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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Observations Show Charge Density of Volcanic Plumes is Higher Than Thunderstorms

We analyze slow electric field change and lightning mapping measurements to provide insight into the characteristics of volcanic lightning and the associated implications on charging processes and the charge structure of a Vulcanian eruption plume. Data were obtained during a multi-instrumental field campaign at Sakurajima volcano in 2015 when the Showa crater was active. We combine the electric field change and lightning mapping data from one explosive eruption on June 6, 2015 to identify individual flashes. From this, we interpret the flash type and polarity. In addition, the long-time constant of the electric field change instrument allowed measurement of the quasi-static field associated with charge separation in the eruption plume. We find that both intracloud and cloud-to-ground discharges occurred, and the polarity of cloud-to-ground discharges were all negative. The quasi-static field measurement showed the plume carried a net negative charge. We calculate both the total charge transferred by cloud-to-ground discharges and the net charge density of the eruption plume. We find that cloud-to-ground discharges transfer an average of –0.41C per flash and the net charge density was –33C/ km 3 . Here, the percent error is at least 200%, due to uncertainty in the antenna gain. We show that these estimates are consistent with lightning that is 100 m in length. Further, the average flash rate during the first 8 s following the onset of eruption was five flashes per second. After that time, the flash rate abruptly decreased, which may be related to the end of gas-thrust forcing.

58 GEOSCIENCES↗
DOE OSTI2025

DARHT Axis 1 Bremsstrahlung Dose Measurements

he Dual-Axis Radiographic Hydrodynamic Test (DARHT) facility provides flash radiography ca pabilities using two electron Linear Induction Accelerators (LIA’s). Axis-1 of DARHT produces, nominally, a 20-MeV, 1.5-kA, 80-ns-Full Width at Half Max (FWHM) electron beam. The elec tron beam is focused on to a tantalum target to produce a Bremsstrahlung x-ray dose for flash radiography of dynamic systems. This paper will describe and compare a variety of techniques for determining the x-ray dose for two different beam currents produced by a 55mm and 70mm diameter cathode. The data was taken in November of 2022 on DARHT Axis 1. The experimen tal measurement techniques include a platinum calorimeter, a diamond radiation detector (DRD), and a Compton diode (CD). Gafchromic EBT3 self-developing dosimetry film was also used, but no useful results were obtained. Beam charge and energy were recorded and dose was calculated with this information using xtr with Dosecalcx, and simple dose charge-energy scaling. In this pa per, we will discuss the target geometry and experimental layout, the methods of measuring dose, dose diagnostics and measurement of beam charge and energy, and the comparisons of data and calculations.

43 PARTICLE ACCELERATORS↗
NASA NTRS1985

Voyager observations of ion phase space densities in the Jovian magnetosphere

Data from the Voyager low-energy charged particle experiment (LECP) were used to calculate ion phase space densities in Jupiter's magnetosphere. The calculation of F at constant mu and J(2) requires the determination of particle fluxes at specific pitch angles and energies. It is shown that the greatest uncertainties in the determination of F from LECP data arise from the fits to the measured pitch angle distributions and differential energy spectra. An estimate is provided of this uncertainty and of others arising from model radial diffusion coefficients and magnetic fields. The general nature of the curves is consistent with inward diffusion of these energetic ions from the outer magnetosphere combined with losses near but starting beyond the orbit of Io. These losses are not consistent with simple satellite sweeping by Io alone and are probably due to an Io-torus wave-particle interaction. The lifetime against loss deduced from the data is approximately 20,000 s near Io and is a value consistent with strong diffusion losses. The measured ion loss rate declines much more rapidly than the strong diffusion loss rate as L increases from 7 to 9, suggesting that the loss rate is well below the strong-diffusion rate beyond 7 Jupiter radii for ions observed by the LECP.

Paonessa, M.↗
NASA NTRS1973

Calculation of energy deposition distributions for simple geometries

When high-energy charged particles pass through a thin detector, the ionization energy loss in that detector is subject to fluctuations or straggling which must be considered in interpreting the data. Under many conditions, which depend upon the charge and energy of the incident particle and the detector geometry, the ionization energy lost by the particle is significantly different from the energy deposited in the detector. This problem divides naturally into a calculation of the energy loss that results in excitation and low-energy secondary electrons which do not travel far from their production points, and a calculation of energy loss that results in high-energy secondary electrons which can escape from the detector. The first calculation is performed using a modification of the Vavilov energy loss distribution. A cutoff energy is introduced above which all electrons are ignored and energy transferred to low energy particles is assumed to be equivalent to the energy deposited by them. For the second calculation, the trajectory of the primary particle is considered as a source of secondary high-energy electrons. The electrons from this source are transported using Monte Carlo techniques and multiple scattering theory, and the energy deposited by them in the detector is calculated. The results of the two calculations are then combined to predict the energy deposition distribution. The results of these calculations are used to predict the charge resolution of parallel-plate pulse ionization chambers that are being designed to measure the charge spectrum of heavy nuclei in the galactic cosmic-ray flux.

Watts, J. W., Jr.↗
DOE OSTI2021

Electric Vehicle Charging Infrastructure Trends from the Alternative Fueling Station Locator: First Quarter 2021

The U.S. Department of Energy’s (DOE’s) Alternative Fueling Station Locator contains information on public and private non-residential alternative fueling stations in the United States and Canada and currently tracks ethanol (E85), biodiesel, compressed natural gas, electric vehicle (EV) charging, hydrogen, liquefied natural gas, and propane stations. Of these fuels, EV charging continues to experience rapidly changing technology and growing infrastructure. This report provides a snapshot of the state of EV charging infrastructure in the United States in the first calendar quarter of 2021 (Q1). Using data from the Station Locator, this report breaks down the growth of public and private charging infrastructure by charging level, network, and location. Additionally, this report measures the current state of charging infrastructure compared with the target infrastructure volume for 2030. This information is intended to help transportation planners, policymakers, researchers, infrastructure developers, and others understand the rapidly changing landscape for EV charging. This is the fifth report in a series. Previous reports from 2020 can be found in the Alternative Fuels Data Center (AFDC) and National Renewable Energy Laboratory (NREL) publication databases, as well as the AFDC Charging Infrastructure Trends page (https://afdc.energy.gov/fuels/electricity_infrastructure_trends.html).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Electric Vehicle Charging Infrastructure Trends from the Alternative Fueling Station Locator: Second Quarter 2021

The U.S. Department of Energy's (DOE's) Alternative Fueling Station Locator contains information on public and private non-residential alternative fueling stations in the United States and Canada and currently tracks ethanol (E85), biodiesel, compressed natural gas, electric vehicle (EV) charging, hydrogen, liquefied natural gas, and propane stations. Of these fuels, EV charging continues to experience rapidly changing technology and growing infrastructure. This report provides a snapshot of the state of EV charging infrastructure in the United States in the second calendar quarter of 2021 (Q2). Using data from the Station Locator, this report breaks down the growth of public and private charging infrastructure by charging level, network, and location. Additionally, this report measures the current state of charging infrastructure compared with the target infrastructure volume for 2030. This information is intended to help transportation planners, policymakers, researchers, infrastructure developers, and others understand the rapidly changing landscape for EV charging. This is the sixth report in a series. Reports from previous quarters can be found in the Alternative Fuels Data Center (AFDC) and National Renewable Energy Laboratory (NREL) publication databases, as well as the AFDC Charging Infrastructure Trends page (https://afdc.energy.gov/fuels/electricity_infrastructure_trends.html).

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Electric Vehicle Charging Infrastructure Trends from the Alternative Fueling Station Locator (Third Quarter 2021)

The U.S. Department of Energy's Alternative Fueling Station Locator contains information on public and private nonresidential alternative fueling stations in the United States and Canada and currently tracks ethanol (E85), biodiesel, compressed natural gas, electric vehicle (EV) charging, hydrogen, liquefied natural gas, and propane stations. Of these fuels, EV charging continues to experience rapidly changing technology and growing infrastructure. This report provides a snapshot of the state of EV charging infrastructure in the United States in the third calendar quarter of 2021 (Q3). Using data from the Station Locator, this report breaks down the growth of public and private charging infrastructure by charging level, network, and location. Additionally, this report measures the current state of charging infrastructure compared with two different 2030 infrastructure requirement scenarios. This information is intended to help transportation planners, policymakers, researchers, infrastructure developers, and others understand the rapidly changing landscape for EV charging. This is the seventh report in a series. Reports from previous quarters can be found in the Alternative Fuels Data Center (AFDC) and National Renewable Energy Laboratory (NREL) publication databases, as well as the AFDC Charging Infrastructure Trends page (https://afdc.energy.gov/fuels/electricity_infrastructure_trends.html).

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Electric Vehicle Charging Infrastructure Trends from the Alternative Fueling Station Locator: Fourth Quarter 2021

The U.S. Department of Energy's Alternative Fueling Station Locator contains information on public and private nonresidential alternative fueling stations in the United States and Canada and currently tracks ethanol (E85), biodiesel, compressed natural gas, electric vehicle (EV) charging, hydrogen, liquefied natural gas, and propane stations. Of these fuels, EV charging continues to experience rapidly changing technology and growing infrastructure. This report provides a snapshot of the state of EV charging infrastructure in the United States in the fourth calendar quarter of 2021 (Q4). Using data from the Station Locator, this report breaks down the growth of public and private charging infrastructure by charging level, network, and location. Additionally, this report measures the current state of charging infrastructure compared with two different 2030 infrastructure requirement scenarios. This information is intended to help transportation planners, policymakers, researchers, infrastructure developers, and others understand the rapidly changing landscape for EV charging. This is the eighth report in a series. Reports from previous quarters can be found in the Alternative Fuels Data Center (AFDC) and National Renewable Energy Laboratory (NREL) publication databases, as well as the AFDC Charging Infrastructure Trends page (https://afdc.energy.gov/fuels/electricity_infrastructure_trends.html).

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1986

Solar wind Fe and CNO measurements in high-speed flows

Solar wind characteristics in driver plasma and coronal hole-associated flow types are analyzed. Measurements of solar wind Fe charge states and densities in well-defined driver plasma and coronal hole-associated high-speed streams, and charge distributions of CNO ions in high speed streams collected with the ultra low energy charge analyzer on ISEE 3 are examined. The Fe-H velocity differences and Fe/H abundance ratios are studied. The data reveal that the driver plasma solar wind has charge states of 15 or 16 with a coronal temperature = 4 x 10 to the 6th K, and the Fe charge states distributions in coronal hole-associated streams = 9 or 10 with a coronal temperature = 1.4 x 10 to the 6th K; the ionization temperature for the CNO group = (1.3 + or - 0.3) x 10 to the 6th K.

Ipavich, F. M.↗
DOE OSTI2025

A method for modeling battery-temperature-aware EV power profiles utilizing Next-Gen Profile data

With the expected increase in the number of electric vehicles (EVs) on the road in the coming years, it is important that analysis tools are capable of modeling and predicting the expected load on the power grid due to both individual EV charging sessions as well as large populations of vehicles. To do this accurately, the power profile of an EV charge session must be accurately modeled, including for scenarios where the temperature is above or below the ideal, and also take into account the nuances of manufacturer charging preferences. This paper introduces a method that utilizes the data in the Next-Gen Profile (NGP) data collection project to build a model of EV charging that takes into account the variations in charging power that occur due to off-nominal battery temperature and manufacturer preferences that limit power due to cold temperatures or high battery state-of-charge.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Measurement of charged-current $\nu_{\mu}$ and $\nu_e$ interactions with wire-cell in MicroBooNE towards a search for low-energy $\nu_e$ excess

This technote summarizes the existing work in searching for $\nu_e$ low-energy excess (eLEE) in MicroBooNE Booster Neutrino Beam (BNB) data stream based on the Wire-Cell event reconstruction paradigm. The charged-current $\nu_{\mu}$ and $\nu_e$ events are selected from the 5.3e19 POT data from the BNB beam and 2.06e20 POT data from Neutrinos at the Main Injector (NuMI) beam. The charged-current $\nu_e$ selection results from the BNB data that are sensitive to the eLEE search are not included. Various comparisons between data and Monte Carlo predictions are performed to validate the overall model and demonstrate the power of the analysis techniques.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1986

Effects of variables upon pyrotechnically induced shock response spectra

Throughout the aerospace industry, large variations of 50 percent (6 dB) or more are continually noted for linear shaped charge (LSC) generated shock response spectra (SRS) from flight data (from the exact same location on different flights) and from plate tests (side by side measurements on the same test). A research program was developed to investigate causes of these large SRS variations. A series of ball drop calibration tests to verify calibration of accelerometers and a series of plate tests to investigate charge and assembly variables were performed. The resulting data were analyzed to determine if and to what degree manufacturing and assembly variables, distance from the shock source, data acquisition instrumentation, and shock energy propagation affect the SRS. LSC variables consisted of coreload, standoff, and apex angle. The assembly variable was the torque on the LSC holder. Other variables were distance from source of accelerometers, accelerometer mounting methods, and joint effects. Results indicated that LSC variables did not affect SRS as long as the plate was severed. Accelerometers mounted on mounting blocks showed significantly lower levels above 5000 Hz. Lap joints did not affect SRS levels. The test plate was mounted in an almost free-free state; therefore, distance from the source did not affect the SRS. Several varieties and brands of accelerometers were used, and all but one demonstrated very large variations in SRS.

Smith, J. L.↗
DOE OSTI2022

Electric Vehicle Charging Infrastructure Trends from the Alternative Fueling Station Locator: First Q2022

The U.S. Department of Energy's Alternative Fueling Station Locator contains information on public and private nonresidential alternative fueling stations in the United States and Canada and currently tracks ethanol (E85), biodiesel, compressed natural gas, electric vehicle (EV) charging, hydrogen, liquefied natural gas, and propane stations. Of these fuels, EV charging continues to experience rapidly changing technology and growing infrastructure. This report provides a snapshot of the state of EV charging infrastructure in the United States in the first calendar quarter of 2022 (Q1). Using data from the Station Locator, this report breaks down the growth of public and private charging infrastructure by charging level, network, and location. Additionally, this report measures the current state of charging infrastructure compared with two different 2030 infrastructure requirement scenarios. This information is intended to help transportation planners, policymakers, researchers, infrastructure developers, and others understand the rapidly changing landscape of EV charging infrastructure. This is the ninth report in a series. Reports from previous quarters can be found in the Alternative Fuels Data Center (AFDC) and National Renewable Energy Laboratory (NREL) publication databases, as well as the AFDC Charging Infrastructure Trends page (https://afdc.energy.gov/fuels/electricity_infrastructure_trends.html).

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Electric Vehicle Charging Infrastructure Trends from the Alternative Fueling Station Locator (Second Quarter 2022)

The U.S. Department of Energy's Alternative Fueling Station Locator contains information on public and private nonresidential alternative fueling stations in the United States and Canada and currently tracks ethanol (E85), biodiesel, compressed natural gas, electric vehicle (EV) charging, hydrogen, liquefied natural gas, and propane stations. Of these fuels, EV charging continues to experience rapidly changing technology and growing infrastructure. This report provides a snapshot of the state of EV charging infrastructure in the United States in the second calendar quarter of 2022 (Q2). Using data from the Station Locator, this report breaks down the growth of public and private charging infrastructure by charging level, network, and location. Additionally, this report measures the current state of charging infrastructure compared with two different 2030 infrastructure requirement scenarios. This information is intended to help transportation planners, policymakers, researchers, infrastructure developers, and others understand the rapidly changing landscape of EV charging infrastructure. This is the tenth report in a series. Reports from previous quarters can be found in the Alternative Fuels Data Center (AFDC) and National Renewable Energy Laboratory (NREL) publication databases, as well as the AFDC Charging Infrastructure Trends page (https://afdc.energy.gov/fuels/electricity_infrastructure_trends.html).

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Electrifying New York City Ride-Hailing fleets: An examination of the need for public fast charging

This report assesses the scale of public fast charging needed to electrify approximately 20,000 vehicles across the yellow cab and for-hire segments in New York City. The analysis considers real-world trip data in conjunction with driver home locations, overnight charging access rates, driver schedules, and more. Outcomes indicate that the existing charging network in New York City is not adequate even in the most optimistic scenario; 1,054 150-kW ports are required when 15% of drivers have access to overnight charging, whereas 367 150-kW ports are needed when 100% of drivers have access. Results also indicate that although charging is demanded in areas nearby high trip demand, fast charging ports are also demanded in areas near driver residences as a supplement for home charging in scenarios with limited overnight charging access. These findings motivate investment into both overnight charging and public fast charging to meet the charging demands of ride-hailing fleets.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

EV Charging Infrastructure Energization: An Overview of Approaches for Simplifying Processes and Accelerating Timelines to Processing EV Charging Load Service Requests

The United States has seen significant growth in electric vehicle (EV) adoption, leading to increased demand for EV charging infrastructure and electricity. Growth in electricity demand from EV charging, coupled with growth in other sectors like data centers and electrification of other sectors, is impacting electricity infrastructure and load service request processes after two decades of relatively flat electricity demand. While most electric vehicle charging occurs at home, enroute and depot charging for medium and heavy-duty vehicles, both using high-powered EV charging, are critical to meet electric vehicle operational needs. Over the past decade, EV charging infrastructure site developers, site hosts, and electric distribution utilities have navigated the process to integrate chargers onto the electric grid. Site developers and site hosts have expressed distress that the integration process for high-powered EV charging projects does not meet the needs of the EV market for timeliness or cost. High-powered charging stations typically require a load service request or an agreement with the local utility to connect to the grid. The process of energizing a new high-powered charging site can be complex and time consuming, often taking up to 2 years. This timeline is the result of current utility energization processes having been designed for construction projects that take longer to build (i.e., buildings). The specific challenges stem from various factors, including compartmentalization in application processes, the integration of EV charging process approvals with other distributed energy resources (DERs), and the need to ensure grid reliability. The energization process needs to evolve to meet the growing demand for high-powered EV charging. This white paper compiles information gathered through various conversations with key stakeholders, including utilities, utility regulators, EV charging operators, site developers, and authorities having jurisdiction (AHJ) as well as through an extensive literature review. This document identifies the challenges and provides potential solutions to streamline the process of connecting EV charging infrastructure to the power grid in the United States, serving as a starting point for future conversations around these solutions.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Rapid Detection of Anomalies in Battery Energy Storage System Data

Data analytics is pivotal in assessing the technical characteristics and performance of Battery Energy Storage Systems (BESS), underpinning BESS modeling, optimization, and control. However, raw datasets frequently harbor anomalies from measurement errors and equipment malfunctions, impacting BESS reliability and analysis accuracy To address the challenge, this paper presents a novel methodology for the rapid detection of anomalous charge or discharge cycles within BESS operational data, expediting the cleaning process while ensuring data integrity. We’ve collected diverse and comprehensive real-world BESS operational datasets in collaboration with the Electric Power Research Institute and multiple Washington State utilities. These datasets serve dual roles: enabling comprehensive data exploration and analysis for understanding underlying challenges and method development, while also acting as a vital validation resource, demonstrating practical effectiveness. The proposed method detects anomalies and aids in their resolution, improving system performance characterization precision. It also reveals recurring data anomaly sources, offering insights for data collection and handling enhancement. Practitioners can gain valuable insights from the identified anomalous cycles in the real-world datasets along with the investigative process for root cause analyses and essential data cleaning steps.

Crawford, Aladsair J.↗
NASA NTRS2015

NASA Ames UV-LED Poster Overview

UV-LED is a small satellite technology demonstration payload being flown on the Saudisat-4 spacecraft that is demonstrating non-contacting charge control of an isolated or floating mass using new solid-state ultra-violet light emitting diodes (UV-LEDs). Integrated to the rest of the spacecraft and launched on a Dnepr in June 19, 2014, the project is a collaboration between the NASA Ames Research Center (ARC), Stanford University, and King Abdulaziz City for Science and Technology (KACST). Beginning with its commissioning in December, 2015, the data collected by UV-LED have validated a novel method of charge control that will improve the performance of drag-free spacecraft allowing for concurrent science collection during charge management operations as well as reduce the mass, power and volume required while increasing lifetime and reliability of a charge management subsystem. UV-LED continues to operate, exploring new concepts in non-contacting charge control and collecting data crucial to understanding the lifetime of ultra-violet light emitting diodes in space. These improvements are crucial to the success of ground breaking missions such as LISA and BBO, and demonstrates the ability of low cost small satellite missions to provide technological advances that far exceed mission costs.

KACST↗