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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 activities at Penn State University are described. During the period Oct. 1990 to Dec. 1991 work on HEAO-1 analysis of the Low Energy Detectors (LED) concentrated on using the improved detector spectral simulation model and fitting diffuse x-ray background spectral data. Spectral fitting results, x-ray point sources, and diffuse x-ray sources are described.
STS-93 was launched on July 23, 1999. There was an anomaly at the end of the launch in that the main engines shut down 0.16 second early because sensors detected a low level of oxidizer in the LOX tank (actually the duct from the tank to the vehicle). This resulted in a cutoff velocity for the vehicle that was 16 ft/sec low. It should have been 25872 ft/sec. The OMS engines were subsequently used to achieve the proper orbit. An investigation was immediately initiated into the cause of this LOX tank low level cutoff. It was noticed during the launch that the turbine temperatures for Main Engine 3 (E2019) were approximately 100 F higher than the preflight prediction. Linear Engine Model matching of the data indicated that a nozzle leak best fit the data. Post launch review of the data showed, that at approximately five seconds into the start, numerous parameters indicated small anomalous shifts. These shifts were all consistent with a rupture of nozzle tubes. Post launch review of the films showed that just after Space Shuttle Main Engine (SSME) ignition and just prior to liftoff a streak is seen in the exhaust plume of E2019. Just after liftoff the streak can be seen emanating from the nozzle wall. This photo confirmed that a leak was coming from the nozzle tubes. Based on the photo, the axial location of the leak was estimated to be 28 in. from the aft end of the nozzle and in line with nozzle coolant feed line #1. The streak continued to be visible during the launch.
Atmospheric temperature retrievals from TES observed radiances make possible the most complete separation of the constituent wave modes evident in Mars atmosphere to date. We use all of the data from the pre-mapping mission phase, which affords good sampling of the diurnal tides and stationary waves. TES retrievals of atmospheric temperature on a grid of pressure levels are the fundamental data set in this study. We then fit this data to selected fourier modes in longitude and time for latitude and L(sub s) bins. From this we have identified the amplitudes and phases of the diurnal and semi-diurnal tides, the first few (gravest) stationary waves and standing waves, as well as an estimate of the zonal and time mean temperature meridional cross sections. These results will be compared with existing models and theory. A possible critical layer for the sun-synchronous diurnal tide may indicate 40 m/s surface zonal wind near 50S for L(sub s) =255-285. Additional information is contained in the original extended abstract.
This quarter was largely devoted to a detailed study of temperature data acquired by the Cryogenic Limb Array Etalon Spectrometer (CLAES) on UARS. Our analysis used the same sequence of methods that have been developed, tested and refined on a more limited subset of temperature data acquired by the CRISTA instrument. We focused on a limited subset of our reasoning that geographical and vertical trends in the small-scale temperature variability could be compared with similar trends observed in November 1994 by the CRISTA-SPAS satellite. Results, backed up with hindcasts from the Mountain Wave Forecast Model (MWFM), reveal strong evidence of mountain waves, most persuasively in the Himalayas on 16-17 November, 1992. These CLAES results are coherent over the 30-50 km range and compare well with MWFM hindcasts for the same period. This constitutes, we believe, the first clear evidence that CLAES explicitly resolved long wavelength gravity waves in its CO2 temperature channel. A series of other tasks, related to mesoscale modeling of mountain waves in CRISTA data and fitting of ground-based and HRDI data on global scales, were seen through to publication stage in peer-reviewed journals.
The Multi-Mission Maximum Likelihood framework (threeML)is a flexi-ble python-based framework for multiwavelength data analysis in astronomy. ThreeMLallows joint likelihood fits of data recorded by many different instruments, from radio to gamma rays. This is achieved by encapsulating data access into instrument-specific plugins, leaving the rest of the analysis agnostic of the data format. In this paper, I out-line threeML’s design and major components, with a focus on the modeling language(astromodels) and the data-access plugins
The considered research had the objective to design a statistical model that could perform an error analysis of curve fits of wind tunnel test data using analysis of variance and regression analysis techniques. Four related subproblems were defined, and by solving each of these a solution to the general research problem was obtained. The capabilities of the evolved true statistical model are considered. The least squares fit is used to determine the nature of the force, moment, and pressure data. The order of the curve fit is increased in order to delete the quadratic effect in the residuals. The analysis of variance is used to determine the magnitude and effect of the error factor associated with the experimental data.
The goal of the BioSentinel small satellite payload is to measure the effect of deep space radiation on the growth and metabolic activity of yeast cells. Raw test data is generated by fluidics cards containing yeast cells rehydrated at different periods, with metabolic activity measured by the reduction of alamarBlue. Each card well has a sensor array that measures the amount of red, green, and infrared light transmitted through the yeast culture. This illumination data is then converted to absorbance values, which are further converted into concentrations. The ultimate objective is to convert these concentrations into biologically-relevant metrics that can be compared against one another to determine changes due to differential radiation exposure. Beginning with IR absorbance data (corresponding to cell density) from ground studies, three parameters from a sigmoidal growth curve were extracted and analyzed: 𝜆 (lag phase), 𝜇 (max growth rate), and A (max cell growth). The data was fit to the Gompertz model of microbial growth using non-linear regression (Minitab), as the fit error was reduced compared to the simpler logistic growth curve. Graphs showed that the data contained a discrepancy (drift) in the lag phase that is attributable to a slow, constant loss of moisture. Correcting this discrepancy by fitting the first 25 hours of the data to a power function and subtracting these values from the absorbance readings obtained a better statistical fit to the growth curve in the lag phase. A power fit was selected over a linear fit because it reflected the effects of constant volume loss. This correction to the BioSentinel data analysis pipeline will enable quantitative statistical analysis of the effect of different levels of deep space radiation on yeast cells. Future work includes automation of drift correction and curve modeling to extract these parameters directly from data.
The author has identified the following significant results. Three small scales of CIR photography were interpreted to determine the number of bark beetle-killed trees detected in each of six spot size categories. A procedure was developed to predict the probability of detecting spots in each spot size category and in turn to estimate the number of infestations and dead trees even on the smallest scale. Statistical tests of the data indicated that the linear model did not fit the data and that other models should be tested. As a result of daily monitoring of Black Hills radiometric instruments it was possible to show the spectral energy relationships in the ponderosa pine ecosystems over time. These data have been helpful for comparison with radiance signatures extracted from ERTS-1 bulk 70mm using precision microdensitometry. Effects of atmospheric interference were shown by a 30 percent increase in scene radiance on channel 4 of the satellite imagery. A calibration and scaling technique was developed and tested to enable interpretation of ERTS-1 bulk and precision data for the Atlanta test site. The technique includes calibration of a photographic copy system for the I2S image combiner and the production of scaled overlays of grid coordinate systems, study area locations, and outline maps of county boundaries.
A criterion for characterizing an iteratively trained classifier is presented. The criterion is based on an information theoretic measure that is developed from modeling classifier training iterations as a set of cascaded channels. The criterion is formulated as a figure of merit and as a performance index to check the appropriateness of application of the characterized classifier to an unknown data base and for implementing classifier updates and data selection respectively.
A criterion for characterizing an iteratively trained classifier is presented. The criterion is based on an information theoretic measure that is developed from modeling classifier training iterations as a set of cascaded channels. The criterion is formulated as a figure of merit and as a performance index to check the appropriateness of application of the characterized classifier to an unknown data base and for implementing classifier updates and data selection, respectively.
An approach toward low-pass, high-pass and band-pass filtering is presented. Convolution coefficients possessing the filtering speed associated with a moving smoothing average without suffering a loss of resolution are discussed. Resolution was retained because the coefficients represented the equivalance of applying high order two-dimensional regression calculations to an image without considering the time-consuming summations associated with the usual normal equations. The smoothing (low-pass) and roughing (high-pass) aspects of the filters are a result of being derived from regression theory. The coefficients are universal integer valves completely described by filter size and surface order, and possess a number of symmetry properties. Double convolution lead to a single set of coefficients with an expanded mask which can yield band-pass filtering and the surface normal. For low order surfaces (0,1), the two-dimensional convolute integers were equivalent to a moving smoothing average.
The extraction of frequency, damping, amplitude, and phase information from unforced transient response data is considered. These quantities are obtained from the parameters determined by fitting the digitized time-history data in a least-squares sense with complex exponential functions. The highlights of the method are described, and the results of several test cases are presented. The effects of noise are considered both by using analytical examples with random noise and by estimating the standard deviation of the parameters from maximum-likelihood theory.
X-ray spectral observations of two nearby low-luminosity clusters of galaxies are presented. For the Centaurus cluster an emission feature at 7.9 keV is detected at about one-third of the strength of the 6.7 keV line. This higher energy line represents K-beta emission from highly ionized iron. In addition, it is demonstrated that for neither the Centaurus nor the A1060 cluster can an isothermal model with an Fe emission line adequately fit the data. Instead, the simplest models which provide acceptable fits include a second, harder component which may be either a second exponential or a power law. The implications of the two-component nature of the continuum on the Fe abundance and the X-ray surface-brightness distribution are discussed.
Prior information for the stationary sea surface topography (SST) may be needed in altimetric solutions that intend to simultaneously improve the gravity field and determine the SST. For this purpose the oceanographically derived SST estimates are represented by a spherical harmonic expansion. The spherical harmonic coefficients are computed from a least squares adjustment of the data covering the majority of the oceanic regions of the world. Several tests are made to determine the optimum maximum degree of solution and the best configuration of the geometry of the data in order to obtain a solution that fits the data and also provides a good spectral representation of the SST.
Techniques are developed for quantitative retrievals of high spatial resolution column atmospheric water vapor that is largely contained in the lower portion of the troposphere. One method consists of curve fitting observed spectra with simulated spectra in the 1.14 microns or the 0.94 micron water vapor band absorption region. The other method is a simple band ratioing technique, which requires less computer time than the curve fitting method. The advantage of the technique over humidity sounding by IR emission measurements is that the retrieved column water vapor amounts over land surfaces have significantly higher precision.
The O2 and N2 pressure-broadening parameters of the 4(1,4)-3(2,1) rotational transition in the ground vibrational state of H2O have been measured in the temperature range between 100 and 520 K. Above 250 K the measurements were made in an equilibrium cell. Below 250 K a cell, which uses collisional cooling to circumvent the temperature limits imposed by the vapor pressure of the sample gas, was used. The data were fitted to an exponential temperature-dependence for data above 150 K with resultant n values of 0.81(3) for O2 and 0.70(3) for N2, where the entries in parentheses represent the error in the last significant figure of the parameter. Below 150 K the measured pressure-broadening parameters are smaller than those calculated using these values of n.
The Near Infrared Mapping Spectrometer (NIMS) has obtained spectra of volcanoes on the surface of the jovian satellite Io.
This paper demonstrates the use of two Bayesian statistical models to analyze single-event sonic boom exposure and human annoyance data from community response surveys. Each model is fit to data from a NASA pilot study.Unlike many community noise surveys, this study used a panel sample to collect multiple observations per participant instead of a single observation. Thus, a multilevel (also known as hierarchical or mixed-effects) model is used to account for the within-subject correlation in the panel sample data. This paper describes a multilevel logistic regression model and a multilevel ordinal regression model. The paper also proposes a method for calculating a summary dose-response curve from the multilevel models that represents the population. The two models’ summary dose-response curves are visually similar. However, their estimates differ when calculating the noise dose at a fixed percent highly annoyed.