sets originally were static maps showing the mean annual wind speed over an area. However, for these data sets to be optimally used for various applications they must be high-resolution time series, seamlessly span large geographic contexts, and account for uncertainty in wind speed. The National Renewable Energy Laboratory is producing public available datasets that meet these criteria and will be bias corrected to yield the most accurate wind resource data. This effort is an augment to the current WIND Toolkit which houses a high resolution data set. In the new iteration of the WIND Toolkit, a 20-year dataset will be used to improve the accuracy and estimate uncertainty using ensemble and machine-learning techniques. The resulting product will be the most accurate dataset of its size and at a 2km x 2km spatial and 5-minute temporal resolution. Through this work, a mesoscale vertical velocity layer will be produced by calculating the likelihood of orographic updraft and thermal updraft conditions across the continental United States. Specifically, we will use WRF model output combined with digital elevation maps to predict updrafts and then determine if vertical velocities are sufficient to support Golden Eagle soaring and gliding. Ultimately this data layer will be made available as a GIS layer in the Wind Prospector (maps.nrel.gov/wind-prospector/) tool or a similar framework. Data that will be incorporated include wind speed, direction temperature, relative humidity, barometric pressure, air density, precipitation rate, solar radiation, atmospheric stability, skin temperature, and upward heat flux. These products will advance research on interactions between volant species and wind energy by providing open access to highly resolved data with uncertainty quantification not previously available at this scale.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Assessment of Updraft Modeling Bias Using Computational Fluid Dynamics

Golden Eagle (Aquila chrysaetos) habitats may overlap with wind energy development in some regions of the US. Eagles, and similar soaring bird species, are therefore at risk of collision with wind turbines when flying through wind farms. Recently developed behavioral modeling approaches can predict the presence of eagles near turbines within the rotor-swept layer but require reliable prediction of atmospheric flowfield conditions. In particular, the vertical component of the wind speed dictates a soaring bird's ability to maintain or gain altitude, since they rely on updrafts to subsidize their flight. In this work, we investigate the atmospheric conditions around a wind farm in complex terrain and compare methods for atmospheric characterization. We use computational fluid dynamics (specifically, large-eddy simulations, or LES) to simulate the atmospheric boundary layer over a region encompassing multiple wind farms with high temporal and spatial resolution (seconds and 10's of meters, respectively). We compare traditional non-simulation-based methods of determining the orographic updraft potential based on wind direction, terrain slope and aspect, with the flowfields from LES that include both orographic updrafts alone and combined thermal and orographic updrafts. Preliminary analysis suggests that although the model captures the horizontal pattern of vertical updrafts, their magnitude can be improved with information about the surface heat flux, which is usually correlated with time of the day. Within our study region, we found that the low-fidelity model may over- or underestimate updraft potential by up to 400% at 80 m AGL, depending on local orographic features. This can result in an inaccurate representation of eagle presence and, consequently, risk. Another important finding is that flowfield time-averaging can hide important details about the flight environment, including how thermally generated flow structures within the atmospheric boundary layer (e.g., convective rolls and/or cells) may be important drivers of eagle flight.

atmospheric turbulence↗

Simulation tools, first results and experimental status of the MURAVES experiment

The MUon RAdiography of VESuvius (MURAVES) project aims at the study of Mt. Vesuvius, an active and hazardous volcano near Naples, Italy, with the use of muons freely and abundantly produced by cosmic rays. In particular, the MURAVES experiment intends to perform muographic imaging of the internal structure of the summit of Mt. Vesuvius. The challenging measurement of the rock density distribution in its summit by muography, in conjunction with data from other geophysical techniques, can help model possible eruption dynamics. The MURAVES apparatus consists of an array of three independent and identical muon trackers, with a total sensitive area of 3 square meters. In each tracker, a sequence of 4 XY tracking planes made of plastic scintillators is complemented by a 60 cm thick lead wall inserted between the two downstream planes to improve rejection of background from low energy muons. The apparatus is currently acquiring data. This paper presents preliminary results from the analysis of the first data samples acquired with trackers pointing towards Mt. Vesuvius, including the first relative measurement of the density projection of two flanks of the volcano at three different altitudes; we also present the workflow of the simulation chain of the MURAVES experiment and its ongoing developments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Pilot decision space for sonic boom impact resolution

A system for predicting pressure waves in a sonic boom footprint calculates alternative footprints and pressure waves along a flight path based on speed and altitude modifiers. The available, adjustable parameters are bounded within a flight envelope and along an approved flight corridor. The system receives and incorporates data from external sources, such as weather data, that impacts sonic boom pressure wave predictions and aircraft performance characteristics.

97 MATHEMATICS AND COMPUTING↗

Hydrogen Leak Modeling for Development of Smart Distributed Monitoring Under Unintended Releases

Hydrogen is a versatile and clean energy carrier that can be produced from various renewable sources such as wind, solar, and hydropower. Hydrogen has the potential to play a crucial role in decarbonizing industrial processes that are currently reliant on fossil fuels and provide long-duration and/or seasonal energy storage to enable electricity decarbonization. Hydrogen can also be used as a fuel for fuel cell vehicles, providing a zero-emission alternative to traditional internal combustion engines. DOE launched the Hydrogen Energy Earthshot (Hydrogen Shot) in June 2021 to reduce the cost of clean hydrogen by 80% to $1 per 1 kilogram in 1 decade ("1 1 1"). While promising, Hydrogen is highly-flammable, and in the presence of oxygen, it can form explosive mixtures. . Therefore, understanding leak scenarios is essential to evaluate and mitigate the safety risks associated with potential hydrogen leaks. An increased understanding of leak behavior, and having tools to model leaks, can help assess how hydrogen would disperse in different environments, influencing emergency response plans and safety measures, and identify potential issues with materials and design systems that can withstand the challenges posed by hydrogen. Recently, researchers have attempted to study hydrogen leaks for development of risk management strategies. However, the focus has been on closed or semi-closed spaces like storage rooms, vehicles, garages, and fueling stations - all promising locations for future hydrogen infrastructure. In this presentation, the modeling environment extends the span of research further by modeling hydrogen leak in an outdoor, open space. We will present the key challenges with modeling hydrogen leaks in an uncontrollable environment, how they were handled, and how modeling results informed sensor selection and placement. A Hydrogen research facility at the National Renewable Energy Laboratory (NREL) was used as a case study to model hydrogen leaks. In the future, Hydrogen wide area detection methodologies will be developed and tested at this site to monitor for unintended and operational hydrogen releases. The data generated from modeling will be used to develop a predictive model to detect hydrogen leak location based on concentration measured by sensors in this open space. Furthermore, the facility was also chosen because controlled hydrogen releases can be performed. A computational fluid dynamics (CFD) based modeling approach was taken to model hydrogen leak. The full-scale hydrogen facility was modeled with a large ambient domain. The electrolyzer at the facility can produce a controlled release rate of 27 kg-H2/hr. Site-specific atmospheric and weather condition data such as wind direction, wind speed at various altitudes, and temperature were used as inputs to the model. To capture the variability of weather conditions, a subset of the weather conditions experienced during daytime hours without precipitation over the course of three months was generated; using established data clustering techniques, a total of 100 condition sets were chosen. The results show statistical distributions and ranges of hydrogen concentrations at locations throughout the domain. These distributions are compared to experimental data from a constant mass flow, controlled hydrogen release at the facility. The stochastic wind conditions of the release make direct validation difficult, therefore, statistical comparison approaches were used. Wind conditions are found to significantly impact the release behavior, including direction and concentration. Sensor selection and placement is proposed for the facility and is now based on release behavior predicted for the facility given its weather patterns; this is much more informed than without the modeling results. The methodology and analysis procedure can be translated to other facilities using modified geometries and site-specific weather conditions. Hydrogen holds great promise as a renewable energy fuel, but ensuring safety in its production, storage, and use is paramount. Studying potential leak scenarios in an open space will help develop sensors to detect hydrogen on a large spectrum of concentration and eventually build a smart distributed monitoring system.

CFD↗

Improving The Simulation Of Muons In The Rock Around The NOvA Near Detector

This thesis investigates the viability of the PROPOSAL lepton propagation tool as an alternative to the commonly used GEANT4 simulation for muon propagation within the NOvA experiment. The main objectives of this research were twofold: first, to validate the accuracy of PROPOSAL data in simulating rock muons when compared to GEANT4 data; second, to explore the practical application of transporting cosmic muons through the NOvA rock region using PROPOSAL. Additionally, an 1D analysis was conducted to determine the optimal altitude at which to hand over the simulation task from PROPOSAL to GEANT4, should we choose to use PROPOSAL for rock simulations and GEANT4 for detector simulations in the NOvA experiment. By thoroughly examining these aspects, this study aims to contribute to the advancement of muon simulation techniques and their implementation in high-energy experiments such as NOvA.

Parvez, Radwan↗

Fleet Hydrogen Basics

Hydrogen is one transportation fuel option public and private heavy-duty vehicle fleets can consider. Heavy-duty hydrogen fuel cell electric vehicles provide the performance benefits of electric drivetrains (e.g., high instantaneous torque for heavy loads and performance improvements at high altitudes and on steep grades) and can contribute to air quality improvements in high-traffic areas. For some regions and duty cycles, hydrogen could be a better fit for heavy-duty fleets than current battery-electric options because of the increased range and faster fueling times.

30 DIRECT ENERGY CONVERSION↗

The General Antiparticle Spectrometer (GAPS) Antarctic Balloon Payload

The General Antiparticle Spectrometer (GAPS) is an Antarctic stratospheric balloon mission designed to provide unmatched sensitivity to low-energy (<0.25 GeV/n) cosmic-ray antiprotons, antideuterons, and antihelium nuclei as signatures of dark matter. The distinctive GAPS particle identification technique relies on measuring the energy loss along the track of an incoming antinucleus as it slows down and is captured into an exotic atom, and then detecting the de-excitation X-rays and the nuclear annihilation products. This measurement is realized using a Tracker composed of more than 1000 custom silicon strip detectors and a plastic scintillator time-of-flight (TOF) system instrumenting more than 40m$^2$. Together, these subsystems provide the velocity and energy resolution, stopping power, particle tracking, and X-ray identification necessary to distinguish rare antinucleus signals from the abundant positive-nucleus backgrounds, all within the constraints of a high-altitude mission. A multi-loop capillary heat pipe system has been developed to maintain the tracker operating temperature with significant mass and power savings over a conventional pump-based system. The first GAPS science payload flew for 25 days during the 2025/26 NASA Antarctic balloon campaign. We detail the design, integration, and commissioning of the payload prior to flight.

Aoyama, Kazutaka [JAXA, Sagamihara]↗

Revealing the Formation of the <20 MeV Inner Proton Radiation Belt at L ∼ 2 During the 10–11 May 2024 Superstorm

A new proton radiation belt was identified during the geomagnetic superstorm of 10–11 May 2024. To investigate its origin, we use an MHD-test particle simulation to model solar energetic proton (SEP) trapping and the evolution of the initial trapped proton population during the storm. The simulation weights the injected SEP population with interplanetary proton measurements and the initial trapped proton radial profile with energy-resolved Weather System Follow-on-Microwave (WSF-M) proton data, enabling quantitative comparison with observations. Results show that SEP contributions are minimal; the new ∼2–18 MeV belt at L ∼ 2 forms mainly through redistribution and energization of the initial trapped population within hours of the CME shock, driven by subsequent electric field impulses, rather than the initial shock alone. These results provide quantitative evidence based on realistic, plasma-dependent field dynamics and significantly advance understanding of inner radiation belt formation.

79 ASTRONOMY AND ASTROPHYSICS↗

Gap-filled meteorological data (2011-2020) and modeled potential evapotranspiration data from the KCOMTCRE2 WeatherUnderground weather station, from the East River Watershed, Colorado

This dataset is a gap-filled meteorological dataset (years 2011-2020) that includes modeled potential evapotranspiration from a station near the pumphouse on the East River (KCOMTCRE2 WeatherUnderground ) near the top of the ridge at the Pumphouse PLM wells. These datasets are useful as model inputs. The KOMTCRE2 station is located on Mt. Crested Butte in Colorado. More information can be found at: https://www.wunderground.com/dashboard/pws/KCOMTCRE2/table/2020-12-7/2020-12-7/daily. The purpose of this dataset is to use as input to hydrological and flow-reactive transport models as meteorological forcing. One attached file includes metadata files with definitions for all variables and units. Subsequent files include the same time series dataset, but aggregated and gap-filled at different time-stamp intervals. These include 5 minute time-series, 15 minute time-series, 30 minute time-series, 1 hour time-series, daily time-series, and hourly and daily modeled evapotranspiration.

54 ENVIRONMENTAL SCIENCES↗

CSAPR2 cell-tracking data collected during TRACER

One of the challenges of analyzing convective cell properties is quick evolution of the individual convective cells. While the operational radar data provide great a data set to analyze the evolution of radar observables of convective precipitation clouds statistically, previous studies also suggested that, because of the quick evolution of cell life cycle, conventional radar volume scan strategies taking ~5-7 minutes might not capture the detailed evolution. The TRACER campaign deployed CSAPR2, which performed frequent update of RHI and sector PPI scans to track convective cells every < 2 minutes guided by a new cell-tracking framework, Multisensor Agile Adaptive Sampling (MAAS; Kollias et al. 2020). This allows for capturing fast-evolving radar observables. The submitted data files are CSAPR2 data in CfRadial format collected during the TRACER field campaign from June to September 2020. The data files include processed radar variables including: noise-masked reflectivity and differential reflectivity corrected for rain attenuation and systematic biases, noise-masked dealiased radial velocity, specific differential phase, locations of target cells (latitude, longitude, radar range), and radar-echo classification.

54 ENVIRONMENTAL SCIENCES↗

Early glacier advance in New Zealand during the Antarctic Cold Reversal

ABSTRACT Glacial landscapes preserve records of past climate change. Investigating the glacier–climate system over the Late Quaternary provides information about past climate change and context for present‐day glacier response to climate warming. Using 28 beryllium‐10 ( 10 Be) surface exposure dates and snowline reconstructions, we present glacier fluctuations and climate changes for the Antarctic Cold Reversal in the Ahuriri River catchment, Southern Alps of New Zealand (44°7′50″S, 169°38′29″E). Prominent terminal and lateral moraine features from the upper right tributary of the Ahuriri River valley have exposure ages of 14.5 ± 0.3, 13.6 ± 0.3 and 12.6 ± 0.2 ka, suggesting retreat of the glacier during the Antarctic Cold Reversal. Maximum elevation of lateral moraines (MELM) and accumulation area ratio (AAR) suggest snowline elevations at these ages were ≤700, ≤630 and ~360 m lower than today, respectively. This equates to air temperatures ≤3.9, ≤3.5 and 2.3 ± 0.7 °C lower than today (1981–2010), assuming no changes in past precipitation. Ice‐sculpted bedrock surfaces bound by a lateral moraine at nearby Canyon Creek have an age of 13.1 ± 0.3 ka, indicating the moraine correlates with those in the Ahuriri upper right tributary. MELM and AAR reconstructions from the Canyon Creek suggest that snowline elevations at 14.5–13.6 ka were ≤500 or ~380 m lower than today, corresponding to air temperatures ≤2.8 or 2.4 ± 0.7 °C lower than the present‐day (1981–2010). Our results provide insight into the structure of the Antarctic Cold Reversal in the Southern Alps, showing that the largest glacier advance occurred at the start of this interval at c . 14.5 ± 0.3 ka and was followed by gradual retreat. We hypothesize that the early cooling and glacier readvance in New Zealand at the onset of the Antarctic Cold Reversal were triggered by a latitudinal shift of the Southern Hemisphere westerly wind belt.

58 GEOSCIENCES↗

Data-driven analysis of relight variability of jet fuels induced by turbulence

For safety purposes, reliable reignition of aircraft engines in the event of flame blow-out is a critical requirement. Typically, an external ignition source in the form of a spark is used to achieve a stable flame in the combustor. However, such forced turbulent ignition may not always successfully relight the combustor, mainly because the state of the combustor cannot be precisely determined. Uncertainty in the turbulent flow inside the combustor, inflow conditions, and spark discharge characteristics can lead to variability in sparking outcomes even for nominally identical operating conditions. Prior studies have shown that of all the uncertain parameters, turbulence is often dominant and can drastically alter ignition behavior. For instance, even when different fuels have similar ignition delay times, their ignition behavior in practical systems can be completely different. In practical operating conditions, it is challenging to understand why ignition fails and how much variation in outcomes can be expected. The focus of this work is to understand relight variability induced by turbulence for two different aircraft fuels, namely Jet-A and a variant named C1. A detailed, previously developed simulation approach is used to generate a large number of successful and failed ignition events. Using this data, the cause of misfire is evaluated based on a discriminant analysis that delineates the difference between turbulent initial conditions that lead to ignition or failure. From the discriminant analysis, a compressed sensing algorithm is then applied to help pinpoint the locations of relevant turbulent features. Findings from the discriminant analysis are confirmed with the time history of near kernel properties. Next, a clustering strategy is used to identify ignition and misfire modes. With this approach, it was determined that the cause of ignition failure is different for the two fuels. While it was found that Jet-A is influenced by fuel entrainment, C1 was found to be more sensitive to small scale turbulence features. Finally, a larger variability is found in the ignition modes of C1, which can be subject to extreme events induced by kernel breakdown.

42 ENGINEERING↗

Geology and elevation shape bacterial assembly in Antarctic endolithic communities

Ice free areas of continental Antarctica are among the coldest and driest environments on Earth, and yet, they support surprisingly diverse and highly adapted microbial communities. Endolithic growth is one of the key adaptations to such extreme environments and often represents the dominant life-form. Despite growing scientific interest, little is known of the mechanisms that influence the assembly of endolithic microbiomes across these harsh environments. Here, we used metagenomics to examine the diversity and assembly of endolithic bacterial communities across Antarctica within different rock types and over a large elevation range. While granite supported richer and more heterogeneous communities than sandstone, elevation had no apparent effect on taxonomic richness, regardless of rock type. Conversely, elevation was clearly associated with turnover in community composition, with the deterministic process of variable selection driving microbial assembly along the elevation gradient. The turnover associated with elevation was modulated by geology, whereby for a given elevation difference, turnover was consistently larger between communities inhabiting different rock types. Overall, selection imposed by elevation and geology appeared stronger than turnover related to other spatially-structured environmental drivers. Our findings indicate that at the cold-arid limit of life on Earth, geology and elevation are key determinants of endolithic bacterial heterogeneity. This also suggests that warming temperatures may threaten the persistence of such extreme-adapted organisms.

54 ENVIRONMENTAL SCIENCES↗

Recovering new viruses from New Mexico soils

Here, we utilized metagenomic and size-filtered virome sequencing to recover 4,157 medium, high, or complete quality viral genomes from soils taken from three high elevation sites in New Mexico, USA. Among recovered viral genomes, 90% were from size-filtered samples, indicating the importance of this enrichment in assessments of complex viromes.

59 BASIC BIOLOGICAL SCIENCES↗