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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 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
NASA NTRS1986

A simple, objective analysis scheme for scatterometer data

A simple economical objective analysis scheme is devised and tested on real scatterometer data. It is designed to treat dense data such as those of the Seasat A Satellite Scatterometer (SASS) for individual or multiple passes, and preserves subsynoptic scale features. Errors are evaluated with the aid of sampling ('bootstrap') statistical methods. In addition, sensitivity tests have been performed which establish qualitative confidence in calculated fields of divergence and vorticity. The SASS wind algorithm could be improved; however, the data at this point are limited by instrument errors rather than analysis errors. The analysis error is typically negligible in comparison with the instrument error, but amounts to 30 percent of the instrument error in areas of strong wind shear. The scheme is very economical, and thus suitable for large volumes of dense data such as SASS data.

Levy, G.↗
NASA NTRS1982

Characterization and measurement of phase-locked loop performance

A set of performance measures and tests are presented which can be implemented at the 'black box' level for the characterization of such statistical aspects of phase-locked loop behavior as the acquisition and tracking threshold, phase error jitter, Doppler accuracy, etc. Also presented is an automatic measurement system, designated the Statistical Loop Analyzer, which has been developed in order to undertake these phase-locked device performance measurements.

Lindsey, W. C.↗
NASA NTRS1986

Statistical corrections to numerical predictions. IV

The National Meteorological Center Barotropic-Mesh Model has been used to test a statistical correction procedure, designated as M-II, that was developed in Schemm et al. (1981). In the present application, statistical corrections at 12 h resulted in significant reductions of the mean-square errors of both vorticity and the Laplacian of thickness. Predictions to 48 h demonstrated the feasibility of applying corrections at every 12 h in extended forecasts. In addition to these improvements, however, the statistical corrections resulted in a shift of error from smaller to larger-scale motions, improving the smallest scales dramatically but deteriorating the largest scales. This effect is shown to be a consequence of randomization of the residual errors by the regression equations and can be corrected by spatially high-pass filtering the field of corrections before they are applied.

Schemm, Jae-Kyung↗
DOE OSTI2016

Single-pixel interior filling function approach for detecting and correcting errors in particle tracking

We present a general method for detecting and correcting biases in the outputs of particle-tracking experiments. Our approach is based on the histogram of estimated positions within pixels, which we term the single-pixel interior filling function (SPIFF). We use the deviation of the SPIFF from a uniform distribution to test the veracity of tracking analyses from different algorithms. Unbiased SPIFFs correspond to uniform pixel filling, whereas biased ones exhibit pixel locking, in which the estimated particle positions concentrate toward the centers of pixels. Although pixel locking is a well-known phenomenon, we go beyond existing methods to show how the SPIFF can be used to correct errors. Here, the key is that the SPIFF aggregates statistical information from many single-particle images and localizations that are gathered over time or across an ensemble, and this information augments the single-particle data. We explicitly consider two cases that give rise to significant errors in estimated particle locations: undersampling the point spread function due to small emitter size and intensity overlap of proximal objects. In these situations, we show how errors in positions can be corrected essentially completely with little added computational cost. Additional situations and applications to experimental data are explored in SI Appendix. In the presence of experimental-like shot noise, the precision of the SPIFF-based correction achieves (and can even exceed) the unbiased Cramér–Rao lower bound. We expect the SPIFF approach to be useful in a wide range of localization applications, including single-molecule imaging and particle tracking, in fields ranging from biology to materials science to astronomy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

A BAYESIAN APPROACH TO DERIVING AGES OF INDIVIDUAL FIELD WHITE DWARFS

We apply a self-consistent and robust Bayesian statistical approach to determine the ages, distances, and zero-age main sequence (ZAMS) masses of 28 field DA white dwarfs (WDs) with ages of approximately 4-8 Gyr. Our technique requires only quality optical and near-infrared photometry to derive ages with <15% uncertainties, generally with little sensitivity to our choice of modern initial-final mass relation. We find that age, distance, and ZAMS mass are correlated in a manner that is too complex to be captured by traditional error propagation techniques. We further find that the posterior distributions of age are often asymmetric, indicating that the standard approach to deriving WD ages can yield misleading results.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2010

Statistical Analysis of Model Data for Operational Space Launch Weather Support at Kennedy Space Center and Cape Canaveral Air Force Station

The 12-km resolution North American Mesoscale (NAM) model (MesoNAM) is used by the 45th Weather Squadron (45 WS) Launch Weather Officers at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) to support space launch weather operations. The 45 WS tasked the Applied Meteorology Unit to conduct an objective statistics-based analysis of MesoNAM output compared to wind tower mesonet observations and then develop a an operational tool to display the results. The National Centers for Environmental Prediction began running the current version of the MesoNAM in mid-August 2006. The period of record for the dataset was 1 September 2006 - 31 January 2010. The AMU evaluated MesoNAM hourly forecasts from 0 to 84 hours based on model initialization times of 00, 06, 12 and 18 UTC. The MesoNAM forecast winds, temperature and dew point were compared to the observed values of these parameters from the sensors in the KSC/CCAFS wind tower network. The data sets were stratified by model initialization time, month and onshore/offshore flow for each wind tower. Statistics computed included bias (mean difference), standard deviation of the bias, root mean square error (RMSE) and a hypothesis test for bias = O. Twelve wind towers located in close proximity to key launch complexes were used for the statistical analysis with the sensors on the towers positioned at varying heights to include 6 ft, 30 ft, 54 ft, 60 ft, 90 ft, 162 ft, 204 ft and 230 ft depending on the launch vehicle and associated weather launch commit criteria being evaluated. These twelve wind towers support activities for the Space Shuttle (launch and landing), Delta IV, Atlas V and Falcon 9 launch vehicles. For all twelve towers, the results indicate a diurnal signal in the bias of temperature (T) and weaker but discernable diurnal signal in the bias of dewpoint temperature (T(sub d)) in the MesoNAM forecasts. Also, the standard deviation of the bias and RMSE of T, T(sub d), wind speed and wind direction indicated the model error increased with the forecast period all four parameters. The hypothesis testing uses statistics to determine the probability that a given hypothesis is true. The goal of using the hypothesis test was to determine if the model bias of any of the parameters assessed throughout the model forecast period was statistically zero. For th is dataset, if this test produced a value >= -1 .96 or <= 1.96 for a data point, then the bias at that point was effectively zero and the model forecast for that point was considered to have no error. A graphical user interface (GUI) was developed so the 45 WS would have an operational tool at their disposal that would be easy to navigate among the multiple stratifications of information to include tower locations, month, model initialization times, sensor heights and onshore/offshore flow. The AMU developed the GUI using HyperText Markup Language (HTML) so the tool could be used in most popular web browsers with computers running different operating systems such as Microsoft Windows and Linux.

Bauman, William H., III↗
DOE OSTI2017

Introducing Human Mandible Identification [(hu)MANid]: A Free, Web‐Based GUI to Classify Human Mandibles

Abstract Statistical programs have revolutionized the way in which forensic anthropologists conduct casework by allowing practitioners to use computationally complex analytics at the click of a button. Importantly, the products of these statistical programs are reproducible and contain measures of error or uncertainty, thereby strengthening conclusions. This paper is an introduction to (hu) MAN id, a free, web‐based application that uses linear and mixture discriminant function analyses to classify human mandibles into one of many worldwide and/or periodic reference groups. The mechanics, development, and use of the application will be discussed. Further, the program was tested against other software to compare model performances and classifications. Total correct classifications among the test cases and programs were identical. Ten mandibles were tested using both statistical procedures. Mixture discriminant analysis improved classification by an average of 9.3% and correctly identified three more mandibles than LDA . Therefore, we believe (hu) MAN id will be an asset to the anthropological community.

Berg, Gregory E.↗
DOE OSTI2020

Detecting differential protein abundance by combining peptide level P -values

The majority of methods for detecting differentially abundant proteins between samples in label-free LC-MS bottom-up proteomics experiments rely on statistically testing inferred protein abundances derived from peptide ionization intensities or averaging peptide level statistics. Here in this paper, we statistically test peptide ionization intensities directly and combine the resulting dependent P-values using the Empirical Brown's Method (EBM), avoiding error introduced through the estimation of protein abundances or summarizing test statistics. We show that on a spike-in proteomics dataset, a peptide level approach using EBM outperforms differential abundance detection using a protein level approach and several analysis workflows, including MSstats. Additionally, we demonstrate the effectiveness of this approach by detecting enriched proteins from an activity-based protein profiling dataset.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Performance Comparison of Circular and Spherical Error Probable Estimators

This report compares the performance of three Circular Error Probable (CEP) estimators: the Grubbs-Patnaik estimator, a new, non-iterative, radial-integration estimator, and a median estimator. It also compares the performance of two Spherical Error Probable (SEP) estimators. The performance of each estimator is assessed in terms of bias, uncertainty, robustness, and computational complexity. Robustness is evaluated with respect to outliers, variations in the underlying statistical distribution characterizing munition impact positions, and impact-position measurement errors. The performance assessments indicate the radial-integration and Grubbs-Patnaik estimators perform nearly identically providing the statistical distribution of impact-position coordinates is jointly normal with zero means. In that case, both estimators outperform the median estimator by about 2% relative to the true CEP in terms of estimator uncertainty. The bias performance of the radial-integration and median estimators is close to zero for jointly normal impacts, however, the Grubbs-Patnaik estimator can be significantly biased for jointly normal impacts with non-zero means. When the statistical distribution characterizing impact positions is known, but not jointly normal, the radial-integration estimator is superior. In this case, the median estimator also outperforms the Grubbs-Patnaik estimator but is not quite as good as the radial-integration estimator. If the statistical distribution characterizing impacts is unknown and not jointly normal, or if distribution parameters are difficult or impractical to estimate, or if test data is corrupted with outliers, then the median estimator dramatically outperforms the other estimators, especially in terms of estimation bias. Unexpectedly, measurement noise did not significantly degrade the performance of any of the estimators, except for cases with signal to noise ratios less than five. Although the Grubbs-Patnaik estimator has remained the gold standard for CEP estimation for over half a century, the performance assessments indicate the new, non-iterative, radial-integration estimator and the median estimator offer significant advantages and, in most practical real-world conditions, are superior estimators. These estimators are also useful for SEP estimation whereas the Grubbs-Patnaik estimator does not extend to three dimensions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Dependence of the compensation error on the error of a sensor and corrector in an adaptive optics phase-conjugating system

In the framework of a statistical model of an adaptive optics system (AOS) of phase conjugation, three algorithms based on an integrated mathematical approach are considered, each of them intended for minimisation of one of the following characteristics: the sensor error (in the case of an ideal corrector), the corrector error (in the case of ideal measurements) and the compensation error (with regard to discreteness and measurement noises and to incompleteness of a system of response functions of the corrector actuators). Functional and statistical relationships between the algorithms are studied and a relation is derived to ensure calculation of the mean-square compensation error as a function of the errors of the sensor and corrector with an accuracy better than 10%. Because in adjusting the AOS parameters, it is reasonable to proceed from the equality of the sensor and corrector errors, in the case the Hartmann sensor is used as a wavefront sensor, the required number of actuators in the absence of the noise component in the sensor error turns out 1.5 – 2.5 times less than the number of counts, and that difference grows with increasing measurement noise. (adaptive optics)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Nature and Consequences of Biological Reductionism for the Immunological Study of Infectious Diseases

Evolution has conserved “economic” systems that perform many functions, faster or better, with less. For example, three to five leukocyte types protect from thousands of pathogens. In order to achieve so much with so little, biological systems combine their limited elements, creating complex structures. Yet, the prevalent research paradigm is reductionist. Focusing on infectious diseases, reductionist and non-reductionist views are here described. Furthermore, the literature indicates that reductionism is associated with information loss and errors, while non-reductionist operations can extract more information from the same data. When designed to capture one-to-many/many-to-one interactions—including the use of arrows that connect pairs of consecutive observations—non-reductionist (spatial–temporal) constructs eliminate data variability from all dimensions, except along one line, while arrows describe the directionality of temporal changes that occur along the line. To validate the patterns detected by non-reductionist operations, reductionist procedures are needed. Integrated (non-reductionist and reductionist) methods can (i) distinguish data subsets that differ immunologically and statistically; (ii) differentiate false-negative from -positive errors; (iii) discriminate disease stages; (iv) capture in vivo, multilevel interactions that consider the patient, the microbe, and antibiotic-mediated responses; and (v) assess dynamics. Integrated methods provide repeatable and biologically interpretable information.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1989

Estimations of ABL fluxes and other turbulence parameters from Doppler lidar data

Techniques for extraction boundary layer parameters from measurements of a short-pulse CO2 Doppler lidar are described. The measurements are those collected during the First International Satellites Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE). By continuously operating the lidar for about an hour, stable statistics of the radial velocities can be extracted. Assuming that the turbulence is horizontally homogeneous, the mean wind, its standard deviations, and the momentum fluxes were estimated. Spectral analysis of the radial velocities is also performed from which, by examining the amplitude of the power spectrum at the inertial range, the kinetic energy dissipation was deduced. Finally, using the statistical form of the Navier-Stokes equations, the surface heat flux is derived as the residual balance between the vertical gradient of the third moment of the vertical velocity and the kinetic energy dissipation. Combining many measurements would normally reduce the error provided that, it is unbiased and uncorrelated. The nature of some of the algorithms however, is such that, biased and correlated errors may be generated even though the raw measurements are not. Data processing procedures were developed that eliminate bias and minimize error correlation. Once bias and error correlations are accounted for, the large sample size is shown to reduce the errors substantially. The principal features of the derived turbulence statistics for two case studied are presented.

Gal-Chen, Tzvi↗
NASA NTRS1990

Statistical analysis of AFE GN&C aeropass performance

Performance of the guidance, navigation, and control (GN&C) system used on the Aeroassist Flight Experiment (AFE) spacecraft has been studied with Monte Carlo techniques. The performance of the AFE GN&C is investigated with a 6-DOF numerical dynamic model which includes a Global Reference Atmospheric Model (GRAM) and a gravitational model with oblateness corrections. The study considers all the uncertainties due to the environment and the system itself. In the AFE's aeropass phase, perturbations on the system performance are caused by an error space which has over 20 dimensions of the correlated/uncorrelated error sources. The goal of this study is to determine, in a statistical sense, how much flight path angle error can be tolerated at entry interface (EI) and still have acceptable delta-V capability at exit to position the AFE spacecraft for recovery. Assuming there is fuel available to produce 380 ft/sec of delta-V at atmospheric exit, a 3-sigma standard deviation in flight path angle error of 0.04 degrees at EI would result in a 98-percent probability of mission success.

Chang, Ho-Pen↗
NASA NTRS2010

Error and Uncertainty Quantification in the Numerical Simulation of Complex Fluid Flows

The failure of numerical simulation to predict physical reality is often a direct consequence of the compounding effects of numerical error arising from finite-dimensional approximation and physical model uncertainty resulting from inexact knowledge and/or statistical representation. In this topical lecture, we briefly review systematic theories for quantifying numerical errors and restricted forms of model uncertainty occurring in simulations of fluid flow. A goal of this lecture is to elucidate both positive and negative aspects of applying these theories to practical fluid flow problems. Finite-element and finite-volume calculations of subsonic and hypersonic fluid flow are presented to contrast the differing roles of numerical error and model uncertainty. for these problems.

Barth, Timothy J.↗
DOE OSTI2014

Systematic Errors in measurement of b1

A class of spin observables can be obtained from the relative difference of or asymmetry between cross sections of different spin states of beam or target particles. Such observables have the advantage that the normalization factors needed to calculate absolute cross sections from yields often divide out or cancel to a large degree in constructing asymmetries. However, normalization factors can change with time, giving different normalization factors for different target or beam spin states, leading to systematic errors in asymmetries in addition to those determined from statistics. Rapidly flipping spin orientation, such as what is routinely done with polarized beams, can significantly reduce the impact of these normalization fluctuations and drifts. Target spin orientations typically require minutes to hours to change, versus fractions of a second for beams, making systematic errors for observables based on target spin flips more difficult to control. Such systematic errors from normalization drifts are discussed in the context of the proposed measurement of the deuteron b(1) structure function at Jefferson Lab.

Wood, S A↗
NASA NTRS1980

Statistical aspects of carbon fiber risk assessment modeling

The probabilistic and statistical aspects of the carbon fiber risk assessment modeling of fire accidents involving commercial aircraft are examined. Three major sources of uncertainty in the modeling effort are identified. These are: (1) imprecise knowledge in establishing the model; (2) parameter estimation; and (3)Monte Carlo sampling error. All three sources of uncertainty are treated and statistical procedures are utilized and/or developed to control them wherever possible.

Gross, D.↗
NASA NTRS2001

Laboratory characterization of silicon avalanche photodiodes (APD) for pulse position modulation (PPM) detection

Two commercially available large area silicon avalanche photodiodes (APD) were characterized in the laboratory. Specifically, the response of the APD’s to a sequence of 8-bit pulse position modulated (256-PPM) laser pulses, with and without background noise, was recorded and stored for post analysis. Empirical probability distribution functions (pdf) were constructed from the signal and noise slot data and compared to pdf’s predicted by an analytical model based on Webb/Gaussian statistics. The limited pulse sequence was also used to generate bit-error rate (BER) versus signal photons per pulse values, albeit with large error bars. These BER measurements were also compared to results predicted using the Gaussian and Webb/Gaussian models for APD channel statistics. While the measurements qualitatively reflect features predicted by theory, significant quantitative deviations are displayed between the measurements and theory. The source of these discrepancies is not currently well understood, but it is surmised that inaccurate knowledge of detector parameters such as gain and noise equivalent temperature models may explain the discrepancies.

Biswas, A.↗
DOE OSTI2014

Joint Seasonal ARMA Approach for Modeling of Load Forecast Errors in Planning Studies

To make informed and robust decisions in the probabilistic power system operation and planning process, it is critical to conduct multiple simulations of the generated combinations of wind and load parameters and their forecast errors to handle the variability and uncertainty of these time series. In order for the simulation results to be trustworthy, the simulated series must preserve the salient statistical characteristics of the real series. In this paper, we analyze day-ahead load forecast error data from multiple balancing authority locations and characterize statistical properties such as mean, standard deviation, autocorrelation, correlation between series, time-of-day bias, and time-of-day autocorrelation. We then construct and validate a seasonal autoregressive moving average (ARMA) model to model these characteristics, and use the model to jointly simulate day-ahead load forecast error series for all BAs.

Power system planning, Operations, Probabilistic m↗