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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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At least 181 records · Page 10

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NASA NTRS1979

Aircraft identification experience

Important aspects of estimating the unknown coefficients of the aircraft equations of motion from dynamic flight data are presented. The primary topic is the application of the maximum likelihood estimation technique. Basic considerations that must be addressed in the estimation of stability and control derivatives from conventional flight maneuvers are discussed. Some complex areas of estimation (such as estimation in the presence of atmospheric turbulence, estimation of acceleration derivatives, and analysis of maneuvers where both kinematic and aerodynamic coupling are present) are also discussed.

Iliff, K. W.↗

NASA NTRS1980

Cycles till failure of silver-zinc cells with completing failures modes: Preliminary data analysis

One hundred and twenty nine cells were run through charge-discharge cycles until failure. The experiment design was a variant of a central composite factorial in five factors. Preliminary data analysis consisted of response surface estimation of life. Batteries fail under two basic modes; a low voltage condition and an internal shorting condition. A competing failure modes analysis using maximum likelihood estimation for the extreme value life distribution was performed. Extensive diagnostics such as residual plotting and probability plotting were employed to verify data quality and choice of model.

Sidik, S. M.↗

NASA NTRS1981

Incorporating partially identified sample segments into acreage estimation procedures: Estimates using only observations from the current year

Several methods of estimating individual crop acreages using a mixture of completely identified and partially identified (generic) segments from a single growing year are derived and discussed. A small Monte Carlo study of eight estimators is presented. The relative empirical behavior of these estimators is discussed as are the effects of segment sample size and amount of partial identification. The principle recommendations are (1) to not exclude, but rather incorporate partially identified sample segments into the estimation procedure, (2) try to avoid having a large percentage (say 80%) of only partially identified segments, in the sample, and (3) use the maximum likelihood estimator although the weighted least squares estimator and least squares ratio estimator both perform almost as well. Sets of spring small grains (North Dakota) data were used.

Sielken, R. L., Jr.↗

NASA NTRS1983

Estimating time and time-lag in time-of-flight velocimetry

Estimating time and time-lag in time-of-flight velocimeters is investigated. Statistics of a filtered Poisson point process is given. A Maximum Likelihood Estimator is compared with suboptimum estimators in terms of robustness. For a dominating background combined spatial and temporal processing can improve the robustness compared with purely temporal processing. Schemes for the spatial filters are given.

Lading, L.↗

NASA NTRS1991

The pulse-pair algorithm as a robust estimator of turbulent weather spectral parameters using airborne pulse Doppler radar

The pulse pair method for spectrum parameter estimation is commonly used in pulse Doppler weather radar signal processing since it is economical to implement and can be shown to be a maximum likelihood estimator. With the use of airborne weather radar for windshear detection, the turbulent weather and strong ground clutter return spectrum differs from that assumed in its derivation, so the performance robustness of the pulse pair technique must be understood. Here, the effect of radar system pulse to pulse phase jitter and signal spectrum skew on the pulse pair algorithm performance is discussed. Phase jitter effect may be significant when the weather return signal to clutter ratio is very low and clutter rejection filtering is attempted. The analysis can be used to develop design specifications for airborne radar system phase stability. It is also shown that the weather return spectrum skew can cause a significant bias in the pulse pair mean windspeed estimates, and that the poly pulse pair algorithm can reduce this bias. It is suggested that use of a spectrum mode estimator may be more appropriate in characterizing the windspeed within a radar range resolution cell for detection of hazardous windspeed gradients.

Baxa, Ernest G., Jr.↗

DOE OSTI2024

Neural simulation-based inference of the neutron star equation of state directly from telescope spectra

Neutron stars provide a unique opportunity to study strongly interacting matter under extreme density conditions. The intricacies of matter inside neutron stars and their equation of state are not directly visible, but determine bulk properties, such as mass and radius, which affect the star's thermal X-ray emissions. However, the telescope spectra of these emissions are also affected by the stellar distance, hydrogen column, and effective surface temperature, which are not always well-constrained. Uncertainties on these nuisance parameters must be accounted for when making a robust estimation of the equation of state. In this study, we develop a novel methodology that, for the first time, can infer the full posterior distribution of both the equation of state and nuisance parameters directly from telescope observations. This method relies on the use of neural likelihood estimation, in which normalizing flows use samples of simulated telescope data to learn the likelihood of the neutron star spectra as a function of these parameters, coupled with Hamiltonian Monte Carlo methods to efficiently sample from the corresponding posterior distribution. Our approach surpasses the accuracy of previous methods, improves the interpretability of the results by providing access to the full posterior distribution, and naturally scales to a growing number of neutron star observations expected in the coming years.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2014

Data Analysis & Statistical Methods for Command File Errors

This paper explains current work on modeling for managing the risk of command file errors. It is focused on analyzing actual data from a JPL spaceflight mission to build models for evaluating and predicting error rates as a function of several key variables. We constructed a rich dataset by considering the number of errors, the number of files radiated, including the number commands and blocks in each file, as well as subjective estimates of workload and operational novelty. We have assessed these data using different curve fitting and distribution fitting techniques, such as multiple regression analysis, and maximum likelihood estimation to see how much of the variability in the error rates can be explained with these. We have also used goodness of fit testing strategies and principal component analysis to further assess our data. Finally, we constructed a model of expected error rates based on the what these statistics bore out as critical drivers to the error rate. This model allows project management to evaluate the error rate against a theoretically expected rate as well as anticipate future error rates.

Correlation Analysis↗

NASA NTRS1986

Maximum-Likelihood Parameter-Estimation Algorithm

Efficient version of maximum-likelihood algorithm devised for calculating normal-mode frequencies and damping parameters of vibrating system from experimental data where both process noise and measurement noise present. Method applicable in vibration analysis of such complicated structures as vehicles, aircraft, and spacecraft. New algorithm simplification of existing maximum-likelihood formulation using Kalman filter that allows for both process and measurement noise.

Eldred, D. B.↗

NASA NTRS1979

Optimization of satellite altimeter and wave height measurements

Two techniques for simultaneously estimating altitude, ocean wave height, and signal-to-noise ratio from the GEOS-C satellite altimeter data are described. One technique was based on maximum likelihood estimation, MLE, and the other on minimum mean square error estimation, MMSE. Performance was determined by comparing the variance and bias of each technique with the variance and bias of the smoothed output from the Geos altimeter tracker. Ocean wave height tracking performance for the MLE and MMSE algorithms was measured by comparing the variance and bias of the wave height estimates with that of the expression for the return waveform obtained by a fit to the average output of the 16 waveform sampling gates.

Dooley, R. P.↗

NASA NTRS1978

A study of parameter identification

A set of definitions for deterministic parameter identification ability were proposed. Deterministic parameter identificability properties are presented based on four system characteristics: direct parameter recoverability, properties of the system transfer function, properties of output distinguishability, and uniqueness properties of a quadratic cost functional. Stochastic parameter identifiability was defined in terms of the existence of an estimation sequence for the unknown parameters which is consistent in probability. Stochastic parameter identifiability properties are presented based on the following characteristics: convergence properties of the maximum likelihood estimate, properties of the joint probability density functions of the observations, and properties of the information matrix.

Herget, C. J.↗

NASA NTRS1983

Spectral moment estimation in MST radars

Signal processing techniques used in Mesosphere-Stratosphere-Troposphere (MST) radars are reviewed. Techniques which produce good estimates of the total power, frequency shift, and spectral width of the radar power spectra are considered. Non-linear curve fitting, autocovariance, autocorrelation, covariance, and maximum likelihood estimators are discussed.

Woodman, R. F.↗

NASA NTRS1972

Evaluation of errors in prior mean and variance in the estimation of integrated circuit failure rates using Bayesian methods

The critical point of any Bayesian analysis concerns the choice and quantification of the prior information. The effects of prior data on a Bayesian analysis are studied. Comparisons of the maximum likelihood estimator, the Bayesian estimator, and the known failure rate are presented. The results of the many simulated trails are then analyzed to show the region of criticality for prior information being supplied to the Bayesian estimator. In particular, effects of prior mean and variance are determined as a function of the amount of test data available.

Fletcher, B. C.↗

NASA NTRS1985

Estimation of transport airplane aerodynamics using multiple stepwise regression

This paper presents an application of multiple stepwise regression to the flight test data of a typical transport airplane. The flight test data was carefully preprocessed to eliminate aliasing, time skews and high frequency noise. The data consisted both of basic certification maneuvers, such as wind-up-turns and maneuvers suitable for parameter estimation, such as responses to elevator pulses and doublets. It is shown that the results of multiple stepwise regression techniques compare favorably with the results obtained from maximum likelihood estimation. Finally, it is concluded that multiple stepwise regression could be a fast economical way to estimate transport airplane aerodynamics.

Keskar, D. A.↗

NASA NTRS1989

Frequency estimation techniques for high dynamic trajectories

A comparison is presented of four different estimation techniques applied to the problem of continuously estimating the rapidly varying parameters of a sinusoidal signal, observed in the presence of additive noise. Frequency estimates are emphasized, although phase and/or frequency rate are also estimated by some of the algorithms. These parameters are related to the velocity, position, and acceleration of the maneuvering receiver or transmitter. Estimated performance at low carrier-to-noise ratios and high dynamics is investigated for the purpose of determining the useful operating range of an approximate maximum-likelihood estimator, an extended Kalman filter, a cross-product automatic frequency loop, and a phase-locked loop. Numerical simulations are used to evaluate performance while tracking a common trajectory exhibiting high dynamics.

Vilnrotter, Victor A.↗

NASA NTRS2010

Using MERRA Gridded Innovations for Quantifying Uncertainties in Analysis Fields and Diagnosing Observing System Inhomogeneities

MERRA is a NASA reanalysis for the satellite era using a major new version of the Goddard Earth Observing System Data Assimilation System Version 5 (GEOS-5). The project focuses on historical analyses of the hydrological cycle on a broad range of weather and climate time scales and places the NASA EOS suite of observations in a climate context. The characterization of uncertainty in reanalysis fields is a commonly requested feature by users of such data. While intercomparison with reference data sets is common practice for ascertaining the realism of the datasets, such studies typically are restricted to long term climatological statistics and seldom provide state dependent measures of the uncertainties involved. In principle, variational data assimilation algorithms have the ability of producing error estimates for the analysis variables (typically surface pressure, winds, temperature, moisture and ozone) consistent with the assumed background and observation error statistics. However, these "perceived error estimates" are expensive to obtain and are limited by the somewhat simplistic errors assumed in the algorithm. The observation minus forecast residuals (innovations) by-product of any assimilation system constitutes a powerful tool for estimating the systematic and random errors in the analysis fields. Unfortunately, such data is usually not readily available with reanalysis products, often requiring the tedious decoding of large datasets and not so-user friendly file formats. With MERRA we have introduced a gridded version of the observations/innovations used in the assimilation process, using the same grid and data formats as the regular datasets. Such dataset empowers the user with the ability of conveniently performing observing system related analysis and error estimates. The scope of this dataset will be briefly described. We will present a systematic analysis of MERRA innovation time series for the conventional observing system, including maximum-likelihood estimates of background and observation errors, as well as global bias estimates. Starting with the joint PDF of innovations and analysis increments at observation locations we propose a technique for diagnosing bias among the observing systems, and document how these contextual biases have evolved during the satellite era covered by MERRA.

da Silva, Arlindo↗

NASA NTRS2005

The Influence of Roughness on Gear Surface Fatigue

Gear working surfaces are subjected to repeated rolling and sliding contacts, and often designs require loads sufficient to cause eventual fatigue of the surface. This research provides experimental data and analytical tools to further the understanding of the causal relationship of gear surface roughness to surface fatigue. The research included evaluations and developments of statistical tools for gear fatigue data, experimental evaluation of the surface fatigue lives of superfinished gears with a near-mirror quality, and evaluations of the experiments by analytical methods and surface inspections. Alternative statistical methods were evaluated using Monte Carlo studies leading to a final recommendation to describe gear fatigue data using a Weibull distribution, maximum likelihood estimates of shape and scale parameters, and a presumed zero-valued location parameter. A new method was developed for comparing two datasets by extending the current methods of likelihood-ratio based statistics. The surface fatigue lives of superfinished gears were evaluated by carefully controlled experiments, and it is shown conclusively that superfinishing of gears can provide for significantly greater lives relative to ground gears. The measured life improvement was approximately a factor of five. To assist with application of this finding to products, the experimental condition was evaluated. The fatigue life results were expressed in terms of specific film thickness and shown to be consistent with bearing data. Elastohydrodynamic and stress analyses were completed to relate the stress condition to fatigue. Smooth-surface models do not adequately explain the improved fatigue lives. Based on analyses using a rough surface model, it is concluded that the improved fatigue lives of superfinished gears is due to a reduced rate of near-surface micropitting fatigue processes, not due to any reduced rate of spalling (sub-surface) fatigue processes. To complete the evaluations, surface inspection were completed. The surface topographies of the ground gears changed substantially due to running, but the topographies of the superfinished gears were essentially unchanged with running.

Krantz, Timothy↗

NASA NTRS1971

Estimating the proportions of objects within a single resolution element of a multispectral scanner.

Description of a procedu*e designed to estimate the proportions of objects and materials contained in the instantaneous field of view (IFOV) of an airborne multispectral device. A mathematical model is derived to relate the signature of a combination of materials in a resolution cell to the signatures of the individual materials considered. Estimation algorithms are generated and digital computer programs are prepared to apply the algorithms in the description of the effects which are observed when several objects are viewed simultaneously. The maximum likelihood estimate of the proportions of various individual materials in an IFOV is discussed. A simulation program is proposed for such estimates. A procedure for analyzing the geometric relations of signatures which affect the accuracy of estimates is set forth.

Horwitz, H. M.↗

NASA NTRS1973

Estimation of proportions of objects and determination of training sample-size in a remote sensing application

A multichannel scanning device may fail to observe objects because of obstructions blocking the view, or different categories of objects may make up a resolution element giving rise to a single observation. Ground truth will be required on any such categories of objects in order to estimate their expected proportions associated with various classes represented in the remote sensing data. Considering the classes to be distributed as multivariate normal with different mean vectors and common covariance, maximum likelihood estimates are given for the expected proportions of objects associated with different classes, using the Bayes procedure for classification of individuals obtained from these classes. An approximate solution for simultaneous confidence intervals on these proportions is given, and thereby a sample-size needed to achieve a desired amount of accuracy for the estimates is determined.

Chhikara, R. S.↗