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Parameter and shape estimates updated from new measurements. Involves statistical structural analysis, statistical electromagneticfield analysis, filtering, measurement modeling, and iterative prediction/correction procedures. Estimating algorithms result from generalizations of Kalman statistical-filter theory.
A new model was developed to predict attenuation statistics for a single Earth-satellite or terrestrial propagation path. The model was extended to provide predictions of the joint occurrences of specified or higher attenuation values on two closely spaced Earth-satellite paths. The joint statistics provide the information required to obtain diversity gain or diversity advantage estimates. The new model is meteorologically based. It was tested against available Earth-satellite beacon observations and terrestrial path measurements. The model employs the rain climate region descriptions of the Global rain model. The rms deviation between the predicted and observed attenuation values for the terrestrial path data was 35 percent, a result consistent with the expectations of the Global model when the rain rate distribution for the path is not used in the calculation. Within the United States the rms deviation between measurement and prediction was 36 percent but worldwide it was 79 percent.
Described here is an approach to automatically assessing the accuracy of various components of a model. In this approach, actual data from the operation of a target system is used to drive statistical measures to evaluate the prediction accuracy of various portions of the model. We describe how these statistical measures of model accuracy can be used in model-based reasoning for monitoring and design. We then describe the application of these techniques to the monitoring and design of the water recovery system of the Environmental Control and Life Support System (ECLSS) of Space Station Freedom.
A probabilistic model of the fracture processes unifying the phenomenological study of long term strength of materials, fracture mechanics and statistical approaches to fracture is briefly outlined. The general framework of irreversible thermodynamics is employed to model the deterministic side of the failure phenomenon. The stochastic calculus is used to account for thg failure mechanisms controlled by chance; particularly, the random roughness of fracture surfaces.
An empirical model of the proton radiation belt is constructed from data taken during 2013-2017 by the Relativistic Electron-Proton Telescopes on the Van Allen Probes satellites. The model intensity is a function of time, kinetic energy in the range 18-600 megaelectronvolts, equatorial pitch angle, and L shell of proton guiding centers. Data are selected, on the basis of energy deposits in each of the nine silicon detectors, to reduce background caused by hard proton energy spectra at low L. Instrument response functions are computed by Monte Carlo integration, using simulated proton paths through a simplified structural model, to account for energy loss in shielding material for protons outside the nominal field of view. Overlap of energy channels, their wide angular response, and changing satellite orientation require the model dependencies on all three independent variables be determined simultaneously. This is done by least squares minimization with a customized steepest descent algorithm. Model uncertainty accounts for statistical data error and systematic error in the simulated instrument response. A proton energy spectrum is also computed from data taken during the 8 January 2014 solar event, to illustrate methods for the simpler case of an isotropic and homogeneous model distribution. Radiation belt and solar proton results are compared to intensities computed with a simplified, on-axis response that can provide a good approximation under limited circumstances.
An improved methodology for quantitatively evaluating failure risk of spaceflight systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with engineering analysis to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in engineering analyses of failure phenomena, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which engineering analysis models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes. These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. Conventional engineering analysis models currently employed for design of failure prediction are used in this methodology. The PFA methodology is described and examples of its application are presented. Conventional approaches to failure risk evaluation for spaceflight systems are discussed, and the rationale for the approach taken in the PFA methodology is presented. The statistical methods, engineering models, and computer software used in fatigue failure mode applications are thoroughly documented.
An improved methodology for quantitatively evaluating failure risk of spaceflight systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with engineering analysis to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in engineering analyses of failure phenomena, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which engineering analysis models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes, These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. Conventional engineering analysis models currently employed for design of failure prediction are used in this methodology. The PFA methodology is described and examples of its application are presented. Conventional approaches to failure risk evaluation for spaceflight systems are discussed, and the rationale for the approach taken in the PFA methodology is presented. The statistical methods, engineering models, and computer software used in fatigue failure mode applications are thoroughly documented.
An improved methodology for quantitatively evaluating failure risk of spaceflight systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with engineering analysis to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in engineering analyses of failure phenomena, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which engineering analysis models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes. These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. Conventional engineering analysis models currently employed for design of failure prediction are used in this methodology. The PFA methodology is described and examples of its application are presented. Conventional approaches to failure risk evaluation for spaceflight systems are discussed, and the rationale for the approach taken in the PFA methodology is presented. The statistical methods, engineering models, and computer software used in fatigue failure mode applications are thoroughly documented.
In this presentation we discuss a new theoretical model and supporting experimental results for the strength and lifetime in creep rupture of unidirectional, carbon fiber/epoxy matrix composites at ambient conditions. First we review the 'standard' Weibull/power-law methodology that has been standard practice. Then we discuss features of a recent model which build on the statistical aspects of fiber strength, micromechanical aspects of stress transfer around fiber breaks, and time-dependent creep of the matrix. The model is applied to 'microcomposites' consisting of seven fibers in a matrix for which strength and creep-rupture data are available. The model yields Weibull distributions in an envelope format for both strength and lifetime. The respective shape, scale and power-law parameters depend on such parameters as the Weibull shape parameter for fiber strength, the exponent for matrix creep, the effective load transfer length (which grows in time due to matrix creep) and the critical cluster size for failed fibers. The experimental results are consistent with the theory, though time-dependent debonding appears to be part of the failure process.
Nontopological solitons can be formed during a phase transition in the early universe as long as some net charge can be trapped in regions of false vacuum. It has been previously suggested that a particle-antiparticle asymmetry would provide a source for such trapped charge. It is pointed out that, for the model and parameters considered, statistical fluctuations provide a much larger concentration of charge, and are therefore, the dominant source of charge fluctuations in solitogenesis.
Airline pilots experience acute and chronic sleep deprivation, sleep inertia, and circadian desynchrony due to the need to schedule flight operations around the clock. This sleep loss and circadian desynchrony gives rise to cognitive impairments, reduced vigilance and inconsistent performance. Several biomathematical models, based principally on patterns observed in circadian rhythms and homeostatic drive, have been developed to predict a pilots levels of fatigue or alertness. These models allow for the Federal Aviation Administration (FAA) and commercial airlines to make decisions about pilot capabilities and flight schedules. Although these models have been validated in a laboratory setting, they have not been thoroughly tested in operational environments where uncontrolled factors, such as environmental sleep disrupters, caffeine use and napping, may impact actual pilot alertness and performance. We will compare the predictions of three prominent biomathematical fatigue models (McCauley Model, Harvard Model, and the privately-sold SAFTE-FAST Model) to actual measures of alertness and performance. We collected sleep logs, movement and light recordings, psychomotor vigilance task (PVT), and urinary melatonin (a marker of circadian phase) from 44 pilots in a short-haul commercial airline over one month. We will statistically compare with the model predictions to lapses on the PVT and circadian phase. We will calculate the sensitivity and specificity of each model prediction under different scheduling conditions. Our findings will aid operational decision-makers in determining the reliability of each model under real-world scheduling situations.
The optimal interpolation method of Lorenc (1981) was used to conduct continuous assimilation of altimetric sea level differences from the simulated Geosat exact repeat mission (ERM) into a three-layer quasi-geostrophic eddy-resolving numerical ocean box model that simulates the statistics of mesoscale eddy activity in the western North Pacific. Assimilation was conducted continuously as the Geosat tracks appeared in simulated real time/space, with each track repeating every 17 days, but occurring at different times and locations within the 17-day period, as would have occurred in a realistic nowcast situation. This interpolation method was also used to conduct the assimilation of referenced altimetric sea level differences into the same model, performing the referencing of altimetric sea sevel differences by using the simulated sea level. The results of this dynamical interpolation procedure are compared with those of a statistical (i.e., optimum) interpolation procedure.
The objective was to evaluate the accuracy of the Goddard Earth Model (GEM)-L2 and its improvement in estimating Lageos orbits in order to provide better baselines for plate tectonics, improved polar motion and Earth rotation. Analyses and comparisons with other models are made to verify accuracy in contrast to the Lambeck and Coleman result which denied the authors' accuracy estimates. The analysis presented completely verifies the accuracy of the GEM-L2 model and disproves the statistical methods of Lambeck and Coleman rejecting the accuracy of GEM-L2 as published in their report. The baselines derived from Lageos with GEM-L2 were estimated to have a 2 cm uncertainty due to the errors in the GEM-L2 gravity model which is supported by the results given. The calibration tests indicate that the errors in GEM-L2 should be reduced by about 30 percent which is in the opposite direction to the result obtained by Lambeck and Coleman.
Turbulence statistics relevant to noise modeling and prediction are computed in a near-sonic, unheated round jet in the presence of a flight stream using Large Eddy Simulation. The Naval Research Laboratory’s Jet Engine Noise REduction (JENRE) code is used to obtain unsteady flow solutions which are post-processed for single- and two-point, space-time statistical quantities, including the nominal noise sources from an acoustic analogy formulation. Jets subject to three different flight-stream speeds, along with a baseline static case, are considered. The impact of flight-stream speed on mean flow and turbulence profiles and the amplitudes, length and time scales of Reynolds-stress auto co-variances are determined and scalings for representing the flight stream effect are explored. The implications for source modeling in jet mixing noise predictions within an acoustic analogy formulation are discussed.
This is a Final Report for a Joint Research Interchange (JRI) between NASA Ames Research Center and San Jose State University, Department of Meteorology. We present below a summary of progress made during the duration of this JRI. The focus of this JRI has been to investigate seasonal water vapor transport in the atmosphere of Mars and its effects on the planet's present climate. To this end, the primary task has been to adapt a new dynamical processor for the adiabatic tendencies of the atmospheric circulation into the NASA Ames Mars general circulation model (MGCM). Using identical boundary and initial conditions, several comparative tests between the new and old MGCMs have been performed and the nature of the simulated circulations have been diagnosed. With confidence that the updated version of the Ames MGCM produces quite similar mean and eddy circulation statistics, the new climate model is well poised as a tool to pursue fundamental questions related to the spatial and seasonal variations of atmospheric water vapor on Mars, and to explore exchanges of water with non-atmospheric reservoirs and transport within its atmosphere. In particular, the role of surface sources and sinks can be explored, the range of water-vapor saturation altitudes can be investigated, and plausible precipitation mechanisms can be studied, for a range of atmospheric dust loadings, such future investigations can contribute to a comprehensive study of surface inventories, exchange mechanisms, and the relative importance of atmospheric transport Mars' water cycle. A listing of presentations made and manuscripts submitted during the course of this project is provided.
This is a Final Report for a Joint Research Interchange (JRI) between NASA Ames Research Center and San Jose State University, Department of Meteorology. We present below a summary of progress made during the duration of this JRI. The focus of this JRI has been to investigate seasonal water vapor transport in the atmosphere of Mars and its effects on the planet's present climate. To this end, the primary task has been to adapt a new dynamical processor for the adiabatic tendencies of the atmospheric circulation into the NASA Ames Mars general circulation model (MGCM). Using identical boundary and initial conditions, several comparative tests between the new and old MGCMs have been performed and the nature of the simulated circulations have been diagnosed. With confidence that the updated version of the Ames MGCM produces quite similar mean and eddy circulation statistics, the new climate model is well poised as a tool to pursue fundamental questions related to the spatial and seasonal variations of atmospheric water vapor on Mars, and to explore exchanges of water with non-atmospheric reservoirs and transport within its atmosphere. In particular, the role of surface sources and sinks can be explored, the range of water-vapor saturation altitudes can be investigated, and plausible precipitation mechanisms can be studied, for a range of atmospheric dust loadings. Such future investigations can contribute to a comprehensive study of surface inventories, exchange mechanisms, and the relative importance of atmospheric transport Mars' water cycle. A listing of presentations made and manuscripts submitted during the course of this project is provided.
Earth-GRAM (Earth-Global Reference Atmospheric Model) Overview: Provides monthly statistics at any point in the atmosphere; Monthly, geographic, altitude variation; Current Version - Earth-GRAM 2016, C++; Output Includes - pressure, density, temperature, horizontal and vertical winds, speed of sound, atmospheric constituents; Used by engineering community to create atmospheric dispersions at a rapid runtime; Not a forecast model. MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, Version 2) Background: Developed by NASA Goddard Modelling and Assimilation Office; Horizontal Resolution: 0.625 degrees by 0.5 degrees longitude-by-latitude grid (NCEP (National Centers for Environmental Prediction) reanalysis I vs. 2.5 degrees by 2.5 degrees currently used in Earth-GRAM); Vertical resolution: 72 model layers or interpolated to 42 pressure levels to 0.1 hectopascals (hPa) (NCEP reanalysis I, surface to 10hPa at 17 pressure levels).
Turbulence statistics relevant to noise modeling and prediction are computed in a series near-sonic, unheated round jets in the presence of a flight stream using Large Eddy Simulation. The Naval Research Laboratory’s Jet Engine Noise REduction (JENRE®) code is used to obtain unsteady flow solutions which are post-processed for single- and two-point, space-time statistical quantities, including the nominal noise sources from an acoustic analogy formulation. Jets subject to three different flight-stream speeds, along with a baseline static case, are considered. The impact of flight-stream speed on mean flow and turbulence profiles and the amplitudes, length and time scales of Reynolds-stress auto co-variances are determined and scalings for representing the flight-stream effect are explored. The implications for source modeling in jet mixing noise predictions within an acoustic analogy formulation are discussed.