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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 NTRS1983

Development of V/STOL methodology based on a higher order panel method

The development of a computational technique to predict the complex flowfields of V/STOL aircraft was initiated in which a number of modules and a potential flow aerodynamic code were combined in a comprehensive computer program. The modules were developed in a building-block approach to assist the user in preparing the geometric input and to compute parameters needed to simulate certain flow phenomena that cannot be handled directly within a potential flow code. The PAN AIR aerodynamic code, which is higher order panel method, forms the nucleus of this program. PAN AIR's extensive capability for allowing generalized boundary conditions allows the modules to interact with the aerodynamic code through the input and output files, thereby requiring no changes to the basic code and easy replacement of updated modules.

Bhateley, I. C.↗
NASA NTRS2018

Improving NASA Earth Science Data and Information Access Through Natural Language Processing Based Data Analysis and Visualization

NASA: The Research Access initiative is part of the agency's framework for increasing public access to scientific publications and digital scientific data. The initiative follows the release of White House Office of Science and Technology Policy's (OSTP) memorandum "Increasing Access to the Results of Federally Funded Research," to ensure federally funded research is available to the public within one year of publication. NASA answered the mandate by creating an agency plan entitled "NASA Plan for Increasing Access to the Results of Scientific Research" and associated policy, NPD 2230.1, Research Data and Publication Access. Principles in NASA SMD Strategic Plan for Scientific Data and Computing: Continued free and open access to scientific data for any use. Improved ease of use and discoverability. Enhanced science applications and new use cases. Incorporates best practices and "state of the art" through partnerships. Earth Data and Systems are Evolving: Increasing archive and file sizes. More complicated data structures. More user-friendly and data services. What is the future direction?

Liu, Zhong↗
NASA NTRS2018

HYPERS Software Development

Providing software support for HYPERS and creating internal tools. NASA has a long and decorated history of spaceflight innovation and achievements. The next great endeavor is NASA’s Journey to Mars, which will be achieved with the Space Launch System (SLS) and Orion capsule. Developing and testing these systems is no easy feat. Commercial-off-the-shelf (COTS) tools do not always provide enough functionality for engineers to do their job efficiently, making internal custom-made tools is necessary to meet the expected launch date. The purpose of this internship was to provide software support to the Storable Propellants and Hydraulic Systems Branch, specifically the Hypergolics Software (HYPERS) team. This included developing tools to parse unique measurements from the vehicle into the format specified by the HYPERS team. Displays were also created per requirements. Another major component of this internship was to create an intuitive interactive offline graphing application. The current tool for plotting vehicle data does not have all the functionality and features that HYPERS would like. By inputting a vehicle data file, the application plots the data based on the time range and components the user would like to view. After the graph is generated, the user is able to zoom in, pan horizontally, add comments, hover over data points, and take a snapshot of the current state of the graph. These additional features will help engineers quickly investigate the relationship between vehicle components through data visualization.

Internal Tools↗
NASA NTRS2017

Educational and Scientific Applications of Climate Model Diagnostic Analyzer

Climate Model Diagnostic Analyzer (CMDA) is a web-based information system designed for the climate modeling and model analysis community to analyze climate data from models and observations. CMDA provides tools to diagnostically analyze climate data for model validation and improvement, and to systematically manage analysis provenance for sharing results with other investigators. CMDA utilizes cloud computing resources, multi-threading computing, machine-learning algorithms, web service technologies, and provenance-supporting technologies to address technical challenges that the Earth science modeling and model analysis community faces in evaluating and diagnosing climate models. As CMDA technology and infrastructure have matured, we have developed the educational and scientific applications of CMDA. Educationally, CMDA supported the summer school of the JPL Center for Climate Sciences in 2014, 2015, and 2016. In the summer school, the students work on group research projects where CMDA provide datasets, analysis tools, and provenance support utility tools. Each student is assigned to a virtual machine with CMDA installed in Amazon Web Services. Scientifically, we have developed several science use cases of CMDA covering various topics, datasets, and analysis types. Each of the science use cases is described in terms of a scientific goal, datasets used, the analysis tools used, scientific results discovered, an analysis result such as output plots and data files, and a link to the corresponding analysis service call with all the input arguments filled.

Bao, Qihao↗
NASA NTRS2019

Lunar Glovebox Balance with Wireless Technology

The most important equipment required for processing lunar samples is a high-quality mass balance for maintaining accurate weight inventory, security, and scientific study. After careful review, a Curation Office memo by Michael Duke in 1978 chose the Mettler PL200 to be used for sample weight measurements inside the gloveboxes (Fig. 3). These commercial off-the-shelf (COTS) balances did not meet the strict accepted material requirements in the Lunar lab. As a result, each balance housing, weighing pan, and wiring was custom retrofitted to meet Lunar Operating Procedure (LOP) 54 requirements [for material construction restrictions]. The original design drawings for the custom housings, readout support stands, and wiring were done by the JSC engineering directorate. The 1977- 1978 schematics, drawings, and files are now housed in the curation Data Center. Per the design specifications, the housing was fabricated from aluminum grade 6061 T6, seamless welds, and anodized per MIL-A-8625 type I, class I. The balance feet were TFE Teflon and any required joints were sealed with Viton A gaskets. The readout display and support stands outside the glovebox were fabricated from 300 series stainless steel with #4 finish and mounted to the glovebox with welded bolts. Wire harnesses that linked the balance with the outside display and power were encapsulated with TFE Teflon and transported through custom Deutsch wire bulk head pass-through systems from inside to outside the glovebox. These Deutsch connectors were custom fabricated with 316L stainless steel bodies, Viton A O-rings, aluminum 6061 with electroless nickel plating, Teflon (replacing the silicone), and gold crimp connectors (no soldering). Many of the Deutsch connectors may have been used in the Apollo program high vacuum complex in building 37 and date to about 1968 to 1970.

Zeigler, Ryan A.↗
NASA NTRS2011

3D Orbit Visualization for Earth-Observing Missions

This software visualizes orbit paths for the Orbiting Carbon Observatory (OCO), but was designed to be general and applicable to any Earth-observing mission. The software uses the Google Earth user interface to provide a visual mechanism to explore spacecraft orbit paths, ground footprint locations, and local cloud cover conditions. In addition, a drill-down capability allows for users to point and click on a particular observation frame to pop up ancillary information such as data product filenames and directory paths, latitude, longitude, time stamp, column-average dry air mole fraction of carbon dioxide, and solar zenith angle. This software can be integrated with the ground data system for any Earth-observing mission to automatically generate daily orbit path data products in Google Earth KML format. These KML data products can be directly loaded into the Google Earth application for interactive 3D visualization of the orbit paths for each mission day. Each time the application runs, the daily orbit paths are encapsulated in a KML file for each mission day since the last time the application ran. Alternatively, the daily KML for a specified mission day may be generated. The application automatically extracts the spacecraft position and ground footprint geometry as a function of time from a daily Level 1B data product created and archived by the mission s ground data system software. In addition, ancillary data, such as the column-averaged dry air mole fraction of carbon dioxide and solar zenith angle, are automatically extracted from a Level 2 mission data product. Zoom, pan, and rotate capability are provided through the standard Google Earth interface. Cloud cover is indicated with an image layer from the MODIS (Moderate Resolution Imaging Spectroradiometer) aboard the Aqua satellite, which is automatically retrieved from JPL s OnEarth Web service.

Jacob, Joseph C.↗
NASA NTRS2013

Dynamic Server-Based KML Code Generator Method for Level-of-Detail Traversal of Geospatial Data

Web-based geospatial client applications such as Google Earth and NASA World Wind must listen to data requests, access appropriate stored data, and compile a data response to the requesting client application. This process occurs repeatedly to support multiple client requests and application instances. Newer Web-based geospatial clients also provide user-interactive functionality that is dependent on fast and efficient server responses. With massively large datasets, server-client interaction can become severely impeded because the server must determine the best way to assemble data to meet the client applications request. In client applications such as Google Earth, the user interactively wanders through the data using visually guided panning and zooming actions. With these actions, the client application is continually issuing data requests to the server without knowledge of the server s data structure or extraction/assembly paradigm. A method for efficiently controlling the networked access of a Web-based geospatial browser to server-based datasets in particular, massively sized datasets has been developed. The method specifically uses the Keyhole Markup Language (KML), an Open Geospatial Consortium (OGS) standard used by Google Earth and other KML-compliant geospatial client applications. The innovation is based on establishing a dynamic cascading KML strategy that is initiated by a KML launch file provided by a data server host to a Google Earth or similar KMLcompliant geospatial client application user. Upon execution, the launch KML code issues a request for image data covering an initial geographic region. The server responds with the requested data along with subsequent dynamically generated KML code that directs the client application to make follow-on requests for higher level of detail (LOD) imagery to replace the initial imagery as the user navigates into the dataset. The approach provides an efficient data traversal path and mechanism that can be flexibly established for any dataset regardless of size or other characteristics. The method yields significant improvements in userinteractive geospatial client and data server interaction and associated network bandwidth requirements. The innovation uses a C- or PHP-code-like grammar that provides a high degree of processing flexibility. A set of language lexer and parser elements is provided that offers a complete language grammar for writing and executing language directives. A script is wrapped and passed to the geospatial data server by a client application as a component of a standard KML-compliant statement. The approach provides an efficient means for a geospatial client application to request server preprocessing of data prior to client delivery. Data is structured in a quadtree format. As the user zooms into the dataset, geographic regions are subdivided into four child regions. Conversely, as the user zooms out, four child regions collapse into a single, lower-LOD region. The approach provides an efficient data traversal path and mechanism that can be flexibly established for any dataset regardless of size or other characteristics.

Baxes, Gregory↗
NASA NTRSDate not supplied

An Oveview of TROPESS Data Products and Services at the NASA GES DISC

The TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project generates Earth System Data Records (ESDRs) of ozone, and other atmospheric constituents (CH4,CO, H2O, HDO, NH3, PAN and temperature) by processing data from multiple satellites through a common retrieval algorithm and ground data system. Satellite Level-1B input data used in generating the TROPESS L2 data products include CrIS NOAA-20 (JPSS-1), CrIS SNPP, AIRS Aqua, OMI Aura, and TROPOMI S5P. The common retrieval framework is known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES) science data processing system (MUSES-SDPS). Several of the TROPESS data products are now available from the NASA Goddard Earth Sciences Data and Information Service Center (GES DISC) for users to download. In this presentation we provide an overview of the various TROPESS data products. These data products can be divided into the following Forward Stream types: Standard Products, Summary Products, and Full-Archival Products. Standard Products are for users that are doing full analysis with avenging kernel and covariance corresponding to retrieved vertical profiles. Summary products have a smaller file size and are more convenient for first-look and rapid analysis, include total and partial columns, as well as column averaging kernels. The Full-Archival Products will contain all information used in creating the data. TROPESS also creates Special Products, provided on an as-needed and as-available basis to support NASA field missions and individual-investigator requests over specific regions. Eventually, TROPESS will also produce and deliver a set of Reanalysis Stream products. Data at the GES DISC are being transitioned into the "Cloud". This will allow users with "Cloud” access to perform data analysis directly on the data without downloading the data to their system. Services, such as subsetting and data visualization, will also be provided for TROPESS data products at the GES DISC.

James Johnson↗
NASA NTRS2019

Collecting and Processing Earth Science Data Metrics at NASA ESDIS

Since the launch of Terra satellite in 1999, the number of Earth Science remote sensing data products created and distributed by NASA's Earth Observing System (EOS) Data and Information System (EOSDIS) has increased from a few hundred to nearly ten thousand. NASA's Earth Science Data and Information System (ESDIS) Metrics System (EMS) collects metrics on data ingest, archive, and distribution by its Distributed Active Archive Centers (DAACs) and the Science Investigator-led Systems (SIPS), known as Data Providers. These metrics are critical in helping NASA management as well as data producers in resource planning and gaining a wide range of knowledge of data users and data usage.EMS receives flat files, or log files of data archive, ingest, and distribution either in their raw format, such as Apache web logs, or text files of log records formatted by the Data Providers. Tens of millions of records are processed each day to extract metrics on data products, user information, distribution protocols and services, and so on. The metrics are then made available to designated parties.This presentation provides an overview of the EMS processing workflow and improvement efforts made in recent years to handle ever-increasing number of data records and new metrics requirements, discusses several key steps including mapping log records to data products and identifying user communities along with geo-distribution, and demonstrates typical metrics capabilities produced by the EMS system. Challenges and potential approaches to improve the system are also discussed.

Pan, Jianfu↗
NASA NTRS2019

Leveraging STARE for Co-aligned Data Locality with netCDF and Python MPI

We have leveraged STARE indexing to package partitioned data chunks from diverse datasets into netCDF files, distributed them on a cluster of 16 lightweight nodes with their placements spatiotemporally co-aligned, and demonstrated a few integrative analyses using netCDF parallel I/O and Python MPI, with single-user performance and scalability comparable to, or even better than, that of a parallel array database management system (ADBMS) such as SciDB. However, records of the node location and STARE index ranges for each data chunk, similar to the chunk maps of SciDB, must be maintained and consulted by the I/O and analysis code for coordinating the analytic operations in parallel, in order to achieve the good performance and scalability.

Kwo-Sen Kuo↗
NASA NTRSDate not supplied

What (and How) MERRA-2 Reanalysis Data are Used in Applied Sciences

The Modern Era Retrospective-analysis for Research and Applications, Version 2 (MERRA-2) is the global atmospheric data reanalysis for the satellite era produced by NASA’s Global Modeling and Assimilation Office (GMAO), using the Goddard Earth Observing System Model (GEOS)version 5.12.4. The data are officially distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). MERRA-2 data have been widely used by the Earth sciences and application community. Since MERRA-2 data were released in early 2016, the number of registered data users has grown steadily from 1,252 in 2016 to 6477 in 2020. By the end of October 2021, ~16 petabytes (over 360 million files) of data have been distributed to more than 18,900 users. Searching in Google Scholar (https://scholar.google.com/), we have found over 7,000 articles, published between January 2017 and May 2021, involving the use ofMERRA-2 data. The figure shows the numbers for various application areas in which theMERRA-2 data have been used, covering almost all of the application areas defined in NASA Applied Sciences (http://appliedsciences.nasa.gov). The largest number of articles are found in disaster research, with the subcategories ordered in flood, wildfires, hurricanes and cyclones, and other forms of severe weather. In this presentation, we will discuss the preliminary findings from a review of the selected literature that uses MERRA-2 data in applied sciences. The current analytic and interoperable data services at GES DISC are listed, such as the on-the-fly subset and analysis service, NASA Giovanni; THREDDS Data Server(TDS); and Python Jupyter notebooks. In addition, we will introduce two new services for supporting the open sciences: My Dashboard and Related Publications.

data management↗