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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Restructuring Big Data to Improve Data Access and Performance in Analytic Services Making Research More Efficient for the Study of Extreme Weather Events and Application User Communities

By developing and enhancing various services and tools, the GES DISC provides users with the capability to access and visualize data, and to make comparisons of data from multiple sensor and models via a number of cross-discipline projects. Discovering Data via Faceted Web Interface Web interface to data products and services Search and Download mechanisms Dataset Landing Pages Accessing Data through Interoperable Services: GDS – GrADS Data Server OPeNDAP - Open-source Project for a Network Data Access Protocol WMS – OGC service GIS connector – allowing IS tools to access data easier (coming soon) HTTPS -- direct online access Downloading Data Basics: Subset and egridding Service – Parameter, Spatial, Time, Vertical, Mean averaging, format conversion, and regridding for L3/L4 gridded data Swath Data Subsetter – Parameter, spatial subset of L2 /L1 data. Visualizing Data Online: Giovanni –Visualization and Analysis L3/L4 gridded data AIRS NRT Viewer – AIRS near-real-time DQVis – L2 data quality visualization

data cube↗
NASA NTRS2017

Investigating Access Performance of Long Time Series with Restructured Big Model Data

Data sets generated by models are substantially increasing in volume, due to increases in spatial and temporal resolution, and the number of output variables. Many users wish to download subsetted data in preferred data formats and structures, as it is getting increasingly difficult to handle the original full-size data files. For example, application research users such as those involved with wind or solar energy, or extreme weather events are likely only interested in daily or hourly model data at a single point (or for a small area) for a long time period, and prefer to have the data downloaded in a single file. With native model file structures, such as hourly data from NASA Modern-Era Retrospective analysis for Research and Applications Version-2 (MERRA-2), it may take over 10 hours for the extraction of parameters-of-interest at a single point for 30 years. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is exploring methods to address this particular user need. One approach is to create value-added data by reconstructing the data files. Taking MERRA-2 data as an example, we have tested converting hourly data from one-day-per-file into different data cubes, such as one-month, or one-year. Performance is compared for reading local data files and accessing data through interoperable services, such as OPeNDAP. Results show that, compared to the original file structure, the new data cubes offer much better performance for accessing long time series. We have noticed that performance is associated with the cube size and structure, the compression method, and how the data are accessed. An optimized data cube structure will not only improve data access, but also may enable better online analysis services

reanalysis↗
NASA NTRS1993

In search of meta-knowledge

Development of an Intelligent Information System (IIS) involves application of numerous artificial intelligence (AI) paradigms and advanced technologies. The National Aeronautics and Space Administration (NASA) is interested in an IIS that can automatically collect, classify, store and retrieve data, as well as develop, manipulate and restructure knowledge regarding the data and its application (Campbell et al., 1987, p.3). This interest stems in part from a NASA initiative in support of the interagency Global Change Research program. NASA's space data problems are so large and varied that scientific researchers will find it almost impossible to access the most suitable information from a software system if meta-information (metadata and meta-knowledge) is not embedded in that system. Even if more, faster, larger hardware is used, new innovative software systems will be required to organize, link, maintain, and properly archive the Earth Observing System (EOS) data that is to be stored and distributed by the EOS Data and Information System (EOSDIS) (Dozier, 1990). Although efforts are being made to specify the metadata that will be used in EOSDIS, meta-knowledge specification issues are not clear. With the expectation that EOSDIS might evolve into an IIS, this paper presents certain ideas on the concept of meta-knowledge and demonstrates how meta-knowledge might be represented in a pixel classification problem.

Lopez, Antonio M., Jr.↗
NASA NTRS2015

Rapid, High-Resolution Detection of Environmental Change over Continental Scales from Satellite Data - the Earth Observation Data Cube

The effort and cost required to convert satellite Earth Observation (EO) data into meaningful geophysical variables has prevented the systematic analysis of all available observations. To overcome these problems, we utilise an integrated High Performance Computing and Data environment to rapidly process, restructure and analyse the Australian Landsat data archive. In this approach, the EO data are assigned to a common grid framework that spans the full geospatial and temporal extent of the observations - the EO Data Cube. This approach is pixel-based and incorporates geometric and spectral calibration and quality assurance of each Earth surface reflectance measurement. We demonstrate the utility of the approach with rapid time-series mapping of surface water across the entire Australian continent using 27 years of continuous, 25 m resolution observations. Our preliminary analysis of the Landsat archive shows how the EO Data Cube can effectively liberate high-resolution EO data from their complex sensor-specific data structures and revolutionise our ability to measure environmental change.

Lewis, Adam↗
NASA NTRS1997

Out-of-Core Streamline Visualization on Large Unstructured Meshes

It's advantageous for computational scientists to have the capability to perform interactive visualization on their desktop workstations. For data on large unstructured meshes, this capability is not generally available. In particular, particle tracing on unstructured grids can result in a high percentage of non-contiguous memory accesses and therefore may perform very poorly with virtual memory paging schemes. The alternative of visualizing a lower resolution of the data degrades the original high-resolution calculations. This paper presents an out-of-core approach for interactive streamline construction on large unstructured tetrahedral meshes containing millions of elements. The out-of-core algorithm uses an octree to partition and restructure the raw data into subsets stored into disk files for fast data retrieval. A memory management policy tailored to the streamline calculations is used such that during the streamline construction only a very small amount of data are brought into the main memory on demand. By carefully scheduling computation and data fetching, the overhead of reading data from the disk is significantly reduced and good memory performance results. This out-of-core algorithm makes possible interactive streamline visualization of large unstructured-grid data sets on a single mid-range workstation with relatively low main-memory capacity: 5-20 megabytes. Our test results also show that this approach is much more efficient than relying on virtual memory and operating system's paging algorithms.

Ueng, Shyh-Kuang↗
NASA NTRS1993

Incorporation of a hierarchical grid component structure into GRIDGEN

The underlying framework of the GRIDGEN multiple block grid generation system has been refined so that grid components are now stored within a hierarchical data structure. This restructuring has enhanced the usability of the software by allowing grids to be generated on a more intuitive level. This new framework also provides a means by which the multiple block system can be edited at most any level in the grid generation process. Editing tools are currently being added to GRIDGEN so that a change to the grid can be propagated backward and forward in the data hierarchy. The new data structure, the editing tools, and other recent GRIDGEN improvements are described in this paper.

Steinbrenner, John P.↗
NASA NTRS1984

Documentation for the machine-readable version of a supplement to the Bright Star catalogue (Hoffleit, Saladyga and Wlasuk 1983)

Detailed descriptions of the three files of the machine-readable catalog are given. The files of the original tape have been restructured and the data records reformatted to produce a uniform data file having a single logical record per star and homogeneous data fields. The characteristics of the tape version as it is presently being distributed from the Astronomical Data Center are given and the changes to the original tape supplied are described.

Warren, W. H., Jr.↗
NASA NTRS1991

Supercomputer optimizations for stochastic optimal control applications

Supercomputer optimizations for a computational method of solving stochastic, multibody, dynamic programming problems are presented. The computational method is valid for a general class of optimal control problems that are nonlinear, multibody dynamical systems, perturbed by general Markov noise in continuous time, i.e., nonsmooth Gaussian as well as jump Poisson random white noise. Optimization techniques for vector multiprocessors or vectorizing supercomputers include advanced data structures, loop restructuring, loop collapsing, blocking, and compiler directives. These advanced computing techniques and superconducting hardware help alleviate Bellman's curse of dimensionality in dynamic programming computations, by permitting the solution of large multibody problems. Possible applications include lumped flight dynamics models for uncertain environments, such as large scale and background random aerospace fluctuations.

Chung, Siu-Leung↗
NASA NTRS1994

Table-driven configuration and formatting of telemetry data in the Deep Space Network

With a restructured software architecture for telemetry system control and data processing, the NASA/Deep Space Network (DSN) has substantially improved its ability to accommodate a wide variety of spacecraft in an era of 'better, faster, cheaper'. In the new architecture, the permanent software implements all capabilities needed by any system user, and text tables specify how these capabilities are to be used for each spacecraft. Most changes can now be made rapidly, outside of the traditional software development cycle. The system can be updated to support a new spacecraft through table changes rather than software changes, reducing the implementation, test, and delivery cycle for such a change from three months to three weeks. The mechanical separation of the text table files from the program software, with tables only loaded into memory when that mission is being supported, dramatically reduces the level of regression testing required. The format of each table is a different compromise between ease of human interpretation, efficiency of computer interpretation, and flexibility.

Manning, Evan↗
NASA NTRS2019

MERRA-2 Data and Analytic Services at NASA GES DISC for Climate Extremes Study

NASA's climate reanalysis datasets from the Modern Era Retrospective-analysis for Research and Applications, Version 2 (MERRA-2) contains numerous long-term atmosphere, land, and ocean data products from 1980-present. MERRA-2 datasets, such as precipitation, soil moisture, and temperature, have been used widely to study extreme events. The native archived MERRA-2 data files are day-file (hourly time interval) and month-file, containing up to 125 parameters in one file. Due to the large number of data files and volumes, it is challenging for users, especially the applications research community, to handle the original hourly data files for long time periods to analyze extreme events. In this presentation, we review MERRA-2 data for studies of extreme conditions, and demonstrate analytic services at the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). One of the current operational services, 'subsetter', allows users to download only specific data of interest, i.e. data selected by parameter, region, and time period. New services are under development that will provide more 'on-the-fly' statistical calculations when downloading data; improve efficiency when accessing long time-series data. We will provide additional "How-to" resources that include step-by-step instructions on data access and usage. We have tested restructuring of day-files in an optimized data cube, which has significantly improved system performance for accessing long time-series. Overall performance is associated with cube size and structure, data compression method, and how the data are accessed. The optimized data cube structure will enable better online analytic services for statistical analysis and extreme events mining. To demonstrate the service, we use an extreme drought associated with the anomalous 2016 monsoon over southern Asia. This prototype time-series service may be augmented in the cloud infrastructure in the future.

data access↗
NASA NTRSDate not supplied

Performance and Portability of a Linear Solver Across Emerging Architectures

A linear solver algorithm used by a large-scale unstructured-grid computational fluid dynamics application is examined for a broad range of familiar and emerging architectures. Efficient implementation of a linear solver is challenging on recent CPUs offering vector architectures. Vector loads and stores are essential to effectively utilize available memory bandwidth on CPUs, and maintaining performance across different CPUs can be difficult in the face of varying vector lengths offered by each. A similar challenge occurs on GPU architectures, where it is essential to have coalesced memory accesses to utilize memory bandwidth effectively. In this work, we demonstrate that restructuring a computation, and possibly data layout, with regard to architecture is essential to achieve optimal performance by establishing a performance benchmark for each target architecture in a low level language such as vector intrinsics or CUDA. In doing so, we demonstrate how a linear solver kernel can be mapped to Intel® Xeon™ and Xeon Phi™, Marvell® ThunderX2®, NEC® SX-Aurora™ TSUBASA Vector Engine, and NVIDIA® and AMD® GPUs. We further demonstrate that the required code restructuring can be achieved in higher level programming environments such as OpenACC, OCCA, and Intel® OneAPI™/SYCL, and that each generally results in optimal performance on the target architecture. Relative performance metrics for all implementations are shown, and subjective ratings for ease of implementation and optimization are suggested.

Programming models↗
NASA NTRS1992

Flight deck engine advisor

The focus of this project is on alerting pilots to impending events in such a way as to provide the additional time required for the crew to make critical decisions concerning non-normal operations. The project addresses pilots' need for support in diagnosis and trend monitoring of faults as they affect decisions that must be made within the context of the current flight. Monitoring and diagnostic modules developed under the NASA Faultfinder program were restructured and enhanced using input data from an engine model and real engine fault data. Fault scenarios were prepared to support knowledge base development activities on the MONITAUR and DRAPhyS modules of Faultfinder. An analysis of the information requirements for fault management was included in each scenario. A conceptual framework was developed for systematic evaluation of the impact of context variables on pilot action alternatives as a function of event/fault combinations.

Shontz, W. D.↗
NASA NTRS1992

Mission modeling for the Manned Transportation System study

To determine the requirements and potential options for the next Manned Transportation System, a 'needs' analysis was performed. The analysis identified the number, mass, type, and destination of manned and unmanned payloads to space. The needs model is based on the NASA Mixed Fleet Manifest and a version of the FY90 Civil Needs Data Base with Space Station Restructure modifications and a 'strawman' DOD mission model.

Gaunce, Michael T.↗
NASA NTRS1998

Method and system for data clustering for very large databases

Multi-dimensional data contained in very large databases is efficiently and accurately clustered to determine patterns therein and extract useful information from such patterns. Conventional computer processors may be used which have limited memory capacity and conventional operating speed, allowing massive data sets to be processed in a reasonable time and with reasonable computer resources. The clustering process is organized using a clustering feature tree structure wherein each clustering feature comprises the number of data points in the cluster, the linear sum of the data points in the cluster, and the square sum of the data points in the cluster. A dense region of data points is treated collectively as a single cluster, and points in sparsely occupied regions can be treated as outliers and removed from the clustering feature tree. The clustering can be carried out continuously with new data points being received and processed, and with the clustering feature tree being restructured as necessary to accommodate the information from the newly received data points.

Zhang, Tian↗
NASA NTRS1983

Minimizing inner product data dependencies in conjugate gradient iteration

The amount of concurrency available in conjugate gradient iteration is limited by the summations required in the inner product computations. The inner product of two vectors of length N requires time c log(N), if N or more processors are available. This paper describes an algebraic restructuring of the conjugate gradient algorithm which minimizes data dependencies due to inner product calculations. After an initial start up, the new algorithm can perform a conjugate gradient iteration in time c*log(log(N)).

Vanrosendale, J.↗
NASA NTRS2020

Satellite Beach Energy - Restructuring the Energy Balance in Satellite Beach, Florida, by Quantifying Solar Energy Production Potential using NASA POWER Data Products and LiDAR

The City of Satellite Beach, Florida, has committed to supplying 100% of its energy use from renewable energy, primarily solar, by the year 2050. The team created a methodology for estimating rooftop solar power potential using a high-resolution Light Detection and Ranging (LiDAR) dataset and the NASA Prediction of Worldwide Energy Resources (POWER) dataset to assist Satellite Beach in reaching their solar renewable energy goals. The POWER dataset provides information on direct and diffuse solar irradiation on horizontal surfaces, surface albedo, and effects of local meteorology, such as clouds. The team integrated the solar irradiance data with the LiDAR data to model slope, aspect, and shadowing in the 7 km2 study area to find suitable roof segments for solar panel installation and estimate the solar potential of each segment. This process was supplemented by an analysis of land surface temperature and urban greenness measured through the Normalized Difference Vegetation Index (NDVI) from Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) observations. These metrics serve to target areas for cooling initiatives aimed at reducing Satellite Beach’s overall energy consumption. The team found the total rooftop solar potential throughout the city to be 221,919,330KWh per year with an average annual rooftop photovoltaic, or PV, potential of 55,647KWh per building. As such, the average building could generate over five times the annual energy needs for an average household if PV panels were installed on all viable areas of its roof.

Spencer Nelson↗
NASA NTRS1999

Earth Science Data Archive and Access at the NASA/Goddard Space Flight Center Distributed Active Archive Center (DAAC)

The Goddard Distributed Active Archive Center (DAAC), as an integral part of the Earth Observing System Data and Information System (EOSDIS), is the official source of data for several important earth remote sensing missions. These include the Sea-viewing Wide-Field-of-view Sensor (SeaWiFS) launched in August 1997, the Tropical Rainfall Measuring Mission (TRMM) launched in November 1997, and the Moderate Resolution Imaging Spectroradiometer (MODIS) scheduled for launch in mid 1999 as part of the EOS AM-1 instrumentation package. The data generated from these missions supports a host of users in the hydrological, land biosphere and oceanographic research and applications communities. The volume and nature of the data present unique challenges to an Earth science data archive and distribution system such as the DAAC. The DAAC system receives, archives and distributes a large number of standard data products on a daily basis, including data files that have been reprocessed with updated calibration data or improved analytical algorithms. A World Wide Web interface is provided allowing interactive data selection and automatic data subscriptions as distribution options. The DAAC also creates customized and value-added data products, which allow additional user flexibility and reduced data volume. Another significant part of our overall mission is to provide ancillary data support services and archive support for worldwide field campaigns designed to validate the results from the various satellite-derived measurements. In addition to direct data services, accompanying documentation, WWW links to related resources, support for EOSDIS data formats, and informed response to inquiries are routinely provided to users. The current GDAAC WWW search and order system is being restructured to provide users with a simplified, hierarchical access to data. Data Browsers have been developed for several data sets to aid users in ordering data. These Browsers allow users to specify spatial, temporal, and other parameter criteria in searching for and previewing data.

Leptoukh, Gregory↗