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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Observation and modeling of compressional Pi 3 magnetic pulsations

Compressional magnetic pulsations with irregular waveforms and periods longer than 150 s (here termed Pi 3) have been studied by using data from Active Magnetospheric Particle Tracer Explorers Charge Composition Explorer (AMPTE/CCE) and GOES 5 and 6 in the dayside magnetosphere and compared with signatures on the ground at low latitudes by using data from Kakioka station (L = 1.25). On the ground, the pulsations appear in the horizontal component. A study of 17 such concurrent events during a 2-month period in 1986 reveals the following pulsation characteristics. (1) The peak-to-peak amplitudes in space (delta B(sub T)) and on the ground (delta H) are comparable and are in the range of 0.5-7 nT. (2) On the ground the pulsations can be seen at all local times, even at midnight, while at geostationary orbit they are observed only on the dayside with a clear amplitude maximum at noon. (3) The pulsations on the ground lag those observed by CCE near local noon, and the lag increases as the local time separation between CCE and the ground station increases. The time lag is 1-2 min longer when the ground station is on the nightside than when it is on the dayside. (4) The time lag between pulsations observed at geostationary orbit and near noon by CCE varies systematically with local time and is about 2 min per 6 hours of local time separation. These observations indicate that some nightside pulsations in the Pi 3 band have dayside origins. The position dependence of the pulsation amplitude can be explained well by changes in the magnetopause current, which are in turn presumably caused by changes in the solar wind dynamic pressure. The time lags observed in space are consistent with signal propagation in the MHD fast mode, but the variation in space-ground time lags with ground station local time must be attributed to another mechanism.

Matsuoka, Hitoshi↗
NASA NTRS2016

An Efficient, FPGA-Based, Cluster Detection Algorithm Implementation for a Strip Detector Readout System in a Time Projection Chamber Polarimeter

A fundamental challenge in a spaceborne application of a gas-based Time Projection Chamber (TPC) for observation of X-ray polarization is handling the large amount of data collected. The TPC polarimeter described uses the APV-25 Application Specific Integrated Circuit (ASIC) to readout a strip detector. Two dimensional photoelectron track images are created with a time projection technique and used to determine the polarization of the incident X-rays. The detector produces a 128x30 pixel image per photon interaction with each pixel registering 12 bits of collected charge. This creates challenging requirements for data storage and downlink bandwidth with only a modest incidence of photons and can have a significant impact on the overall mission cost. An approach is described for locating and isolating the photoelectron track within the detector image, yielding a much smaller data product, typically between 8x8 pixels and 20x20 pixels. This approach is implemented using a Microsemi RT-ProASIC3-3000 Field-Programmable Gate Array (FPGA), clocked at 20 MHz and utilizing 10.7k logic gates (14% of FPGA), 20 Block RAMs (17% of FPGA), and no external RAM. Results will be presented, demonstrating successful photoelectron track cluster detection with minimal impact to detector dead-time.

Polarimeter↗
DOE OSTI2023

Estimate long-term impact on battery degradation by considering electric vehicle real-world end-use factors

Many estimates of battery capacity degradation are based on accelerated lab tests that involve charge-discharge cycles or rely on data or electrochemical modeling. These methods are reasonable for technology benchmarking but rarely consider real-world end-use factors. To address this issue, this study develops the Battery Run-down under Electric Vehicle Operation (BREVO) model. It links the driver's travel pattern to physics-based battery degradation and powertrain energy consumption models. The model simulates the impacts of charging behavior, charging rate, driving patterns, and multiple energy management modules on battery capacity degradation. It finds that, over a 10-year timespan, firstly, for a random driver situated in the New England area, daily direct-current fast charging (60 kW) could lead to up to 22% less battery capacity when compared to daily Level-1 charging (1.8 kW). Second, the battery thermal management system can delay battery degradation by approximately 0.5% in the New England area. Third, warmer ambient temperatures enhance BEV battery usage. The model indicates that the battery capacity in the Los Angeles area is 6% higher than that in the New England area. The BREVO model provides crucial information for consumers and BEV manufacturers on range anxiety, BEV battery design, and decision support of battery warranty.

25 ENERGY STORAGE↗
NASA NTRS1984

A Battery Charger and State of Charge Indicator

A battery charger which has a full wave rectifier in series with a transformer isolated 20 kHz dc-dc converter with high frequency switches, which are programmed to actively shape the input dc line current to be a mirror image of the ac line voltage is discussed. The power circuit operates at 2 kW peak and 1 kW average power. The BC/SCI has two major subsystems: (1) the battery charger power electronics with its controls; and (2) a microcomputer subsystem which is used to acquire battery terminal data and exercise the state of charge software programs. The state of charge definition employed is the energy remaining in the battery when extracted at a 10 kW rate divided by the energy capacity of a fully charged new battery. The battery charger circuit is an isolated boost converter operating at an internal frequency of 20 kHz. The switches selected for the battery charger are the single most important item in determining its efficiency. The combination of voltage and current requirements dictate the use of high power NPN Darlington switching transistors. The power circuit topology is a three switch design which utilizes a power FET on the center tap of the isolation transformer and the power Darlingtons on each of the two ends. An analog control system is employed to accomplish active input current waveshaping as well as the necessary regulation.

Latos, T. S.↗
NASA NTRS1999

Investigation of a Frequency Spectrum of Discharges Generated by a Charged Aerosol Cloud

Charged aerosol clouds allow to create strong electric fields which generate discharges. A character of appearance and development of the discharges is similar to thundercloud processes. That is why the charged aerosol clouds are used for experiments on studying of discharges in air. Experimental data on electromagnetic fields investigation produced by discharges between charged cloud and ground are considered.

Christian, H. J.↗
NASA NTRS1980

The charge distribution of heavy cosmic ray nuclei

In the analysis of data from a balloon flight of a detector of cosmic ray nuclei designed to study the isotopic composition, the charge composition has been measured with high resolution. Some 24,000 nuclei with Z not less than 10 have been measured with a charge resolution of 0.2 charge units. The limitations on the significance of these results are those due to the corrections for the effects of matter in the detectors and the residual atmospheric matter. These corrections are discussed and corrected results are compared with other high resolution charge spectra reported recently.

Freier, P. S.↗
DOE OSTI2023

Electric Vehicle Charging Infrastructure Trends from the Alternative Fueling Station Locator: Third Quarter 2022

Electric vehicle (EV) charging infrastructure continues to rapidly change and grow. Using data from the U.S. Department of Energy's (DOE) Alternative Fueling Station Locator, this report provides a snapshot of the state of EV charging infrastructure in the United States in the third calendar quarter of 2022 (Q3 2022) 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 eleventh 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 OSTI2023

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

Electric vehicle (EV) charging infrastructure continues to rapidly change and grow. Using data from the U.S. Department of Energy's (DOE) Alternative Fueling Station Locator, this report provides a snapshot of the state of EV charging infrastructure in the United States in the fourth calendar quarter of 2022 (Q4 2022) 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 twelfth 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 OSTI2023

On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experiments

This work describes the investigation of neuromorphic computing-based spiking neural network (SNN) models used to filter data from sensor electronics in high energy physics experiments conducted at the High Luminosity Large Hadron Collider. We present our approach for developing a compact neuromorphic model that filters out the sensor data based on the particle's transverse momentum with the goal of reducing the amount of data being sent to the downstream electronics. The incoming charge waveforms are converted to streams of binary-valued events, which are then processed by the SNN. We present our insights on the various system design choices - from data encoding to optimal hyperparameters of the training algorithm - for an accurate and compact SNN optimized for hardware deployment. Our results show that an SNN trained with an evolutionary algorithm and an optimized set of hyperparameters obtains a signal efficiency of about 91% with nearly half as many parameters as a deep neural network.

R. Kulkarni, Shruti↗
DOE OSTI2023

Machine-Learning Assisted Identification of Battery Life Models

Predictive battery life models are commonly utilized to extrapolate degradation trends observed during accelerated aging tests for simulation of degradation in real-world applications. Thus, fitting accelerated aging data as accurately as possible and with low uncertainty is crucial for making believable projections of battery lifetime, but it is challenging to identify algebraic expressions that accurately fit multivariate degradation trends. A review of models published in literature reveal some common expressions for fitting calendar aging data, which is only dependent on temperature and state-of-charge, but no consistency across many models for fitting cycle aging data, indicating the need for a statistically rigorous data driven approach for developing empirical models. This talk will describe a machine-learning assisted method for identification of predictive battery life models utilizing bilevel optimization and symbolic regression. Bilevel optimization with cross-validation is used to statistically determine cell- and stress-dependent model parameters, while symbolic regression identifies both linear and multiplicative candidate expressions to predict stress-dependent degradation rates by selecting low-order subsets of features from a generated feature library. Because model expressions are identified empirically, it is crucial to ensure resulting models behave according to physical expectations, so the stability of models for interpolation or extrapolation is interrogated qualitatively through simulation and quantitatively through cross-validation and uncertainty quantification via bootstrap resampling. This model identification approach substantially improves upon models identified purely using expert judgement in terms of both accuracy and uncertainty. Model simulation and validation is then conducted by deriving a state-equation form of the predictive model, enabling simulation of battery aging under dynamic stresses. This enables validation of the predictive battery model on lab-based tests with varying conditions or on drive-cycle or application-cycle testing protocols. Parameter uncertainty can be carried forward into model simulation, giving lifetime estimates and confidence windows for cell- or system-level lifetime. The financial impact of battery model uncertainty can be estimated by incorporating uncertainty into a technoeconomic model.

battery↗
DOE OSTI2023

Landau-Ginzburg theory of charge density wave formation accompanying lattice and electronic long-range ordering

Here we propose an analytical Landau-Ginzburg (LG) theory of the charge density waves coupled with lattice and electronic long-range order parameters. Examples of long-range order include the electronic wave function of superconducting Cooper pairs, structural distortions, electric polarization, and magnetization. We formulate the LG free energy density as a power expansion with respect to the charge density and other long-range order parameters as well as their spatial gradients and biquadratic coupling terms. We introduced a biquadratic coupling between the charge density gradient and long-range order parameters as well as nonlinear higher gradients of the long-range order parameters. The biquadratic gradient coupling is critical to the appearance of different spatially modulated phases in charge-ordered ferroics and high-temperature superconductors. We derived the thermodynamic conditions for the stability of the spatially modulated phases, which are the intertwined spatial waves of charge density and lattice/electronic long-range order. The analytical expressions for the energies of different phases, corresponding order parameters, charge density waves amplitudes, and modulation periods obtained in this paper can be employed to guide the comprehensive physical explanation, deconvolution, and Bayesian analysis of experimental data on quantum materials ranging from charge-ordered ferroics to high-temperature superconductors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2003

Toroidal wave frequency at L=6-10: AMPTE/CCE Observations and Comparison with Theoretical Model

Magnetospheric standing Alfven waves are guided along the ambient magnetic field and their frequency depends on the mass density of the plasma distributed along the field lines. These properties allow us to use Alfen waves to map time-dependent phenomena between space and ground and to estimate the mass density. In this paper we present a statistical study of the spatial variation of the fundamental frequency f(sub T1) of toroidal-mode standing Alfen waves in the L range from 6 to 10, where L indicates the maximum geocentric distance on the field line. The data used for this analysis are energetic particle flux anisotropy (proxy of transverse electric field) and magnetic field measurements from the Active Magnetospheric Particle Tracer Explorers Charge Composition Explorer (CCE) spacecraft. Using CCE data covering 4 years we obtained approximately 5000 20-min intervals containing a clear signature of toroidal waves. The median f(sub T1), is 6-10 mHz at L=7 and decreases to 4-8 mHz at L = . The frequency tends to be lower at noon than at midnight. The observed frequencies are compared with numerically derived frequencies using an empirical mass density model and a magnetic field model. We found a good agreement for 12-24 MLT but a large discrepancy near MLT= 3. This may indicate that the flux tubes at this local time are more heavily loaded than specified in the Gallagher et al. model.

Takahashi, Kazue↗
NASA NTRS2004

Electrical Charging of Aerosols and Conductivity of Titan's Atmosphere

We have used recent data on graphitic cloud particles in the atmosphere of Titan to compute the electrical charging of the particles (radii ranging from 0.01 microns to 0.26 microns). The charging on the nightside was rather similar to that obtained earlier except that charge distributions on the particles are now computed and recently obtained cloud particle sizes and density distributions were employed. The negative charge on particles of 0.26 microns peaked at 9 at 150 km altitude. The computations were repeated for the dayside with the addition of photoelectron emission by the particles as a result of the absorption of solar UV radiation. Particles (except the very smallest) now became positively charged with particles of radius 0.26 microns being charged up to +47. Next, very small particles (radii approx. 3 x 10 (sup -4) microns) of polycyclic aromatic hydrocarbons (PAHs) were introduced and treated as sources of negative ions since they could be either neutral or carry one negative charge. Moreover, they are mobile so that they had to be treated like molecular size negative ions although much more massive. They had the effect of substantially reducing the electron densities in the altitude range 190 to 310 km to values less than the negative PAH densities and increasing the peak electron charge on the larger particles. Particles of radius 0.26 microns bore peak charges of approx. +47 at altitudes of approx. 250 km. The simulated effect of PAHs on the nightside proved to be much less pronounced; at the peak negative PAH density, it was less than the electron density. The physics governing these results will be discussed.

Borucki, W. J.↗
DOE OSTI2021

Learning electric vehicle driver range anxiety with an initial state of charge-oriented gradient boosting approach

This manuscript focuses on the modeling of electric vehicle (EV) driver’s range anxiety, a fear that a vehicle does not have sufficient range, or state of charge (SOC) of the battery pack, to reach its destination and would strand its occupants. Despite numerous research studies on the modeling of charging behaviors, modeling efforts to understand at what battery percentages do EV drivers charge their vehicles, and what are the associated contributing factors, are rather limited. To this end, an ensemble learning model based on gradient boosting is developed. The model sequentially fits new predictors to new residuals of the previous prediction and, then, minimizes the loss when adding the latest prediction. A total of 18 features are defined and extracted from the multisource data, which cover information on driver, vehicles, stations, traffic conditions, as well as spatial-temporal context information of the charging events. The analyzed dataset includes 4.5-year’s charging event log data from 3,096 users and 468 public charging stations in Kansas City Missouri, and the macroscopic travel demand model maintained by the metropolitan planning organization. Here, the result shows the proposed model achieved a satisfactory result with a R square value of 0.54 and root mean square error of 0.14, both better than multiple linear regression model and random forest model. To reduce range anxiety, it is suggested that the priorities of deploying new charging facilities should be given to the areas with higher daily traffic prediction, with more conservative EV users or that are further from residential areas.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1987

Performance of laminar-flow leading-edge test articles in cloud encounters

An extensive data bank of concurrent measurements of laminar flow (LF), particle concentration, and aircraft charging state was gathered for the first time. From this data bank, 13 flights in the simulated airline service (SAS) portion were analyzed to date. A total of 6.86 hours of data at one-second resolution were analyzed. An extensive statistical analysis, for both leading-edge test articles, shows that there is a significant effect of cloud and haze particles on the extent of laminar flow obtained. Approximately 93 percent of data points simulating LFC flight were obtained in clear air conditions; approximately 7 percent were obtained in cloud and haze. These percentages are consistent with earlier USAF and NASA estimates and results. The Hall laminar flow loss criteria was verified qualitatively. Larger particles and higher particle concentrations have a more marked effect on LF than do small particles. A particle spectrometer of a charging patch are both acceptable as diagnostic indicators of the presence of particles detrimental to laminar flow.

Davis, Richard E.↗
DOE OSTI2024

Soil properties and root characteristics across four lowland Panamanian forests from 0 - 1 m soil depths

Objectives:Fine roots significantly influence ecosystem-scale cycling of nutrients, carbon (C), and water, yet there is limited understanding of how fine root traits vary across and within tropical forests, some of Earth's most C-rich ecosystems. The biomass of fine roots can impact soil carbon storage, as root mortality is a primary source of new carbon to soils. A positive relationship has been observed between fine root biomass and soil carbon stocks in Panama (Cusack et al 2018). Beyond biomass, root characteristics like specific root length (SRL) could also influence soil carbon, as roots with higher SRL are less dense and thinner, potentially decomposing more easily or promoting soil aggregation. Understanding the effects of root morphology and tissue quality on soil carbon storage and with soil properties in general can improve predictions of landscape-scale carbon patterns. We aggregated new data of root biomass, morphology and nutrient content at 0-10 cm, 10-20 cm, 20-50 cm and 50-100 cm depth increments across four distinct lowland Panamanian forests and paired with already published datasets (Cusack et al 2018; Cusack and Turner 2020) of soil chemistry from the same sites and soil depths to explore relationship between soil carbon stocks and root characteristics.Datasets included:The datasets provided include .csv and .xlsx files for fine root characteristics and soil chemistry from four different forests across 0-10 cm, 10-20 cm, 20-50 cm, and 50-100 cm depth increments. Root characteristics include live fine root biomass, dead fine root biomass, coarse root biomass, specific root length, root diameter, root tissue density, specific root area, root %N, root %C, and root C/N ratio. Soil chemistry data includes total carbon (TC), dissolved organic carbon (DOC), bulk density, total phosphorus (TP), available phosphorus (AEM Pi), and various Mehlich-extractable elements such as aluminum, calcium, iron, potassium, manganese, phosphorus, and zinc. Nitrogen content measures include ammonium, nitrate, total dissolved nitrogen (TDN), dissolved inorganic nitrogen (DIN), and dissolved organic nitrogen (DON). The dataset also includes total exchangeable bases (TEB) and effective cation exchange capacity (ECEC) in both centimoles of charge per kilogram and micromoles of charge per gram. The soil chemistry data was obtained from Cusack et al (2018) and Cusack and Turner (2020) and paired with root characteristics data for the same depth increments and sites. Additionally, a .kml file is provided with coordinates for all 32 plots included in the study across four forests (n = 8 plots per site). Root data was averaged across these 8 plots per site and soil data was collected in one pit in each site. This dataset serves as baseline data before a throughfall exclusion experiment, Panama Rainforest Changes with Experimental Drying (PARCHED), was implemented. No special software is needed to open these files.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1985

Abundances of secondary elements among the ultra heavy cosmic rays: Results from HEAO-3

Observations of the abundances of elements of charge 62 or = Z or = 73 in the cosmic radiation from the HEAO-3 Heavy Nuclei Experiment (HNE) are discussed. These elements, having solar, and presumably source, abundances much less than the heavier Pt and Pb groups, are expected to be largely products of spallation. Thus they are indicators of the conditions prevailing during the propagation of cosmic rays. The abundances have changed from those reported previously due to a different data selection. This resulted in better charge resolution and in a higher mean energy for the particles. All the particles included in this paper were required to have had a cutoff rigidity R sub c 5 GV. This allowed the charge determination to be based solely on the Cerenkov measurement.

Klarmann, J.↗
NASA NTRS1985

Abundances of secondary elements among the ultra heavy cosmic rays: Results from HEAO-3

Observations of the abundances of elements of charge 62 or Z or = 73 in the cosmic radiation from the HEAO-3 Heavy Nuclei Experiment (HNE) are discussed. These elements, having solar, and presumably source, abundances much less than the heavier Pt and Pb groups, are expected to be largely products of spallation. Thus they are indicators of the conditions prevailing during the propagation of cosmic rays. The abundances have changed from those reported previously due to a different data selection. This results in better charge resolution and in a higher mean energy for the particles. All the particles included were required to have had a cutoff rigidity R sub c 5 GV. This allowed the charge determination to be based solely on the Cherenkov measurement.

Klarmann, J.↗