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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Nanoscale covariance magnetometry with diamond quantum sensors

Nitrogen vacancy (NV) centers in diamond are atom-scale defects that can be used to sense magnetic fields with high sensitivity and spatial resolution. Typically, the magnetic field is measured by averaging sequential measurements of single NV centers, or by spatial averaging over ensembles of many NV centers, which provides mean values that contain no nonlocal information about the relationship between two points separated in space or time. In this work, we propose and implement a sensing modality whereby two or more NV centers are measured simultaneously, and we extract temporal and spatial correlations in their signals that would otherwise be inaccessible. We demonstrate measurements of correlated applied noise using spin-to-charge readout of two NV centers and implement a spectral reconstruction protocol for disentangling local and nonlocal noise sources.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Covariant Density Functional Theory in Nuclear Physics and Astrophysics

How does subatomic matter organize itself? Neutron stars are cosmic laboratories uniquely poised to answer this fundamental question that lies at the heart of nuclear science. Newly commissioned rare isotope facilities, telescopes operating across the entire electromagnetic spectrum, and ever more sensitive gravitational wave detectors will probe the properties of neutron-rich matter with unprecedented precision over an enormous range of densities. A coordinated effort between observation, experiment, and theoretical research is of paramount importance for realizing the full potential of these investments. Theoretical nuclear physics provides valuable insights into the properties of neutron-rich matter in regimes that are not presently accessible to experiment or observation. In particular, nuclear density functional theory is likely the only tractable framework that can bridge the entire nuclear landscape by connecting finite nuclei to neutron stars. This compelling connection is the main scope of the present review.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Observed Covariations in Boundary Layer and Cumulus Cloud Layer Processes

In this paper we examine variations in boundary-layer processes spanning the shallow to deep cumulus transition. This is accomplished by differentiating boundary layer properties on the basis of convective outcomes, ranging from shallow to deep, as observed at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in Oklahoma, USA. Doppler lidar, radar, and radiosonde data are combined to determine statistical differences in boundary layer and cloud layer properties using a large sample (236) of days with a range of convective outcomes: shallow, congestus, and deep convection. In these analyses, the radar characterizes diurnal cloud depth, the lidar quantifies updraft and downdraft properties in the subcloud layer, and daily radiosonde data provides the convective inhibition (CIN). Combined, these data are used to test the hypothesis that deep convection occurs when the strength of the boundary layer turbulence (i.e., TKE) exceeds the strength of the energy barrier (i.e., CIN) at the top of the CBL. Results show that days with deep convective clouds have significantly lower vertical velocity variance and weaker updrafts within the subcloud layer. However, CIN values are also found to be significantly lower on deep convective days, allowing for these weaker updrafts to penetrate the energy barrier and reach the level of free convection (LFC). In contrast, shallow convective outcomes occur when the updrafts are strong in an absolute sense, but are weak when compared to the strength of the energy barrier. These findings support the use of the CIN/TKE framework in parameterizing convection in coarse resolution models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Vegetation classification map and covariates associated with NEON AOP survey, East River, CO 2018

This package includes geospatial data layers developed to investigate how environmental gradients—specifically topography and near-surface soil properties—drive the spatial arrangement of dominant plant communities in mountainous watersheds. The geospatial products, which support the analysis of these ecological relationships, are derived from airborne hyperspectral and LiDAR datasets acquired by the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP), in conjunction with an extensive ground field campaign conducted in summer 2018. This work is part of the DOE Watershed Function Science Focus Area (SFA) and features geospatial datasets developed based on observations and ground data collected at East River, Colorado, in collaboration with the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) survey in June 2018. Classification Map: - Classification Map (PNG, GeoTIFF): Derived from hyperspectral and LiDAR airborne data using a machine learning approach. - Class Code Mapper (CSV): Associates pixel values with corresponding vegetation/non-vegetation classes. - Classification Reference Data (CSV): Reference data used in the machine learning procedure. LiDAR-Derived Products: - Topographical Metrics (GeoTIFFs): Elevation, slope, curvature, TWI, TPI, solar insolation, and canopy height model (CHM), smoothed with a 5x5 pixel window. Vegetation Indices: - GeoTIFFs of NDVI, NDNI, NDWI: Vegetation indices derived from hyperspectral data. Urban Masks: - Urban Mask (GeoTIFF): Applied to the mapping to convert bare soil classes to urban classes. Software Compatibility: GeoTIFFs: Can be visualized with GIS software or libraries that support GeoTIFF images. CSV Files: Can be opened with any software that handles comma-separated values. The FLMD file provides details and links to the source datasets used to derive the products. The manuscript (in the Method session) provides details on how each product was derived. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. Update on 2026-03-25: Since the original dataset publication date of 02/28/2020, this package has a new classification map derived by an improved methodology. This update also includes additional ground data that improved the representation of some of the communities. See the methods for further details on what has changed between versions.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2022

Theoretical and calculable dependent variables and their covariance in nuclear data libraries [Slides]

This presentation begins by defining observables, including theoretical observable, calculable observable, and measured observable. It also provides definitions and examples of experimental effects. Additionally, uncertainty in evaluated libraries and a metric to probe evaluated uncertainty is presented. In conclusion, being related to theoretical quantities such as resonance parameters, the uncertainty in ENDF libraries (e.g., ENDF/B-VIII.0) can be overestimated because it is usually evaluated under the guidance of experimental uncertainty: lim Δσexp.corr.→0 Δσ = Δσ theoretical model . The presentation states that a clear distinction of the uncertainty between nuclear theoretical models and experimental corrections should be revisited and this is important since (e.g., transport) simulations need theoretical quantities convoluted with specific operational parameters and material configurations. It also states that strong coupling between uncertainty quantification methodologies and optimization procedures exists, and it is necessary to develop methodologies to obtain uncertainty on theoretical models from physical and mathematical constraints.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗