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At least 271 records · Page 15

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NASA NTRS1973

Design relationships for holographic memories.

The maximum capacity of a block-oriented, random-access memory is determined primarily by the geometrical parameters of the lenses required to create a Fourier transform of a spatial bit pattern and to image the reconstructed bits onto a photodetector array. Furthermore, the maximum packing density is determined primarily by the same parameters. Several important relationships are developed that are useful in the preliminary design of holographic memories.

Lugt, A. V.↗

NASA NTRS1984

Scale lengths in quasi-parallel shocks

A review was carried out of ISEE and Voyager spacecraft magnetometer data to determine if quasi-parallel bow shocks are really broad, disordered regions. The key parameter was the deceleration scale (thickness, Lp) across which random energy would need to increase and a localized electrostatic field (E) would be present. Lp would define the breadth of the shock and be associated with a plasma deceleration. The ISEE 1 satellite collected data on the electron density, bulk speed, magnetic intensity, and electron temperature in November 1977 during five traverses of the bow shock. Similar data were gathered from an interplanetary shock wave in 1981. The evidence supported the concept of a plasma deceleration across a thin layer (Lp) in both types of shocks. The layers were about 50 times (interplanetary) and 20 times (earth) thinner than surrounding magnetic fluctuation regions. It is asserted that the regions of deceleration, although much thinner, are the actual shocks and not the entire regions of magnetic fluctuations.

Scudder, J. D.↗

NASA NTRS1990

A system performance throughput model applicable to advanced manned telescience systems

As automated space systems become more complex, autonomous, and opaque to the flight crew, it becomes increasingly difficult to determine whether the total system is performing as it should. Some of the complex and interrelated human performance measurement issues are addressed that are related to total system validation. An evaluative throughput model is presented which can be used to generate a human operator-related benchmark or figure of merit for a given system which involves humans at the input and output ends as well as other automated intelligent agents. The concept of sustained and accurate command/control data information transfer is introduced. The first two input parameters of the model involve nominal and off-nominal predicted events. The first of these calls for a detailed task analysis while the second is for a contingency event assessment. The last two required input parameters involving actual (measured) events, namely human performance and continuous semi-automated system performance. An expression combining these four parameters was found using digital simulations and identical, representative, random data to yield the smallest variance.

Haines, Richard F.↗

NASA NTRS1993

The galaxy velocity field and CDM models

It is generally accepted that some kind of non-baryonic dark matter accounts for most of the mass density of the universe. Considering such a component has become, in the last decade, a key ingredient in current theories of structure formation. In particular, the Cold Dark Matter (CDM) scenario has proven to be quite successful in explaining most of the observed properties of galaxies and of their large-scale distribution. The standard CDM model is characterized by a primordial Zel'dovich spectrum, of random-phase adiabatic perturbations in a universe with density parameter omega sub 0 = 1 and vanishing cosmological constant. This poster paper presents an analysis of observational data on peculiar motion of optical galaxies in comparison to the predictions of CDM models where the assumptions of the standard scenario: omega sub 0 = 1, n = 1, and bias parameter b = 1 are relaxed. In particular, CDM models with 0 less than n less than 1 and 0.4 less than omega sub 0 less than 1 are considered.

Tormen, Giuseppe↗

NASA NTRS2010

CARS Spectral Fitting with Multiple Resonant Species using Sparse Libraries

The dual pump CARS technique is often used in the study of turbulent flames. Fast and accurate algorithms are needed for fitting dual-pump CARS spectra for temperature and multiple chemical species. This paper describes the development of such an algorithm. The algorithm employs sparse libraries, whose size grows much more slowly with number of species than a conventional library. The method was demonstrated by fitting synthetic "experimental" spectra containing 4 resonant species (N2, O2, H2 and CO2), both with noise and without it, and by fitting experimental spectra from a H2-air flame produced by a Hencken burner. In both studies, weighted least squares fitting of signal, as opposed to least squares fitting signal or square-root signal, was shown to produce the least random error and minimize bias error in the fitted parameters.

Cutler, Andrew D.↗

NASA NTRS1982

Application of a computerized vibroacoustic data bank for random vibration criteria development

A computerized data bank system was developed for utilization of large amounts of vibration and acoustic data to formulate component random vibration design and test criteria. This system consists of a computer, graphics tablets, and a dry silver hard copier which are all desk top type hardware and occupy minimal space. Currently, the data bank contains data from the Saturn 5 and Titan 3 flight and static test programs. The vibration and acoustic data are stored in the form of power spectral density and one third octave band plots over the frequency range from 20 to 2000 Hz. The data were stored by digitizing each spectral plot by tracing with the graphics tablet. The digitized data were statistically analyzed, and the resulting 97.5 percent confidence levels were stored on tape along with the appropriate structural parameters. Standard extrapolation procedures were programmed for prediction of component random vibration test criteria for new launch vehicle and payload configurations. A user's manual is included to guide potential users through the programs.

Ferebee, R. C.↗

NASA NTRS1982

Automation of vibroacoustic data bank for random vibration criteria development

A computerized data bank system was developed for utilization of large amounts of vibration and acoustic data to formulate component random vibration design and test criteria. This system consists of a computer, graphics tablet, and a dry-silver hard copier which are all desk-top type hardware and occupy minimal space. The data bank contains data from the Saturn V and Titan III flight and static test programs. The vibration and acoustic data are stored in the form of power spectral density and one-third octave band plots over the frequency range from 20 to 2000 Hz. The data was stored by digitizing each spectral plot by tracing with the graphics tablet. The digitized data was statistically analyzed and the resulting 97.5% probability levels were stored on tape along with the appropriate structural parameters. Standard extrapolation procedures were programmed for prediction of component random vibration test criteria for new launch vehicle and payload configurations. This automated vibroacoustic data bank system greatly enhances the speed and accuracy of formulating vibration test criteria. In the future, the data bank will be expanded to include all data acquired from the space shuttle flight test program.

Ferebee, R. C.↗

NASA NTRS2007

Collocation and Pattern Recognition Effects on System Failure Remediation

Previous research found that operators prefer to have status, alerts, and controls located on the same screen. Unfortunately, that research was done with displays that were not designed specifically for collocation. In this experiment, twelve subjects evaluated two displays specifically designed for collocating system information against a baseline that consisted of dial status displays, a separate alert area, and a controls panel. These displays differed in the amount of collocation, pattern matching, and parameter movement compared to display size. During the data runs, subjects kept a randomly moving target centered on a display using a left-handed joystick and they scanned system displays to find a problem in order to correct it using the provided checklist. Results indicate that large parameter movement aided detection and then pattern recognition is needed for diagnosis but the collocated displays centralized all the information subjects needed, which reduced workload. Therefore, the collocated display with large parameter movement may be an acceptable display after familiarization because of the possible pattern recognition developed with training and its use.

Trujillo, Anna C.↗

NASA NTRS1993

Application of neural network to remote sensing of soil moisture using theoretical polarimetric backscattering coefficients

This paper investigates the potential application of neural network to inversion of soil moisture using polarimetric remote sensing data. The neural network used for the inversion of soil parameters is multi-layer perceptron trained with the back-propagation algorithm. The training data include the polarimetric backscattering coefficients obtained from theoretical surface scattering models together with an assumed nominal range of soil parameters which are comprised of the soil permittivity and surface roughness parameters. Soil permittivity is calculated from the soil moisture and the assumed soil texture based on an empirical formula at C-, L-, and P-bands. The rough surface parameters for the soil surface, which is described by the Gaussian random process, are the root-mean-square (rms) height and correlation length. For the rough surface scattering, small perturbation method is used for the L-band frequency, and Kirchhoff approximation is used for the C-band frequency to obtain the corresponding backscattering coefficients. During the training, the backscattering coefficients are the inputs to the neural net and the output from the net are compared with the desired soil parameters to adjust the interconnecting weights. The process is repeated for each input-output data entry and then for the entire training data until convergence is reached. After training, the backscattering coefficients are applied to the trained neural net to retrieve the soil parameters which are compared with the desired soil parameters to verify the effectiveness of this technique. Several cases are examined. First, for simplicity, the correlation length and rms height of the soil surface are fixed while soil moisture is varied. Soil moisture obtained using the neural networks with either L-band or C-band backscattering coefficients for the HH and VV polarizations as inputs is in good agreement with the desired soil moisture. The neural net output matches the desired output for the soil moisture range of 16 to 60 percent for the C-band case. The next case investigated is to vary both soil moisture and rms height while keeping the correlation length fixed. For this case, C-band backscattering coefficients are not sufficient for retrieving two parameters because the Kirchhoff approximation gives the same HH and VV backscattering coefficients. Therefore, the backscattering coefficients at two different frequency bands are necessary to find both the soil moisture and rms height. Finally, the neural nets are also applied to simultaneously invert soil moisture, rms height, and correlation length. Overall, the soil moisture retrieved from the neural network agrees very well with the desired soil moisture. This suggests that the neural network shows potential for retrieval of soil parameters from remote sensing data.

Wang, L.↗

NASA NTRS2013

Extracting Tree Height from Repeat-Pass PolInSAR Data : Experiments with JPL and ESA Airborne Systems

In this paper we present our latest developments and experiments with the random-motion-over-ground (RMoG) model used to extract canopy height and other important forest parameters from repeat-pass polarimetricinterferometric SAR (Pol-InSAR) data. More specifically, we summarize the key features of the RMoG model in contrast with the random-volume-over-ground (RVoG) model, describe in detail a possible inversion scheme for the RMoG model and illustrate the results of the RMoG inversion using airborne data collected by the Jet Propulsion Laboratory (JPL) and the European Space Agency (ESA).

Pol-InSAR↗

NASA NTRS2018

Far-Field Lorenz-Mie Scattering in an Absorbing Host Medium: Theoretical Formalism and FORTRAN Program

In this paper we make practical use of the recently developed first-principles approach to electromagnetic scattering by particles immersed in an unbounded absorbing host medium. Specifically, we introduce an actual computational tool for the calculation of pertinent far-field optical observables in the context of the classical Lorenzâ€"Mie theory. The paper summarizes the relevant theoretical formalism, explains various aspects of the corresponding numerical algorithm, specifies the input and output parameters of a FORTRAN program available at https://www.giss.nasa.gov/staff/mmishchenko/Lorenz-Mie.html, and tabulates benchmark results useful for testing purposes. This public-domain FORTRAN program enables one to solve the following two important problems: (i) simulate theoretically the reading of a remote well-collimated radiometer measuring electromagnetic scattering by an individual spherical particle or a small random group of spherical particles; and (ii) compute the single-scattering parameters that enter the vector radiative transfer equation derived directly from the Maxwell equations.

Far-field electromagnetic scattering; Absorbing ho↗

DOE OSTI2025

How does ion temperature gradient turbulence depend on magnetic geometry? Insights from data and machine learning

Magnetic geometry has a significant effect on the level of turbulent transport in fusion plasmas. Here, we model and analyse this dependence using multiple machine learning methods and a dataset of >200 000 nonlinear gyrokinetic simulations of ion-temperature-gradient turbulence in diverse non-axisymmetric geometries. The dataset is generated using a large collection of both optimised and randomly generated stellarator equilibria. At fixed gradients and other input parameters, the turbulent heat flux varies between geometries by several orders of magnitude. Trends are apparent among the configurations with particularly high or particularly low heat flux. Regression and classification techniques from machine learning are then applied to extract patterns in the dataset. Due to a symmetry of the gyrokinetic equation, the heat flux and regressions thereof should be invariant to translations of the raw features in the parallel coordinate, similar to translation invariance in computer vision applications. Multiple regression models including convolutional neural networks (CNNs) and decision trees can achieve reasonable predictive power for the heat flux in held-out test configurations, with highest accuracy for the CNNs. Using Spearman correlation, sequential feature selection and Shapley values to measure feature importance, it is consistently found that the most important geometric lever on the heat flux is the flux surface compression in regions of bad curvature. The second most important geometric feature relates to the magnitude of geodesic curvature. These two features align remarkably with surrogates that have been proposed based on theory, while the methods here allow a natural extension to more features for increased accuracy. The dataset, released with this publication, may also be used to test other proposed surrogates, and we find that many previously published proxies do correlate well with both the heat flux and stability boundary.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Isoconversional Kinetic Analysis of Oxygen Desorption for Sr 0.75 Ca 0.25 FeO 3-δ Perovskite in Dry and Steam-Based Environments

The desorption kinetics of Sr 0.75 Ca 0.25 FeO 3-δ perovskite material are examined in both dry and steam-based environments using redox gaseous products of a laboratory-scale fixed bed. First, desorption kinetics associated with the dry environment are proposed based on Friedman’s isoconversional method. It is found from the reconstructed reaction model that the desorption kinetics of this perovskite are controlled by a three-step mechanism, where hypothetically, the phase-boundary reaction rapidly prevails first, followed by diffusion and/or nucleation, and finally, unimolecular decay or random nucleation-controlled reaction. Subsequent analyses of the Arrhenius parameters indicated that both the early and later desorption stages, respectively, controlled by the phase boundary and unimolecular/random nucleation reactions, are associated with low energetic demand. In contrast, a high energetic penalty is required for the diffusion/nucleation reaction. Further, a satisfactory a priori verification of the proposed kinetics suggested that the three-step reaction mechanism reasonably describes the desorption of this perovskite in a dry environment. With the presence of steam in the desorption environment, oxygen production is substantially inhibited, but the hypothetical three-step mechanism is still found to control the desorption. Meanwhile, the primary effects of the steam on these mechanisms include the widening of the conversion extent associated with the diffusion/nucleation reaction, along with a higher energetic demand compared to the dry environment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1971

Application of Monte Carlo techniques to optimization of high-energy beam transport in a stochastic environment

An algorithm employing a modified sequential random perturbation, or creeping random search, was applied to the problem of optimizing the parameters of a high-energy beam transport system. The stochastic solution of the mathematical model for first-order magnetic-field expansion allows the inclusion of state-variable constraints, and the inclusion of parameter constraints allowed by the method of algorithm application eliminates the possibility of infeasible solutions. The mathematical model and the algorithm were programmed for a real-time simulation facility; thus, two important features are provided to the beam designer: (1) a strong degree of man-machine communication (even to the extent of bypassing the algorithm and applying analog-matching techniques), and (2) extensive graphics for displaying information concerning both algorithm operation and transport-system behavior. Chromatic aberration was also included in the mathematical model and in the optimization process. Results presented show this method as yielding better solutions (in terms of resolutions) to the particular problem than those of a standard analog program as well as demonstrating flexibility, in terms of elements, constraints, and chromatic aberration, allowed by user interaction with both the algorithm and the stochastic model. Example of slit usage and a limited comparison of predicted results and actual results obtained with a 600 MeV cyclotron are given.

Parrish, R. V.↗

NASA NTRS1973

Identification of an object by input and output spectral characteristics

The problem is discussed of identification of a linear object of known structure, the movement of which is described by a system of differential equations of the type y = Ay + Bu, where y is an n-dimensional output vector, u is an m-dimensional vector of stationary, random disturbances (inputs), A and B are matrices of unknown parameters of the dimension, n x n and n x m, respectively. The spectral and reciprocal spectral densities of the inputs and outputs are used as the initial information on the object.

Redko, S. F.↗

NASA NTRS1975

A generalized correlation of experimental flat-plate collector performance

A generalized collector performance correlation was derived and shown by experimental verification to be of the proper form to account for the majority of the variable conditions encountered both in outdoor and in indoor collector tests. This correlation permits a determination of collector parameters which are essentially nonvarying under conditions which do vary randomly (outdoors) or conditions which vary in a controlled manner (indoors - simulator). It was shown that correlation of the experimental performance of collectors allows the following: (1) comparisons of different collector designs; (2) collector performance prediction under conditions that differ from the conditions of the test program; and (3) monitoring performance degradation effects.

Simon, F. F.↗

NASA NTRS1981

NOSS/ALDCS analysis and system requirements definition

The results of system analyses and implementation studies of an advanced location and data collection system (ALDCS) , proposed for inclusion on the National Oceanic Satellite System (NOSS) spacecraft are reported. The system applies Doppler processing and radiofrequency interferometer position location technqiues both alone and in combination. Aspects analyzed include: the constraints imposed by random access to the system by platforms, the RF link parameters, geometric concepts of position and velocity estimation by the two techniques considered, and the effects of electrical measurement errors, spacecraft attitude errors, and geometric parameters on estimation accuracy. Hardware techniques and trade-offs for interferometric phase measurement, ambiguity resolution and calibration are considered. A combined Doppler-interferometer ALDCS intended to fulfill the NOSS data validation and oceanic research support mission is also described.

Reed, D. L.↗

NASA NTRS1981

Correlation function studies for snow and ice

The random medium model is used to characterize snow and ice fields in the interpretation of active and passive microwave remote sensing data. A correlation function is used to describe the random permittivity fluctuations with the associated mean and variance and correlation lengths; and several samples are investigated to determine typical correlation functions for snow and ice. It is shown that correlation functions are extracted directly from appropriate ground truth data, and an exponential correlation function is observed for snow and ice with lengths corresponding to the actual size of ice particles or air bubbles. Thus, given that a medium has spatially stationary statistics and a small medium, the random medium model can interpret remote sensing data where theoretical parameters correspond to actual physical parameters of the terrain.

Vallese, F.↗