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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The S-PLUS Fornax Project (S+FP): A first 12-band glimpse of the Fornax galaxy cluster

ABSTRACT The Fornax galaxy cluster is the richest nearby (D ∼ 20 Mpc) galaxy association in the southern sky. As such, it provides a wealth of opportunities to elucidate on the processes where environment holds a key role in transforming galaxies. Although it has been the focus of many studies, Fornax has never been explored with contiguous homogeneous wide-field imaging in 12 photometric narrow and broad bands like those provided by the Southern Photometric Local Universe Survey (S-PLUS). In this paper, we present the S-PLUS Fornax Project (S+FP) that aims to comprehensively analyse the galaxy content of the Fornax cluster using S-PLUS. Our data set consists of 106 S-PLUS wide-field frames (FoV∼1.4 × 1.4 deg2) observed in five Sloan Digital Sky Survey-like ugriz broad bands and seven narrow bands covering specific spectroscopic features like [O ii], Ca ii H+K, Hδ, G band, Mg b triplet, Hα, and the Ca ii triplet. Based on S-PLUS specific automated photometry, aimed at correctly detecting Fornax galaxies and globular clusters in S-PLUS images, our data set provides the community with catalogues containing homogeneous 12-band photometry for ∼3 × 106 resolved and unresolved objects within a region extending over ∼208 deg2 (∼5 Rvir in RA) around Fornax’ central galaxy, NGC 1399. We further explore the eagle and IllustrisTNG cosmological simulations to identify 45 Fornax-like clusters and generate mock images on all 12 S-PLUS bands of these structures down to galaxies with M⋆ ≥ 108 M⊙. The S+FP data set we put forward in this first paper of a series will enable a variety of studies some of which are briefly presented.

Astronomy & Astrophysics↗
NASA NTRS2019

On the Persistence of QPOs During the SGR 1806-20 Giant Flare

The discovery of quasi-periodic brightness oscillations (QPOs) in the X-ray emission accompanying the giant flares of the soft gamma-ray repeaters SGR 1806–20 and SGR 1900+14 has led to intense speculation about their nature and what they might reveal about the interiors of neutron stars. Here we take a fresh look at the giant flare data for SGR 1806–20, and in particular we analyze short segments of the post-peak emission using a Bayesian procedure, which has not previously been applied to these data. We find at best weak evidence that any QPO persists for more than ~1 s; instead, almost all the data are consistent with a picture in which there are numerous independently excited modes that decay within a few tenths of a second. This has interesting implications for the rapidity of decay of the QPO modes, which could occur by the previously suggested mechanism of coupling to the MHD continuum. The strongest QPOs favor certain rotational phases, which might suggest special regions of the crust or of the magnetosphere. We also find several previously unreported QPOs in these data, which may help in tracking down their origin.

Miller, M Coleman↗
NASA NTRS1994

The HST Quasar Absorption-Line Key Project. 8: CCD imaging of Hubble Space Telescope quasar fields

We present an analysis of CCD images of more than 100 quasars; many of the objects have been or will be observed in the Hubble Space Telescope (HST) Key Project Quasar Absorption-Line Survey. The data set consists of B, g, and i photometry of 117 quasars and deeper g (limiting magnitude of approximately equal 22) images of 101 quasar fields. Positions accurate to approximately equal 1 sec, measured with the Space Telescope Science Institute's Astrometric Support Program, are listed for all of the quasars in this study. Positions, magnitudes, and classifications of stars and galaxies located within approximately equal 100 sec of the quasars are given for all of the deep g data. The positions of the stars and galaxies relative to the quasar are accurate to approximately equal 0.5 sec. The results presented here can be used to prepare spectroscopic programs designed to obtain redshifts of galaxies in the fields of these quasars.

Kirhakos, Sofia↗
NASA NTRS1993

Computer networking at SLR stations

There are several existing communication methods to deliver data from the satellite laser ranging (SLR) station to the SLR data center and back: telephonmodem, telex, and computer networks. The SLR scientific community has been exploiting mainly INTERNET, BITNET/EARN, and SPAN. The total of 56 countries are connected to INTERNET and the number of nodes is exponentially growing. The computer networks mentioned above and others are connected through E-mail protocol. The scientific progress of SLR requires the increase of communication speed and the amount of the transmitted data. The TOPEX/POSEIDON test campaign required to deliver Quick Look data (1.7 kB/pass) from a SLR site to SLR data center within 8 hours and full rate data (up to 500 kB/pass) within 24 hours. We developed networking for the remote SLR station in Helwan, Egypt. The reliable scheme for data delivery consists of: compression of MERIT2 format (up to 89 percent), encoding to ASCII Me (files); and e-mail sending from SLR station--e-mail receiving, decoding, and decompression at the center. We do propose to use the ZIP method for compression/decompression and the UUCODE method for ASCII encoding/decoding. This method will be useful for stations connected via telephonemodems or commercial networks. The electronics delivery could solve the problem of the too late receiving of the FR data by SLR data center.

Novotny, Antonin↗
DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗
NASA NTRS1986

Modeling of coronal X-ray emission from active cool stars. I Hyades cluster

X-ray pulse height spectra of the most active cool stars in the Hyades cluster obtained with the Einstein IPC cannot be satisfactorily fitted using isothermal thin plasma emission models. Addition of a second isothermal component provides acceptable fits. However, a more physically meaningful set of coronal parameters is provided by models which consist of an ensemble of loops wih a single maximum temperature, but with the temperature distribution within the loop determined by loop physics. Such models have been successfully fitted to the IPC pulse height spectra. Constraints on loop parameters are discussed for the F-G dwarfs BD + 14 deg 693, BD + 14 deg 690, BD + 15 deg 640, and 71 Tau. Models with a large variation of loop cross section from base to top do not fit the data. A consistent physical description is an ensemble of small high-pressure loops of similar maximum temperature which dominate the coronal X-ray spectrum.

Stern, R. A.↗
NASA NTRS2001

Spatial/Spectral Identification of Endmembers from AVIRIS Data using Mathematical Morphology

During the last several years, a number of airborne and satellite hyperspectral sensors have been developed or improved for remote sensing applications. Imaging spectrometry allows the detection of materials, objects and regions in a particular scene with a high degree of accuracy. Hyperspectral data typically consist of hundreds of thousands of spectra, so the analysis of this information is a key issue. Mathematical morphology theory is a widely used nonlinear technique for image analysis and pattern recognition. Although it is especially well suited to segment binary or grayscale images with irregular and complex shapes, its application in the classification/segmentation of multispectral or hyperspectral images has been quite rare. In this paper, we discuss a new completely automated methodology to find endmembers in the hyperspectral data cube using mathematical morphology. The extension of classic morphology to the hyperspectral domain allows us to integrate spectral and spatial information in the analysis process. In Section 3, some basic concepts about mathematical morphology and the technical details of our algorithm are provided. In Section 4, the accuracy of the proposed method is tested by its application to real hyperspectral data obtained from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) imaging spectrometer. Some details about these data and reference results, obtained by well-known endmember extraction techniques, are provided in Section 2. Finally, in Section 5 we expose the main conclusions at which we have arrived.

Plaza, Antonio↗
DOE OSTI2021

SPRUCE Ground Observations of Phenology in Experimental Plots, 2020

This data set consists of one comma separated (*.csv) file containing phenological transition dates, as derived from direct observations of vegetative and reproductive phenology recorded by a human observer, from the SPRUCE experiment during 2020, the fifth full year of whole-ecosystem warming (Hanson et al. 2017). Both spring and autumn phenological events are included. Since April 2016, human observers have been directly tracking the phenology of both woody and herbaceous species on a weekly schedule within the SPRUCE experimental chambers, these data are reported in annual ground observations data sets (see Related Data Sets). The observed date reported here is the first survey date in 2020 on which an event/phenophase was definitively observed. This data set also contains a companion file in HTML (*.html) format containing figures showing the relationship between the day of year and temperature treatment for different phenological phases by species for 2020. User note: Ground observations of phenology from 2016-2021 are available. SPRUCE Ground Observations of Phenology in Experimental Plots 2021, https://doi.org/10.25581/spruce.099/1874936 for the most recent data as well as links to all other ground phenology datasets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

SPRUCE Ground Observations of Phenology in Experimental Plots, 2021

This data set consists of one comma separated (*.csv) file containing phenological transition dates, as derived from direct observations of vegetative and reproductive phenology recorded by a human observer, from the SPRUCE experiment during 2021, the sixth full year of whole-ecosystem warming (Hanson et al. 2017). Both spring and autumn phenological events are included. Since April 2016, human observers have been directly tracking the phenology of both woody and herbaceous species on a weekly schedule within the SPRUCE experimental chambers, these data are reported in annual ground observations data sets (see Related Data Sets). The observed date reported here is the first survey date in 2021 on which an event/phenophase was definitively observed. This data set also contains a companion file in HTML (*.html) format containing figures showing the relationship between the day of year and temperature treatment for different phenological phases by species for 2021. User note: Ground observations of phenology from 2016-2020 are available. See the user guide for links to all other ground phenology datasets.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1993

Are existing magnetospheric models excessively stretched?

The distribution of the average north-south magnetic field component Bz in the vicinity of the neutral sheet has been investigated. This component is crucial for mappings between the nightside polar ionosphere and the equatorial magnetosphere. Data sets consisting of about 0.5 R(E) averages of magnetic field observations by the IMP/HEOS and ISEE spacecraft have been compared to the field predicted by the Tsyganenko (1987, 1989) models T87 and T89. It was found that both T87 and T89 underestimate Bz in the near tail region by as much as a factor of 2. Modified versions of the T87 model, incorporating plasma sheet warping, were obtained by fitting the model parameters via nonlinear least squares to the ISEE data set and yielded Bz values in agreement with the ISEE data. The study reveals an enormous scatter among the observed baseline values of Bz (on a time scale of 10-20 min), as well as intrinsic biases imposed by the mathematical structure of tail models, and these two factors (especially the first one) greatly limit the accuracy of model predictions of tail Bz.

Peredo, Mauricio↗
DOE OSTI2025

Machine learning inversion from scattering for mechanically driven polymers

A machine learning inversion method is developed for analyzing scattering functions of mechanically driven polymers and extracting the corresponding feature parameters, which include energy parameters and conformation variables. The polymer is modeled as a chain of fixed-length bonds constrained by bending energy, and it is subject to external forces such as stretching and shear. We generate a data set consisting of random combinations of energy parameters, including bending modulus, stretching and shear force, along with Monte Carlo-calculated scattering functions and conformation variables such as end-to-end distance, radius of gyration and off-diagonal component of the gyration tensor. The effects of the energy parameters on the polymer are captured by the scattering function, and principal component analysis ensures the feasibility of the machine learning inversion. Finally, we train a Gaussian process regressor using part of the data set as a training set and validate the trained regressor for inversion using the rest of the data. The regressor successfully extracts the feature parameters.

Gaussian process regressors↗
DOE OSTI2026

Data and script associated with “Shifts in Rain-Snow Partitioning Drive Faster Water Transit Times in the US Pacific Northwest”

This data package contains the data and code to use and run the Water Tracer enabled version of the Weather Research and Forecasting Hydrologic model (WT-WRF-Hydro) with the Sequential Precipitation Input Tagging (SPIT) framework. It is associated with the publication “Shifts in Rain-Snow Partitioning Drive Faster Water Transit Times in the US Pacific Northwest” published in Scientific Reports (Butler et al., 2026; https://doi.org/10.1038/s41598-026-46539-1). We use the Continental U.S. (CONUSII; Rasmussen et al., 2021) dataset to force the model with an historical climate (2006–2013) and a future climate (2086–2093) with a representative carbon pathway (RCP) 8.5 scenario. We use the model to calculate water transit times in five headwater catchments within the U.S. Pacific Northwest. We also show key hydrologic and environmental variables that affect water transit times and changes in the future. Finally, we use observed data to validate the model such as stream water isotopes, snowpack characteristics, and stream discharge. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. The data package consists of 11 folders: (1) "Figures" contains the exported figures used in the manuscript; (2) "Model_Isotope_Date" contains the WT-WRF-Hydro isotope date used in model validation; (3) “Model_Outputs_Future” contains the WT-WRF-Hydro future climate outputs; (4) “Model_Outputs_Historical” contains the WT-WRF-Hydro historical climate outputs; (5) “Model_Outputs_Weights_Areas” contains the WT-WRF-Hydro weights per catchment used to calculate water transit times and isotopes in stream water; (6) “MODIS_data_scripts” contains data used to validate snow conditions in the study area; (7) “Observed_Flow_Data” contains the observed streamflow data used in model validation; (8) “Observed_Isotope_Data” contains the observed stream water isotope data used in model validation; (9) “Scripts” contains the Python scripts used to general results and the figures; (10) “Statistic_Outputs” contains the water transit time statistical outputs reported in this manuscript; (11) “Validation_SNOTEL” contains the SNOTEL data used in model validation. The files in this data package have the following file extensions: .tif, .txt, .csv, .pdf, .py, .jpg, and .png.

American River↗
NASA NTRS1983

LANDSAT-4 horizon scanner full orbit data averages

Averages taken over full orbit data spans of the pitch and roll residual measurement errors of the two conical Earth sensors operating on the LANDSAT 4 spacecraft are described. The variability of these full orbit averages over representative data throughtout the year is analyzed to demonstrate the long term stability of the sensor measurements. The data analyzed consist of 23 segments of sensor measurements made at 2 to 4 week intervals. Each segment is roughly 24 hours in length. The variation of full orbit average as a function of orbit within a day as a function of day of year is examined. The dependence on day of year is based on association the start date of each segment with the mean full orbit average for the segment. The peak-to-peak and standard deviation values of the averages for each data segment are computed and their variation with day of year are also examined.

Stanley, J. P.↗
NASA NTRS1984

Velocity spike at the poleward edge of the auroral zone

Data from coordinated observations of the Chatanika incoherent scatter radar and the Atmosphere Explorer C satellite are examined to study the poleward edge of the auroral zone for one pass in the morning sector. A very intense, spikelike electric field is seen at the boundary between the auroral zone and the polar cap, coincident with the convection reversal. The particle detector data are consistent with the presence of a pair of oppositely oriented potential drops parallel to the magnetic field. These potential drops are of a few hundred electron volts, and their direction is such that the precipitating electrons are accelerated on the poleward side and decelerated on the equatorward side of the electric field spike. These observations are examined in the light of recent theoretical investigations that have shown that parallel acceleration can indeed be expected to occur in the regions of large velocity shear. The Chatanika data suggest that such spikes may occur repeatedly at the poleward edge of the diffuse aurora, but that they are confined in either time or space.

De La Beaujardiere, O.↗
DOE OSTI2023

Alpha-heating analysis of burning plasma and ignition experiments on the National Ignition Facility

A recent experiment conducted on the National Ignition Facility (NIF) described in the study by Abu-Shawareb et al. achieved a fusion yield output of 1.3 MJ from ~ 220 kJ of x-ray energy absorbed by the capsule, demonstrating remarkable progress in the field of laser driven inertial confinement fusion. In the study by A. R. Christopherson [“Effects of charged particle heating on the hydrodynamics of inertially confined plasmas,” Ph.D. thesis (2020)], the plasma conditions needed to claim the onset of ignition and burn propagation were outlined and multiple criterion were provided to assess progress in inertial fusion experiments. In this work, we modify the metrics from A. R. Christopherson to accurately calculate performance metrics for indirect-drive experiments on the NIF. We also show that performance metric trends observed in NIF data are consistent with theory and simulations. This analysis indicates that all the identified criterion for ignition and burn propagation have been exceeded by experiment 210 808.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

SPRUCE Ground Observations of Phenology in Experimental Plots, 2022

This data set consists of one comma separated (*.csv) file containing phenological transition dates, as derived from direct observations of vegetative and reproductive phenology recorded by a human observer, from the SPRUCE experiment during 2022 (2022-03-28 to 2021-11-17), the seventh full year of whole-ecosystem warming (Hanson et al. 2017). Both spring and autumn phenological events are included. Since April 2016, human observers have been directly tracking the phenology of both woody and herbaceous species on a weekly schedule within the SPRUCE experimental chambers, these data are reported in annual ground observations data sets (see Related Data Sets). The observed date reported here is the first survey date in 2022 on which an event/phenophase was definitively observed. This data set also contains a companion file in HTML (*.html) containing figures showing the relationship between the day of year and temperature treatment for different phenological phases by species for 2022.

SPRUCE experiment, plant phenological phases, Spru↗
DOE OSTI2024

SPRUCE Ground Observations of Phenology in Experimental Plots, 2023

This data set consists of one comma separated (*.csv) file containing phenological transition dates, as derived from direct observations of vegetative and reproductive phenology recorded by a human observer, from the SPRUCE experiment during 2023 (2023-03-30 to 2023-11-29), the eighth full year of whole-ecosystem warming (Hanson et al. 2017). Both spring and autumn phenological events are included. Since April 2016, human observers have been directly tracking the phenology of both woody and herbaceous species on a weekly schedule within the SPRUCE experimental chambers, these data are reported in annual ground observations data sets (see Related Data Sets). The observed date reported here is the first survey date in 2023 on which an event/phenophase was definitively observed. This data set also contains a companion file in HTML (*.html) containing figures showing the relationship between the day of year and temperature treatment for different phenological phases by species for 2023.

Marcell Experimental Forest↗
DOE OSTI2025

SPRUCE Ground Observations of Phenology in Experimental Plots, 2024

This data set consists of one comma separated (*.csv) file containing phenological transition dates, as derived from direct observations of vegetative and reproductive phenology recorded by a human observer, from the SPRUCE experiment during 2024 (2025-03-06 to 2025-11-21), the ninth full year of whole-ecosystem warming (Hanson et al. 2017). Both spring and autumn phenological events are included. Since April 2016, human observers have been directly tracking the phenology of both woody and herbaceous species on a weekly schedule within the SPRUCE experimental chambers, these data are reported in annual ground observations data sets (see Related Data Sets). The observed date reported here is the first survey date in 2024 on which an event/phenophase was definitively observed. This data set also contains a companion file in HTML (*.html) containing figures showing the relationship between the day of year and temperature treatment for different phenological phases by species for 2024.

54 ENVIRONMENTAL SCIENCES↗