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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

MINERvA s Open Data Product: A First for Neutrino Data Preservation

Access to information on neutrino nucleus interactions is critical to the success of all neutrino oscillation experiments. MINERvA's rich dataset covers a range of energies and nuclei unique amongst experiments, and as such is critical to the community in building the important shared knowledge needed to unravel the mysteries of the neutrino. In particular, its dataset provides the greatest statistical coverage in in the range of neutrino energies pertinent for DUNE until DUNE's near detector begins operation. Historically, such significant datasets in neutrino physics have been preserved primarily through their published results. While meaningful and useful, this limits the ability to explore the data to its fullest extent as new perspectives continue to form. MINERvA has undertaken a major effort to break this trend and preserve its data in a format to be as analyzable as possible from outside the collaboration. This has culminated in the officially-released MINERvA Open Data Product for the community to take advantage of and utilize. Maintaining direct access to the dataset in an analyzable form will allow new insights to continue to be extracted indefinitely. This talk will cover the contents of this product, the information included (and excluded), the tools provided to utilize the product effectively, the support MINERvA intends to provide in its use, and some lessons learned through the process.

Last, David [Rochester U.] (ORCID:0000000245147183↗
NASA NTRS2005

Effect of Forest Canopy on Remote Sensing Soil Moisture at L-band

Global maps of soil moisture are needed to improve understanding and prediction of the global water and energy cycles. Accuracy requirements imply the use of lower frequencies (L-band) to achieve adequate penetration into the soil and to minimize attenuation by the vegetation canopy and effects of surface roughness. Success has been demonstrated over agricultural areas, but canopies with high biomass (e.g. forests) still present a challenge. Examples from recent measurements over forests with the L-band radiometer, 2D-STAR, and its predecessor, ESTAR, will be presented to illustrate the problem. ESTAR and 2D-STAR are aircraft-based synthetic aperture radiometers developed to help resolve both the engineering and algorithm issues associated with future remote sensing of soil moisture. ESTAR, which does imaging across track, was developed to demonstrate the viability of aperture synthesis for remote sensing. The instrument has participated several soil moisture experiments (e.g. at the Little Washita Watershed in 1992 and the Southern Great Plains experiments in 1997 and 1999). In addition, measurements have been made at a forest site near Waverly, VA which contains conifer forests with a variety of biomass. These data have demonstrated the success of retrieving soil moisture at L-band over agricultural areas and the response of passive observations at L-band to biomass over forests. 2D-STAR is a second generation instrument that does aperture synthesis in two dimensions (along track and cross track) and is dual polarized. This instrument has the potential to provide measurements at L-band that simulate the measurements that will be made by the two L-band sensors currently being developed for future remote sensing of soil moisture from space: Hydros (conical scan and real aperture) and SMOS (multiple incidence angle and synthetic aperture). 2D-STAR participated in the SMEX-03 soil moisture experiment, providing images from the NASA P-3 aircraft. Preliminary results include images of the experiment site area near Huntsville, AL that included a mixture of forest and agriculture. Changes during a rain event further illustrate the issues presented by forests. Work is continuing to reduce the 2D-STAR data and to support the two future remote sensing missions. Among the goals is to process the 2D-STAR data to create multiple looks (at the same pixel) with different incidence angles. Data in this format can be used to test algorithms for retrieving soil moisture and biomass such as are planned for SMOS. Also, the data are being processed to provide images at constant incidence angles such as will be obtained by Hydros. Although Hydros will have only one incidence angle, it will also carry an L-band radar, The goal is to use the radar to improve spatial resolution, an issue for remote sensing from space at the long wavelengths. Simultaneous observations with active and passive sensors also offers interesting prospects for treating areas of high biomass (forests) and irregular terrain and may be the challenge for the future.

LeVine, D. M.↗
NASA NTRS2001

Thermodynamics of Volatile Silicon Hydroxides Studied

Silicon-based ceramics are promising candidate structural materials for heat engines. The long-term stability of these materials to environmental degradation is dependent on the formation and retention of a protective SiO2 layer. It is well known that SiO2 forms stable volatile hydroxides in the presence of water vapor at elevated temperatures. Combustion conditions, which characteristically are at high velocities, contain significant water vapor pressures, and high temperatures tend to promote continuous formation of these hydroxides with resulting material degradation. For the degradation of silicon-based ceramics to be predicted, accurate thermodynamic data on the formation of silicon hydroxides are needed.

Copland, Evan H.↗
NASA NTRS2012

The Thermodynamic Properties of Cubanite

CuFe2S3 exists in two polymorphs, a low-temperature orthorhombic form (cubanite) and a high-temperature cubic form (isocubanite). Cubanite has been identified in the CI-chondrite and Stardust collections. However, the thermodynamic properties of cubanite have neither been measured nor estimated. Our derivation of a thermodynamic model for cubanite allows constraints to be placed on the formation conditions. This data, along with the temperature constraint afforded by the crystal structure, can be used to assess the environments in which cubanite formation is (or is not) thermodynamically favored.

Berger, E. L.↗
NASA NTRS2015

The Process of Bringing Dark Data to Light: The Rescue of the Early Nimbus Satellite Data

Myriad environmental satellite missions are currently orbiting the earth. The comprehensive monitoring by these sensors provide scientists, policymakers, and the public critical information on the earths weather and climate system. The state of the art technology of our satellite monitoring system is the legacy of the first environment satellites, the Nimbus systems launched by NASA in the mid-1960s. Such early data can extend our climate record and provide important context in longer-term climate changes. However, the data was stowed away and, over the years, largely forgotten. It was nearly lost before its value was recognized and attempts to recover the data were undertaken. This paper covers what it took the authors to recover, navigate and reprocess the data into modern formats so that it could be used as a part of the satellite climate record. The procedures to recover the Nimbus data, from both film and tape, could be used by other data rescue projects, however the algorithms presented will tend to be Nimbus specific. Data rescue projects are often both difficult and time consuming but the data they bring back to the science community makes these efforts worthwhile.

dark↗
DOE OSTI2022

Data Curation for Machine Learning Applied to Geothermal Power Plant Operational Data for GOOML: Geothermal Operational Optimization with Machine Learning: Preprint

Geothermal Operational Optimization with Machine Learning (GOOML) is a transferable and extensible component-based geothermal asset modeling framework that considers complex steamfield relationships and identifies optimization prospects using a data-driven approach to physics-guided, data-centric machine learning. This framework has been used to develop digital twins that provide steamfield operators with operational environments to analyze and understand historical and forecasted power production, explore new steamfield configuration possibilities, and seek optimal asset management in real world applications. To create, test, and apply the GOOML framework, diverse time-series datasets spanning multiple years were sourced from various geothermal power plant components within several complex real-world geothermal operations. These operations are based in the United States and New Zealand and include a variety of technologies, end-uses and configurations, collectively covering nearly all relevant operating conditions for modern geothermal fields. Datasets were acquired from multiple sources to ensure that machine learning experiments generalized properly to various operating conditions. It was found that the data varied in quality, format, and completeness. To ensure consistency between the various datasets, a standardized data curation process was developed to reliably streamline data preparation. This paper will discuss best practices as learned from the GOOML data curation process which takes the following steps: 1) acquisition of large quantities of data from power plant operators, 2) digestion of data to gain an initial understanding of what is included, 3) data transformation, which includes converting the data into a standardized machine-readable format so that they can be visualized, quality checked, and cleaned, 4) quality assurance and quality control, involving identification of significant data gaps and apparent anomalies through mapping of data features to real world componentry via the GOOML historical model, followed by discussion with modelers and power plant operators to identify additional data needs and to resolve issues, 5) use in machine learning algorithms, and 6) repetition of steps one through five until all data needs are met and data are deemed suitable for producing trustworthy modeling results which may be disseminated, ideally along with the curated dataset. This iterative process is focused on improving the quality of the data rather than tuning machine learning model parameters and supports a shift towards data-centric AI as a means to improving real-world applicability of geothermal machine learning projects.

access↗
DOE OSTI2015

THE PANCHROMATIC STARBURST IRREGULAR DWARF SURVEY (STARBIRDS): OBSERVATIONS AND DATA ARCHIVE

Understanding star formation in resolved low mass systems requires the integration of information obtained from observations at different wavelengths. We have combined new and archival multi-wavelength observations on a set of 20 nearby starburst and post-starburst dwarf galaxies to create a data archive of calibrated, homogeneously reduced images. Named the panchromatic “STARBurst IRregular Dwarf Survey” archive, the data are publicly accessible through the Mikulski Archive for Space Telescopes. This first release of the archive includes images from the Galaxy Evolution Explorer Telescope (GALEX), the Hubble Space Telescope (HST), and the Spitzer Space Telescope (Spitzer) Multiband Imaging Photometer instrument. The data sets include flux calibrated, background subtracted images, that are registered to the same world coordinate system. Additionally, a set of images are available that are all cropped to match the HST field of view. The GALEX and Spitzer images are available with foreground and background contamination masked. Larger GALEX images extending to 4 times the optical extent of the galaxies are also available. Finally, HST images convolved with a 5″ point spread function and rebinned to the larger pixel scale of the GALEX and Spitzer 24 μm images are provided. Future additions are planned that will include data at other wavelengths such as Spitzer IRAC, ground-based Hα, Chandra X-ray, and Green Bank Telescope H i imaging.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRSDate not supplied

Making NASA GES DISC Level 2 Data GIS Analysis Ready

There are many valuable data hosted by NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for GIS applications in the areas of extreme weather events, climatic anomaly, and public health. However, using NASA Earth Science Data poses some challenges for GIS users. Many of these users are not experts in Earth Observation and have little knowledge about NASA's Earth science data. In the GIS community, GeoTiff is the most widely used raster format, whereas NASA's data is primarily in complex multidimensional netCDF and HDF formats. This complexity makes it difficult for GIS users, especially those who are unfamiliar with these formats. Although GIS software like ArcGIS has made progress in processing multidimensional netCDF data, certain issues still remain, particularly with level 2 data. In this study, we use TROPSpheric Monitoring instrument (TROPOMI) level 2 data as an example to demonstrate how to make such data GIS analysis ready. The process involves: 1. Creating a feature layer from the TROPOMI level 2 data. 2. Converting the feature layer to a gridded raster dataset. 3. Mosaicking gridded raster datasets into a raster dataset covering the entire desired extent. 4. Generating a symbology with a GIBS-specific style that aligns with the visual standards and requirements of GIBS. 5. Publishing image services. By performing these preprocessing and transformation steps, NASA level 2 data can be made compatible and ready for use within GIS software for various spatial analysis and visualization tasks.

Geographic Information System↗
NASA NTRS2015

Improving the Accessibility and Use of NASA Earth Science Data

Many of the NASA Langley Atmospheric Science Data Center (ASDC) Distributed Active Archive Center (DAAC) multidimensional tropospheric and atmospheric chemistry data products are stored in HDF4, HDF5 or NetCDF format, which traditionally have been difficult to analyze and visualize with geospatial tools. With the rising demand from the diverse end‐user communities for geospatial tools to handle multidimensional products, several applications, such as ArcGIS, have refined their software. Many geospatial applications now have new functionalities that enable the end user to: Store, serve, and perform analysis on each individual variable, its time dimension, and vertical dimension. Use NetCDF, GRIB, and HDF raster data formats across applications directly. Publish output within REST image services or WMS for time and space enabled web application development. During this webinar, participants will learn how to leverage geospatial applications such as ArcGIS, OPeNDAP and ncWMS in the production of Earth science information, and in increasing data accessibility and usability.

Tisdale, Matthew↗
NASA NTRS1981

Bit error rate performance of Image Processing Facility high density tape recorders

The Image Processing Facility at the NASA/Goddard Space Flight Center uses High Density Tape Recorders (HDTR's) to transfer high volume image data and ancillary information from one system to another. For ancillary information, it is required that very low bit error rates (BER's) accompany the transfers. The facility processes about 10 to the 11th bits of image data per day from many sensors, involving 15 independent processing systems requiring the use of HDTR's. When acquired, the 16 HDTR's offered state-of-the-art performance of 1 x 10 to the -6th BER as specified. The BER requirement was later upgraded in two steps: (1) incorporating data randomizing circuitry to yield a BER of 2 x 10 to the -7th and (2) further modifying to include a bit error correction capability to attain a BER of 2 x 10 to the -9th. The total improvement factor was 500 to 1. Attention is given here to the background, technical approach, and final results of these modifications. Also discussed are the format of the data recorded by the HDTR, the magnetic tape format, the magnetic tape dropout characteristics as experienced in the Image Processing Facility, the head life history, and the reliability of the HDTR's.

Heffner, P.↗
DOE OSTI2026

Leveraging large language models to address data scarcity in machine learning for graphene synthesis

Machine learning in experimental materials science faces significant challenges due to the scarcity of data, which are costly and time-consuming to generate, particularly when relying on in-house experiments. Literature data mining offers a potential solution but introduces issues like mixed data quality, inconsistent formats, and non-uniform reporting of synthesis parameters, resulting in partially missing and heterogeneous features across the dataset. Here, we propose data imputation and feature engineering methods that employ pre-trained large language models (LLMs) to enhance machine learning performance on scarce, heterogeneous datasets, demonstrated on graphene CVD synthesis data and the ML-HydPARK hydrogen storage dataset. GPT models perform data imputation via tailored prompting and semantic normalization of inconsistently reported features through embeddings, for example, to harmonize the complex nomenclature of CVD substrates. Beyond yielding more diverse and richer feature representations than traditional methods such as K-nearest neighbors (KNN) and Multivariate Imputation by Chained Equations (MICE), LLM-based data imputation is evaluated against dataset characteristics and prompting strategies. We vary the level of autonomy granted to the LLM, from generic prompting that leverages pre-trained knowledge for autonomous data generation to data-informed prompting that constrains outputs using target-specific information, and demonstrate which level of autonomy yields superior imputation performance across datasets and feature types. The proposed data engineering methods markedly improve downstream performance; for example, in graphene layer number classification using a support vector machine (SVM), binary accuracy increases from 39% to 65% and ternary accuracy from 52% to 72%. Fine-tuning experiments on both datasets show that combining our proposed LLM-based data imputation and feature encoding methods with numerical machine learning predictors outperforms standalone fine-tuned LLM predictors in data-scarce settings. The proposed strategies emphasize data enhancement techniques rather than refining learning architectures or regularizing loss functions, offering a broadly applicable framework for improving machine learning performance on scarce, inhomogeneous datasets.

Chemical vapor deposition↗
NASA NTRS1985

Earth radiation budget experiment software development

Computer programming and analysis efforts were carried out in support of the Earth Radiation Budget Experiment (ERBE) at NASA/Langley. The Earth Radiation Budget Experiment is described as well as data acquisition, analysis and modeling support for the testing of ERBE instruments. Also included are descriptions of the programs developed to analyze, format and display data collected during testing of the various ERBE instruments. Listings of the major programs developed under this contract are located in an appendix.

Edmonds, W. L.↗
NASA NTRS1988

VLSI high speed packet processor

The Goddard Space Flight Center Mission Operations and Data Systems Directorate has developed a packet processor card utilizing semicustom very large scale integration (VLSI) devices, microprocessors, and programmable gate arrays to support the implementation of multichannel telemetry data capture systems. This card will receive synchronized error corrected telemetry transfer frames and output annotated application packets derived from this data. An adaptable format capability is provided by the programmability of three microprocessors while the throughput capability of the packet processor is achieved by a data pipeline consisting of two separate RAM systems controlled by specially designed semicustom VLSI logic.

Grebowsky, Gerald J.↗
NASA NTRS1995

Software Aids In Graphical Depiction Of Flow Data

Interactive Data Display System (IDDS) computer program is graphical-display program designed to assist in visualization of three-dimensional flow in turbomachinery. Grid and simulation data files in PLOT3D format required for input. Able to unwrap volumetric data cone associated with centrifugal compressor and display results in easy-to-understand two- or three-dimensional plots. IDDS provides majority of visualization and analysis capability for Integrated Computational Fluid Dynamics and Experiment (ICE) system. IDDS invoked from any subsystem, or used as stand-alone package of display software. Generates contour, vector, shaded, x-y, and carpet plots. Written in C language. Input file format used by IDDS is that of PLOT3D (COSMIC item ARC-12782).

Stegeman, J. D.↗
DOE OSTI2025

Data and Scripts associated with a manuscript on ecosystem responses to wildfires in the Columbia River Basin

This data package is associated with the publication “Ecosystem leaf area, gross primary production, and evapotranspiration responses to wildfire in the Columbia River Basin” submitted to Biogeosciences (Shi et al., 2024; doi: 10.22541/au.171053013.30286044/v1). In this research, data products, leaf area index (LAI), gross primary production (GPP), and evapotranspiration (ET), from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used to quantify the resistance and resilience of different ecosystem types in the Columbia River Basin (CRB). A machine learning algorithm, random forest (RF), was used to examine the impacts of precipitation, vapor pressure deficit (VPD), and burn severity from Monitoring Trends in Burn Severity (MTBS) on ecosystem resilience. The data package includes the processed MODIS data products, precipitation, VPD, and burn severity in 138 fire regions in CRB and the input files for RF model training. This data package includes six folders. The MODIS products are included in three MODIS_* folders with shell scripts for data clipping and *ncl files for data processing: (1) “/MODIS_LAI_CRB”; (2) “/MODIS_GPP_CRB”; and (3) “/MODIS_ET_CRB”. All the processed data for each fire event are NetCDF formatted. The MTBS burn severity data and the shell and *ncl scripts used for data processing are in the folder named (4) “MTBS_fire”. The ERA meteorological fields and the data processing scritps are in (5) “ERA_Var_CR”. All the scripts for figure development are in the format of *ncl and in the folder (6) “paper_scripts”. See the file ending in “flmd.csv” for a list of all files contained in this data package and descriptions for each. Tabular column headers and units are described in the data dictionary file ending in “dd.csv”.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2004

Bolt Analysis Program

In designing and testing bolted joints there are multiple parameters to be considered and calculations that must be performed to predict the joint behavior. Each different set of parameters may call for a different set of equations. Determining every parameter in each bolted joint is impractical and in many cases impossible. On the other hand, it is much easier to reduce these calculations to a universal set that can be used for all bolted joints. This is the purpose of the Bolt Analysis Program. My project under the Mechanical and Rotating Systems branch of the Engineering Development and Analysis Division was to take the Bolt Analysis Program Version 2.0 and update the program to a modem and user-friendly format. Version 2.0 of the Bolt Analysis Program is a useful program, but lacks the dynamic capabilities that are needed for current applications. Version 2.0 of the Bolt Analysis Program was written in 1993 using the Pascal programming language in a DOS format. This program allows you to input data in a step-by-step format, calculates the data, and then on a final screen displays the input and the output fiom the calculations. Version 2.0 is still applicable for all bolted joint anaiysis, but has updates that are desired. First, the program runs in DOS format. With the applications available today, my mentor decided it would be best to update the program into Excel using Visual Basic for Applications (VBA). This would allow the program to have multiple Graphical User Interfaces (GUI s) while retaining all functions of the previous program. Version 2.0 only allows you to input data in a step-by-step process. If you make a mistake and need to go back, you must run through the entire program before you can return to fix your error. This becomes tedious when needing to change one parameter or test multiple sets of data. In Version 3.0, the program allows you to enter and change data at any time while displaying real-time output data. If you realize an error, it is as simple as scrolling back to your mistake and changing the data. Additional information is included in the original extended abstract.

Travis, Brandon E.↗
NASA NTRS1986

Resonant AC power system proof-of-concept test program, volume 2, appendix 1

This report contains two volumes. The main text (Volume 1) summarizes the tests results and gives a detailed discussion of the response of three early, first generation configurations of ac power system IRAD breadboards to the contracted tests imposed on them. It explains photographs, measurements, and data calculations, as well as any observed anomalies or lessons learned. This volume (No 2, Appendix 1, Test Results and Data), published under separate cover, includes all of the data taken on the 1.0 kW single-phase; 5.0 kW three-phase; and 25.0-kW three-phase system breadboards. The format of this data is raw, i.e., it is a direct copy of the data sheets for the test data notebook.

Source record↗