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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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NASA NTRSDate not supplied

TPSAS-NF1676L-13204-DND

Invasive perennial pepperweed (Lepidium latifolium) has spread rapidly throughout the western United States in the past fifteen years. Pepperweed outcompetes many native species for water and nutrients, disturbing sensitive ecosystems. The purpose of this study is to map the contemporary distribution of pepperweed throughout the wetland ecosystem of the restored South San Francisco Bay Salt Ponds and create a habitat suitability model to predict future spread. Pepperweed reflectance data were collected in-situ with the GER 1500 spectroradiometer along with presence and absence pepperweed GPS data points. A Spectral Angle Mapper (SAM) classification algorithm (Method A) was used to distinguish pepperweed spectra and map its distribution on an EO-1 Hyperion image of the study area. Similarly, a supervised classification was run on an ASTER image of the study area. A GIS multivariate habitat suitability model was created to predict areas most susceptible to pepperweed colonization. Variables incorporated into the model were tidal extent, propensity for disturbance, pond salinity, proximity to levees and channels, and terrain curvature. A Generalized Additive Model (GAM) was also used to generate a suitability map (Method B) and investigate the statistical probability that each variable contributed to predict pepperweed spread. Results from the GAM revealed distance to channels, distance to disturbance factors, extreme pond salinity and curvature as statistically significant in determining the locations of suitable pepperweed habitats

Andrew Nguyen↗
NASA NTRS2003

Software Reliability 2002

In FY01 we learned that hardware reliability models need substantial changes to account for differences in software, thus making software reliability measurements more effective, accurate, and easier to apply. These reliability models are generally based on familiar distributions or parametric methods. An obvious question is 'What new statistical and probability models can be developed using non-parametric and distribution-free methods instead of the traditional parametric method?" Two approaches to software reliability engineering appear somewhat promising. The first study, begin in FY01, is based in hardware reliability, a very well established science that has many aspects that can be applied to software. This research effort has investigated mathematical aspects of hardware reliability and has identified those applicable to software. Currently the research effort is applying and testing these approaches to software reliability measurement, These parametric models require much project data that may be difficult to apply and interpret. Projects at GSFC are often complex in both technology and schedules. Assessing and estimating reliability of the final system is extremely difficult when various subsystems are tested and completed long before others. Parametric and distribution free techniques may offer a new and accurate way of modeling failure time and other project data to provide earlier and more accurate estimates of system reliability.

Wallace, Dolores R.↗
NASA NTRS1977

The reality of comet groups and pairs

Although the common genetic origin of the Kreutz family of sun-grazing comets has generally been accepted, there remains uncertainty with regard to genetic identity among other groups of comets whose orbital elements are nearly alike. Porter (1952) has listed a number of such groups, and Opik (1971) has made a statistical study of the orbits of 472 comets with aphelion distances beyond Saturn. Opik lists 97 groups that show similarities among their three angular elements, calculates an overall probability of some 10 to the -39th power that these similarities could have occurred by chance, and thus concludes that 60% or more of such comets fall into genetic groups containing from two to seven members. This paper explores the statistical reality of Opik's groups utilizing the Monte Carlo method of statistics as well as ordinary probability theory. The conclusion is reached that except for a few pairs, the similarity among orbital elements within the groups is no greater than random expectation.

Whipple, F. L.↗
NASA NTRS1979

Short-term solar activity forecasting

A method of forecasting the level of activity of every active region on the surface of the Sun within one to three days is proposed in order to estimate the possibility of the occurrence of ionospheric disturbances and proton events. The forecasting method is a probability process based on statistics. In many of the cases, the accuracy in predicting the short term solar activity was in the range of 70%, although there were many false alarms.

Xie-Zhen, C.↗
NASA NTRS2018

A Fast Monte Carlo Method for Model-Based Prognostics Based on Stochastic Calculus

This work proposes a fast Monte Carlo method to solve differential equations utilized in model-based prognostics. The methodology is derived from the theory of stochastic calculus, and the goal of such a method is to speed up the estimation of the probability density functions describing the independent variable evolution over time. In the prognostic scenarios presented in this paper, the stochastic differential equations describe variables directly or indirectly related to the degradation of a monitored system. The method allows the estimation of the probability density functions by solving the deterministic equation and approximating the stochastic integrals using samples of the model noise. By so doing, the prognostic problem is solved without the Monte Carlo simulation based on Euler's forward method, which is typically the most time consuming task of the prediction stage. Three different prognostic scenarios are presented as proof of concept: (i) life prediction of electrolytic capacitors, (ii) remaining time to discharge of Lithium-ion batteries, and (iii) prognostic of cracked structures under fatigue loading. The paper shows how the method produces probability density functions that are statistically indistinguishable from the distributions estimated with Euler's forward Monte Carlo simulation. However, the proposed solution is orders of magnitude faster when computing the time-to-failure distribution of the monitored system. The approach may enable complex real-time prognostics and health management solutions with limited computing power.

Corbetta, M.↗
NASA NTRS2019

A Fast Monte Carlo Method for Model-Based Prognostics Based on Stochastic Calculus

This work proposes a fast Monte Carlo method to solve differential equations utilized in model-based prognostics. The methodology is derived from the theory of stochastic calculus, and the goal of such a method is to speed up the estimation of the probability density functions describing the independent variable evolution over time. In the prognostic scenarios presented in this paper, the stochastic differential equations describe variables directly or indirectly related to the degradation of a monitored system. The method allows the estimation of the probability density functions by solving the deterministic equation and approximating the stochastic integrals using samples of the model noise. By so doing, the prognostic problem is solved without the Monte Carlo simulation based on Euler's forward method, which is typically the most time consuming task of the prediction stage. Three different prognostic scenarios are presented as proof of concept: (i) life prediction of electrolytic capacitors, (ii) remaining time to discharge of Lithium-ion batteries, and (iii) prognostic of cracked structures under fatigue loading. The paper shows how the method produces probability density functions that are statistically indistinguishable from the distributions estimated with Euler's forward Monte Carlo simulation. However, the proposed solution is orders of magnitude faster when computing the time-to-failure distribution of the monitored system. The approach may enable complex real-time prognostics and health management solutions with limited computing power.

stochastic calculus↗
NASA NTRS1976

Comparison of rate one-half, equivalent constraint length 24, binary convolutional codes for use with sequential decoding on the deep-space channel

Virtually all previously-suggested rate 1/2 binary convolutional codes with KE = 24 are compared. Their distance properties are given; and their performance, both in computation and in error probability, with sequential decoding on the deep-space channel is determined by simulation. Recommendations are made both for the choice of a specific KE = 24 code as well as for codes to be included in future coding standards for the deep-space channel. A new result given in this report is a method for determining the statistical significance of error probability data when the error probability is so small that it is not feasible to perform enough decoding simulations to obtain more than a very small number of decoding errors.

Massey, J. L.↗
NASA NTRS2014

The Effect of General Statistical Fiber Misalignment on Predicted Damage Initiation in Composites

A micromechanical method is employed for the prediction of unidirectional composites in which the fiber orientation can possess various statistical misalignment distributions. The method relies on the probability-weighted averaging of the appropriate concentration tensor, which is established by the micromechanical procedure. This approach provides access to the local field quantities throughout the constituents, from which initiation of damage in the composite can be predicted. In contrast, a typical macromechanical procedure can determine the effective composite elastic properties in the presence of statistical fiber misalignment, but cannot provide the local fields. Fully random fiber distribution is presented as a special case using the proposed micromechanical method. Results are given that illustrate the effects of various amounts of fiber misalignment in terms of the standard deviations of in-plane and out-of-plane misalignment angles, where normal distributions have been employed. Damage initiation envelopes, local fields, effective moduli, and strengths are predicted for polymer and ceramic matrix composites with given normal distributions of misalignment angles, as well as fully random fiber orientation.

Micromechanical↗
NASA NTRS1976

Time-dependent MOS breakdown

A general model for time-dependent breakdown in metal-oxide-silicon (MOS) structures is developed and related to experimental measurements on samples deliberately contaminated with Na. A statistical method is used for measuring the breakdown probability as a function of log time and applied field. It is shown that three time regions of breakdown can be explained respectively in terms of silicon surface defects, ion emission from the metal interface, and lateral ion diffusion at the silicon interface.

Li, S. P.↗
NASA NTRS1978

Gamma ray spectroscopy in astrophysics: An observational overview (50 keV-20 MeV)

The observational status of gamma ray astronomy based on results reported prior to April 1978 is discussed. Specific line observations are reviewed from the Sun, the galactic plane, the Galactic Center region, the active Galaxy NGC 5128 (Cen A), the Crab Nebula region, and a transient source in the general direction of the anti-Galactic Center region. A statistical method is described which determines the relative probability that a given, reported observation is due to an external source as compared to a random fluctuation in the experimental background counting rate. Only a few of the many reported observations can be considered likely extraterrestrial observations of an extraterrestrial source made with a confidence level of 99%.

Chupp, E. L.↗
NASA NTRS1991

A method for classification of multisource data using interval-valued probabilities and its application to HIRIS data

A method of classifying multisource data in remote sensing is presented. The proposed method considers each data source as an information source providing a body of evidence, represents statistical evidence by interval-valued probabilities, and uses Dempster's rule to integrate information based on multiple data source. The method is applied to the problems of ground-cover classification of multispectral data combined with digital terrain data such as elevation, slope, and aspect. Then this method is applied to simulated 201-band High Resolution Imaging Spectrometer (HIRIS) data by dividing the dimensionally huge data source into smaller and more manageable pieces based on the global statistical correlation information. It produces higher classification accuracy than the Maximum Likelihood (ML) classification method when the Hughes phenomenon is apparent.

Kim, H.↗
NASA NTRS1990

A method for classification of multisource data using interval-valued probabilities and its application to HIRIS data

A method of classifying multisource data in remote sensing is presented. The proposed method considers each data source as an information source providing a body of evidence, represents statistical evidence by interval-valued probabilities, and uses Dempster's rule to integrate information based on multiple data sources. The method is applied to the problems of ground-cover classification of multispectral data combined with digital terrain data such as elevation, slope, and aspect. Then this method is applied to simulated 201-band High Resolution Imaging Spectrometer (HIRIS) data by dividing the dimensionally huge data source into smaller and more manageable pieces based on the global statistical correlation information. It produces higher classification accuracy than the Maximum Likelihood (ML) classification method when the Hughes phenomenon is apparent.

Kim, H.↗
NASA NTRS1989

Bayesian learning

In 1983 and 1984, the Infrared Astronomical Satellite (IRAS) detected 5,425 stellar objects and measured their infrared spectra. In 1987 a program called AUTOCLASS used Bayesian inference methods to discover the classes present in these data and determine the most probable class of each object, revealing unknown phenomena in astronomy. AUTOCLASS has rekindled the old debate on the suitability of Bayesian methods, which are computationally intensive, interpret probabilities as plausibility measures rather than frequencies, and appear to depend on a subjective assessment of the probability of a hypothesis before the data were collected. Modern statistical methods have, however, recently been shown to also depend on subjective elements. These debates bring into question the whole tradition of scientific objectivity and offer scientists a new way to take responsibility for their findings and conclusions.

Denning, Peter J.↗
NASA NTRS1971

Accelerated battery-life testing - A concept

Test program, employing empirical, statistical and physical methods, determines service life and failure probabilities of electrochemical cells and batteries, and is applicable to testing mechanical, electrical, and chemical devices. Data obtained aids long-term performance prediction of battery or cell.

Mccallum, J.↗
NASA NTRS1971

The application of signal detection theory to optics

The restoration of images focused on a photosensitive surface is treated from the standpoint of maximum likelihood estimation, taking into account the Poisson distributions of the observed data, which are the numbers of photoelectrons from various elements of the surface. A detector of an image focused on such a surface utilizes a certain linear combination of those numbers as the optimum detection statistic. Methods for calculating the false alarm and detection probabilities are proposed. It is shown that measuring noncommuting observables in an ideal quantum receiver cannot yield a lower Bayes cost than that attainable by a system measuring only commuting observables.

Helstrom, C. W.↗
NASA NTRS1990

The pdf approach to turbulent flow

This paper provides a detailed discussion of the theory and application of probability density function (pdf) methods, which provide a complete statistical description of turbulent flow fields at a single point or a finite number of points. The basic laws governing the flow of Newtonian fluids are set up in the Eulerian and the Lagrangian frame, and the exact and linear equations for the characteristic functionals in those frames are discussed. Pdf equations in both frames are derived as Fourier transforms of the equations of the characteristic functions. Possible formulations for the nonclosed terms in the pdf equation are discussed, their properties are assessed, and closure modes for the molecular-transport and the fluctuating pressure-gradient terms are reviewed. The application of pdf methods to turbulent combustion flows, supersonic flows, and the interaction of turbulence with shock waves is discussed.

Kollmann, W.↗