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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Interplanetary space science data base and access/display tool on the NSSDC heliospheric CD-ROM

The National Space Science Data Center (NSSDC) has accumulated a rich archive of heliospheric, magnetospheric, and ionospheric data, as well as data from most other NASA-involved science disciplines. To facilitate access to and use of these data, NSSDC has begun to put selected data onto CD-ROM's. This paper describes one such CD-ROM, and the access and display software developed at NSSDC to support its use. The data on the CD-ROM consist primarily of hourly solar wind magnetic field and plasma data from many near-Earth spacecraft (OMNI) and deep space spacecraft (Voyagers, Pioneers, Helios, Pioneer Venus Orbiter). In addition, 5-minute resolution IMP-8 and ISEE-3 magnetic field and plasma data are also included. Data are stored in both ASCII and CDF formats.

Papitashvili, N. E.↗
NASA NTRSDate not supplied

Compressive Sensing Based Data Acquisition Architecture for Transient Stellar Events in Crowded Star Fields

Compressive sensing is a mathematical technique for simultaneous data acquisition and compression. In this work, we show a CS based architecture for acquiring and reconstructing transient stellar events. This architecture recon-structs a differenced image itself, eliminating the need for any sparse domain transforms, otherwise required for traditional CS reconstruction. The resulting reconstructed differenced image is of critical importance as the information required for generating a time-series photometric light curve is obtained only from a differenced image. Hence, reconstructing a crowded star spatial image, followed by differencing is wasteful. This architecture eliminates the need to 1.) transform an image to a sparse domain, 2.) Reconstruct a dense field, and then apply differencing on the image to obtain the image of critical value. We study the case of microlensing to depict a star source experiencing magnification in time. Our results show that this architecture is able to reconstruct star source magnitudes with magnification factors greater than 1 for clean images with error less than 2% using only 10% of the Nyquist rate samples.

Compressive Sensing, Data acquisition, Image diffe↗
NASA NTRSDate not supplied

Compressive Sensing Based Data Acquisition Architecture for Transient Stellar Events in Crowded Star Fields

Compressive sensing is a mathematical technique for simultaneous data acquisition and compression. In this work, we show a CS based architecture for acquiring and reconstructing transient stellar events. This architecture reconstructs a differenced image itself, eliminating the need for any sparse domain transforms, otherwise required for traditional CS reconstruction. The resulting reconstructed differenced image is of critical importance as the information required for generating a time-series photometric light curve is obtained only from a differenced image. Hence, reconstructing a crowded star spatial image, followed by differencing is wasteful. This architecture eliminates the need to 1.) transform an image to a sparse domain, 2.) Reconstruct a dense field, and then apply differencing on the image to obtain the image of critical value. We study the case of microlensing to depict a star source experiencing magnification in time. Our results show that this architecture is able to reconstruct star source magnitudes with magnification factors greater than 1 for clean images with error less than 2% using only 10% of the Nyquist rate samples.

Asmita Korde-Patel↗
NASA NTRSDate not supplied

Compressive Sensing Based Data Acquisition Architecture for Transient Stellar Events in Crowded Star Fields

Compressive sensing is a mathematical technique for simultaneous data acquisition and compression. In this work, we show a CS based architecture for acquiring and reconstructing transient stellar events. This architecture recon-structs a differenced image itself, eliminating the need for any sparse domain transforms, otherwise required for traditional CS reconstruction. The resulting reconstructed differenced image is of critical importance as the information required for generating a time-series photometric light curve is obtained only from a differenced image. Hence, reconstructing a crowded star spatial image, followed by differencing is wasteful. This architecture eliminates the need to 1.) transform an image to a sparse domain, 2.) Reconstruct a dense field, and then apply differencing on the image to obtain the image of critical value. We study the case of microlensing to depict a star source experiencing magnification in time. Our results show that this architecture is able to reconstruct star source magnitudes with magnification factors greater than 1 for clean images with error less than 2% using only 10% of the Nyquist rate samples.

Asmita Korde-patel↗
DOE OSTI2024

Improving Text Classification with Large Language Model-Based Data Augmentation

Large Language Models (LLMs) such as ChatGPT possess advanced capabilities in understanding and generating text. These capabilities enable ChatGPT to create text based on specific instructions, which can serve as augmented data for text classification tasks. Previous studies have approached data augmentation (DA) by either rewriting the existing dataset with ChatGPT or generating entirely new data from scratch. However, it is unclear which method is better without comparing their effectiveness. This study investigates the application of both methods to two datasets: a general-topic dataset (Reuters news data) and a domain-specific dataset (Mitigation dataset). Our findings indicate that: 1. ChatGPT generated new data consistently enhanced model’s classification results for both datasets. 2. Generating new data generally outperforms rewriting existing data, though crafting the prompts carefully is crucial to extract the most valuable information from ChatGPT, particularly for domain-specific data. 3. The augmentation data size affects the effectiveness of DA; however, we observed a plateau after incorporating 10 samples. 4. Combining the rewritten sample with new generated sample can potentially further improve the model’s performance.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1977

Tabular data base construction and analysis from thematic classified Landsat imagery of Portland, Oregon

A systematic verification of Landsat data classifications of the Portland, Oregon metropolitan area has been undertaken on the basis of census tract data. The degree of systematic misclassification due to the Bayesian classifier used to process the Landsat data was noted for the various suburban, industrialized and central business districts of the metropolitan area. The Landsat determinations of residential land use were employed to estimate the number of automobile trips generated in the region and to model air pollution hazards.

Bryant, N. A.↗
NASA NTRS1985

Operational maintenance data base development for kinematic Stirling engines

In the initial stages of developing the automotive Stirling engine (ASE), data has been accumulated under the program's Quality Assurance Report (QAR) program to identify problem areas encountered during engine operation. This data has been used as the basis for developing design modifications to existing hardware, identifying diagnostic techniques and instrumentation, and providing guidance towards component and system development requirements for future engine designs, such as the Mod II engine currently in the initial design stage. The QAR has proven itself to be an essential part of the ASE Program, and has successfully guided the development of the automotive application of this emerging engine technology.

Richey, A.↗
NASA NTRS2013

Probabilistic Analysis for Comparing Fatigue Data Based on Johnson-Weibull Parameters

Leonard Johnson published a methodology for establishing the confidence that two populations of data are different. Johnson's methodology is dependent on limited combinations of test parameters (Weibull slope, mean life ratio, and degrees of freedom) and a set of complex mathematical equations. In this report, a simplified algebraic equation for confidence numbers is derived based on the original work of Johnson. The confidence numbers calculated with this equation are compared to those obtained graphically by Johnson. Using the ratios of mean life, the resultant values of confidence numbers at the 99 percent level deviate less than 1 percent from those of Johnson. At a 90 percent confidence level, the calculated values differ between +2 and 4 percent. The simplified equation is used to rank the experimental lives of three aluminum alloys (AL 2024, AL 6061, and AL 7075), each tested at three stress levels in rotating beam fatigue, analyzed using the Johnson- Weibull method, and compared to the ASTM Standard (E739 91) method of comparison. The ASTM Standard did not statistically distinguish between AL 6061 and AL 7075. However, it is possible to rank the fatigue lives of different materials with a reasonable degree of statistical certainty based on combined confidence numbers using the Johnson- Weibull analysis. AL 2024 was found to have the longest fatigue life, followed by AL 7075, and then AL 6061. The ASTM Standard and the Johnson-Weibull analysis result in the same stress-life exponent p for each of the three aluminum alloys at the median, or L(sub 50), lives

Vlcek, Brian L.↗
NASA NTRS1993

Space Station Freedom (SSF) Data Management System (DMS) performance model data base

The purpose of this document was originally to be a working document summarizing Space Station Freedom (SSF) Data Management System (DMS) hardware and software design, configuration, performance and estimated loading data from a myriad of source documents such that the parameters provided could be used to build a dynamic performance model of the DMS. The document is published at this time as a close-out of the DMS performance modeling effort resulting from the Clinton Administration mandated Space Station Redesign. The DMS as documented in this report is no longer a part of the redesigned Space Station. The performance modeling effort was a joint undertaking between the National Aeronautics and Space Administration (NASA) Johnson Space Center (JSC) Flight Data Systems Division (FDSD) and the NASA Ames Research Center (ARC) Spacecraft Data Systems Research Branch. The scope of this document is limited to the DMS core network through the Man Tended Configuration (MTC) as it existed prior to the 1993 Clinton Administration mandated Space Station Redesign. Data is provided for the Standard Data Processors (SDP's), Multiplexer/Demultiplexers (MDM's) and Mass Storage Units (MSU's). Planned future releases would have added the additional hardware and software descriptions needed to describe the complete DMS. Performance and loading data through the Permanent Manned Configuration (PMC) was to have been included as it became available. No future releases of this document are presently planned pending completion of the present Space Station Redesign activities and task reassessment.

Stovall, John R.↗
NASA NTRS1998

A Data Base of Nutrient Use, Water Use, CO2 Exchange, and Ethylene Production by Soybeans in a Controlled Environment

A data set is given describing daily nutrient and water uptake, carbon dioxide (CO2) exchange, ethylene production, and carbon and nutrient partitioning from a 20 sq m stand of soybeans (Glycine max (L.) Merr. cv. McCall] for use in bioregenerative life support systems. Stand CO2 exchange rates were determined from nocturnal increases in CO2 (respiration) and morning drawdowns (net photosynthesis) to a set point of 1000 micromol/ mol each day (i.e., a closed system approach). Atmospheric samples were analyzed throughout growth for ethylene using gas chromatography with photoionization detection (GC/PH)). Water use was monitored by condensate production from the humidity control system, as well as water uptake from the nutrient solution reservoirs each day. Nutrient uptake data were determined from daily additions of stock solution and acid to maintain an EC of 0.12 S/m and pH of 5.8. Dry mass yields of seeds, pods (without seeds), leaves, stems, and roots are provided, as well as elemental and proximate nutritional compositions of the tissues. A methods section is included to qualify any assumptions that might be required for the use of the data in plant growth models, along with a daily event calendar documenting set point adjustments and the occasional equipment or sensor failure.

Wheeler, R. M.↗
NASA NTRSDate not supplied

Pipeline for Applications-Based Data Discovery

From disaster response and mitigation to monitoring water quality or protecting wildlife habitat, satellite Earth observation data can be applied in countless ways to meet pressing needs and benefit society. The crucial first step toward successful data application is data discovery. Potential users often know exactly what data they need--what Earth feature or phenomenon they need to observe, how frequently, and at what resolution or level of accuracy--but may still struggle to discover the existing observations that meet their needs. We have developed a pipeline to connect applications-based users to specific satellites and data collections within NASA's Earth observation program of record that are highly relevant to their data needs. This pipeline combines available information on satellite and instrument measurement characteristics with an innovative machine learning-based approach that identifies instruments that are most relevant to the feature or phenomenon of interest.

Katrina S Virts↗
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 NTRS1980

Effect of impurities and processing on silicon solar cells. Volume 1: Characterization methods for impurities in silicon and impurity effects data base

Two major topics are treated: methods to measure and evaluate impurity effects in silicon and comprehensive tabulations of data derived during the study. Discussions of deep level spectroscopy, detailed dark I-V measurements, recombination lifetime determination, scanned laser photo-response, conventional solar cell I-V techniques, and descriptions of silicon chemical analysis are presented and discussed. The tabulated data include lists of impurity segregation coefficients, ingot impurity analyses and estimated concentrations, typical deep level impurity spectra, photoconductive and open circuit decay lifetimes for individual metal-doped ingots, and a complete tabulation of the cell I-V characteristics of nearly 200 ingots.

Hopkins, R. H.↗
NASA NTRS1994

Steps toward determination of the size and structure of the broad-line region in active galactic nuclei. 6: Variability of NGC 3783 from ground-based data

The Seyfert 1 galaxy NGC 3783 was intensely monitored in several bands between 1991 December and 1992 August. This paper presents the results from the ground-based observations in the optical and near-IR bands, which complement the data set formed by the International Ultraviolet Explorer (IUE) spectra, discussed elsewhere. Spectroscopic and photometric data from several observatories were combined in order to obtain well-sampled light curves of the continuum and of H(beta). During the campaign the source underwent significant variability. The light curves of the optical continuum and of H(beta) display strong similarities to those obtained with the IUE. The near-IR flux did not vary significantly except for a slight increase at the end of the campaign. The cross-correlation analysis shows that the variations of the optical continuum have a lag of 1 day or less with respect to those of the UV continuum, with an uncertainty of is less than or equal to 4 days. The integrated flux of H(beta) varies with a delay of about 8 days. These results confirm that (1) the continuum variations occur simultaneously or with a very small lag across the entire UV-optical range, as in the Seyfert galaxy NGC 5548; and (2) the emission lines of NGC 3783 respond to ionizing continuum variations with less delay than those of NGC 5548. As observed in NGC 5548, the lag of H(beta) with respect to the continuum is greater than those of the high-ionization lines.

Stirpe, G. M.↗
NASA NTRS2000

The SHADOZ Data Base: History, Archive Web Guide, and Sample Climatologies

SHADOZ (Southern Hemisphere Additional Ozonesonde) is a project to augment and archive ozonesonde data from ten tropical and subtropical ozone stations. Started in 1998 by NASA's Goddard Space Flight Center and other US and international co-investigators, SHADOZ is an important tool for tropospheric ozone research in the equatorial region. The rationale for SHADOZ is to: (1) validate and improve remote sensing techniques (e.g., the Total Ozone Mapping Spectrometer (TOMS) satellite) for estimating tropical ozone, (2) contribute to climatology and trend analyses of tropical ozone and (3) provide research topics to scientists and educate students, especially in participating countries. SHADOZ is envisioned as a data service to the global scientific community by providing a central public archive location via the internet: http://code9l6.gsfc.nasa.gov/Data_services/shadoz. While the SHADOZ website maintains a standard data format for the archive, it also informs the data users on the differing stations' preparation techniques and data treatment. The presentation navigates through the SHADOZ website to access each station's sounding data and summarize each station's characteristics. Since the start of the project in 1998, the SHADOZ archive has accumulated over 600 ozonesonde profiles and received over 30,000 outside data requests. Data also includes launches from various SHADOZ supported field campaigns, such as, the Indian Ocean Experiment (INDOEX), Sounding of Ozone and Water in the Equatorial Region (SOWER) and Aerosols99 Atlantic Cruise. Using data from the archive, sample climatologies and profiles from selected stations and campaigns will be shown.

White, J. C.↗
NASA NTRS2003

Compendium of NASA Data Base for the Global Tropospheric Experiment's Transport and Chemical Evolution Over the Pacific (TRACE-P): DC-8 - Volume 1

This report provides a compendium of NASA aircraft data that are available from NASA's Global Tropospheric Experiment's (GTE) Transport and Chemical Evolution over the Pacific (TRACE-P) Mission. The broad goal of TRACE-P was to characterize the transit and evolution of the Asian outflow over the western Pacific. Conducted from February 24 through April 10, 2001, TRACE-P integrated airborne, satellite- and ground-based observations, as well as forecasts from aerosol and chemistry models. The format of this compendium utilizes data plots (time series) of selected data acquired aboard the NASA/Dryden DC-8 (vol. 1) and NASA/Wallops P-3B (vol. 2) aircraft during TRACE-P. The purpose of this document is to provide a representation of aircraft data that are available in archived format via NASA Langley s Distributed Active Archive Center (DAAC) and through the GTE Project Office archive. The data format is not intended to support original research/analyses, but to assist the reader in identifying data that are of interest.

Kleb, Mary M.↗