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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 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

A versatile system for processing geostationary satellite data with run-time visualization capability

To better predict global climate change, scientists are developing climate models that require interdisciplinary and collaborative efforts in their building. We are currently involved in several such projects but will briefly discuss activities in support of two such complementary projects: the Atmospheric Radiation Measurement (ARM) program of the Department of Energy and Sequoia 2000, a joint venture of the University of California, the private sector, and government agencies. Our contribution to the ARM program is to investigate the role of clouds on the top of the atmosphere and on surface radiance fields through the data analysis of surface and satellite observations and complex modeling of the interaction of radiation with clouds. One of our first ARM research activities involves the computation of the broadband shortwave surface irradiance from satellite observations. Geostationary satellite images centered over the first ARM observation site are received hourly over the Internet network and processed in real time to compute hourly and daily composite shortwave irradiance fields. The images and the results are transferred via a high-speed network to the Sequoia 2000 storage facility in Berkeley, where they are archived These satellite-derived results are compared with the surface observations to evaluate the accuracy of the satellite estimate and the spatial representation of the surface observations. In developing the software involved in calculating the surface shortwave irradiance, we have produced an environment whereby we can easily modify and monitor the data processing as required. Through the principles of modular programming, we have developed software that is easily modified as new algorithms for computation are developed or input data availability changes. In addition, the software was designed so that it could be run from an interactive, icon-driven, graphical interface, TCL-TK, developed by Sequoia 2000 participants. In this way, the data flow can be interactively assessed and altered as needed. In this environment, the intermediate data processing 'images' can be viewed, enabling the investigator to easily monitor the various data processing steps as they progress. Additionally, this environment allows the rapid testing of new processing modules and allows their effects to be visually compared with previous results.

Landsfeld, M.↗
NASA NTRS2006

Secure Display of Space-Exploration Images

Java EDR Display Interface (JEDI) is software for either local display or secure Internet distribution, to authorized clients, of image data acquired from cameras aboard spacecraft engaged in exploration of remote planets. ( EDR signifies experimental data record, which, in effect, signifies image data.) Processed at NASA s Multimission Image Processing Laboratory (MIPL), the data can be from either near-realtime processing streams or stored files. JEDI uses the Java Advanced Imaging application program interface, plus input/output packages that are parts of the Video Image Communication and Retrieval software of the MIPL, to display images. JEDI can be run as either a standalone application program or within a Web browser as a servlet with an applet front end. In either operating mode, JEDI communicates using the HTTP(s) protocol(s). In the Web-browser case, the user must provide a password to gain access. For each user and/or image data type, there is a configuration file, called a "personality file," containing parameters that control the layout of the displays and the information to be included in them. Once JEDI has accepted the user s password, it processes the requested EDR (provided that user is authorized to receive the specific EDR) to create a display according to the user s personality file.

Cheng, Cecilia↗
NASA NTRS1980

Three computer codes to read, plot and tabulate operational test-site recorded solar data

Computer programs used to process data that will be used in the evaluation of collector efficiency and solar system performance are described. The program, TAPFIL, reads data from an IBM 360 tape containing information (insolation, flowrates, temperatures, etc.) from 48 operational solar heating and cooling test sites. Two other programs, CHPLOT and WRTCNL, plot and tabulate the data from the direct access, unformatted TAPFIL file. The methodology of the programs, their inputs, and their outputs are described.

Stewart, S. D.↗
NASA NTRS1986

Test processing system (SEE)

The SEE data processing system, developed in 1985, manages and process test results. General information is provided on the SEE system: objectives, characteristics, basic principles, general organization, and operation. Full documentation is accessible by computer using the HELP SEE command.

Gaulene, P.↗
NASA NTRS2008

Reusing Information Management Services for Recommended Decadal Study Missions to Facilitate Aerosol and Cloud Studies

NASA Earth Sciences Division (ESD) has made great investments in the development and maintenance of data management systems and information technologies, to maximize the use of NASA generated Earth science data. With information management system infrastructure in place, mature and operational, very small delta costs are required to fully support data archival, processing, and data support services required by the recommended Decadal Study missions. This presentation describes the services and capabilities of the Goddard Space Flight Center (GSFC) Earth Sciences Data and Information Services Center (GES DISC) and the reusability for these future missions. The GES DISC has developed a series of modular, reusable data management components currently in use. They include data archive and distribution (Simple, Scalable, Script-based, Science [S4] Product Archive aka S4PA), data processing (S4 Processor for Measurements aka S4PM), data search (Mirador), data browse, visualization, and analysis (Giovanni), and data mining services. Information management system components are based on atmospheric scientist inputs. Large development and maintenance cost savings can be realized through their reuse in future missions.

Kempler, Steve↗
NASA NTRS2008

Hail Size Distribution Mapping

A 3-D weather radar visualization software program was developed and implemented as part of an experimental Launch Pad 39 Hail Monitor System. 3DRadPlot, a radar plotting program, is one of several software modules that form building blocks of the hail data processing and analysis system (the complete software processing system under development). The spatial and temporal mapping algorithms were originally developed through research at the University of Central Florida, funded by NASA s Tropical Rainfall Measurement Mission (TRMM), where the goal was to merge National Weather Service (NWS) Next-Generation Weather Radar (NEXRAD) volume reflectivity data with drop size distribution data acquired from a cluster of raindrop disdrometers. In this current work, we adapted these algorithms to process data from a cluster of hail disdrometers positioned around Launch Pads 39A or 39B, along with the corresponding NWS radar data. Radar data from all NWS NEXRAD sites is archived at the National Climatic Data Center (NCDC). That data can be readily accessed at . 3DRadPlot plots Level III reflectivity data at four scan elevations (this software is available at Open Channel Software, ). By using spatial and temporal interpolation/extrapolation based on hydrometeor fall dynamics, we can merge the hail disdrometer array data coupled with local Weather Surveillance Radar-1988, Doppler (WSR-88D) radial velocity and reflectivity data into a 4-D (3-D space and time) picture of hail size distributions. Hail flux maps can then be generated and used for damage prediction and assessment over specific surfaces corresponding to structures within the disdrometer array volume. Immediately following a hail storm, specific damage areas and degree of damage can be identified for inspection crews.

Source record↗
NASA NTRSDate not supplied

Laser Communications Relay Demonstration Optimetrics Experiment

Within this paper, we report on an Optimetrics experiment conducted on the National Aeronautics and Space Administration (NASA) Laser Communication Relay Demonstration (LCRD). The high precision two-way ranging is conducted on the optical communications link between the LCRD space terminal in a geosynchronous orbit and the Optical Ground Station 1 (OGS1) at Table Mountain, California, and is referred to as optimetrics ranging. The receiver to transmitter clock loopback is implemented in the LCRD space modem. A digital dual mixer time difference phase meter (DDMTD) is implemented in the OGS1 ground modem. The round-trip time is measured with the DDMTD by counting the frame and data clock ticks between transmitter and receiver frame synchronization markers for coarse range, and the relative phase of the transmit and receive clock for high precision range. This approach provides coverage for all potential ranges with no ranging ambiguity. The flight modem FPGA code was modified for frame and data clock loopback and the OGS1 FPGA code was modified for the DDMTD phase meter implementation. The experiment team conducted a 36-hour continuous ranging measurement. Preliminary two-way optimetrics ranging data show a noise floor of 3cm (rms). Based on our theoretical noise analysis and ground tests with spare flight modems, we expected one order of magnitude improvement on ranging precision. We are conducting detailed post data processing to identify the differences to further reduce the noise level and formulating an improved implementation approach for future missions. For reference and comparison, traditional Radio Frequency (RF) ranging between LCRD and OGS1 was conducted at the same time and indicates a ranging noise floor of 30cm(rms). Other telemetry and relevant environmental parameters such as weather and atmosphere temperature were also collected for offline data processing. The high precision ranging and range rate measurements from the laser communications data link clock and clock phase are performed simultaneously with continuous optical communication. No extra hardware added to existing system. This high precision optimetrics measurement implementation provides alternative ranging and range rate measurements for orbit determination (OD) and flight dynamics calculations with higher precision. We see significant improvement on OD for navigation upon implementation in future optical communication systems.

Rafael Garcia↗
NASA NTRS2020

Application of IRIG 106 Digital Data Recorder Standards for Flight Test

During flight tests, data is acquired from different sources and in different formats. Sensor data, aircraft avionics bus data, time, voice and video are essential for analysis of these flight tests. IRIG 106 standards exist for recording and transferring these data types. However, to enhance interoperability of flight test data between (multinational) test teams, a standard was needed to encapsulate all these data types. For this purpose the Telemetry Group of the Range Commanders Council (RCC) first published in 2004 the IRIG 106 Chapter 10 Standard, “Solid-State On-Board Recorder Standard”. This standard is evolving up to present. This AGARDograph gives an introduction to the standard, explains how the standard is positioned among other relevant standards, and describes the equipment and their application in flight tests with some examples. The topics covered include IRIG 106 Chapter 10 compatible recording of (multiple) data streams, synchronization of data, data recorders, data retrieval, data processing, and examples of flight test programs. Further development of the standard, originally published for on-board recorders, is described for extending the requirements for ground-based data recorders. The last chapter addresses a number of issues related to the application of the Chapter 10 standard with references to information and possible solutions. This document shows how data acquisition and data processing in accordance to IRIG 106 Chapter 10 standard can be applied in NATO flight tests and will thereby contribute to collaboration and cost efficiency.

flight test instrumentation↗
NASA NTRSDate not supplied

GL4U: Bioinformatics training for students and educators using space omics data

NASA’s GeneLab project provides researchers open access to space-relevant experiment multi-omics data that can be mined to understand the effects of spaceflight on biological systems. To maximize the number of scientists who understand and utilize GeneLab data and data processing pipelines, GeneLab has created GeneLab for Colleges and Universities (GL4U). GL4U provides space biology-relevant training in bioinformatics to the next generation of scientists through direct and indirect approaches. The GeneLab team plans to host two annual data processing bootcamps, one for college-level students (direct) and one for college educators (indirect – training of trainers), in which participants learn to analyze GeneLab’s space-relevant omics data. The GL4U direct training pilot program was conducted in June 2021. During the pilot, students participated in a week-long bootcamp consisting of space biology-specific lectures and hands-on instruction using Jupyter Notebooks to analyze RNA sequence data. This pilot demonstrated the capacity of GL4U for training young scientists and encouraging data re-use. During the educator pilot, scheduled for June 2022, educators will receive materials and training to enable them to run the bootcamp at their home institutions or alternatively to adapt the content to implement within existing courses, thereby extending the reach of this initiative.

Amanda Marie Saravia-butler↗
NASA NTRSDate not supplied

GL4U: Using Space Omics Data to Provide Bioinformatics Training for Students and Educators

NASA’s GeneLab project provides researchers open access to space-relevant experiment multi-omics data that can be mined to understand the effects of spaceflight on biological systems. To maximize the number of scientists who understand and utilize GeneLab data and data processing pipelines, GeneLab has created GeneLab for Colleges and Universities (GL4U). GL4U provides space biology-relevant training in bioinformatics to the next generation of scientists through direct and indirect approaches. The GeneLab team plans to host two annual data processing bootcamps, one for college-level students (direct) and one for college educators (indirect – training of trainers), in which participants learn to analyze GeneLab’s space-relevant omics data. The GL4U direct training pilot program was conducted in June 2021. During the pilot, students participated in a week-long bootcamp consisting of space biology-specific lectures and hands-on instruction using Jupyter Notebooks to analyze RNA sequence data. This pilot demonstrated the capacity of GL4U for training young scientists and encouraging data re-use. In June 2022, GL4U partnered with Jet Propulsion Laboratory’s (JPL) Planetary Protection Center of Excellence to conduct the indirect training pilot program by training educators at historically black colleges and universities (HBCUs) and minority serving institutions (MSIs). During the educator pilot, participants received materials, training, and will be provided the necessary compute resources to enable them to run the bootcamp at their home institutions or alternatively to adapt the content to implement within existing courses, thereby extending the reach of this initiative. The GL4U training program provides undergraduate students from underrepresented groups the opportunity to learn about NASA and Space Biology, and to enhance their career prospects by gaining hands-on experience analyzing omics data, a skillset that is highly applicable and marketable in the life sciences. Pre- and post-bootcamp surveys were completed by all participants and show the overwhelming success of the bootcamps.

Amanda M. Saravia-Butler↗
NASA NTRSDate not supplied

GL4U: Bioinformatics Training for Students and Educators Using Space Omics Data

NASA’s GeneLab project provides researchers open access to space-relevant experiment multi-omics data that can be mined to understand the effects of spaceflight on biological systems. To maximize the number of scientists who understand and utilize GeneLab data and data processing pipelines, GeneLab has created GeneLab for Colleges and Universities (GL4U). GL4U provides space biology-relevant training in bioinformatics to the next generation of scientists through direct and indirect approaches. The GeneLab team plans to host two annual data processing bootcamps, one for college-level students (direct) and one for college educators (indirect – training of trainers), in which participants learn to analyze GeneLab’s space-relevant omics data. The GL4U direct training pilot program was conducted in June 2021. During the pilot, students participated in a week-long bootcamp consisting of space biology-specific lectures and hands-on instruction using Jupyter Notebooks to analyze RNA sequence data. This pilot demonstrated the capacity of GL4U for training young scientists and encouraging data re-use. During the educator pilot, scheduled for June 2022, educators will receive materials and training to enable them to run the bootcamp at their home institutions or alternatively to adapt the content to implement within existing courses, thereby extending the reach of this initiative.

Amanda Marie Saravia-butler↗
NASA NTRS2005

On the Hilbert-Huang Transform Theoretical Developments

One of the main heritage tools used in scientific and engineering data spectrum analysis is the Fourier Integral Transform and its high performance digital equivalent - the Fast Fourier Transform (FFT). Both carry strong a-priori assumptions about the source data, such as linearity, of being stationary, and of satisfying the Dirichlet conditions. A recent development at the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC), known as the Hilbert-Huang Transform (HHT), proposes a novel approach to the solution for the nonlinear class of spectrum analysis problems. Using a-posteriori data processing based on the Empirical Mode Decomposition (EMD) sifting process (algorithm), followed by the normalized Hilbert Transform of the decomposition data, the HHT allows spectrum analysis of nonlinear and nonstationary data. The EMD sifting process results in a non-constrained decomposition of a source real value data vector into a finite set of Intrinsic Mode Functions (IMF). These functions form a near orthogonal adaptive basis, a basis that is derived from the data. The IMFs can be further analyzed for spectrum interpretation by the classical Hilbert Transform. A new engineering spectrum analysis tool using HHT has been developed at NASA GSFC, the HHT Data Processing System (HHT-DPS). As the HHT-DPS has been successfully used and commercialized, new applications post additional questions about the theoretical basis behind the HHT and EMD algorithms. Why is the fastest changing component of a composite signal being sifted out first in the EMD sifting process? Why does the EMD sifting process seemingly converge and why does it converge rapidly? Does an IMF have a distinctive structure? Why are the IMFs near orthogonal? We address these questions and develop the initial theoretical background for the HHT. This will contribute to the developments of new HHT processing options, such as real-time and 2-D processing using Field Programmable Array (FPGA) computational resources, enhanced HHT synthesis, and broaden the scope of HHT applications for signal processing.

Kizhner, Semion↗
NASA NTRS1984

Interplanetary navigation - An overview

The major elements of the Voyager navigational system are described, within the context of a general review of current interplanetary navigational techniques and equipment. The data processing components of the Voyager navigational system are described, including the ground-based computational facilities and software, and the different support functions. A block diagram of the ground based data processing system is presented. The development of VLBI techniques for a high-precision quasi-relative navigational system to be incorporated into the navigational payload of the Galileo satellite in 1986 is also discussed.

Jordan, J. F.↗
NASA NTRS1988

Adapting high-level language programs for parallel processing using data flow

EASY-FLOW, a very high-level data flow language, is introduced for the purpose of adapting programs written in a conventional high-level language to a parallel environment. The level of parallelism provided is of the large-grained variety in which parallel activities take place between subprograms or processes. A program written in EASY-FLOW is a set of subprogram calls as units, structured by iteration, branching, and distribution constructs. A data flow graph may be deduced from an EASY-FLOW program.

Standley, Hilda M.↗
NASA NTRS2006

On Certain Theoretical Developments Underlying the Hilbert-Huang Transform

One of the main traditional tools used in scientific and engineering data spectral analysis is the Fourier Integral Transform and its high performance digital equivalent - the Fast Fourier Transform (FFT). Both carry strong a-priori assumptions about the source data, such as being linear and stationary, and of satisfying the Dirichlet conditions. A recent development at the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC), known as the Hilbert-Huang Transform (HHT), proposes a novel approach to the solution for the nonlinear class of spectral analysis problems. Using a-posteriori data processing based on the Empirical Mode Decomposition (EMD) sifting process (algorithm), followed by the normalized Hilbert Transform of the decomposed data, the HHT allows spectral analysis of nonlinear and nonstationary data. The EMD sifting process results in a non-constrained decomposition of a source real-value data vector into a finite set of Intrinsic Mode Functions (IMF). These functions form a nearly orthogonal derived from the data (adaptive) basis. The IMFs can be further analyzed for spectrum content by using the classical Hilbert Transform. A new engineering spectral analysis tool using HHT has been developed at NASA GSFC, the HHT Data Processing System (HHT-DPS). As the HHT-DPS has been successfully used and commercialized, new applications pose additional questions about the theoretical basis behind the HHT and EMD algorithms. Why is the fastest changing component of a composite signal being sifted out first in the EMD sifting process? Why does the EMD sifting process seemingly converge and why does it converge rapidly? Does an IMF have a distinctive structure? Why are the IMFs nearly orthogonal? We address these questions and develop the initial theoretical background for the HHT. This will contribute to the development of new HHT processing options, such as real-time and 2-D processing using Field Programmable Gate Array (FPGA) computational resources,

Kizhner, Semion↗
NASA NTRS1998

Parallel processing spacecraft communication system

An uplink controlling assembly speeds data processing using a special parallel codeblock technique. A correct start sequence initiates processing of a frame. Two possible start sequences can be used; and the one which is used determines whether data polarity is inverted or non-inverted. Processing continues until uncorrectable errors are found. The frame ends by intentionally sending a block with an uncorrectable error. Each of the codeblocks in the frame has a channel ID. Each channel ID can be separately processed in parallel. This obviates the problem of waiting for error correction processing. If that channel number is zero, however, it indicates that the frame of data represents a critical command only. That data is handled in a special way, independent of the software. Otherwise, the processed data further handled using special double buffering techniques to avoid problems from overrun. When overrun does occur, the system takes action to lose only the oldest data.

Bolotin, Gary S.↗