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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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Studying vaporization of indium-gallium oxide, and gallium-aluminum oxide by Knudsen effusion mass spectroscopy
Remote identification and measurement of subsurface water turbidity and oil on water was accomplished with analytical models which describe the backscatter from smooth surface turbid water, including single scatter and multiple scatter effects. Lidar measurements from natural waterways are also presented and compared with ground observations of several physical water quality parameters.
Papers on major issues and trends that affect the future of intercity transportation are presented. Specific areas covered include: political, social, technological, institutional, and economic mechanisms, the workings of which determine how future intercity transporation technologies will evolve and be put into service; the major issues of intercity transportation from the point of view of reform, including candidate transporation technologies; and technical analysis of trends affecting the evolution of intercity transportation technologies.
Given a Volterra series representations of an input-output system, the authors present a result concerning conditions under which there exists a feedback linearizable realization of the nonlinear system. In addition, they have developed a method to compute the unknown parameters in a canonical form realization from the known Volterra kernels. A finite number of parameters can be calculated from an associated finite number of the kernels. The authors restrict themselves to single-input, single-output continuous-time systems. It is assumed that the systems have real analytic state-space representations. The realization results are local, taking place a neighborhood of the origin in state space.
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The topics are presented in viewgraph form and include: Frequency Modulated Continuous Wave (FMCW) Laser Radar; a FMCW laser block diagram, current problems; and an individual fiber sensor.
The following paper is presented in viewgraph format and covers topics including: (1) linear state feedback control system; (2) Kalman filter state estimation; (3) relation between residual and stochastic part of output; (4) obtaining Kalman filter gain; (5) state estimation under unknown system model and unknown noises; and (6) relationship between filter Markov parameters and system Markov parameters.
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We have measured the properties of He I and Ca II optical absorption lines in the four Trapezium stars of M42 and Theta-exp-2 A Ori using CCD spectra of 3.3 km/s full width at half-maximum intensity (FWHM). A similar analysis was also done of previously published Na I spectra. Some lines are seen in all of the stars and others in as few as one. The 24 different line systems noted in specific ions are matched into 12 absorption velocity systems, three of which coincide with 21 cm H I absorption. Data from 20 different emission-line systems seen in a variety of ionization states are combined into eight emission-line velocity systems, one of which agrees with an absorption system. These data are combined to produce a line-of-sight model to M42, which has the special feature of identifying ionization fronts not only at the near side of the parent molecular cloud, but also on both sides of the neutral lid that overlies the optical nebula.
Identification of large space structures' distributed mass, stiffness, and energy dissipation characteristics poses formidable analytical, numerical, and implementation difficulties. Development of reliable on-orbit structural identification methods is important for implementing active vibration suppression concepts which are under widespread study in the large space structures community. Near the heart of the identification problem lies the necessity of making a large number of spatially distributed measurements of the structure's vibratory response and the associated force/moment inputs with sufficient spatial and frequency resolution. In the present paper, we discuss a method whereby tens of active or passive (retro-reflecting) targets on the structure are tracked simultaneously by the focal planes of two or more video cameras mounted on an adjacent platform. Triangulation (optical ray intersection) of the conjugate image centroids yield inertial trajectories of each target on the structure. Given the triangulated motion of the targets, we apply and extend methodology developed by Creamer, Junkins, and Juang to identify the frequencies, mode shapes, and updated estimates for the mass/stiffness/damping parameterization of the structure. The methodology is semi-automated, for example, the post experiment analysis of the video imagery to determine the inertial trajectories of the targets typically requires less than thirty minutes of real time. Using methodology discussed herein, the frequency response of a large number of points on the structure (where reflective targets are mounted) on the structure can be determined from optical measurements alone. For comparison purposes, we also utilize measurements from accelerometers and a calibrated impulse hammer. While our experimental work remains in a research stage of development, we have successfully tracked and stereo triangulated 20 targets (on a vibrating cantilevered grid structure) at a sample frequency of 200 HZ, and have established conclusively the feasibility and desirability of this approach. We discuss, in summary, recent advances in analog and digital video processing methodology, actuation methods, and bring them to bear on the structural identification problem. We include a brief discussion of our experimental hardware and some recent experimental results which support the practical feasibility of this structural vibration sensing approach.
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Decision-making is the process of identifying and choosing alternatives based on an agreed-upon set of metrics and preferences established by the decision-maker. There are options to be considered during the decision-making process and each option offers a different trajectory and associated success profile in moving from a given system state to the desired system state. The decision-making process typically involves uncertainties associated with the current component and system states. In this sense, probabilistic risk assessment (PRA) can be an analytical method and tool for accomplishing the probabilistic aspect of the decision-making process. Dynamic PRA is an evolution of conventional PRA methodology in which driving forces on modeled plant elements and the element behaviors are explicitly modeled over time. In the recent past, risk assessment methodologies have evolved to address risk issues in a continuously evolving environment and a novel probabilistic dynamics framework in continuous time and state-space discretization forms has been proposed. While state-space discretization has shown its strength in both consequence and causal reasoning modes, several challenges, including the computational requirement and physically meaningful system state identification, exist. Conventional system space discretization has usually been done by either the equal width discretization method or a data-driven method. Those methods naturally possess challenges coming from the physical understanding of discretized system space (i.e., system state) and the trajectory moving from a given system state to another system state. The purpose of this paper is to present a physics-based and data-driven system state discretization method such that one can justify what the discretized system space implies and understand the state trajectory from the viewpoint of operational actions.
Abstract not provided.
An algorithm for failure detection and identification for redundant non-orthogonal arrays of single degree of freedom gyros and accelerometers is described which is optimum in the sense that detection occurs as soon as it is no longer possible to account for the instrument outputs as the outputs of good instruments operating within their noise tolerances, and identification occurs as soon as it is true that only a particular instrument failure could account for the actual instrument outputs within the noise tolerance of good instruments. An estimation algorithm is described which minimizes the maximum possible error magnitude for the given set of instrument outputs. Monte Carlo simulation results are presented for the application of the algorithms to an inertial reference unit consisting of six gyros and six accelerometers in two alternate configurations.
This paper deals with the identification of spatially varying parameters in systems of finite spatial extent which can be described by second order hyperbolic differential equations. Two questions have been addressed. The first deals with 'partial identification' and inquires into the possibility of retrieving all the eigenvalues of the system from response data obtained at one location x-asterisk epsilon (0, 1). The second deals with the identification of the distributed coefficients rho(x), a(x) and b(x). Sufficient conditions for unique identification of all the eigenvalues of the system are obtained, and conditions under which the coefficients can be uniquely identified using suitable response data obtained at one point in the spatial domain are determined. Application of the results and their usefulness is demonstrated in the identification of the properties of tall building structural systems subjected to dynamic load environments.
Failure detection and identification logic for a fault-tolerant longitudinal control system were investigated. Aircraft dynamics were based upon the cruise condition for a hypothetical transonic business jet transport configuration. The fault-tolerant control system consists of conventional control and estimation plus a new outer loop containing failure detection, identification, and reconfiguration (FDIR) logic. It is assumed that the additional logic has access to all measurements, as well as to the outputs of the control and estimation logic. The pilot may also command the FDIR logic to perform special tests.
Identification and control of linear dynamic systems with unknown parameters
An identification algorithm that uses the maximum likelihood technique to identify the unknown time delays, plant parameters, and noise covariances of linear discrete stochastic systems is presented. Cases of additive white noise and colored measurement noises are considered. The likelihood function is evaluated using either a minimum-variance (Kalman) filter or a minimal-order observer. The Kalman filter is used in the identification algorithm to provide minimum-variance estimates. The minimal-order observer is a lower-dimensional and computationally simpler filter, and is advantageous especially for systems with long delays. It provides a less optimal solution to the minimum-mean-square state estimation problem. The colored-noise observer algorithm has the disadvantage of having to compute an extra error covariance matrix of lower order.