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Pb, Sr, Nd and Hf isotopic compositions of Oahu volcanics indicate that the three principal volcanic series on Oahu Koolau, Honolulu and Waianae - were derived from isotopically distinct sources. Honolulu and Waianae basalts plot on the Nd-Pb-Sr 'mantle plane' whereas Koolau data plot distinctly below the plane.
A wind tunnel investigation was conducted in the Langley Unitary Plan Wind Tunnel to determine the effectiveness of a technique to measure aircraft sonic boom signatures using a single conical survey probe while continuously moving the model past the probe. Sonic boom signatures were obtained using both move-pause and continuous data acquisition methods for comparison. The test was conducted using a generic business jet model at a constant angle of attack and a single model-to-survey-probe separation distance. The sonic boom signatures were obtained at a Mach number of 2.0 and a unit Reynolds number of 2 million per foot. The test results showed that it is possible to obtain sonic boom signatures while continuously moving the model and that the time required to acquire the signature is at least 10 times faster than the move-pause method. Data plots are presented with a discussion of the results. No tabulated data or flow visualization photographs are included.
A wind tunnel investigation was conducted in the Langley Unitary Plan Wind Tunnel (UPWT) to determine the effectiveness of a wedge probe to measure sonic boom pressure signatures compared to a slender conical probe. A generic business jet model at a constant angle of attack and at a single model to probe separation distance was used to generate a sonic boom signature. Pressure signature data were acquired with both the wedge probe and a slender conical probe for comparison. The test was conducted at a Mach number of 2.0 and a free-stream unit Reynolds number of 2 million per foot. The results showed that the wedge probe was not effective in measuring the sonic boom pressure signature of the aircraft model in the supersonic wind tunnel. Data plots and a discussion of the results are presented. No tabulated data or flow visualization photographs are included.
The research results described in this paper presents a new understanding of the behavior of fatigue crack growth in the threshold region. It is believed by some crack growth experts that the ASTM load shedding test method does not produce true or valid threshold properties. The concern involves the observed fanning of threshold region da/dN data plots for some materials in which the low R-ratio data fans out or away from the high R-ratio data. This data fanning or elevation of threshold values is obviously caused by an increase in crack closure in the low R-ratio tested specimens. This increase in crack closure is assumed by some investigators to be caused by a plastic wake on the crack surfaces that was created during the load shedding test phase. This study shows that the increase in crack closure is the result of an extensive occurrence of crack bifurcation behavior in some materials, particularly in aluminum alloys, when the crack tip cyclic yield zone size becomes less than the grain size of the alloy. This behavior is related to the high stacking fault energy (SFE) property of aluminum alloys which results in easier slip characteristics. Therefore, the particular fanning behavior in aluminum alloys is a function of intrinsic dislocation property of the materials and that the fanned data represents valid material properties. However, for corrosion sensitive steel alloys used in this study the fanning was caused by a build-up of iron oxide at the crack tip from fretting corrosion.
Routine called CRTRPM meets needs of applications programer to plot data in interactive environment on Tektronix graphics terminal. CRTRPM designed specifically for applications where data is viewed and responded to at terminal. CRTRPM produces from one to four grids on terminal screen at one time, with from one to ten plots of X-Y data on each grid. CRTRPM written in FORTRAN V for interactive execution.
Forest structure and composition regulate a range of ecosystem services, including biodiversity, water and nutrient cycling, and wood volume for resource extraction. Forest type is an important metric measured in the US Forest Service Forest Inventory and Analysis (FIA) program, the national forest inventory of the USA. Forest type information can be used to quantify carbon and other forest resources within specific domains to support ecological analysis and forest management decisions, such as managing for disease and pests. In this study, we developed a methodology that uses a combination of airborne hyperspectral and lidar data to map FIA-defined forest type between sparsely sampled FIA plot data collected in interior Alaska. To determine the best classification algorithm and remote sensing data for this task, five classification algorithms were tested with six different combinations of raw hyperspectral data, hyperspectral vegetation indices, and lidar-derived canopy and topography metrics. Models were trained using forest type information from 632 FIA subplots collected in interior Alaska. Of the thirty model and input combinations tested, the random forest classification algorithm with hyperspectral vegetation indices and lidar-derived topography and canopy height metrics had the highest accuracy (78% overall accuracy). This study supports random forest as a powerful classifier for natural resource data. It also demonstrates the benefits from combining both structural (lidar) and spectral (imagery) data for forest type classification.
Present requirements for realistic solar energy system evaluations necessitate a comprehensive body of solar-radition data. The data should include both diffuse and direct solar radiation as well as their total on an hourly (or shorter) basis. In general, however, only the total solar radiation values were recorded. This report presents a correlation that relates the diffuse component of an hourly total solar radiation value to the total radiation ratio of the maximum value attainable. The data used were taken at the Blue Hill Observatory in Milton, Massachusetts, for the period 1952. The relation - in the form of the data plots - can be used in situations in which only the hourly total radiation data are available but the diffuse component is desired.
This paper presents the utility of multipolarization Synthetic Aperture Radar (SAR) data for surface feature delineation and forest vegetation characterization. Three channels of radioed data (VV/HH, VH/HH, and VH/VV) are generated from the HH, VV, and VH polarization data (V = vertical, H = horizontal). The radioed data are linearly stretched to yield a digital number within a range of 0 to 255. The techniques for reducing SAR speckle noise and for measuring the degree of separation are discussed. For surface feature delineation, the results indicate that cross polarization as well as cross polarization radioed data better delineate those surface features that are difficult to separate by like polarization data. The results suggest using a median value filtering technique to reduce within-plot data fluctuation to increase the separability measure. For forest vegetation characterization, the results indicate that multipolarization SAR data may be used to estimate forest properties such as total-tree biomass, basal area, and tree height.
One common characteristic of the USMP-4 experiments is that various effects of gravity make it difficult, if not impossible, to achieve usable results when performing the experiments on Earth's surface. Therefore, the investigators took advantage of the microgravity environment afforded by being in low-Earth orbit to perform their research. Interpretation of the experiment results both during the mission and upon post-mission analyses of data and samples required an understanding of the microgravity environment in which the experiments were conducted. To achieve that understanding, data were collected using the Orbital Acceleration Research Experiment (OARE) and two Space Acceleration Measurement Systems (SAMS). Data from those systems, combined with an assessment of mission and experiment activities, were used to characterize the microgravity environment that existed on Columbia during the mission. The text herein gives details about some characteristics of the environment that were noted during the mission and during post-mission data analysis. The disturbances studied include the Ku-band antenna 17 Hz dither; the effect of changing the Orbiter attitude deadband limits; the effects of different bicycle ergometer configurations; and the effect of IDGE (Isothermal Dendritic Growth Experiment) experiment fans and SAMS computer hard drives. Additional information about the microgravity environment is provided. Supplementary data plots representing the environment throughout the majority of the mission are available at the Uniform Resource Locator (URL). Data files for both SAMS and OARE are accessible via anonymous file transfer protocol from the file server.
This invention is a TecPlot macro of a computer program in the TecPlot programming language that processes data from DPLR solutions in TecPlot format. DPLR (Data-Parallel Line Relaxation) is a NASA computational fluid dynamics (CFD) code, and TecPlot is a commercial CFD post-processing tool. The Tec- Plot data is in SI units (same as DPLR output). The invention converts the SI units into British units. The macro modifies the TecPlot data with unit conversions, and adds some extra calculations. After unit conversions, the macro cuts a slice, and adds vectors on the current plot for output format. The macro can also process surface solutions. Existing solutions use manual conversion and superposition. The conversion is complicated because it must be applied to a range of inter-related scalars and vectors to describe a 2D or 3D flow field. It processes the CFD solution to create superposition/comparison of scalars and vectors. The existing manual solution is cumbersome, open to errors, slow, and cannot be inserted into an automated process. This invention is quick and easy to use, and can be inserted into an automated data-processing algorithm.
Detailed orbiter aerodynamic and aeroacoustic pressure data were obtained in a three-part experimental investigation (OA-310A, B and C). The tests were conducted in three NASA facilities: OA-310A in the Ames 11x11-foot Transonic Wind Tunnel; OA-310B in the Lewis 8x6-foot Supersonic Wind Tunnel; and OA-310C in the Lewis 10x10-foot Supersonic Wind Tunnel. Test data were obtained to support analysis of the Space Transportation System (STS)-6 advanced flexible reusable surface insulation (AFRSI) anomaly using the 0.035-scale space shuttle vehicle pressure-loads Model 84-0. Data were obtained in the areas of the orbiter where AFRSI is to be applied to OV-099 and OV-103. Emphasis was placed on acquiring detailed aeroacoustic data and time-averaged pressure distributions on five affected areas: (1) canopy; (2) side of fuselage; (3) upper surface of wing; (4) OMS pods; and (5) vertical tail. Data were obtained at nominal ascent and entry atmospheric flight trajectory conditions between M=0.6 through M-3.5. Sample plotted data are given. aba M.G.
The object of this project was to develop and calibrate quantitative models for predicting the quality of software. Reliable flight and supporting ground software is a highly important factor in the successful operation of the space shuttle program. The models used in the present study consisted of SMERFS (Statistical Modeling and Estimation of Reliability Functions for Software). There are ten models in SMERFS. For a first run, the results obtained in modeling the cumulative number of failures versus execution time showed fairly good results for our data. Plots of cumulative software failures versus calendar weeks were made and the model results were compared with the historical data on the same graph. If the model agrees with actual historical behavior for a set of data then there is confidence in future predictions for this data. Considering the quality of the data, the models have given some significant results, even at this early stage. With better care in data collection, data analysis, recording of the fixing of failures and CPU execution times, the models should prove extremely helpful in making predictions regarding the future pattern of failures, including an estimate of the number of errors remaining in the software and the additional testing time required for the software quality to reach acceptable levels. It appears that there is no one 'best' model for all cases. It is for this reason that the aim of this project was to test several models. One of the recommendations resulting from this study is that great care must be taken in the collection of data. When using a model, the data should satisfy the model assumptions.
Recent updates to a geographical information system (GIS) called VICAR (Video Image Communication and Retrieval)/IBIS are described. The system is designed to handle data from many different formats (vector, raster, tabular) and many different sources (models, radar images, ground truth surveys, optical images). All the data are referenced to a single georeference plane, and average or typical values for parameters defined within a polygonal region are stored in a tabular file, called an info file. The info file format allows tracking of data in time, maintenance of links between component data sets and the georeference image, conversion of pixel values to `actual' values (e.g., radar cross-section, luminance, temperature), graph plotting, data manipulation, generation of training vectors for classification algorithms, and comparison between actual measurements and model predictions (with ground truth data as input).
A computer program has been developed that analyzes Deep Space Network monitor data, looking for changes of trends in critical parameters. This program represents a significant improvement over the previous practice of manually plotting data and visually inspecting the resulting graphs to identify trends. This program uses proven numerical techniques to identify trends. When a statistically significant trend is detected, then it is characterized by means of a symbol that can be used by pre-existing model-based reasoning software. The program can perform any of the following functions: Given an expectation that data in a given list should exhibit an upward, downward, constant, or unknown trend, it can determine whether the data do or do not follow such a trend. Given a list of data, it can identify which of the aforementioned trends the data follow. Given two lists of data, it can determine whether or not both follow the same trend. This program can be executed on a variety of computers. It can be distributed in either source code or binary code form. It must be run in conjunction with any one of a number of Lisp compilers that are available commercially or as shareware.
Continuous atmospheric data analysis is an important factor for space launch vehicle design and operations. The balloon quality control tool was developed by NASA’s Marshall Space Flight Center (MSFC) Natural Environments Branch (NEB) for monitoring quality control processes and verifying the automated flags created on the balloon data sets analyzed. The data sets currently analyzed are comprised of high-resolution and low-resolution balloon data from NASA Kennedy Space Center (KSC), co-located on the United States Air Force’s Eastern range (ER) at the Cape Canaveral Air Force Station. The NEB was tasked to perform a quality assessment of these data sets and needed a tool to confirm the quality control (QC) flags produced from an automated process and add additional QC flags if necessary. This Graphical User Interface (GUI) was developed to visualize all of the data from these balloon sets, display any flags from the automated QC process, and add additional flags to variables if necessary. The GUI was developed in Python 3.6 utilizing different packages available such as pandas for data analysis and manipulation, NumPy for high-performance multidimensional array and tools to compute with and manipulate arrays, Matplotlib for plotting data and Tkinter to build the GUI.
Continuous atmospheric data analysis is an important factor for space launch vehicle design and operations. The balloon quality control tool was developed by NASA’s Marshall Space Flight Center (MSFC) Natural Environments Branch (NEB) for monitoring quality control processes and verifying the automated flags created on the balloon data sets analyzed. The data sets currently analyzed are comprised of high-resolution and low-resolution balloon data from NASA Kennedy Space Center (KSC), co-located on the United States Air Force’s Eastern range (ER) at the Cape Canaveral Air Force Station. The NEB was tasked to perform a quality assessment of these data sets and needed a tool to confirm the quality control (QC) flags produced from an automated process and add additional QC flags if necessary. This Graphical User Interface (GUI) was developed to visualize all of the data from these balloon sets, display any flags from the automated QC process, and add additional flags to variables if necessary. The GUI was developed in Python 3.6 utilizing different packages available such as pandas for data analysis and manipulation, NumPy for high-performance multidimensional array and tools to compute with and manipulate arrays, Matplotlib for plotting data and Tkinter to build the GUI.
Weightless conditions of space flight accelerate bone loss. There are no reports to date that address whether the bone that is lost during spaceflight could ever be recovered. Spaceinduced bone loss in astronauts is evaluated at the Johnson Space Center (JSC) by measurement of bone mineral density (BMD) by Dual-energy x-ray absorptiometry (DXA) scans. Astronauts are routinely scanned preflight and at various time points postflight (greater than or equal to Return+2 days). Two sets of BMD data were used to model spaceflight-induced loss and skeletal recovery in crewmembers following long-duration spaceflight missions (4-6 months). Group I was from astronauts (n=7) who were systematically scanned at multiple time points during the postflight period as part of a research protocol to investigate skeletal recovery. Group II came from a total of 49 sets of preflight and postflight data obtained by different protocols. These data were from 39 different crewmembers some of whom served on multiple flights. Changes in BMD (between pre- and postflight BMD) were plotted as a function of time (days-after-landing); plotted data were fitted to an exponential equation which enabled estimations of i) BMD change at day 0 after landing and ii) the number of days by which 50% of the lost bone is recovered (half-life). These fits were performed for BMD of the lumbar spine, trochanter, pelvis, femoral neck and calcaneus. There was consistency between the models for BMD recovery. Based upon the exponential model of BMD restoration, recovery following long-duration missions appears to be substantially complete in crewmembers within 36 months following return to Earth.
Introduction: The loss of bone mineral in astronauts during spaceflight has been investigated throughout the more than 40 years of bone research in space. Consequently, it is a medical requirement at NASA that changes in bone mass be monitored in crew members by measurements of bone mineral density (BMD) with dual-energy x-ray absorptiometry (DXA). This report is the first to evaluate medical data to address the recovery of bone mineral that is lost during spaceflight. Methods: DXA scans are performed before and after flight in astronauts who serve on long-duration missions (4-6 months) to ensure that medical standards for flight certification are met, to evaluate the effects of spaceflight and to monitor the restoration to preflight BMD status after return to Earth. Through cooperative agreements with the Russian Space Agency, the Bone and Mineral Lab at NASA Johnson Space Center (Houston, TX), also had access to BMD data from cosmonauts who had flown on long-duration missions yielding data from a total of 45 individual crew members. Changes in BMD (between 56 different sets of pre- and postflight measurements) were plotted as a function of time (days after landing); plotted data were fitted to an exponential mathematical model that determined i) BMD change at day 0 after landing and ii) the number of days after which 50% of the lost bone was recovered ("Recovery Half-Life"). These fits were performed for BMD of the lumbar spine, trochanter, pelvis, femoral neck and calcaneus. Results: In sum, averaged losses of bone mineral after spaceflight ranged between 2-9% for sites in the axial and appendicular skeleton. The fitted postflight BMD values predicted a 50% recovery of bone loss for all sites within 9 months.