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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

MatPhase: Material phase prediction for Li-ion Battery Reconstruction using Hierarchical Curriculum Learning

Li-ion Batteries (LIB), one of the most efficient energy storage devices, are used extensively in many industrial applications. These batteries consist of electrodes that are put together with heterogeneous material compositions. Imaging data of these battery electrodes obtained from X-ray tomography can explain the distribution of material constituents and allow reconstructions to study electron transport pathways. Such reconstructions of material constituents help quantify various associated properties of electrodes (e.g., volume-specific surface area, porosity) which determine the performance of batteries. These images often suffer from low image contrast between multiple material constituents, hence making it difficult for humans to distinguish and characterize these constituents through visual inspection. A minor error in detecting distributions of the material constituents can lead to magnified errors in the calculated parameters of material properties (e.g., porosity). We present MatPhase, a novel hierarchical curriculum learning technique to address the complex task of estimating material constituent distribution in battery electrodes. MatPhase comprises three modules: (i) an uncertainty-aware global model trained to yield inferences conditioned upon global knowledge of material distribution, (ii) a local model to capture relatively more fine-grained (local) distributional signals, (iii) an aggregator model to appropriately fuse the local and global effects towards obtaining the final distribution. On average, MatPhase improves prediction up to 8.5% relative to other sophisticated modeling pipelines and state-of-the-arts (SOTA) object detection models employed in the performance comparison.

Tabassum, Anika↗
DOE OSTI2021

Visualizing quasiparticles from quantum entanglement for general one-dimensional phases

In this work, we present a quantum information framework for the entanglement behavior of the low-energy quasiparticle (QP) excitations in various quantum phases in one-dimensional (1D) systems. We first establish an exact correspondence between the correlation matrix and the QP entanglement Hamiltonian for free fermions and find an extended in-gap state in the QP entanglement Hamiltonian as a consequence of the position uncertainty of the QP. A more general understanding of such an in-gap state can be extended to a Kramers theorem for the QP entanglement Hamiltonian, which also applies to strongly interacting systems. Further, we present a set of ubiquitous entanglement spectrum features, dubbed entanglement fragmentation, conditional mutual information, and measurement-induced nonlocal entanglement for QPs in 1D symmetry protected topological phases. Our result thus provides another framework to identify different phases of matter in terms of their QP entanglement.

1-dimensional spin chains↗
DOE OSTI2020

Stereoscopic detection of hot spots in superfluid 4 He (He II) for accelerator-cavity diagnosis

Superconducting radio-frequency (SRF) cavities cooled by superfluid 4He (He II) are building blocks of many modern particle accelerators due to their high quality factor. However, Joule heating from sub-millimeter surface defects on cavities can lead to cavity quenching, which limits the maximum acceleration gradient of the accelerators. Developing a non-contacting detection technology to accurately locate these surface hot spots is the key to improve the performance of SRF cavities and hence the accelerators. In a recent proof-of-concept experiment, we demonstrated that a molecular tagging velocimetry (MTV) technique based on the tracking of a He molecular tracer line created nearby a surface hot spot in He II can be utilized to locate the hot spot. In order to make this technique practically useful, here we describe our further development of a stereoscopic MTV setup for tracking the tracer line’s motion in three-dimensional (3D) space. We simulate a quench spot by applying a transient voltage pulse to a small heater mounted on a substrate plate. Images of the drifted tracer line, taken with two cameras from orthogonal directions, are used to reconstruct the line profile in 3D space. Here, a new algorithm for analyzing the 3D line profile is developed, which incorporates the finite size effect of the heater. We show that the center location of the heater can be reproduced on the substrate surface with an uncertainty of only a few hundred microns, thereby proving the practicability of this method.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

County Land-Use Regulations for Solar Energy Development in Colorado

We present a survey of county-level policies on ground-mounted solar development across Colorado, including both solar-specific ordinances as well as general land-use code that might be applicable in counties without solar-specific policies. This report provides an accessible reference for stakeholders interested in identifying counties with particular regulations or in analyzing the diversity of regulations across Colorado. We defined a set of search criteria to find information on solar definitions and classifications, permitting processes, and use-specific requirements in each of Colorado's 64 counties. With those criteria, we reviewed relevant ordinances, land-use code, and comprehensive and master plans. If any uncertainties were identified, we contacted county officials for clarification. The findings are categorized and mapped to illustrate the distribution of key policies adopted across Colorado's counties on the following topics: solar definitions, solar siting policy documentation, categorization of PV systems for permitting, 1041 permitting, solar on agricultural land, panel height restrictions, fencing requirements, vegetation management, visual impacts, decommissioning plans, and financial assurance for decommissioning. Additionally, we identify and discuss policies that might impact the deployment of agrivoltaics, a dual land use combining both agriculture and solar on the same land, which might not fit neatly in existing zoning definitions and solar-specific regulations.

14 SOLAR ENERGY↗
NASA NTRS1989

Multifrequency survey of the intergalactic cloud in the M96 group

The intergalactic cloud of neutral hydrogen in the M96 group are examined for signs of emission over a wide range of frequencies, from radio waves to X rays. Past or present stellar activity in the gas might have been expected to produce detectable visual infrared, CO, OH, or radio recombination-line emission. None was detected. The limits are used to study physical conditions in the intergalactic gas. In particular, B and V band limits on starlight and IRAS limits on the presence of dust strongly constrain the presence of stars or stellar by-products. However, given the uncertainties about physical conditions in the intergalactic environment, it is difficult to rule out entirely the presence of stellar-processed materials. Results of neutral hydrogen mapping from a large-scale survey of the intergalactic cloud and surrounding region are also presented. These observations confirm that the gas is confined to a large ringlike structure. The simplest interpretation remains that the intergalactic gas in Leo is primordial.

Schneider, Stephen E.↗
NASA NTRS1981

A high-resolution optical survey of interstellar absorption lines toward globular clusters and extragalactic objects. II - Further data

A status report is given on an ongoing survey of narrow-line, visual absorption components, which has been expanded to include lines of sight to the 3C 273 quasar, the nucleus of M87, and the NGC 1851, NGC 2808, and 47 Tuc globular cluster cores. The number density of components of the halo is calculated to lie between one-third and one per 100 kpc, though with substantial uncertainty. This estimate is held to be consistent with the galactic halo interpretation of the origin of high column density, quasar absorption-line systems.

Songaila, A.↗
NASA NTRS2015

Forest Disturbance Across the Conterminous United States from 1985-2012: The Emerging Dominance of Forest Decline

Evidence of shifting dominance among major forest disturbance agent classes regionally to globally has been emerging in the literature. For example, climate-related stress and secondary stressors on forests (e.g., insect and disease, fire) have dramatically increased since the turn of the century globally, while harvest rates in the western US and elsewhere have declined. For shifts to be quantified, accurate historical forest disturbance estimates are required as a baseline for examining current trends. We report annual disturbance rates (with uncertainties) in the aggregate and by major change causal agent class for the conterminous US and five geographic subregions between 1985 and 2012. Results are based on human interpretations of Landsat time series from a probability sample of 7200 plots (30 m) distributed throughout the study area. Forest disturbance information was recorded with a Landsat time series visualization and data collection tool that incorporates ancillary high-resolution data. National rates of disturbance varied between 1.5% and 4.5% of forest area per year, with trends being strongly affected by shifting dominance among specific disturbance agent influences at the regional scale. Throughout the time series, national harvest disturbance rates varied between one and two percent, and were largely a function of harvest in the more heavily forested regions of the US (Mountain West, Northeast, and Southeast). During the first part of the time series, national disturbance rates largely reflected trends in harvest disturbance. Beginning in the mid-90s, forest decline-related disturbances associated with diminishing forest health (e.g., physiological stress leading to tree canopy cover loss, increases in tree mortality above background levels), especially in the Mountain West and Lowland West regions of the US, increased dramatically. Consequently, national disturbance rates greatly increased by 2000, and remained high for much of the decade. Decline-related disturbance rates reached as high as 8% per year in the western regions during the early-2000s. Although low compared to harvest and decline, fire disturbance rates also increased in the early- to mid-2000s. We segmented annual decline-related disturbance rates to distinguish between newly impacted areas and areas undergoing gradual but consistent decline over multiple years. We also translated Landsat reflectance change into tree canopy cover change information for greater relevance to ecosystem modelers and forest managers, who can derive better understanding of forest-climate interactions and better adapt management strategies to changing climate regimes. Similar studies could be carried out for other countries where there are sufficient Landsat data and historic temporal snapshots of high-resolution imagery.

Cohen, Warren B.↗
DOE OSTI2022

Li-ion Battery Material phase prediction through Hierarchical Curriculum Learning

Li-ion Batteries (LIB), one of the most efficient energy storage devices, are widely adopted in many industrial applications. Imaging data of these battery electrodes obtained from X-ray tomography can explain the distribution of material constituents and allow reconstructions to study electron transport pathways. Therefore, it can eventually help quantify various associated properties of electrodes (e.g., volume-specific surface area, porosity) which determine the performance of batteries. However, these images often suffer from low image contrast between multiple material constituents , making it difficult for humans to distinguish and characterize these constituents through visualization. A minor error in detecting distributions among the material constituents can lead to a high error in the calculated parameters of material properties.We present a novel hierarchical curriculum learning framework to address the complex task of estimating material constituent distribution in battery electrodes. To provide spatially smooth prediction, our framework comprises three modules: (i) an uncertainty-aware model trained to yield inferences conditioned upon global knowledge of material distribution, (ii) a technique to capture relatively more fine-grained (local) distributional signals, (iii) an aggregator to appropriately fuse the local and global effects towards obtaining the final distribution.

Tabassum, Anika↗
NASA NTRS2015

Explore GPM IMERG and Other Global Precipitation Products with GES DISC GIOVANNI

New features and capabilities in the newly released GIOVANNI allow exploring GPM IMERG (Integrated Multi-satelliE Retrievals for GPM) Early, Late and Final Run global half-hourly and monthly precipitation products as well as other precipitation products distributed by the GES DISC such as TRMM Multi-Satellite Precipitation Analysis (TMPA), MERRA (Modern Era Retrospective-Analysis for Research and Applications), NLDAS (North American Land Data Assimilation Systems), GLDAS (Global Land Data Assimilation Systems), etc. GIOVANNI is a web-based tool developed by the GES DISC (Goddard Earth Sciences and Data Information Services Center) to visualize and analyze Earth science data without having to download data and software. The new interface in GIOVANNI allows searching and filtering precipitation products from different NASA missions and projects and expands the capabilities to inter-compare different precipitation products in one interface. Knowing differences in precipitation products is important to identify issues in retrieval algorithms, biases, uncertainties, etc. Due to different formats, data structures, units and so on, it is not easy to inter-compare precipitation products. Newly added features and capabilities (unit conversion, regridding, etc.) in GIOVANNI make inter-comparisons possible. In this presentation, we will describe these new features and capabilities along with examples.

Giovanni↗
NASA NTRSDate not supplied

NASA POWER: Providing Analysis-Ready, Cloud-Optimized Data for AI /ML Training and Applications in Earth Science

As global demand for sustainable development grows, the integration of Earth Observation (EO) data into decision making frameworks has become a primary objective for the scientific community. The NASA Prediction of Worldwide Energy Resources (POWER) project serves as a bridge between NASA EO data and the specialized needs of the renewable energy, sustainable infrastructure and agroclimatology communities. In this poster presentation we will present an overview of POWER data products and services along with its use in diverse research to decision-making workflows. By providing over 40 years of high-resolution historical, hourly and daily solar and meteorological data, POWER transforms satellite observations and global model reanalysis into actionable, Analysis-Ready Dataset (ARD). Currently, the project delivers over 250 industry-friendly parameters to the users from different NASA datasets like CERES SYN1Deg, MERRA-2, and IMERG alongside downscaled CMIP6 climate model data, fulfilling over 16 million requests from 50,000 unique users monthly. To ensure data quality and traceability, these parameters are rigorously validated against the ground-based observations from the Baseline Surface Radiation Network (BSRN) and the Global Surface Summary of the Day (GSOD) – these results will be discussed in the presentation. A newly introduced web-based PaRameter Uncertainty ViEwer (PRUVE) tool will be presented that provides an online validation platform to the users that benchmarks satellite-based and assimilation data products against these surface measurements. To reduce technical barriers to data adoption, POWER data is accessible through RESTful APIs, ESRI ArcGIS Image Services, a web-based Data Access Viewer tool, allowing users to visualize, validate and apply the dataset. For efficient data delivery POWER data is cloud-optimized into Zarr datastore accessible through NASA managed Amazon S3 ensures high-performance allowing users to integrate EO directly into operational pipelines. These customized services will be presented. Use cases from application will be presented from the energy sector - such as for design of generation systems, performance monitoring of solar power plants, in infrastructure sector- optimizing building energy efficiency and thermal comfort, in agriculture – such as driving crop simulation and yield forecasting models to enable climate resilient farming. Furthermore, the shift toward machine learning (ML) in EO research that has positioned POWER as a key provider for training datasets which will be discussed. Use-cases will be presented to showcase how NASA data is enabling the development of predictive tools for climate variability and resource management. The poster will present POWER’s future plans including technology development to enhance data traceability and reproducibility and improving I/O performance to support the rapid integration of new EO products, ensuring that POWER remains a robust scalable backend for the evolving landscape of AI-driven Earth Science. Additionally, POWER is developing an AI Agent and an MCP-Server to enable industry AI-Agentic workflows.

Neha Khadka↗
NASA NTRS2011

The Cosmic-Ray and Gas Content of the Cygnus Region as Measured in Gamma Rays by the Fermi Large Area Telescope

Context. The Cygnus region hosts a giant molecular-cloud complex which actively forms massive stars. Interactions of cosmic rays with interstellar gas and radiation fields make it shine at y-ray energies. Several gamma-ray pulsars and other energetic sources are seen in this direction. Aims. In this paper we analyse the gamma-ray emission measured by the Fermi Large Area Telescope in the energy range from 100 Me V to 100 Ge V in order to probe the gas and cosmic-ray content over the scale of the whole Cygnus complex. The gamma-ray emission on the scale of the central massive stellar clusters and from individual sources is addressed elsewhere. Methods. The signal from bright pulsars is largely reduced by selecting photons in their off-pulse phase intervals. We compare the diffuse gamma-ray emission with interstellar gas maps derived from radio/mm-wave lines and visual extinction data. and a global model of the region, including other pulsars and gamma-ray sources, is sought. Results. The integral H I emissivity above 100 MeV averaged over the whole Cygnus complex amounts to 12.06 +/- 0.11 (stat.) (+0.15 -0.84) (syst.J] x 10(exp -26) photons /s / sr / H-atom, where the systematic error is dominated by the uncertainty on the H I opacity to calculate its column densities. The integral emissivity and its spectral energy distribution are both consistent within the systematics with LAT measurements in the interstellar space near the solar system. The average X(sub co) N(H2)/W(sub co) ratio is found to be [1.68 +/- 0.05 (stat.) (H I opacity)] x 1020 molecules cm-2 (K km/s /r, consistent with other LAT measurements in the Local Arm. We detect significant gamma-ray emission from dark neutral gas for a mass corresponding to approx 40% of that traced by CO. The total interstellar mass in the Cygnus complex inferred from its gamma-ray emission amounts to 8(+5 -1) x 10(exp 6) Solar M at a distance of 1.4 kpc. Conclusions. Despite the conspicuous star formation activity and large masses of the interstellar clouds, the cosmic-ray population in the Cygnus complex averaged over a few hundred parsecs is similar to that of the local interstellar space.

Ackermann, M.↗
NASA NTRSDate not supplied

Reduced Dimensionality Analysis of TEMPO Ozone Profile Retrievals Using the Compact Phase Space (CPSR) Algorithm

TEMPO ozone (O 3 ) profile retrievals are expected to have fidelity in the troposphere due the sensitivities of the associated averaging kernels. However, those averaging kernels are severely rank deficiency meaning that a visual inspection of the vertical structure of the averaging kernel profile sensitivities is misleading due linear dependencies in the profile. The Compact Phase Space Retrieval (CPSR) algorithm use singular value decompositions of the averaging kernels and the ‘compressed’ retrieval solution error covariance to project the transformed averaging kernels into a space that removes the linear dependencies and accounts for the solution error uncertainties. In this oral presentation and poster, we apply the CPSR dimensional reduction analysis to TEMPO and TROPOMI O 3 profile retrievals for 13:45 UTC March 29, 2024 to study the phase space characteristics of the transformed averaging kernels as a function of latitude for North America. Our results show that TEMPO generally has more phase space vertical structure in the troposphere than TROPOMI. TEMPO has four to five dominant modes, and TROPOMI has five to six dominant modes. That means that dimensional reduction can reduce the TEMPO resource requirements by ~77% and the TROPOMI requirements by ~81%. Finally, we found that after removing linear dependences and after accounting for solution uncertainties TEMPO still has sensitivities throughout the troposphere.

TEMPO↗
DOE OSTI2026

Decision Making Under Uncertainty Human Subjects Data - Fire Evacuation Task

This dataset contains de-identified data from human subjects experiments, along with the images and code that were used to run the experiments (as a crowdsourced online study). In this study, participants were shown the probability of a house being in the burn zone of a wildfire. They were asked if they would stay in the house or evacuate in that scenario. The probability information was presented in different ways, including text and maps. The studies tested the impact of different visual cues on the participants' patterns of decisions.

Matzen, Laura E. [Sandia National Laboratories (SN↗
NASA NTRS2005

Expansion of direction space around the cardinal axes revealed by smooth pursuit eye movements

It is well established that perceptual direction discrimination shows an oblique effect; thresholds are higher for motion along diagonal directions than for motion along cardinal directions. Here, we compare simultaneous direction judgments and pursuit responses for the same motion stimuli and find that both pursuit and perceptual thresholds show similar anisotropies. The pursuit oblique effect is robust under a wide range of experimental manipulations, being largely resistant to changes in trajectory (radial versus tangential motion), speed (10 versus 25 deg/s), directional uncertainty (blocked versus randomly interleaved), and cognitive state (tracking alone versus concurrent tracking and perceptual tasks). Our data show that the pursuit oblique effect is caused by an effective expansion of direction space surrounding the cardinal directions and the requisite compression of space for other directions. This expansion suggests that the directions around the cardinal directions are in some way overrepresented in the visual cortical pathways that drive both smooth pursuit and perception.

Visual Pathways/physiology↗
NASA NTRS2009

Rover Slip Validation and Prediction Algorithm

A physical-based simulation has been developed for the Mars Exploration Rover (MER) mission that applies a slope-induced wheel-slippage to the rover location estimator. Using the digital elevation map from the stereo images, the computational method resolves the quasi-dynamic equations of motion that incorporate the actual wheel-terrain speed to estimate the gross velocity of the vehicle. Based on the empirical slippage measured by the Visual Odometry software of the rover, this algorithm computes two factors for the slip model by minimizing the distance of the predicted and actual vehicle location, and then uses the model to predict the next drives. This technique, which has been deployed to operate the MER rovers in the extended mission periods, can accurately predict the rover position and attitude, mitigating the risk and uncertainties in the path planning on high-slope areas.

Yen, Jeng↗
DOE OSTI2019

Eastern North Atlantic (ENA) Aerosol Supplementary Site (S1) Data Analysis Report

We present strategies to identify time periods when local aerosol sources impact the data collected at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility in the Eastern North Atlantic (ENA). Data from the aerosol observing system (AOS) at the Central Facility and a temporary aerosol Supplementary Site (S1) were evaluated. S1 instrumentation was deployed within 0.75 km of the ENA Central Facility (C1) for approximately one year. Two time periods, one during the summer and one during the winter, were examined in support of the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) intensive operating periods (IOPs) in 2017 and 2018. As the criteria for identifying regionally representative data varies based on the specific needs of the user, we present and discuss different strategies using measured aerosol properties and their associated meteorological parameters to isolate periods in AOS data that were impacted by local aerosol at ENA. We identify high-concentration aerosol events at ENA using submicron number concentrations, size distributions, high-time-resolution measurements, and diurnal profiles. An improved understanding of local aerosol to select the optimal aerosol mask was obtained with an understanding of local meteorology during the different seasons. We developed a mathematical algorithm that employs AOS data to mask high-concentration aerosol events at ENA. We validated the ENA Aerosol Mask with aerosol data from C1 and S1 and associated meteorological data. The Supplementary Site was ideal to understand and constrain local aerosol sources within the region. In its absence, collocated data from the ENA AOS could be used to select appropriate parameters to develop an aerosol mask that would limit over- or underestimating the regional baseline aerosol data. When available, associated metadata, such as airport flight logs and visual identification with AOS cameras should also be used to validate the application of an aerosol mask at a new site. Our results confirm the importance of optimizing an aerosol mask prior its application. In the future, the application of an aerosol mask at ARM observatories and during mobile deployments could be used to increase the data quality of continuous AOS data and to reduce uncertainties when parameterizing regional aerosol processes and impacts on clouds for incorporation in models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2016

National Climate Assessment - Land Data Assimilation System (NCA-LDAS) Data at NASA GES DISC

As part of NASA's active participation in the Interagency National Climate Assessment (NCA) program, the Goddard Space Flight Center's Hydrological Sciences Laboratory (HSL) is supporting an Integrated Terrestrial Water Analysis, by using NASA's Land Information System (LIS) and Land Data Assimilation System (LDAS) capabilities. To maximize the benefit of the NCA-LDAS, on completion of planned model runs and uncertainty analysis, NASA will provide open access to all NCA-LDAS components, including input data, output fields, and indicator data, to other NCA-teams and the general public. The NCA-LDAS data will be archived at the NASA GES DISC (Goddard Earth Sciences Data and Information Services Center) and can be accessed via direct ftp, THREDDS, Mirador search and download, and Giovanni visualization and analysis system.

land surface↗
DOE OSTI2020

Machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion battery cathodes

The microstructure of a composite electrode determines how individual battery particles are charged and discharged in a lithium-ion battery. It is a frontier challenge to experimentally visualize and, subsequently, to understand the electrochemical consequences of battery particles’ evolving (de)attachment with the conductive matrix. Herein, we tackle this issue with a unique combination of multiscale experimental approaches, machine-learning-assisted statistical analysis, and experiment-informed mathematical modeling. Our results suggest that the degree of particle detachment is positively correlated with the charging rate and that smaller particles exhibit a higher degree of uncertainty in their detachment from the carbon/binder matrix. We further explore the feasibility and limitation of utilizing the reconstructed electron density as a proxy for the state-of-charge. Our findings highlight the importance of precisely quantifying the evolving nature of the battery electrode’s microstructure with statistical confidence, which is a key to maximize the utility of active particles towards higher battery capacity.

25 ENERGY STORAGE↗