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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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A quick reference for obtaining estimates of available solar insolation for numerous locations and array angles is presented. A model and a computer program are provided which considered the effects of array shadowing reflector augmentation as design variables.
Giovanni is an exploration tool at the NASA Goddard Earth Sciences Data Information Services Center (GES DISC), providing 22 analysis and visualization services for over 1600 Earth Science data variables. Owing to its popularity, Giovanni has experienced a consistent growth in overall demand, with periodic usage spikes attributed to trainings by education organizations, extensive data analysis in response to natural disasters, preparations for science meetings, etc. Furthermore, the new generation of spaceborne sensors and high resolution models have resulted in an exponential growth in data volume with data distributed across the traditional boundaries of data centers. Seamless exploration of data (without users having to worry about data center boundaries) has been a key recommendation of the GES DISC User Working Group. These factors have required new strategies for delivering acceptable performance. The cloud-based Giovanni, built on Amazon Web Services (AWS), evaluates (1) AWS native solutions to provide a scalable, serverless architecture; (2) open standards for data storage in the Cloud; (3) a cost model for operations; and (4) end-user performance. Our preliminary findings indicate that the use of serverless architecture has a potential to significantly reduce development and operational cost of Giovanni. The combination of using AWS managed services, storage of data in open standards, and schema-on-read data access strategy simplifies data access and analytics, in addition to making data more accessible to the end users of Giovanni through popular programming languages.
Analyzing data sets collected in experiments or by observations is a Core scientific activity. Typically, experimentd and observational data are &aught with uncertainty, and the analysis is based on a statistical model of the conjectured underlying processes, The large data volumes collected by modern instruments make computer support indispensible for this. Consequently, scientists spend significant amounts of their time with the development and refinement of the data analysis programs. AutoBayes [GF+02, FS03] is a fully automatic synthesis system for generating statistical data analysis programs. Externally, it looks like a compiler: it takes an abstract problem specification and translates it into executable code. Its input is a concise description of a data analysis problem in the form of a statistical model as shown in Figure 1; its output is optimized and fully documented C/C++ code which can be linked dynamically into the Matlab and Octave environments. Internally, however, it is quite different: AutoBayes derives a customized algorithm implementing the given model using a schema-based process, and then further refines and optimizes the algorithm into code. A schema is a parameterized code template with associated semantic constraints which define and restrict the template s applicability. The schema parameters are instantiated in a problem-specific way during synthesis as AutoBayes checks the constraints against the original model or, recursively, against emerging sub-problems. AutoBayes schema library contains problem decomposition operators (which are justified by theorems in a formal logic in the domain of Bayesian networks) as well as machine learning algorithms (e.g., EM, k-Means) and nu- meric optimization methods (e.g., Nelder-Mead simplex, conjugate gradient). AutoBayes augments this schema-based approach by symbolic computation to derive closed-form solutions whenever possible. This is a major advantage over other statistical data analysis systems which use numerical approximations even in cases where closed-form solutions exist. AutoBayes is implemented in Prolog and comprises approximately 75.000 lines of code. In this paper, we take one typical scientific data analysis problem-analyzing planetary nebulae images taken by the Hubble Space Telescope-and show how AutoBayes can be used to automate the implementation of the necessary anal- ysis programs. We initially follow the analysis described by Knuth and Hajian [KHO2] and use AutoBayes to derive code for the published models. We show the details of the code derivation process, including the symbolic computations and automatic integration of library procedures, and compare the results of the automatically generated and manually implemented code. We then go beyond the original analysis and use AutoBayes to derive code for a simple image segmentation procedure based on a mixture model which can be used to automate a manual preproceesing step. Finally, we combine the original approach with the simple segmentation which yields a more detailed analysis. This also demonstrates that AutoBayes makes it easy to combine different aspects of data analysis.
Tabulated pressure data are presented on the left lower wing surface of a scale model of the integrated space shuttle configuration. For Vol. 1, see N76-15246.
Pressure data are presented in tabular form on the left and right vertical tail surface of the space shuttle launch vehicle configuration model. For Vol. 3, see N75-23657.
Pressure data are presented in tabular form on the external tank and the SRM nozzle of the space shuttle launch vehicle configuration model. For Vol. 3, see N75-23657.
During the post-launch SMAP calibration and validation (Cal/Val) phase there are two objectives for each science data product team: 1) calibrate, verify, and improve the performance of the science algorithm, and 2) validate the accuracy of the science data product as specified in the science requirements and according to the Cal/Val schedule. This report provides an assessment of the SMAP Level 4 Surface and Root Zone Soil Moisture Passive (L4_SM) product specifically for the product's public beta release scheduled for 30 October 2015. The primary objective of the beta release is to allow users to familiarize themselves with the data product before the validated product becomes available. The beta release also allows users to conduct their own assessment of the data and to provide feedback to the L4_SM science data product team. The assessment of the L4_SM data product includes comparisons of SMAP L4_SM soil moisture estimates with in situ soil moisture observations from core validation sites and sparse networks. The assessment further includes a global evaluation of the internal diagnostics from the ensemble-based data assimilation system that is used to generate the L4_SM product. This evaluation focuses on the statistics of the observation-minus-forecast (O-F) residuals and the analysis increments. Together, the core validation site comparisons and the statistics of the assimilation diagnostics are considered primary validation methodologies for the L4_SM product. Comparisons against in situ measurements from regional-scale sparse networks are considered a secondary validation methodology because such in situ measurements are subject to upscaling errors from the point-scale to the grid cell scale of the data product. Based on the limited set of core validation sites, the assessment presented here meets the criteria established by the Committee on Earth Observing Satellites for Stage 1 validation and supports the beta release of the data. The validation against sparse network measurements and the evaluation of the assimilation diagnostics address Stage 2 validation criteria by expanding the assessment to regional and global scales.
This study aims to the validation of the left ventricular (LV) volume estimates obtained by processing volumetric data utilizing a segmentation model based on level set technique. The validation has been performed by comparing real-time volumetric echo data (RT3DE) and magnetic resonance (MRI) data. A validation protocol has been defined. The validation protocol was applied to twenty-four estimates (range 61-467 ml) obtained from normal and pathologic subjects, which underwent both RT3DE and MRI. A statistical analysis was performed on each estimate and on clinical parameters as stroke volume (SV) and ejection fraction (EF). Assuming MRI estimates (x) as a reference, an excellent correlation was found with volume measured by utilizing the segmentation procedure (y) (y=0.89x + 13.78, r=0.98). The mean error on SV was 8 ml and the mean error on EF was 2%. This study demonstrated that the segmentation technique is reliably applicable on human hearts in clinical practice.
Pressure data are presented in tabular form on the external tank base and SRM booster base of the space shuttle launch vehicle configuration model. For Vol. 3, see N75-23657.
Aerodynamic force data are presented in tables and graphs for the NASA Langley V/STOL Transition Research Wind Tunnel tests on a 0.04 scale model of the 747 with a 0.0405 scale Orbiter space shuttle. The investigation included the effects of flap setting, stabilizer angle, elevator angle, ground proximity, and Orbiter tailcone fairing. Data were obtained in the pitch plane only. The test was run at M = 0.15, with a dynamic pressure of 35 psf. Six static pressures were measured on each side of the 747 CAM nose to determine the effects of the Orbiter on the 747 airspeed and altitude indicators.
Using the ANSYS finite element program, a global model of the aft skirt and a detailed nonlinear model of the failure region was made. The analysis confirmed the area of failure in both STA-2B and STA-3 tests as the forging heat affected zone (HAZ) at the aft ring centerline. The highest hoop strain in the HAZ occurs in this area. However, the analysis does not predict failure as defined by ultimate elongation of the material equal to 3.5 percent total strain. The analysis correlates well with the strain gage data from both the Wyle influence test of the original design aft sjirt and the STA-3 test of the redesigned aft skirt. it is suggested that the sensitivity of the failure area material strength and stress/strain state to material properties and therefore to small manufacturing or processing variables is the most likely cause of failure below the expected material ultimate properties.
Through analyses of the model simulated data-base, we developed a technique to estimate surface soil moisture under HYDROS radar sensor (L-band multi-polarizations and 40deg incidence) configuration. This technique includes two steps. First, it decomposes the total backscattering signals into two components - the surface scattering components (the bare surface backscattering signals attenuated by the overlaying vegetation layer) and the sum of the direct volume scattering components and surface-volume interaction components at different polarizations. From the model simulated data-base, our decomposition technique works quit well in estimation of the surface scattering components with RMSEs of 0.12,0.25, and 0.55 dB for VV, HH, and VH polarizations, respectively. Then, we use the decomposed surface backscattering signals to estimate the soil moisture and the combined surface roughness and vegetation attenuation correction factors with all three polarizations.
Mass distribution properties of the adult human body are summarized. The summary is user-oriented for design engineers and mathematical modeling. Properties are dicussed in terms of the musculoskeletal linkage system, axes systems, mass, volume, center of mass, and inertial properties. Data and prediction equations or coefficients for modeling these properties are provided. Predictive formulas use total body weight and stature as independent variables. The data are based on small samples of living and cadaveric subjects typical of the white European male.
Pressure data are presented in tabular form on the external tank attach points and the external tank base rake of the space shuttle launch vehicle configuration model. For Vol. 3, see N75-23657.
Tabulated data from wind tunnel tests conducted to evaluate the aerodynamic performance of an advanced coannular exhaust nozzle for a future supersonic propulsion system are presented. Tests were conducted with two test configurations: (1) a short flap mechanism for fan stream control with an isentropic contoured flow splitter, and (2) an iris fan nozzle with a conical flow splitter. Both designs feature a translating primary plug and an auxiliary inlet ejector. Tests were conducted at takeoff and simulated cruise conditions. Data were acquired at Mach numbers of 0, 0.36, 0.9, and 2.0 for a wide range of nozzle operating conditions. At simulated supersonic cruise, both configurations demonstrated good performance, comparable to levels assumed in earlier advanced supersonic propulsion studies. However, at subsonic cruise, both configurations exhibited performance that was 6 to 7.5 percent less than the study assumptions. At takeoff conditions, the iris configuration performance approached the assumed levels, while the short flap design was 4 to 6 percent less. Data are provided through test run 25.
Tabulated aerodynamic data from coannular nozzle performance tests are given for test runs 26 through 37. The data include nozzle thrust coefficient parameters, nozzle discharge coefficients, and static pressure tap measurements.
Tabulated data from wind tunnel tests evaluating the aerodynamic performance of coannular exhaust nozzles are given for test runs 37 through 65.
Graphical data from wind tunnel tests of variable cycle engine coannular exhaust nozzles are given. Specifically, aerodynamic data for takeoff conditions are presented.