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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 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

Incorporating Parallel Computing into the Goddard Earth Observing System Data Assimilation System (GEOS DAS)

Atmospheric data assimilation is a method of combining actual observations with model forecasts to produce a more accurate description of the earth system than the observations or forecast alone can provide. The output of data assimilation, sometimes called the analysis, are regular, gridded datasets of observed and unobserved variables. Analysis plays a key role in numerical weather prediction and is becoming increasingly important for climate research. These applications, and the need for timely validation of scientific enhancements to the data assimilation system pose computational demands that are best met by distributed parallel software. The mission of the NASA Data Assimilation Office (DAO) is to provide datasets for climate research and to support NASA satellite and aircraft missions. The system used to create these datasets is the Goddard Earth Observing System Data Assimilation System (GEOS DAS). The core components of the the GEOS DAS are: the GEOS General Circulation Model (GCM), the Physical-space Statistical Analysis System (PSAS), the Observer, the on-line Quality Control (QC) system, the Coupler (which feeds analysis increments back to the GCM), and an I/O package for processing the large amounts of data the system produces (which will be described in another presentation in this session). The discussion will center on the following issues: the computational complexity for the whole GEOS DAS, assessment of the performance of the individual elements of GEOS DAS, and parallelization strategy for some of the components of the system.

Larson, Jay W.↗
NASA NTRS1985

Fast Dual Analog-to-Digital Converter

Unit delivers blocks of quadrature data to computer memory. Contains 105 integrated circuit packages in 19 by 3 1/2 inch (48.26 by 8.39 cm) rack mounted drawer with internal force air blower. Intended for synchronous sampling of quadrature pairs of data, samples with minimal skew (about 10 nanoseconds) using video-type high-speed sampling technology. Unit equipped for computer controlled self-testing. Originally developed for planetary radar data-acquisition system.

Wallis, D. E.↗
NASA NTRS2004

A Comparison of Experimental EPMA Data and Monte Carlo Simulations

Monte Carlo (MC) modeling shows excellent prospects for simulating electron scattering and x-ray emission from complex geometries, and can be compared to experimental measurements using electron-probe microanalysis (EPMA) and phi(rho z) correction algorithms. Experimental EPMA measurements made on NIST SRM 481 (AgAu) and 482 (CuAu) alloys, at a range of accelerating potential and instrument take-off angles, represent a formal microanalysis data set that has been used to develop phi(rho z) correction algorithms. The accuracy of MC calculations obtained using the NIST, WinCasino, WinXray, and Penelope MC packages will be evaluated relative to these experimental data. There is additional information contained in the extended abstract.

Carpenter, P. K.↗
NASA NTRS2015

Python-Based Scientific Analysis and Visualization of Precipitation Systems at NASA Marshall Space Flight Center

At NASA Marshall Space Flight Center (MSFC), Python is used several different ways to analyze and visualize precipitating weather systems. A number of different Python‐based software packages have been developed, which are available to the larger scientific community. The approach in all these packages is to utilize pre‐existing Python modules as well as to be object‐oriented and scalable. The first package that will be described and demonstrated is the Python Advanced Microwave Precipitation Radiometer (AMPR) Data Toolkit, or PyAMPR for short. PyAMPR reads geolocated brightness temperature data from any flight of the AMPR airborne instrument over its 25‐year history into a common data structure suitable for user‐defined analyses. It features rapid, simplified (i.e., one line of code) production of quick‐look imagery, including Google Earth overlays, swath plots of individual channels, and strip charts showing multiple channels at once. These plotting routines are also capable of significant customization for detailed, publication‐ready figures. Deconvolution of the polarization‐varying channels to static horizontally and vertically polarized scenes is also available. Examples will be given of PyAMPR's contribution toward real‐time AMPR data display during the Integrated Precipitation and Hydrology Experiment (IPHEx), which took place in the Carolinas during May‐June 2014. The second software package is the Marshall Multi‐Radar/Multi‐Sensor (MRMS) Mosaic Python Toolkit, or MMM‐Py for short. MMM‐Py was designed to read, analyze, and display three‐dimensional national mosaicked reflectivity data produced by the NOAA National Severe Storms Laboratory (NSSL). MMM‐Py can read MRMS mosaics from either their unique binary format or their converted NetCDF format. It can also read and properly interpret the current mosaic design (4 regional tiles) as well as mosaics produced prior to late July 2013 (8 tiles). MMM‐Py can easily stitch multiple tiles together to provide a larger regional or national picture of precipitating weather systems. Composites, horizontal and vertical crosssections, and combinations thereof are easily displayed using as little as one line of code. MMM‐Py can also write to the native MRMS binary format, and sub‐sectioning of tiles (or multiple stitched tiles) is anticipated to be in place by the time of this meeting. Thus, MMM‐Py also can be used to power the creation of custom mosaics for targeted regional studies. Overlays of other data (e.g., lightning observations) are easily accomplished. Demonstrations of MMM‐Py, including the creation of animations, will be shown. Finally, Marshall has done significant work to interface Python‐based analysis routines with the U.S. Department of Energy's Py‐ART software package for radar data ingest, processing, and analysis. One example of this is the Python Turbulence Detection Algorithm (PyTDA), an MSFC‐based implementation of the National Center for Atmospheric Research (NCAR) Turbulence Detection Algorithm (NTDA) for the purposes of convective‐scale analysis, situational awareness, and forensic meteorology. PyTDA exploits Py‐ART's radar data ingest routines and data model to rapidly produce aviation‐relevant turbulence estimates from Doppler radar data. Work toward processing speed optimization and better integration within the Py‐ART framework will be highlighted. Python‐based analysis within the Py‐ART framework is also being done for new research related to intercomparison of ground‐based radar data with satellite estimates of ocean winds, as well as research on the electrification of pyrocumulus clouds.

Lang, Timothy J.↗
NASA NTRS1990

SPARSKIT: A basic tool kit for sparse matrix computations

Presented here are the main features of a tool package for manipulating and working with sparse matrices. One of the goals of the package is to provide basic tools to facilitate the exchange of software and data between researchers in sparse matrix computations. The starting point is the Harwell/Boeing collection of matrices for which the authors provide a number of tools. Among other things, the package provides programs for converting data structures, printing simple statistics on a matrix, plotting a matrix profile, and performing linear algebra operations with sparse matrices.

Saad, Youcef↗
NASA NTRS2012

NASA Electronic Parts and Packaging (NEPP) Program

Recent Findings from Audits, New Technology Data Reviews a) Disabled Chip Burn-ins A recent audit for a QML device discovered that the chip was disabled during the static burn-in, thus it was not drawing any current. Recommendation: For new SMDs add a statement within the burn-in paragraphs stating that the parts shall be kept in their enabled state during the burn-in. b) Class Q 160-hr/125oC Burn-in This is being interpreted as a static burn-in (even for CMOS technology). Recommendation: Provide clarification in MIL-STD-883, Test Method 5004. c) At Frequency (Dynamic) Burn-ins Test equipment limitation is being cited for not doing burn-ins at the application frequency. Recommendation: The burn-in task group to discuss and provide guidance. When the SMD says that the part can be used at 200 MHz, then doing burn-in at 6 MHz (cited as burn-in equipment limitation frequency) is not going to be meaningful! d) Two Static Burn-ins Some manufacturers are doing electrical testing between the two static burn-ins, whereas others do electricals after completing both static burn-ins. Recommendation: Provide clarification in MIL-STD-883, Test Method 5004. e) Thermal Imaging For a device with hot spots, the thermal resistance, junction-to-case, would be much higher than the guidelines given in MIL-STD-1835. One of the suppliers used thermal imaging to find hot spots on the die. Recommendation: Assign a task group to evaluate the effectiveness of thermal imaging at the product development stage.

Class Y↗
NASA NTRS1992

Portable microwave test packages for beam-waveguide antenna performance evaluations

Portable microwave test packages used to evaluate a new 34-m-diameter beam-waveguide (BWG) antenna are described. The experimental methodology involved transporting test packages to different focal points of the BWG system and making noise temperature, antenna efficiency, and holography measurements. Comparisons of data measured at the different focal points enabled determinations of performance degradations caused by various mirrors in the BWG system. It is shown that, due to remarkable stabilities and accuracies of radiometric data obtained through the use of the microwave test packages, degradations caused by the BWG system were successfully determined.

Otoshi, Tom Y.↗
NASA NTRS2001

Retrieval of Temperature and Species Distributions from Multispectral Image Data of Surface Flame Spread in Microgravity

Weight, size, and power constraints severely limit the ability of researchers to fully characterize temperature and species distributions in microgravity combustion experiments. A powerful diagnostic technique, infrared imaging spectrometry, has the potential to address the need for temperature and species distribution measurements in microgravity experiments. An infrared spectrum imaged along a line-of-sight contains information on the temperature and species distribution in the imaged path. With multiple lines-of-sight and approximate knowledge of the geometry of the combustion flowfield, a three-dimensional distribution of temperature and species can be obtained from one hyperspectral image of a flame. While infrared imaging spectrometers exist for collecting hyperspectral imagery, the remaining challenge is retrieving the temperature and species information from this data. An initial version of an infrared analysis software package, called CAMEO (Combustion Analysis Model et Optimizer), has been developed for retrieving temperature and species distributions from hyperspectral imaging data of combustion flowfields. CAMEO has been applied to the analysis of multispectral imaging data of flame spread over a PMMA surface in microgravity that was acquired in the DARTFire program. In the next section of this paper, a description of CAMEO and its operation is presented, followed by the results of the analysis of microgravity flame spread data.

Annen, K. D.↗
NASA NTRS1994

Develop advanced nonlinear signal analysis topographical mapping system

The Space Shuttle Main Engine (SSME) has been undergoing extensive flight certification and developmental testing, which involves some 250 health monitoring measurements. Under the severe temperature, pressure, and dynamic environments sustained during operation, numerous major component failures have occurred, resulting in extensive engine hardware damage and scheduling losses. To enhance SSME safety and reliability, detailed analysis and evaluation of the measurements signal are mandatory to assess its dynamic characteristics and operational condition. Efficient and reliable signal detection techniques will reduce catastrophic system failure risks and expedite the evaluation of both flight and ground test data, and thereby reduce launch turn-around time. The basic objective of this contract are threefold: (1) develop and validate a hierarchy of innovative signal analysis techniques for nonlinear and nonstationary time-frequency analysis. Performance evaluation will be carried out through detailed analysis of extensive SSME static firing and flight data. These techniques will be incorporated into a fully automated system; (2) develop an advanced nonlinear signal analysis topographical mapping system (ATMS) to generate a Compressed SSME TOPO Data Base (CSTDB). This ATMS system will convert tremendous amount of complex vibration signals from the entire SSME test history into a bank of succinct image-like patterns while retaining all respective phase information. High compression ratio can be achieved to allow minimal storage requirement, while providing fast signature retrieval, pattern comparison, and identification capabilities; and (3) integrate the nonlinear correlation techniques into the CSTDB data base with compatible TOPO input data format. Such integrated ATMS system will provide the large test archives necessary for quick signature comparison. This study will provide timely assessment of SSME component operational status, identify probable causes of malfunction, and indicate feasible engineering solutions. The final result of this program will yield an ATMS system of nonlinear and nonstationary spectral analysis software package integrated with the Compressed SSME TOPO Data Base (CSTDB) on the same platform. This system will allow NASA engineers to retrieve any unique defect signatures and trends associated with different failure modes and anomalous phenomena over the entire SSME test history across turbo pump families.

Source record↗
DOE OSTI2022

Automated Shift Detection in Sensor-Based PV Power and Irradiance Time Series: Preprint

PV power and irradiance sensor-based measurements are prone to error, resulting in issues such as abrupt time series data shifts. These shifts, which are usually unintentional, may be caused by software or hardware configuration changes on a PV system, and do not reflect an actual change in overall system performance. Locating these shifts and segmenting the associated time series aids in more accurate future PV analysis. In this research, an offline changepoint detection (CPD) algorithm that automatically detects these abrupt data shifts in sensor-based time series is introduced. Data shift periods in 101 daily PV power and irradiance time series were labeled manually by two solar experts. These data streams represent sensor-based measurements, and display a variety of data shift behaviors. A changepoint detection algorithm was tuned using the 101 labeled data streams, with each model configuration's ability to detect labeled changepoints benchmarked using metrics such as F1-score, recall, and Rand Index. Best performing models on seasonality-corrected data streams include the Pruned Exact Linear (PELT) method, the Binary Segmentation method, and the Bottom-Up method, all scoring an average F1-score of 0.76 or greater at detecting labeled changepoints within a 30-day window for the labeled data sets. To promote further research in this space, we are releasing the labeled data shift sets on U.S. Department of Energy's (DOE) DuraMAT Data Hub, and the associated algorithm in the Python PVAnalytics package.

changepoint detection↗
DOE OSTI2022

Automated Shift Detection in Sensor-Based PV Power and Irradiance Time Series

PV power and irradiance sensor-based measurements are prone to error, resulting in issues such as abrupt time series data shifts. These shifts, which are usually unintentional, may be caused by software or hardware configuration changes on a PV system, and do not reflect an actual change in overall system performance. Locating these shifts and segmenting the associated time series aids in more accurate future PV analysis. In this research, an offline changepoint detection (CPD) algorithm that automatically detects these abrupt data shifts in sensor-based time series is introduced. Data shift periods in 101 daily PV power and irradiance time series were labeled manually by two solar experts. These data streams represent sensor-based measurements, and display a variety of data shift behaviors. A changepoint detection algorithm was tuned using the 101 labeled data streams, with each model configuration's ability to detect labeled changepoints benchmarked using metrics such as F1-score, recall, and Rand Index. Best performing models on seasonality-corrected data streams include the Pruned Exact Linear (PELT) method, the Binary Segmentation method, and the Bottom-Up method, all scoring an average F1-score of 0.76 or greater at detecting labeled changepoints within a 30-day window for the labeled data sets. To promote further research in this space, we are releasing the labeled data shift sets on U.S. Department of Energy's (DOE) DuraMAT Data Hub, and the associated algorithm in the Python PVAnalytics package.

changepoint detection↗
NASA NTRS1993

Develop advanced nonlinear signal analysis topographical mapping system

The SSME has been undergoing extensive flight certification and developmental testing, which involves some 250 health monitoring measurements. Under the severe temperature pressure, and dynamic environments sustained during operation, numerous major component failures have occurred, resulting in extensive engine hardware damage and scheduling losses. To enhance SSME safety and reliability, detailed analysis and evaluation of the measurements signal are mandatory to assess its dynamic characteristics and operational condition. Efficient and reliable signal detection techniques will reduce catastrophic system failure risks and expedite the evaluation of both flight and ground test data, and thereby reduce launch turn-around time. The basic objective of this contract are threefold: (1) Develop and validate a hierarchy of innovative signal analysis techniques for nonlinear and nonstationary time-frequency analysis. Performance evaluation will be carried out through detailed analysis of extensive SSME static firing and flight data. These techniques will be incorporated into a fully automated system. (2) Develop an advanced nonlinear signal analysis topographical mapping system (ATMS) to generate a Compressed SSME TOPO Data Base (CSTDB). This ATMS system will convert tremendous amounts of complex vibration signals from the entire SSME test history into a bank of succinct image-like patterns while retaining all respective phase information. A high compression ratio can be achieved to allow the minimal storage requirement, while providing fast signature retrieval, pattern comparison, and identification capabilities. (3) Integrate the nonlinear correlation techniques into the CSTDB data base with compatible TOPO input data format. Such integrated ATMS system will provide the large test archives necessary for a quick signature comparison. This study will provide timely assessment of SSME component operational status, identify probable causes of malfunction, and indicate feasible engineering solutions. The final result of this program will yield an ATMS system of nonlinear and nonstationary spectral analysis software package integrated with the Compressed SSME TOPO Data Base (CSTDB) on the same platform. This system will allow NASA engineers to retrieve any unique defect signatures and trends associated with different failure modes and anomalous phenomena over the entire SSME test history across turbo pump families.

Jong, Jen-Yi↗
NASA NTRS1988

The GONG Data Reduction and Analysis System

Each of the six GONG observing stations will produce three, 16-bit, 256X256 images of the Sun every 60 sec of sunlight. These data will be transferred from the observing sites to the GONG Data Management and Analysis Center (DMAC), in Tucson, on high-density tapes at a combined rate of over 1 gibabyte per day. The contemporaneous processing of these data will produce several standard data products and will require a sustained throughput in excess of 7 megaflops. Peak rates may exceed 50 megaflops. Archives will accumulate at the rate of approximately 1 terabyte per year, reaching nearly 3 terabytes in 3 yr of observing. Researchers will access the data products with a machine-independent GONG Reduction and Analysis Software Package (GRASP). Based on the Image Reduction and Analysis Facility, this package will include database facilities and helioseismic analysis tools. Users may access the data as visitors in Tucson, or may access DMAC remotely through networks, or may process subsets of the data at their local institutions using GRASP or other systems of their choice. Elements of the system will reach the prototype stage by the end of 1988. Full operation is expected in 1992 when data acquisition begins.

Pintar, James A.↗
NASA NTRS1991

ALDAS user's manual

The Acoustic Laboratory Data Acquisition System (ALDAS) is an inexpensive, transportable means to digitize and analyze data. The system is based on the Macintosh 2 family of computers, with internal analog-to-digital boards providing four channels of simultaneous data acquisition at rates up to 50,000 samples/sec. The ALDAS software package, written for use with rotorcraft acoustics, performs automatic acoustic calibration of channels, data display, two types of cycle averaging, and spectral amplitude analysis. The program can use data obtained from internal analog-to-digital conversion, or discrete external data imported in ASCII format. All aspects of ALDAS can be improved as new hardware becomes available and new features are introduced into the code.

Watts, Michael E.↗
NASA NTRS1979

DAM package version 7807: Software fixes and enhancements

The Detection and Mapping package is an integrated set of manual procedures, computer programs, and graphic devices designed for efficient production of precisely registered, formatted, and interpreted maps from digital LANDSAT multispectral scanner data. This report documents changes to the DAM package in support of its use by the Corps of Engineers for inventorying impounded surface water. Although these changes are presented in terms of their application to detecting and mapping surface water, they are equally relevant to other land surface materials.

Schlosser, E.↗
NASA NTRS2003

The SEIS Experiment: A Mars Seismic Package

This experiment will integrate a VBB (Very Broad Band) two axis seismometer, a three axis Short Period seismometer and a series of environmental sensors for pressure, infra-sounds and temperature. IPGP (France) has the overall responsibility of the experiment and is responsible for the VBB and environmental sensors. ETHZ (Switzerland) is responsible for the electronics of the experiment and JPL (USA) for the SP (Short Period) sensors. SEIS instrument was first proposed and accepted for NetLander mission (and will also be in charge of data acquisition for SPICE experiment). This seismic package should also be proposed for future missions.

Schibler, P.↗
NASA NTRS1971

Implementation of radiation shielding calculation methods. Volume 1: Synopsis of methods and summary of results

The work performed in the following areas is summarized: (1) Analysis of Realistic nuclear-propelled vehicle was analyzed using the Marshall Space Flight Center computer code package. This code package includes one and two dimensional discrete ordinate transport, point kernel, and single scatter techniques, as well as cross section preparation and data processing codes, (2) Techniques were developed to improve the automated data transfer in the coupled computation method of the computer code package and improve the utilization of this code package on the Univac-1108 computer system. (3) The MSFC master data libraries were updated.

Capo, M. A.↗
DOE OSTI2026

Datashare

Datashare facilitates communication and data sharing within local networks in potentially dangerous situations such as an explosive ordnance disposal. During such events, there is a need to transmit information rapidly around the incident area. It is a distributed database that does not require an internet connection for operation. In addition, Datashare interfaces with XTK and other software applications, allowing for seamless integration and data management. Datashare supports video calls over the network, enabling real-time communication among users. This software serves to organize, package, and share between responders on location and export data to those off location. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Eldridge, Bryce [Sandia National Lab. (SNL-CA), Li↗