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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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Nonlinear behavior of second-order phase locked loop in presence of noise, discussing probability distribution of phase error, statistical properties of loop behavior, etc
Single and joint terminal slant path attenuation statistics at frequencies of 28.56 and 19.04 GHz were derived from radar data. Statistics were independently obtained for path angles of 20, 45, and 90 deg, in order to examine how path angle influences both single terminal and joint probability distributions. A prediction technique is demonstrated to work well for calculating both single and joint terminal distributions at other path angles. Diversity gains and autocorrelation function dependence on site spacing were determined employing the radar modeling results.
The evaluation of simulated TM data obtained on an ER-2 aircraft at twenty-five predesignated sample sites for mapping water quality factors such as conductivity, pH, suspended solids, turbidity, temperature, and depth, is discussed. Using a multiple regression for the seven TM bands, an equation is developed for the suspended solids. TM bands 1, 2, 3, 4, and 6 are used with logarithm conductivity in a multiple regression. The assessment of regression equations for a high coefficient of determination (R-squared) and statistical significance is considered. Confidence intervals about the mean regression point are calculated in order to assess the robustness of the regressions used for mapping conductivity, turbidity, and suspended solids, and by regressing random subsamples of sites and comparing the resultant range of R-squared, cross validation is conducted.
It has been shown by Brown and Emslie (1988) that any optically thin thermal bremsstrahlung source must emit an energy spectrum L(epsilon)(keV/s per keV) which has the property that higher derivatives alternate in sign. In this short note, this test is applied to the 'superhot' component discussed by Lin et al. (1981) in order to determine whether a strictly thermal interpretation of this component is valid. All statistically significant higher derivatives do indeed have the correct sign; this strengthens the identification of this component as due to a thermal source.
This paper presents a general framework for forecasting the smoothed maximum level of solar activity in a given cycle, based on a simple understanding of the solar dynamo. This type of forecasting requires knowledge of the sun's polar magnetic field strength at the preceding activity minimum. Because direct measurements of this quantity are difficult to obtain, the quality of a number of proxy indicators already used by other authors is evaluated, which are physically related to the sun's polar field. These indicators are subjected to a rigorous statistical analysis, and the analysis technique for each indicator is specified in detail in order to simplify and systematize reanalysis for future use. It is found that several of these proxies are in fact poorly correlated or uncorrelated with solar activity, and thus are of little value for predicting activity maxima. Also presented is a scheme in which the predictions of the individual proxies are combined via an appropriately weighted mean to produce a compound prediction. The scheme is then applied to the current cycle 22, and a maximum smoothed international sunspot number of 171 + or - 26 is estimated.
The reflection spectrum and density of the Martian satellite, Phobos, suggests that it may be a captured C-type asteroid and hence may contain relatively unaltered organic material from the early history of the solar system. Since there is evidence from isotopic anomalies that meteorites presumably derived from such asteroids contain preserved interstellar material, it is conceivable that evidence for interstellar matter might also be present on Phobos. The present research concerns interpretation of the chemical composition of Phobos' surface and analysis of images of Phobos taken by the FREGAT camera equipment aboard the Phobos 2 Spacecraft, including both disk-integrated and disk-resolved photometry. Because the surface of Phobos is a regolith, spectral information on the surface composition will be convolved with information on the surface texture. A primary goal was to untangle these effects. Unfortunately, the Phobos 1 Spacecraft lost contact with Earth during its transit to Mars, and Phobos 2 likewise lost contact after two months in orbit around Mars. Thus, the hovering phase of very close approach and the landing of instruments on the surface of Phobos did not take place, so no data on the elemental composition of the surface is available. However, 37 images were obtained of the satellite. The results include the first such observations at near infrared wavelengths, for which disk-integrated results show that the corresponding geometric albedo is quite small and very similar to the value in the blue. The resolved surface photometry was analyzed by developing new methods of radiative transfer in rough surface layers and a procedure referred to as statistical photoclinometry. The results provide root mean square surface slopes on scales from the order of 1 millimeter to 250 meters, albedo fluctuations, and corresponding correlation lengths.
A backscattering model for scattering from a randomly rough dielectric surface is developed based on an approximate solution of a pair of integral equations for the tangential surface fields. Both like and cross-polarized scattering coefficients are obtained. It is found that the like polarized scattering coefficients contain two types of terms: single scattering terms and multiple scattering terms. The single scattering terms in like polarized scattering are shown to reduce the first-order solutions derived from the small perturbation method when the roughness parameters satisfy the slightly rough conditions. When surface roughnesses are large but the surface slope is small, only a single scattering term corresponding to the standard Kirchhoff model is significant. If the surface slope is large, the multiple scattering term will also be significant. The cross-polarized backscattering coefficients satisfy reciprocity and contain only multiple scattering terms. The difference between vertical and horizontal scattering coefficients is found to increase with the dielectric constant and is generally smaller than that predicted by the first-order small perturbation model. Good agreements are obtained between this model and measurements from statistically known surfaces.
A neural network approach to the inversion of surface scattering parameters is presented. Simulated data sets based on a surface scattering model are used so that the data may be viewed as taken from a completely known randomly rough surface. The fast learning (FL) neural network and a multilayer perceptron (MLP) trained with backpropagation learning (BP network) are tested on the simulated backscattering data. The RMS error of training the FL network is found to be less than one half the error of the BP network while requiring one to two orders of magnitude less CPU time. When applied to inversion of parameters from a statistically rough surface, the FL method is successful at recovering the surface permittivity, the surface correlation length, and the RMS surface height in less time and with less error than the BP network. Further applications of the FL neural network to the inversion of parameters from backscatter measurements of an inhomogeneous layer above a half space are shown.
Current deterministic structural methods are easily applied to substructures and components, and analysts have built great design insights and confidence in them over the years. However, deterministic methods cannot support systems risk analyses, and it was recently reported that deterministic treatment of statistical data is inconsistent with error propagation laws that can result in unevenly conservative structural predictions. Assuming non-nal distributions and using statistical data formats throughout prevailing stress deterministic processes lead to a safety factor in statistical format, which integrated into the safety index, provides a safety factor and first order reliability relationship. The embedded safety factor in the safety index expression allows a historically based risk to be determined and verified over a variety of quasi-static metallic substructures consistent with the traditional safety factor methods and NASA Std. 5001 criteria.
The design, analysis, and demonstration of a digital-correlation microwave polarimeter for use in earth remote sensing is presented. We begin with an analysis of three-level digital correlation and develop the correlator transfer function and radiometric sensitivity. A fifth-order polynomial regression is derived for inverting the digital correlation coefficient into the analog statistic. In addition, the effects of quantizer threshold asymmetry and hysteresis are discussed. A two-look unpolarized calibration scheme is developed for identifying correlation offsets. The developed theory and calibration method are verified using a 10.7 GHz and a 37.0 GHz polarimeter. The polarimeters are based upon 1-GS/s three-level digital correlators and measure the first three Stokes parameters. Through experiment, the radiometric sensitivity is shown to approach the theoretical as derived earlier in the paper and the two-look unpolarized calibration method is successfully compared with results using a polarimetric scheme. Finally, sample data from an aircraft experiment demonstrates that the polarimeter is highly-useful for ocean wind-vector measurement.
This report describes work performed by MTC Technologies (MTCT) for NASA Glenn Research Center (GRC) under Contract NAS3-00178, Task Order No. 15. MTCT previously developed a first-generation empirical model that correlates the core/combustion noise of four GE engines, the CF6, CF34, CFM56, and GE90 for General Electric (GE) under Contract No. 200-1X-14W53048, in support of GRC Contract NAS3-01135. MTCT has demonstrated in earlier noise modeling efforts that the improvement of predictive modeling is greatly enhanced by an iterative approach, so in support of NASA's Quiet Aircraft Technology Project, GRC sponsored this effort to improve the model. Since the noise data available for correlation are total engine noise spectra, it is total engine noise that must be predicted. Since the scope of this effort was not sufficient to explore fan and turbine noise, the most meaningful comparisons must be restricted to frequencies below the blade passage frequency. Below the blade passage frequency and at relatively high power settings jet noise is expected to be the dominant source, and comparisons are shown that demonstrate the accuracy of the jet noise model recently developed by MTCT for NASA under Contract NAS3-00178, Task Order No. 10. At lower power settings the core noise became most apparent, and these data corrected for the contribution of jet noise were then used to establish the characteristics of core noise. There is clearly more than one spectral range where core noise is evident, so the spectral approach developed by von Glahn and Krejsa in 1982 wherein four spectral regions overlap, was used in the GE effort. Further analysis indicates that the two higher frequency components, which are often somewhat masked by turbomachinery noise, can be treated as one component, and it is on that basis that the current model is formulated. The frequency scaling relationships are improved and are now based on combustor and core nozzle geometries. In conjunction with the Task Order No. 10 jet noise model, this core noise model is shown to provide statistical accuracy comparable to the jet noise model for frequencies below blade passage. This model is incorporated in the NASA FOOTPR code and a user s guide is provided.
Shewhart control charts have been established as an expedient method for analyzing dynamic, trending data in order to identify anomalous subsystem performance as soon as such performance would exceed a statistically established baseline. Additionally, this leading indicator tool integrates a selection methodology that reduces false positive indications, optimizes true leading indicator events, minimizes computer processor unit duty cycles, and addresses human factor concerns (i.e., the potential for flight-controller data overload). This innovation leverages statistical process control, and provides a relatively simple way to allow flight controllers to focus their attention on subtle system changes that could lead to dramatic off-nominal system performance. Finally, this capability improves response time to potential hardware damage and/or crew injury, thereby improving space flight safety. Shewhart control charts require normalized data. However, the telemetry from the ISS Early External Thermal Control System (EETCS) was not normally distributed. A method for normalizing the data was implemented, as was a means of selecting data windows, the number of standard deviations (Sigma Level), the number of consecutive points out of limits (Sequence), and direction (increasing or decreasing trend data). By varying these options, and treating them like dial settings, the number of nuisance alerts and leading indicators were optimized. The goal was to capture all leading indicators while minimizing the number of nuisances. Lean Six Sigma (L6S) design of experiment methodologies were employed. To optimize the results, Perl programming language was used to automate the massive amounts of telemetry data, control chart plots, and the data analysis.
- Our primary objectives: Incorporate all A-Train water vapor, including small-scale, horizontal and vertical water vapor structure from MODIS (Moderate Resolution Imaging Spectroradiometer) and GPS (Global Positioning System); Use MODIS cloud classification that is collocated at the pixel-scale over the full AIRS/AMSU (Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit) swath; Extend summaries to PDFs (Probability Distribution Functions)/higher order moments of water vapor sorted by cloud classes; Establish robust, scale-dependent statistical relationships between cloud, temperature, and water vapor PDFs for the climate modeling community. - We will also: Update our current data record to new CloudSat/CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) cloud classification; Provide merged AIRS and MLS (Microwave Limb Sounder) water vapor profiles using averaging kernels; Include temperature and water vapor from models (ECMWF - European Centre for Medium-Range Weather Forecasts) and re-analyses (MERRA - Modern Era-Retrospective Analysis for Research and Applications); Classify METOP-A (Meteorological Operational Satellite-A) and NOAA (National Oceanic and Atmospheric Administration) satellite water vapor with AVHRR (Advanced Very High Resolution Radiometer) during SNOs (Solar Neutrino Oscillations); Compare with the NVAP-M (re-analysis and extension of the NASA Water Vapor Project (NVAP)) water vapor climatologies; Use existing data sources and mature algorithms, and document processing algorithms.
Lagrangian statistics and particle transport in edge plasma turbulence are investigated using the Hasegawa–Wakatani model and its modified version. The latter shows the emergence of pronounced zonal flows. Different values of the adiabaticity parameter are considered. The main goal is to characterize the role of coherent structures, i.e., vortices and zonal flows, and their impact on the Lagrangian statistics of particles. Computationally intensive long time simulations following ensembles of test particles over hundreds of eddy turnover times are considered in statistically stationary turbulent flows. The flow topology is characterized using the Lagrangian Okubo–Weiss criterion in order to split the flow into topologically different domains. In elliptic and hyperbolic regions, the probability density functions (PDFs) of the residence time have self-similar algebraic decaying tails. However, in the intermediate regions, the PDFs exhibit exponentially decaying tails. Topologically conditioned PDFs of the Lagrangian velocity, and acceleration and density fluctuations are likewise computed. The differences between the classical Hasegawa–Wakatani system and its modified version are assessed, and the role of zonal flows is highlighted. Here, the density flux spectrum, which characterizes the contributions of different length scales, is studied, and its inertial scaling is found to be in agreement with predictions based on dimensional arguments. Analyzing the angular change of particle tracers at different time scales, corresponding to coarse grained curvature, completes the study, and these multiscale geometric statistics quantify the directional properties of the particle motion in different flow regimes.
In situ measurements of stratospheric ozone and laboratory studies of ozone production in electric discharge through oxygen have shown previously that ozone containing heavy isotopes of oxygen (O-17, O-18) may be formed preferentially. In order to assess the relevance of thee latter experiment to the stratospheric measurements, detailed understanding of the effect of isotopic substitution on the O3 formation reaction O + O2 + M yields O3 + M and on the O atom exchange reaction O + O2 + O yields O2 + O is necessary. In this work, an estimate of the effect of isotopic substitution on the recombination rate is made by us of approximate dynamical theories and statistical mechanics. The results indicate the possibility of isotope effects on the O + O2 recombination rate of the order of several percent at stratospheric temperatures. In general, recombination reactions involving heavy (mass 49, 50) O3 formation are found to be slower than the reaction leading to normal (mass 48) O3 formation. The calculated isotope effects are sufficiently small that the uncertainties in the model input and the approximations in the dynamical theories will probably make the quantitative nature of these results subject to considerable uncertainty. This isotope effect should not be observable in the atmosphere given the precision of the current measurements but may be crucial in the understanding of the laboratory experiments, where observed enhancements are only of the order of several percent. Possible reasons for this discrepancy between the observed enhancement and predicted depletion are presented.
Some popular functions used to test global optimization algorithms have multiple local optima, all with the same value, making them all global optima. It is easy to make them more challenging by fortifying them via adding a localized bump at the location of one of the optima. In previous work the authors illustrated this for the Branin-Hoo function and the popular differential evolution algorithm, showing that the fortified Branin-Hoo required an order of magnitude more function evaluations. This paper examines the effect of fortifying the Branin-Hoo function on surrogate- based optimization, which usually proceeds by adaptive sampling. Two algorithms are considered. The EGO algorithm, which is based on a Gaussian process (GP) and an algorithm based on radial basis functions (RBF). EGO is found to be more frugal in terms of the number of required function evaluations required to identify the correct basin, but it is expensive to run on a desktop, limiting the number of times the runs could be repeated to establish sound statistics on the number of required function evaluations. The RBF algorithm was cheaper to run, providing more sound statistics on performance. A four-dimensional version of the Branin-Hoo function was introduced in order to assess the effect of dimensionality. Furthermore, it was found that the difference between the ordinary function and the fortified one was much more pronounced for the four-dimensional function compared to the two dimensional one.
Many rare weather events, including hurricanes, droughts, and floods, dramatically impact human life. To accurately forecast these events and characterize their climatology requires specialized mathematical techniques to fully leverage the limited data that are available. Here we describe transition path theory (TPT), a framework originally developed for molecular simulation, and argue that it is a useful paradigm for developing mechanistic understanding of rare climate events. TPT provides a method to calculate statistical properties of the paths into the event. As an initial demonstration of the utility of TPT, we analyze a low-order model of sudden stratospheric warming (SSW), a dramatic disturbance to the polar vortex that can induce extreme cold spells at the surface in the midlatitudes. SSW events pose a major challenge for seasonal weather prediction because of their rapid, complex onset and development. Climate models struggle to capture the long-term statistics of SSW, owing to their diversity and intermittent nature. We use a stochastically forced Holton–Mass-type model with two stable states, corresponding to radiative equilibrium and a vacillating SSW-like regime. In this stochastic bistable setting, from certain probabilistic forecasts TPT facilitates estimation of dominant transition pathways and return times of transitions. These “dynamical statistics” are obtained by solving partial differential equations in the model’s phase space. With future application to more complex models, TPT and its constituent quantities promise to improve the predictability of extreme weather events through both generation and principled evaluation of forecasts.
When performing a validation exercise for determining the upper subcritical limit of a nuclear criticality safety application, an analyst should select and perform a statistical analysis on a population of benchmark experiments that are neutronically similar to the application. The size of this population should be sufficiently large such that the statistical analysis has a high degree of confidence that the bias plus bias uncertainty (calculational margin) has been accurately quantified. A complication arises because many benchmark experiments share common components, leading to correlations in their measured effective multiplication factors. Correlations between benchmark experiments within the population reduces its predictive power. This motivates the need for methods that consider benchmark experiment correlations and ensure adequate statistical significance of results. The Whisper code is a statistical analysis pack- age that incorporates nuclear data sensitivity coefficients from MCNP to assess benchmark experiment similarity and then performs an extreme-value analysis to estimate the bias plus bias uncertainty. The original methodology in Whisper does not consider the effect of benchmark experiment correlations when making this estimation, and this summary proposes the uniformly ordered binary decision algorithm to address this shortcoming. The original methodology in Whisper computes similarity coefficients ck for an application compared to all benchmark experiments in its library and develops weighting factors for a selected population proportional to the ck values. The methodology can be interpreted as statistically emulating a validation exercise for a particular application where the weighting factors may be viewed as the likelihood that an analyst would include a particular benchmark experiment within the population. The effective sample size of the population is the expected or mean number of benchmark experiments in the population. The uniformly ordered binary decision algorithm identifies clusters of correlated benchmark experiments within the population and then computes adjusted weighting factors based on the magnitude of the correlation coefficients within the cluster to compute a reduced effective sample size accounting for the lower information content because of correlations. Benchmark experiments within the cluster are ordered randomly with equal probability and probabilistic decisions are made as to whether a benchmark. experiment within the cluster should treated as redundant with a previous one; if two redundant benchmark experiments are included, then the conservative worst case bias plus bias uncertainty is used and the pair is counted as a single benchmark experiment in the population. Results are provided for HEU solutions in a research version of the Whisper software using benchmark experiment correlations provided by DICE, the Database for the International Criticality Safety Benchmark Evaluation Project (ICSBEP). These show that there can be a significant increase in the bias plus bias uncertainty because the effective sample size is reduced, and therefore the algorithm, needing to meet sample size requirements, expands the benchmark experiment population by accepting less similar benchmark experiments that would have otherwise not been included.