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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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The large scale seasonal variability is estimated jointly with the mean sea surface and the ocean tide signal not recovered by the Cartwright and Ray ocean tide model.
We propose to develop an interactive environment for the analysis of large Earth science observation and model data sets. We will use a standard scientific data storage format and a large capacity (greater than 20 GB) optical disk system for data management; develop libraries for coordinate transformation and regridding of data sets; modify the NCSA X Image and X Data Slice software for typical Earth observation data sets by including map transformations and missing data handling; develop analysis tools for common mathematical and statistical operations; integrate the components described above into a system for the analysis and comparison of observations and model results; and distribute software and documentation to the scientific community.
We propose to develop an interactive environment for the analysis of large Earth science observation and model data sets. We will use a standard scientific data storage format and a large capacity (greater than 20 GB) optical disk system for data management; develop libraries for coordinate transformation and regridding of data sets; modify the NCSA X Image and X DataSlice software for typical Earth observation data sets by including map transformations and missing data handling; develop analysis tools for common mathematical and statistical operations; integrate the components described above into a system for the analysis and comparison of observations and model results; and distribute software and documentation to the scientific community.
We present a new analysis of mass spectral data obtained by the Pioneer Venus (PV) Large Probe Neutral Mass Spectrometer (LNMS). To analyze the LNMS data, we constructed an analytical model that accounts for spectrometer performance at each altitude, provides CO2 abundances in units of density (kg/m3), and retains the resolving power of the LNMS through use of a targeted data-fitting routine. Our results provide new insights into the composition of Venus’ atmosphere and show that densities for CO2 increase towards the surface, which is suggestive of surface outgassing. Additionally, the data reveal partial obstructions of the LNMS inlet at <17 km, which is an likely important consideration for future missions. Re-analysis of the LNMS data may assist in revealing the past, present and/or future habitability of Venus’ clouds.
The purposes of the Array System are: To fulfill the mission needs for higher data rates in the post-2010 era - Reduce the cost per data bit by two orders of magnitude. Specific Goal: Achieve a factor of 10 increase in DSN reception capability per decade from 2012 through 2027 (an average of 1 dB per year).
Large collections of oceanographic data and other large collections of data published on CD-ROM's in formats facilitating access and analysis. Involves four major steps: preprocessing, premastering, mastering, and verification. Large capacity, small size, commercial availability, long-life, and standard format of CD-ROM's offer advantages over computer-compatible magnetic tape.
The data acquisition system monitors five types of transducers: (1) platinum and germanium resistance thermometers; (2) superconducting liquid level indicators for both cryogen tanks; (3) pressure transducers for both tanks; (4) strain gauges at critical structural locations; and (5) flowmeters for monitoring cryogen tank boiloff which are linear voltage output transducers. All the transducers produce a voltage proportional to the parameter they measure; this is routed to a single six and one-half digit voltmeter by way of an 80-channel multiplexer. The data acquisition program has six main functions: (1) definition and modification of the channels to be stimulated; (2) sensor stimulation and measurement; (3) raw data storage; (4) raw data conversion; (5) real-time formatting and output of raw or converted data; and (6) editing. The heart of the program is a driver section that represents approximately 20 percent of the code. The driver mode sequences and controls the six main functions.
Detailed land cover classifications were performed on the Thematic Mapper Simulator (TMS) and MSS data of the Clinton River Basin (acquired on August 19, 1981, and June 28, 1980, respectively) using supervised classification techniques. Differences in interclass separability were compared to select several promising TMS band combinations, selected from the 27 covering the Clinton River Basin. The TMS data produced a more accurate and spatially contiguous classification than MSS for this study site. While the accuracy of the 4-band TM data set was as good as the 7-band, the 3-band TMS data sets were also better than the MSS. These results indicate that both the increased spectral discrimination and spatial resolution contribute to improved classification accuracy. The possibility of reducing the data analysis burden associated with large TM data volumes through effective band selection therefore appears promising. The implications of the improved classification accuracy of TMS data are important for hydrologic and economic modeling. In particular, the higher accuracies for the developed categories (residential and commercial) should improve the predictions of runoff in flood forecasting models and of flood damage for damage calculation models appreciably.
SAMPIE, the Solar Array Module Plasma Interactions Experiment, flew in the Space Shuttle Columbia payload bay as part of the Office of Aeronautics and Space Technology-2 (OAST-2) mission on STS-62, March, 1994. SAMPIE biased samples of solar arrays and space power materials to varying potentials with respect to the surrounding space plasma, and recorded the plasma currents collected and the arcs which occurred, along with a set of plasma diagnostics data. A large set of high quality data was obtained on the behavior of solar arrays and space power materials in the space environment. This paper is the first report on the data SAMPIE telemetered to the ground during the mission. It will be seen that the flight data promise to help determine arcing thresholds, snapover potentials, and floating potentials for arrays and spacecraft in LEO.
SAMPIE, the Solar Array Module Plasma Interactions Experiment, flew in the Space Shuttle Columbia payload bay as part of the OAST-2 mission on STS-62, March, 1994. SAMPIE biased samples of solar arrays and space power materials to varying potentials with respect to the surrounding space plasma, and recorded the plasma currents collected and the arcs which occurred, along with a set of plasma diagnostics data. A large set of high quality data was obtained on the behavior of solar arrays and space power materials in the space environment. This paper is the first report on the data SAMPIE telemetered to the ground during the mission. It will be seen that the flight data promise to help determine arcing thresholds, snapover potentials and floating potentials for arrays and spacecraft in LEO.
The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is one of twelve NASA Earth science data centers that document, process, archive and distribute data from Earth observation missions and projects. GES DISC maintains an archive of several hydrology datasets, including the Land Data Assimilation Systems (LDAS) and the Gravity Recovery and Climate Experiment (GRACE) Data Assimilation for Drought Monitoring (GRACE-DA-DM) data products. These datasets include model output of heat fluxes, rain, snow, soil temperature, soil moisture, and runoff; and observational forcing data, including surface pressure, temperature, precipitation, downward shortwave and longwave radiation, humidity, and wind. The temporal resolution of the hydrology data at GES DISC ranges from hourly to monthly, and spatial resolutions range from 0.1° to 1.0°. The GES DISC provides services which enable users to aggregate, temporally and spatially subset, regrid, and visualize archived data including the GES DISC Subsetter, Hydrology Data Rods, and the Geospatial Interactive Online Visualization and Analysis Infrastructure (GIOVANNI). The Hydrology Data Rods service optimally reorganizes large hydrological data sets as extended time series, providing more efficient access for the hydrological community. The time series data (aka “data rods”) were integrated into hydrology community tools, such as the Data Rods Explorer on HydroShare. Furthermore, the GES DISC is in the process of migrating its data and services to the cloud. Hydrological data available at the GES DISC are now available in the Amazon Web Services (AWS) cloud (us-west-2 region) providing users Direct S3 data access and the capability for cloud computing operations. In this presentation, the hydrology data products and services currently available at the GES DISC will be summarized. Also discussed are the migration to the cloud, user support through this transition, and the status of migrating the data rods service to the cloud.
The Wind spacecraft has yielded several decades of high-resolution magnetic field data, a large fraction of which displays small-scale structures. In particular, the solar wind is full of wavelike fluctuations that appear in both the field magnitude and its components. The nature of these fluctuations can be tied to the properties of other structures in the solar wind, such as shocks, that have implications for the time evolution of the solar wind. As such, having a large collection of wave events would facilitate further study of the effects that these fluctuations have on solar wind evolution. Given the large volume of magnetic field data available, machine learning is the most practical approach to classifying the myriad small-scale structures observed. To this end, a subset of Wind data is labeled and used as a training set for a multi-branch 1D convolutional neural network aimed at classifying circularly polarized wave modes. Using this algorithm, a preliminary statistical study of one year of data is performed, yielding about 300,000 wave intervals out of about 5,000,000 solar wind intervals. The wave intervals come about more often in the fast solar wind and at higher temperatures, and the number of waves per day is highly periodic. This machine learning-based approach to wave detection has the potential to be a powerful, inexpensive way to catalog waves throughout decades of spacecraft data.
The effect of airspace factors on air traffic controller (ATC) workload has been an active area of study for almost three decades due to the importance of safety considerations necessary to design and maintain operations. Existing literature has examined several traffic-related (e.g., number of aircraft under control, loss of separation) contributors to ATC workload and proposed mathematical functions to best describe controller response. However, future air traffic continues to increase in complexity with the introduction of urban air mobility (UAM) – or the transportation of humans and cargo using electric vertical takeoff and landing (eVTOL) aircraft. UAM aims to alleviate congestion for existing ground transportation systems and improve mobility within urban centers and other high-demand locations. This shift in the traditional airspace paradigm necessitates an evolved understanding of model use and development for ATC workload prediction. This study aimed to develop an ATC workload forecasting model based on human-in-the-loop (HITL) simulation data for UAM operations at large airports. Data collected from the HITL simulation served as the training and testing data for a Long Short-Term Memory recurrent neural network and enabled time-series forecasting of ATC workload from traffic characteristics. Results demonstrated the potential of LSTM models for forecasting ATC workload 40 minutes into the future and highlighted important considerations for future development.
The effect of airspace factors on air traffic controller (ATC) workload has been an active area of study for almost three decades due to the importance of safety considerations necessary to design and maintain operations. Existing literature has examined several traffic-related (e.g., number of aircraft under control, loss of separation) contributors to ATC workload and proposed mathematical functions to best describe controller response. However, future air traffic continues to increase in complexity with the introduction of urban air mobility (UAM) – or the transportation of humans and cargo using electric vertical takeoff and landing (eVTOL) aircraft. UAM aims to alleviate congestion for existing ground transportation systems and improve mobility within urban centers and other high-demand locations. This shift in the traditional airspace paradigm necessitates an evolved understanding of model use and development for ATC workload prediction. This study aimed to develop an ATC workload forecasting model based on human-in-the-loop (HITL) simulation data for UAM operations at large airports. Data collected from the HITL simulation served as the training and testing data for a Long Short-Term Memory recurrent neural network and enabled time-series forecasting of ATC workload from traffic characteristics. Results demonstrated the potential of LSTM models for forecasting ATC workload 40 minutes into the future and highlighted important considerations for future development.
During the four years of this grant (including the one year extension), we have explored many aspects of the visualization of large CFD (Computational Fluid Dynamics) datasets. These have included new direct volume rendering approaches, hierarchical methods, volume decimation, error metrics, parallelization, hardware texture mapping, and methods for analyzing and comparing images. First, we implemented an extremely general direct volume rendering approach that can be used to render rectilinear, curvilinear, or tetrahedral grids, including overlapping multiple zone grids, and time-varying grids. Next, we developed techniques for associating the sample data with a k-d tree, a simple hierarchial data model to approximate samples in the regions covered by each node of the tree, and an error metric for the accuracy of the model. We also explored a new method for determining the accuracy of approximate models based on the light field method described at ACM SIGGRAPH (Association for Computing Machinery Special Interest Group on Computer Graphics) '96. In our initial implementation, we automatically image the volume from 32 approximately evenly distributed positions on the surface of an enclosing tessellated sphere. We then calculate differences between these images under different conditions of volume approximation or decimation.
Several geometric distortions are present in remotely sensed images depending on the type of sensors and the object being observed. It is often desirable to compensate for these distortions and store the images in reference to a standard coordinate system. Digital techniques for correction are versatile and introduce a minimum of radiometric errors. The main problems to be considered in this area are the determination of the corrective transformation, resampling, and the management of the large quantities of data. It is shown that, by a judicious rearrangement of the input data, considerable reductions in the required memory capacity can be achieved. The rearrangement can be accomplished in several stages. The method presented here is amenable to pipeline implementation for processing a continuous stream of images.
A large class of data parallel computations is characterized by a sequence of phases, with phase changes occurring unpredictably. Dynamic remapping of the workload to processors may be required to maintain good performance. The problem considered, for which the utility of remapping and the future behavior of the workload are uncertain, arises when phases exhibit stable execution requirements during a given phase, but requirements change radically between phases. For these situations, a workload assignment generated for one phase may hinder performance during the next phase. This problem is treated formally for a probabilistic model of computation with at most two phases. The authors address the fundamental problem of balancing the expected remapping performance gain against the delay cost, and they derive the optimal remapping decision policy. The promise of the approach is shown by application to multiprocessor implementations of an adaptive gridding fluid dynamics program and to a battlefield simulation program.
We use the term "Grid" to refer to a software system that provides uniform and location independent access to geographically and organizationally dispersed, heterogeneous resources that are persistent and supported. These emerging data and computing Grids promise to provide a highly capable and scalable environment for addressing large-scale science problems. We describe the requirements for science Grids, the resulting services and architecture of NASA's Information Power Grid (IPG) and DOE's Science Grid, and some of the scaling issues that have come up in their implementation.