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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 pricing policies of different data-base services offering Predicast data bases are examined from a user perspective. The services carrying these data bases are listed; the problems introduced by varying exchange rates and seemingly idiosyncratic price structures are discussed; and numerous specific examples are given.
Changes of mean annual net accumulation at the surface on the grounded ice sheets of East Antarctica, West Antarctica, and Greenland in response to variations in sea ice extent are estimated using grid-point values 100 km apart. The data bases are assembled principally by bilinear interpolation of remotely sensed brightness temperature (Nimbus-5 ESMR, Nimbus-7 SMMR), surface temperature (Nimbus-7 THIR), and surface elevation (ERS-1 radar altimeter). These data, complemented by field data where remotely sensed data are not available, are used in multivariate analyses in which mean annual accumulation (derived from firn emissivity) is the dependent variable; the independent variables are latitude, surface elevation, mean annual surface temperature, and mean annual distance to open ocean (as a source of energy and moisture). The last is the shortest distance measured between a grid point and the mean annual position of the 10% sea ice concentration boundary, and is used as an index of changes in sea ice extent as well as of mean concentration. Stepwise correlation analyses indicate that variations in sea ice extent of +/-50 km would lead to changes in accumulation inversely of +/-4% on East Antarctica, +/- 10% on West Antarctica, and +4% on Greenland. These results are compared with those obtained in a previous study using visually interpolated values from contoured compilations of field data; they substantiate the findings for the Antarctic ice sheets (+/-4% on East Antarctica, +/-9% in West Antarctica), and suggest a reduction by one half of the probable change of accumulation on Greenland (from +/-8%). The results also suggest a reduction of the combined contribution to sea level variability to +/- 0.19 mm/a (from +/- 0.22 mm/a).
APL provides convenient associative data manipulation functions in a high level language. Six statements were added to PL/1 via a preprocessor: CREATE, INSERT, FIND, FOR EACH, REMOVE, and DELETE. They allow complete control of all data base operations. During execution, data base management programs perform the functions required to support the APL language. VAAM is the data base management system designed to support the APL language. APL/VAAM is used by CADANCE, an interactive graphic computer system. VAAM is designed to support heavily referenced files. Virtual memory files, which utilize the paging mechanism of the operating system, are used. VAAM supports a full network data structure. The two basic blocks in a VAAM file are entities and sets. Entities are the basic information element and correspond to PL/1 based structures defined by the user. Sets contain the relationship information and are implemented as arrays.
There is a growing need in the AEC industry for the digitalization of model-based data exchange in BIM workflows. However, users continue to face difficulties exchanging data between BIM-based computer-aided design (CAD) and computer-aided engineering (CAE) software, even with the open, non-proprietary data exchange format called Industry Foundation Classes (IFC). Proposed solutions in academic research focus primarily on building systems, with comparatively little attention to piping models. Therefore, this paper introduces an interoperability tool for enabling piping model data exchange between architectural and structural analysis domains. The tool is tested using a piping model created in Autodesk Revit and shows marked improvement for IFC-based CAD-to-CAE interoperability over existing practice.
Acoustic and diagnostic data that were obtained to determine the influence of selected geometric and aerodynamic flow variables of coannular nozzles with thermal acoustic shields are summarized in this comprehensive data report. A total of 136 static and simulated flight acoustic test points were conducted with 9 scale-model nozzles. Aerodynamic laser velocimeter measurements were made for four selected plumes. In addition, static pressure data in the chute base region of the suppressor configurations were obtained to assess the influence of the shield stream on the suppressor base drag.
Ultralow-frequency (ULF) waves are observed almost everywhere in the dayside magnetosphere. The mechanism by which these waves are generated and transformed in the dayside magnetosphere is still not understood. Here we report a statistical study of these waves based on magnetic field data from the International Sun-Earth Explorer 1 (ISEE 1) spacecraft. Data from the first traversal of the spacecraft through the entire dayside magnetosphere have been examined to determine the spatial distribution of wave occurrence. Successive 20-min segments of data were transformed to a field-aligned coordinate system. The parallel component was detrended and all three components of the field spectrally analyzed. Wave occurrence was defined by the presence of significant peaks in the power spectra. Wave events were categorized by three wave frequency bands: Pc 3 with T approximately 10-45 s; Pc 4 with T approximately 45-150 s; the short-period part of the Pc 5 wave band with T approximately 150-324 s. Properties of the spectral peaks were then entered into a data base. The data base was next sorted to determine the spatial occurrence pattern for the waves. Our results show that Pc 3 waves most frequently occur just outside synchronous orbit and are approximately centered on local noon. Pc 4 waves have a similar distribution with its peak further out. Pc 5 waves have high occurrence rate at the two flanks of the magnetosphere. Peaks in spectra obtained near the magnetopause are less clearly defined than those deeper in the magnetosphere.
The final Shuttle-derived atmospheric data base is presented. The relational data base is comprised of data from 32 Space Transportation System (STS) descent flights, to include available meteorology data taken in support of each flight. For the most part, the available data are restricted to the middle atmosphere. In situ accelerations, sensed by the tri-redundant Inertial Measurement Unit (IMU) to an accuracy better than 1 mg, are combined with post-flight Best Estimate Trajectory (BET) information and predicted, flight-substantiated Orbiter aerodynamics to provide determinations up to altitudes of 95 km. In some instances, alternate accelerometry data with micro-g resolution were utilized to extend the data base well into the thermosphere. Though somewhat limited, the ensemble of flights permit a reasonable sampling of monthly, seasonal, and latitudinal variations which can be utilized for atmospheric science investigations and model evaluations and upgrades as appropriate. More significantly, the unparallel vertical resolution in the Shuttle-derived results indicate density shears normally associated with internal gravity waves or local atmospheric instabilities. Consequently, these atmospheres can also be used as stress-atmospheres for Guidance, Navigation and Control (GN and C) system development and analysis as part of any advanced space vehicle design activities.
A technique for the direct comparison of LDV signal processors is developed, based on the use of a data base of digitized signal bursts obtained from an LDV under various configurations. This data base can be used to evaluate the response of signal processors and processor algorithms to specific signal characteristics and not to generalized simplistic waveforms. Examples from such a data base are presented to illustrate the capabilities of the proposed method. This data base includes signal ensembles obtained with three laser power settings at two transmitted focal lengths.
The Atmosphere Explorer (AE) program provided assistance to participants in the International Magnetosphere Study (IMS). The AE program, which began about 1970 employed three spacecraft in three complementary orbits. The program initiated a concept of a common data base, which is, a data base shared by all the participating investigators, to facilitate the correlation of the measured parameters. The immediate goal of the AE program has been study of the thermosphere, giving particular attention to photochemistry. Three satellites, AE-C, D, and E, were launched into orbits of 68 deg, 90 deg, and 19 deg inclination respectively in December 1973, October 1975, and November 1975. An instrument complement description is provided, and the AE data base is discussed.
Current and future ground-based cosmological surveys, such as the Dark Energy Survey (DES), and the Vera Rubin Observatory Legacy Survey of Space and Time (LSST), are predicted to discover thousands to tens of thousands of strong gravitational lenses. The large number of strong lenses discoverable in future surveys will make strong lensing a highly competitive and complementary cosmic probe. However, conventional lens modeling techniques are unable to scale up to the sheer number of lenses that will be discovered through upcoming surveys. Therefore, the use of automated lens analysis techniques is necessary. We demonstrate that machine learning methods can be used to automate the inference of informative model posteriors of strong lensing systems in ground-based surveys with credible uncertainty estimation. We present two Simulation-Based Inference (SBI) approaches for lens parameter estimation of galaxy-galaxy lenses. We demonstrate applications of Neural Posteriors Estima tors (NPEs) and Bayesian Neural Network (BNNs) to automate the inference of a 12-parameter lensing system for DES-like ground-based imaging data. We apply a suite of diagnostics (e.g., posterior coverage and SBC) to validate the performance of our methods. We find that NPEs outperform the BNN, producing posterior distributions that are for the most part both more accurate and more precise; in particular, several source-light model parameters are systematically biased in the BNN implementation.
A memory-based parallel data output controller employs associative memories and memory mapping to decommutate multiple channels of telemetry data. The output controller contains a random access memory (RAM) which has at least as many address locations as there are channels. A word counter addresses the RAM which provides as it outputs an encoded peripheral device number and a MSB/LSB-first flag. The encoded device number and a bit counter address a second RAM which contains START and STOP flags to pick out the required bits from the specified word number. The LSB/MSB, START and STOP flags, along with the serial input digital data go to a control block which selectively fills a shift register used to drive the parallel data output bus.
The expansion of distribution power system and the growing penetration of distributed energy resources present new challenges for situational awareness. Calibrating the extended system model with sensor measurements and maintaining the usability is critical for utilities. This paper presents a distribution network parameter estimation (DNPE) approach using machine learning (ML) and metering data that improve the quality of extended distribution power system modeling. The reliability model can improve the ability of endpoint data to be translated into network-level situational awareness in real time and help distribution system operators (DSOs) solve branch flow and voltage problems. In addition, a data analytic and automate processing scheme is proposed to improve the sensor data quality and prevent misleading information. The effectiveness of the proposed method is verified with actual advanced metering infrastructure (AMI) data on a real utility feeder model, while considering the higher penetration of photovoltaic power generation. The test of DNPE and study results are demonstrated in this paper.
While proper orbital elements are currently available for more than 1 million asteroids, taxonomical information is still lagging behind. Surveys like SDSS-MOC4 provided preliminary information for more than 100 000 objects, but many asteroids still lack even a basic taxonomy. In this study, we use Dark Energy Survey (DES) data to provide new information on asteroid physical properties. By cross-correlating the new DES data base with other data bases, we investigate how asteroid taxonomy is reflected in DES data. While the resolution of DES data is not sufficient to distinguish between different asteroid taxonomies within the complexes, except for V-type objects, it can provide information on whether an asteroid belongs to the C- or S-complex. Here, machine learning methods optimized through the use of genetic algorithms were used to predict the labels of more than 68 000 asteroids with no prior taxonomic information. Using a high-quality, limited set of asteroids with data on gri slopes and i − z colours, we detected 409 new possible V-type asteroids. Their orbital distribution is highly consistent with that of other known V-type objects.
The purpose of this experimental investigation was to produce a data base of endwall heat transfer data under conditions that simulate those in the passage of the first-stage stator in advanced turbine engines. The data base is intended to be sufficiently complete to provide verification data for refined computational models, and to provide a basis for advanced core engine endwall cooling designs. A linear, two-dimensional cascade was used to generate the data base. The test plan provided data to examine the effects of exit Mach number, exit Reynolds number, inlet boundary layer thickness, gas-to-wall temperature ratio, inlet pressure gradients, and inlet temperature gradients. The data generated consist of inlet, intrapassage, and exit aerodynamic data plus intrapassage endwall heat flux, adiabatic wall temperature measurements, and inlet turbulence data.
A miscomparison between a channel's configuration data base and a voted system configuration data base in a redundant channel system having identically operating, frame synchronous channels triggers autoequalization of the channel's historical signal data bases in a hierarchical, chronological manner with that of a correctly operating channel. After equalization, symmetrization of the channel's configuration data base with that of the system permits upgrading of the previously degraded channel to full redundancy. An externally provided equalization command, e.g., manually actuated, can also trigger equalization.
Engineering data base development which has become increasingly widespread to industry with the availability of data management systems is examined. A large data base was developed for wind tunnel data and related model test information, using RIM as the data base manager. The arrangement of the wind tunnel data into the proper schema for the most efficient database utilization is discussed. The FORTRAN interface program of RIM is used extensively in the loading phases of the data base and by the users. Several examples to illustrate how the Wind Tunnel Data base might be searched for specific data items and test information using RIM are presented.
Abstract not provided.
A primary operational goal of the United States Department of Agriculture (USDA) is to improve foreign market access for U.S. agricultural products. A large fraction of this crop condition assessment is based on satellite imagery and ground data analysis. The baseline soil moisture estimates that are currently used for this analysis are based on output from the modified Palmer two-layer soil moisture model, updated to assimilate near-real time observations derived from the Soil Moisture Ocean Salinity (SMOS) satellite. The current data assimilation system is based on a 1-D Ensemble Kalman Filter approach, where the observation error is modeled as a function of vegetation density. This allows for offsetting errors in the soil moisture retrievals. The observation error is currently adjusted using Normalized Difference Vegetation Index (NDVI) climatology. In this paper we explore the possibility of utilizing microwave-based vegetation optical depth instead.