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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
NASA NTRS1997

Scientific and Technical Support for the Galileo Net Flux Radiometer Experiment

This report describes work in support of the Galileo Net Flux Radiometer (NFR), an instrument mounted on the Galileo probe, a spacecraft designed for entry into and direct measurements of Jupiter's atmosphere. Tasks originally proposed for the post launch period are briefly as follows: attend and support PSG (Project Science Group) and other project science meetings; support in-flight checkouts; maintain and keep safe the spare instrument and GSE (Ground Support Equipment); organize and maintain documentation; finish NFR calibration measurements, documentation, and analysis; characterize and diagnose instrument anomalies; develop descent data analysis tools; and science data analysis and publication. Because we had the capability to satisfy a project support need we also subsequently proposed and were funded to make ground- based observations of Jupiter during the period surrounding the Galileo arrival at Jupiter, using the Swedish Solar Telescope at La Palma, Canary Islands. The following section (11) provides background information on the NFR instrument.

Sromovsky, Lawrence A.↗
NASA NTRS1997

Scientific and Technical Support for the Galileo Net Flux Radiometer Experiment

This report describes work in support of the Galileo Net Flux Radiometer (NFR), an instrument mounted on the Galileo probe, a spacecraft designed for entry into and direct measurements of Jupiter's atmosphere. Tasks originally proposed for the post launch period covered by NCC 2-854 are briefly as follows: attend and support PSG (Project Science Group) and other project science meetings; support in-flight checkouts; maintain and keep safe the spare instrument and GSE (Ground Support Equipment); organize and maintain documentation; finish NFR calibration measurements, documentation, and analysis; characterize and diagnose instrument anomalies; develop descent data analysis tools; and science data analysis and publication. Because we had the capability to satisfy a project support need we also subsequently proposed and were funded to make ground-based observations of Jupiter during the period surrounding the Galileo arrival at Jupiter, using the Swedish Solar Telescope at La Palma, Canary Islands. The following section provides background information on the NFR instrument. Section 3 contains the final report of work done.

Sromovsky, Lawrence A.↗
NASA NTRS1994

Scientific and technical support for the Galileo net flux radiometer experiment

This report describes work supported by a post-launch grant (NCC 2-679) that originally covered the period from 1 January 1990 through 31 December 1992. This was extended an additional nine months at no cost until 30 September 1993, and once more until to 30 November 93. A final report on the effort from 1 January 1990 through 30 November 1993 (anticipated) is provided following the background discussion. Tasks required during the post launch period are briefly as follows: (1) attend PSG (Project Science Group) meetings; (2) support in-flight checkouts; maintain and keep safe the spare instrument and GSE (Ground Support Equipment); (3) organize and maintain documentation; (4) finish calibration measurements, documentation, and analysis; (5) characterize and diagnose instrument anomalies; (6) develop descent data analysis tools; and (7) science data analysis and publication. The following sections provide background information on the NFR instrument followed by the complete progress report.

Sromovsky, Lawrence A.↗
NASA NTRS1977

ASTP science demonstration data analysis

Analyses of the Apollo-Soyuz science demonstrations on chemical foams and liquid spreading are presented. The chemical foams demonstation showed that aqueous foams and gas/liquid dispersions are more stable in low-g than on the ground. Unique chemical reactions in low-g foams and gas/liquid dispersions are therefore possible. Further ground tests on the formaldehyde clock reaction led to the rather surprising conclusions that surfaces can exert a nucleation effect and that long-range surface influences on chemical reaction rates are apparently operative.

Grodzka, P. G.↗
NASA NTRS1993

DataHub: Science Data Management in Support of Interactive Exploratory Analysis

The DataHub addresses four areas of significant need: scientific visualization and analysis; science data management; interactions in a distributed, heterogeneous environment; and knowledge-based assistance for these functions. The fundamental innovation embedded within the DataHub is the integration of three technologies, videlicet knowledge-based expert systems, science visualization, and science data management. With the DataHub concept, science investigators are able to apply a more complete solution to all nodes of a distributed system.

science↗
NASA NTRS1993

DataHub - Science data management in support of interactive exploratory analysis

DataHub addresses four areas of significant need: scientific visualization and analysis; science data management; interactions in a distributed, heterogeneous environment; and knowledge-based assistance for these functions. The fundamental innovation embedded within the DataHub is the integration of three technologies, viz. knowledge-based expert systems, science visualization, and science data management. This integration is based on a concept called the DataHub. With the DataHub concept, science investigators are able to apply a more complete solution to all nodes of a distributed system. Both computational nodes and interactive nodes are able to effectively and efficiently use the data services (access, retrieval, update, etc.) in a distributed, interdisciplinary information system in a uniform and standard way. This allows the science investigators to concentrate on their scientific endeavors, rather than to involve themselves in the intricate technical details of the systems and tools required to accomplish their work. Thus, science investigators need not be programmers. The emphasis is on the definition and prototyping of system elements with sufficient detail to enable data analysis and interpretation leading to information. The DataHub includes all the required end-to-end components and interfaces to demonstrate the complete concept.

Handley, Thomas H., Jr.↗
NASA NTRS1993

DataHub: Science data management in support of interactive exploratory analysis

The DataHub addresses four areas of significant needs: scientific visualization and analysis; science data management; interactions in a distributed, heterogeneous environment; and knowledge-based assistance for these functions. The fundamental innovation embedded within the DataHub is the integration of three technologies, viz. knowledge-based expert systems, science visualization, and science data management. This integration is based on a concept called the DataHub. With the DataHub concept, science investigators are able to apply a more complete solution to all nodes of a distributed system. Both computational nodes and interactives nodes are able to effectively and efficiently use the data services (access, retrieval, update, etc), in a distributed, interdisciplinary information system in a uniform and standard way. This allows the science investigators to concentrate on their scientific endeavors, rather than to involve themselves in the intricate technical details of the systems and tools required to accomplish their work. Thus, science investigators need not be programmers. The emphasis on the definition and prototyping of system elements with sufficient detail to enable data analysis and interpretation leading to information. The DataHub includes all the required end-to-end components and interfaces to demonstrate the complete concept.

Handley, Thomas H., Jr.↗
NASA NTRS1998

AXAF: Current Developments and Future Plans for the Scientific Data Analysis

AXAF, the Advanced X-ray Astrophysics Facility, is one of NASA's great observatories scheduled for launch in January of 1999 by a Space Shuttle. This facility will be available to scientists in the United States and to the international astronomical community over an anticipated mission lifetime of at least 5 years. The objective of AXAF is to make astrophysical observations in the 0.09 to 10.0 keV energy range with two imaging detectors, and two sets of transmission gratings giving high spatial (0.5 arcsec) and spectral (E/(Delta)E=100-2000) resolution. The AXAF Science Center (ASC) in Cambridge, MA, USA is responsible for obtaining and reviewing observation proposals submitted by users, providing long range science planning and performing all science data calibration, data analysis, science instrument trend analysis, and science data archiving. The ASC will distribute science data products to the users. We will review here the current developments and future plans for the AXAF scientific data analysis system, its infrastructure and new key features, including the global modelling and fitting environment, integrated data analysis GUIs and new analysis tools.

Fruscione, Antonella↗
NASA NTRS1992

Knowledge-based assistance for science visualization and analysis using large distributed databases

Within this decade, the growth in complexity of exploratory data analysis and the sheer volume of space data require new and innovative approaches to support science investigators in achieving their research objectives. To date, there have been numerous efforts addressing the individual issues involved in inter-disciplinary, multi-instrument investigations. However, while successful in small scale, these efforts have not proven to be open and scaleable. This proposal addresses four areas of significant need: scientific visualization and analysis; science data management; interactions in a distributed, heterogeneous environment; and knowledge-based assistance for these functions. The fundamental innovation embedded within this proposal is the integration of three automation technologies, namely, knowledge-based expert systems, science visualization and science data management. This integration is based on the concept called the Data Hub. With the Data Hub concept, NASA will be able to apply a more complete solution to all nodes of a distributed system. Both computation nodes and interactive nodes will be able to effectively and efficiently use the data services (access, retrieval, update, etc.) with a distributed, interdisciplinary information system in a uniform and standard way. This will allow the science investigators to concentrate on their scientific endeavors, rather than to involve themselves in the intricate technical details of the systems and tools required to accomplish their work. Thus, science investigators need not be programmers. The emphasis will be on the definition and prototyping of system elements with sufficient detail to enable data analysis and interpretation leading to publishable scientific results. In addition, the proposed work includes all the required end-to-end components and interfaces to demonstrate the completed concept.

Handley, Thomas H., Jr.↗
NASA NTRS1993

Knowledge-based assistance for science visualization and analysis using large distributed databases

Within this decade, the growth in complexity of exploratory data analysis and the sheer volume of space data require new and innovative approaches to support science investigators in achieving their research objectives. To date, there have been numerous efforts addressing the individual issues involved in inter-disciplinary, multi-instrument investigations. However, while successful in small scale, these efforts have not proven to be open and scalable. This proposal addresses four areas of significant need: scientific visualization and analysis; science data management; interactions in a distributed, heterogeneous environment; and knowledge-based assistance for these functions. The fundamental innovation embedded with this proposal is the integration of three automation technologies, namely, knowledge-based expert systems, science visualization and science data management. This integration is based on concept called the DataHub. With the DataHub concept, NASA will be able to apply a more complete solution to all nodes of a distributed system. Both computation nodes and interactive nodes will be able to effectively and efficiently use the data services (address, retrieval, update, etc.) with a distributed, interdisciplinary information system in a uniform and standard way. This will allow the science investigators to concentrate on their scientific endeavors, rather than to involve themselves in the intricate technical details of the systems and tools required to accomplish their work. Thus, science investigators need not be programmers. The emphasis will be on the definition and prototyping of system elements with sufficient detail to enable data analysis and interpretation leading to publishable scientific results. In addition, the proposed work includes all the required end-to-end components and interfaces to demonstrate the completed concept.

Handley, Thomas H., Jr.↗
NASA NTRS2017

Mars Science Laboratory Heatshield Flight Data Analysis

NASA Mars Science Laboratory (MSL), which landed the Curiosity rover on the surface of Mars on August 5th, 2012, was the largest and heaviest Mars entry vehicle representing a significant advancement in planetary entry, descent and landing capability. Hypersonic flight performance data was collected using MSLs on-board sensors called Mars Entry, Descent and Landing Instrumentation (MEDLI). This talk will give an overview of MSL entry and a description of MEDLI sensors. Observations from flight data will be examined followed by a discussion of analysis efforts to reconstruct surface heating from heatshields in-depth temperature measurements. Finally, a brief overview of MEDLI2 instrumentation, which will fly on NASAs Mars2020 mission, will be presented with a discussion on how lessons learned from MEDLI data affected the design of MEDLI2 instrumentation.

Mars↗
NASA NTRS2018

The Advantages of Synergy-Quantitative Earth Science Data Visualization and Analysis with Giovanni, Panoply, and Excel

The NASA Giovanni data analysis system provides a multitude of basic analysis capabilities for numerous Earth science data products which are available in the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) archive, as well as for additional selected data products provided by other NASA Distributed Active Archive Center (DAAC) archives. In Giovanni, users can easily generate time-averaged data maps, area-averaged time-series, Latitude-Time and Longitude-Time Hovmoeller diagrams, correlation maps, accumulation maps, and map animations (22 analysis options are available in total). While ASCII text output is available for time-series plots, it is not included as an option for data maps. In order to provide a quantitative, easy-to-use numerical output in ASCII text form, the NetCDF file output from a Giovanni visualization is downloaded and then opened with the free NASA visualization software package Panoply. Panoply provides the capability of translating the Giovanni file into comma-separated-variable (CSV) output. Panoply also provides additional visualization options, including the facile calculation of difference maps and quasi-anomaly maps using Giovanni output files. The CSV files from Panoply can then be imported into an Excel spreadsheet, where an Excel macro converts the CSV files. The output consists of latitude-longitude-data value triads in text form for maps, and either longitude-time-data value or latitude-time-data value triads in text form for Hovmoeller diagrams. This presentation will explicate the basic procedure for the conversion, and then provide several examples where the procedure is applied to Giovanni output from different analysis options.

Earth science↗
NASA NTRS2004

Onboard Autonomy on the Earth Observing One Mission

The Earth Observing One Spacecraft is currently flying The Autonomous Sciencecraft Experiment (ASE) - onboard autonomy software to improve science return. The ASE software enables the spacecraft to autonomously detect and respond to science events occurring on the Earth. ASE includes software systems that perform science data analysis, mission planning, and run-time robust execution. In this article we describe the autonomy flight software and how it enables a new paradigm of autonomous science and mission operations.

autonomy↗
NASA NTRS2004

Supporting Weather Data

Since its founding in 1992, Global Science & Technology, Inc. (GST), of Greenbelt, Maryland, has been developing technologies and providing services in support of NASA scientific research. GST specialties include scientific analysis, science data and information systems, data visualization, communications, networking and Web technologies, computer science, and software system engineering. As a longtime contractor to Goddard Space Flight Center s Earth Science Directorate, GST scientific, engineering, and information technology staff have extensive qualifications with the synthesis of satellite, in situ, and Earth science data for weather- and climate-related projects. GST s experience in this arena is end-to-end, from building satellite ground receiving systems and science data systems, to product generation and research and analysis.

Source record↗
NASA NTRS2014

An Analysis of Earth Science Data Analytics Use Cases

The increase in the number and volume, and sources, of globally available Earth science data measurements and datasets have afforded Earth scientists and applications researchers unprecedented opportunities to study our Earth in ever more sophisticated ways. In fact, the NASA Earth Observing System Data Information System (EOSDIS) archives have doubled from 2007 to 2014, to 9.1 PB (Ramapriyan, 2009; and https:earthdata.nasa.govaboutsystem-- performance). In addition, other US agency, international programs, field experiments, ground stations, and citizen scientists provide a plethora of additional sources for studying Earth. Co--analyzing huge amounts of heterogeneous data to glean out unobvious information is a daunting task. Earth science data analytics (ESDA) is the process of examining large amounts of data of a variety of types to uncover hidden patterns, unknown correlations and other useful information. It can include Data Preparation, Data Reduction, and Data Analysis. Through work associated with the Earth Science Information Partners (ESIP) Federation, a collection of Earth science data analytics use cases have been collected and analyzed for the purpose of extracting the types of Earth science data analytics employed, and requirements for data analytics tools and techniques yet to be implemented, based on use case needs. ESIP generated use case template, ESDA use cases, use case types, and preliminary use case analysis (this is a work in progress) will be presented.

data analytics↗
NASA NTRS2006

Engineering the LISA Project: Systems Engineering Challenges

The Laser Interferometer Space Antenna (LISA) is a joint NASA/ESA mission to detect and measure gravitational waves with periods from 1 s to 10000 s. The systems engineering challenges of developing a giant interferometer, 5 million kilometers on a side, an: numerous. Some of the key challenges are presented in this paper. The organizational challenges imposed by sharing the engineering function between three centers (ESA ESTEC, NASA GSFC, and JPL) across nine time zones are addressed. The issues and approaches to allocation of the acceleration noise and measurement sensitivity budget terms across a traditionally decomposed system are discussed. Additionally, using LISA to detect gravitational waves for the first time presents significant data analysis challenges, many of which drive the project system design. The approach to understanding the implications of science data analysis on the system is also addressed.

systems engineering↗
NASA NTRS1990

Satellite image analysis using neural networks

The tremendous backlog of unanalyzed satellite data necessitates the development of improved methods for data cataloging and analysis. Ford Aerospace has developed an image analysis system, SIANN (Satellite Image Analysis using Neural Networks) that integrates the technologies necessary to satisfy NASA's science data analysis requirements for the next generation of satellites. SIANN will enable scientists to train a neural network to recognize image data containing scenes of interest and then rapidly search data archives for all such images. The approach combines conventional image processing technology with recent advances in neural networks to provide improved classification capabilities. SIANN allows users to proceed through a four step process of image classification: filtering and enhancement, creation of neural network training data via application of feature extraction algorithms, configuring and training a neural network model, and classification of images by application of the trained neural network. A prototype experimentation testbed was completed and applied to climatological data.

Sheldon, Roger A.↗