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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

NASA Tech Briefs, January 2007

Topics covered include: Flexible Skins Containing Integrated Sensors and Circuitry; Artificial Hair Cells for Sensing Flows; Video Guidance Sensor and Time-of-Flight Rangefinder; Optical Beam-Shear Sensors; Multiple-Agent Air/Ground Autonomous Exploration Systems; A 640 512-Pixel Portable Long-Wavelength Infrared Camera; An Array of Optical Receivers for Deep-Space Communications; Microstrip Antenna Arrays on Multilayer LCP Substrates; Applications for Subvocal Speech; Multiloop Rapid-Rise/Rapid Fall High-Voltage Power Supply; The PICWidget; Fusing Symbolic and Numerical Diagnostic Computations; Probabilistic Reasoning for Robustness in Automated Planning; Short-Term Forecasting of Radiation Belt and Ring Current; JMS Proxy and C/C++ Client SDK; XML Flight/Ground Data Dictionary Management; Cross-Compiler for Modeling Space-Flight Systems; Composite Elastic Skins for Shape-Changing Structures; Glass/Ceramic Composites for Sealing Solid Oxide Fuel Cells; Aligning Optical Fibers by Means of Actuated MEMS Wedges; Manufacturing Large Membrane Mirrors at Low Cost; Double-Vacuum-Bag Process for Making Resin- Matrix Composites; Surface Bacterial-Spore Assay Using Tb3+/DPA Luminescence; Simplified Microarray Technique for Identifying mRNA in Rare Samples; High-Resolution, Wide-Field-of-View Scanning Telescope; Multispectral Imager With Improved Filter Wheel and Optics; Integral Radiator and Storage Tank; Compensation for Phase Anisotropy of a Metal Reflector; Optical Characterization of Molecular Contaminant Films; Integrated Hardware and Software for No-Loss Computing; Decision-Tree Formulation With Order-1 Lateral Execution; GIS Methodology for Planning Planetary-Rover Operations; Optimal Calibration of the Spitzer Space Telescope; Automated Detection of Events of Scientific Interest; Representation-Independent Iteration of Sparse Data Arrays; Mission Operations of the Mars Exploration Rovers; and More About Software for No-Loss Computing.

Source record↗
NASA NTRS2011

Telemetry-Based Ranging

A telemetry-based ranging scheme was developed in which the downlink ranging signal is eliminated, and the range is computed directly from the downlink telemetry signal. This is the first Deep Space Network (DSN) ranging technology that does not require the spacecraft to transmit a separate ranging signal. By contrast, the evolutionary ranging techniques used over the years by NASA missions, including sequential ranging (transmission of a sequence of sinusoids) and PN-ranging (transmission of a pseudo-noise sequence) whether regenerative (spacecraft acquires, then regenerates and retransmits a noise-free ranging signal) or transparent (spacecraft feeds the noisy demodulated uplink ranging signal into the downlink phase modulator) relied on spacecraft power and bandwidth to transmit an explicit ranging signal. The state of the art in ranging is described in an emerging CCSDS (Consultative Committee for Space Data Systems) standard, in which a pseudo-noise (PN) sequence is transmitted from the ground to the spacecraft, acquired onboard, and the PN sequence is coherently retransmitted back to the ground, where a delay measurement is made between the uplink and downlink signals. In this work, the telemetry signal is aligned with the uplink PN code epoch. The ground station computes the delay between the uplink signal transmission and the received downlink telemetry. Such a computation is feasible because symbol synchronizability is already an integral part of the telemetry design. Under existing technology, the telemetry signal cannot be used for ranging because its arrival-time information is not coherent with any Earth reference signal. By introducing this coherence, and performing joint telemetry detection and arrival-time estimation on the ground, a high-rate telemetry signal can provide all the precision necessary for spacecraft ranging.

Hamkins, Jon↗
NASA NTRS1978

Derivation of the equations of motion for complex structures by symbolic manipulation

This paper outlines a computer program especially tailored to the task of deriving explicit equations of motion for structures with point-connected substructures. The special purpose program is written in FORTRAN and is designed for performing the specific algebraic operations encountered in the derivation of explicit equations of motion. The derivation is by the Lagrangian approach. Using an orderly kinematical procedure and a discretization and/or truncation scheme, it is possible to write the kinetic and potential energy of each substructure in a compact vector-matrix form. Then, if each element of the matrices and vectors encountered in the kinetic and potential energy is a known algebraic expression, the computer program performs the necessary operations to evaluate the kinetic and potential energy of the system explicitly. Lagrange's equations for small motions about equilibrium can be deduced directly from the explicit form of the system kinetic and potential energy.

Hale, A. L.↗
NASA NTRS2014

Exact and Approximate Probabilistic Symbolic Execution

Probabilistic software analysis seeks to quantify the likelihood of reaching a target event under uncertain environments. Recent approaches compute probabilities of execution paths using symbolic execution, but do not support nondeterminism. Nondeterminism arises naturally when no suitable probabilistic model can capture a program behavior, e.g., for multithreading or distributed systems. In this work, we propose a technique, based on symbolic execution, to synthesize schedulers that resolve nondeterminism to maximize the probability of reaching a target event. To scale to large systems, we also introduce approximate algorithms to search for good schedulers, speeding up established random sampling and reinforcement learning results through the quantification of path probabilities based on symbolic execution. We implemented the techniques in Symbolic PathFinder and evaluated them on nondeterministic Java programs. We show that our algorithms significantly improve upon a state-of- the-art statistical model checking algorithm, originally developed for Markov Decision Processes.

Symbolic Execution↗
NASA NTRS2002

An Efficient Scheme for Updating Sparse Cholesky Factors

Raghavan had earlier developed the software package DCSPACK which can be used for solving sparse linear systems where the coefficient matrix is symmetric and positive definite (this project was not funded by NASA but by agencies such as NSF). DSCPACK-S is the serial code and DSCPACK-P is a parallel implementation suitable for multiprocessors or networks-of-workstations with message passing using MCI. The main algorithm used is the Cholesky factorization of a sparse symmetric positive positive definite matrix A = LL(T). The code can also compute the factorization A = LDL(T). The complexity of the software arises from several factors relating to the sparsity of the matrix A. A sparse N x N matrix A has typically less that cN nonzeroes where c is a small constant. If the matrix were dense, it would have O(N2) nonzeroes. The most complicated part of such sparse Cholesky factorization relates to fill-in, i.e., zeroes in the original matrix that become nonzeroes in the factor L. An efficient implementation depends to a large extent on complex data structures and on techniques from graph theory to reduce, identify, and manage fill. DSCPACK is based on an efficient multifrontal implementation with fill-managing algorithms and implementation arising from earlier research by Raghavan and others. Sparse Cholesky factorization is typically a four step process: (1) ordering to compute a fill-reducing numbering, (2) symbolic factorization to determine the nonzero structure of L, (3) numeric factorization to compute L, and, (4) triangular solution to solve L(T)x = y and Ly = b. The first two steps are symbolic and are performed using the graph of the matrix. The numeric factorization step is of dominant cost and there are several schemes for improving performance by exploiting the nested and dense structure of groups of columns in the factor. The latter are aimed at better utilization of the cache-memory hierarchy on modem processors to prevent cache-misses and provide execution rates (operations/second) that are close to the peak rates for dense matrix computations. Currently, EPISCOPACY is being used in an application at NASA directed by J. Newman and M. James. We propose the implementation of efficient schemes for updating the LL(T) or LDL(T) factors computed in DSCPACK-S to meet the computational requirements of their project. A brief description is provided in the next section.

Raghavan, Padma↗
NASA NTRS1985

Carrier tracking by smoothing filter can improve symbol SNR

The potential benefit of using a smoothing filter to estimate carrier phase over use of phase locked loops (PLL) is determined. Numerical results are presented for the performance of three possible configurations of the deep space network advanced receiver. These are residual carrier PLL, sideband aided residual carrier PLL, and finally sideband aiding with a Kalman smoother. The average symbol signal to noise ratio (CNR) after losses due to carrier phase estimation error is computed for different total power SNRs, symbol rates and symbol SNRs. It is found that smoothing is most beneficial for low symbol SNRs and low symbol rates. Smoothing gains up to 0.4 dB over a sideband aided residual carrier PLL, and the combined benefit of smoothing and sideband aiding relative to a residual carrier loop is often in excess of 1 dB.

Hurd, W. J.↗
NASA NTRS1986

Carrier tracking by smoothing filter improves symbol SNR

The potential benefit of using a smoothing filter to estimate carrier phase over use of phase locked loops (PLL) is determined. Numerical results are presented for the performance of three possible configurations of the deep space network advanced receiver. These are residual carrier PLL, sideband aided residual carrier PLL, and finally sideband aiding with a Kalman smoother. The average symbol signal to noise ratio (SNR) after losses due to carrier phase estimation error is computed for different total power SNRs, symbol rates and symbol SNRs. It is found that smoothing is most beneficial for low symbol SNRs and low symbol rates. Smoothing gains up to 0.4 dB over a sideband aided residual carrier PLL, and the combined benefit of smoothing and sideband aiding relative to a residual carrier loop is often in excess of 1 dB.

Pomalaza-Raez, Carlos A.↗
NASA NTRS1980

Symbolic-numeric interface: A review

A survey of the use of a combination of symbolic and numerical calculations is presented. Symbolic calculations primarily refer to the computer processing of procedures from classical algebra, analysis, and calculus. Numerical calculations refer to both numerical mathematics research and scientific computation. This survey is intended to point out a large number of problem areas where a cooperation of symbolic and numerical methods is likely to bear many fruits. These areas include such classical operations as differentiation and integration, such diverse activities as function approximations and qualitative analysis, and such contemporary topics as finite element calculations and computation complexity. It is contended that other less obvious topics such as the fast Fourier transform, linear algebra, nonlinear analysis and error analysis would also benefit from a synergistic approach.

Ng, E. W.↗
NASA NTRS2003

Automated Modeling and Simulation Using the Bond Graph Method for the Aerospace Industry

Bond graph modeling was originally developed in the late 1950s by the late Prof. Henry M. Paynter of M.I.T. Prof. Paynter acted well before his time as the main advantage of his creation, other than the modeling insight that it provides and the ability of effectively dealing with Mechatronics, came into fruition only with the recent advent of modern computer technology and the tools derived as a result of it, including symbolic manipulation, MATLAB, and SIMULINK and the Computer Aided Modeling Program (CAMPG). Thus, only recently have these tools been available allowing one to fully utilize the advantages that the bond graph method has to offer. The purpose of this paper is to help fill the knowledge void concerning its use of bond graphs in the aerospace industry. The paper first presents simple examples to serve as a tutorial on bond graphs for those not familiar with the technique. The reader is given the basic understanding needed to appreciate the applications that follow. After that, several aerospace applications are developed such as modeling of an arresting system for aircraft carrier landings, suspension models used for landing gears and multibody dynamics. The paper presents also an update on NASA's progress in modeling the International Space Station (ISS) using bond graph techniques, and an advanced actuation system utilizing shape memory alloys. The later covers the Mechatronics advantages of the bond graph method, applications that simultaneously involves mechanical, hydraulic, thermal, and electrical subsystem modeling.

Granda, Jose J.↗
NASA NTRS2011

Engineering and Scientific Applications: Using MatLab(Registered Trademark) for Data Processing and Visualization

MatLab(R) (MATrix LABoratory) is a numerical computation and simulation tool that is used by thousands Scientists and Engineers in many cou ntries. MatLab does purely numerical calculations, which can be used as a glorified calculator or interpreter programming language; its re al strength is in matrix manipulations. Computer algebra functionalities are achieved within the MatLab environment using "symbolic" toolbo x. This feature is similar to computer algebra programs, provided by Maple or Mathematica to calculate with mathematical equations using s ymbolic operations. MatLab in its interpreter programming language fo rm (command interface) is similar with well known programming languag es such as C/C++, support data structures and cell arrays to define c lasses in object oriented programming. As such, MatLab is equipped with most ofthe essential constructs of a higher programming language. M atLab is packaged with an editor and debugging functionality useful t o perform analysis of large MatLab programs and find errors. We belie ve there are many ways to approach real-world problems; prescribed methods to ensure foregoing solutions are incorporated in design and ana lysis of data processing and visualization can benefit engineers and scientist in gaining wider insight in actual implementation of their perspective experiments. This presentation will focus on data processing and visualizations aspects of engineering and scientific applicati ons. Specifically, it will discuss methods and techniques to perform intermediate-level data processing covering engineering and scientifi c problems. MatLab programming techniques including reading various data files formats to produce customized publication-quality graphics, importing engineering and/or scientific data, organizing data in tabu lar format, exporting data to be used by other software programs such as Microsoft Excel, data presentation and visualization will be discussed. The presentation will emphasize creating practIcal scripts (pro grams) that extend the basic features of MatLab TOPICS mclude (1) Ma trix and vector analysis and manipulations (2) Mathematical functions (3) Symbolic calculations & functions (4) Import/export data files (5) Program lOgic and flow control (6) Writing function and passing parameters (7) Test application programs

Sen, Syamal K.↗
NASA NTRS1976

An algorithm for automatic reduction of complex signal flow graphs

A computer algorithm is developed that provides efficient means to compute transmittances directly from a signal flow graph or a block diagram. Signal flow graphs are cast as directed graphs described by adjacency matrices. Nonsearch computation, designed for compilers without symbolic capability, is used to identify all arcs that are members of simple cycles for use with Mason's gain formula. The routine does not require the visual acumen of an interpreter to reduce the topology of the graph, and it is particularly useful for analyzing control systems described for computer analyses by means of interactive graphics.

Young, K. R.↗
NASA NTRS1991

Computational attributes of the integral form of the equation of transfer

Difficulties can arise in radiative and neutron transport calculations when a highly anisotropic scattering phase function is present. In the presence of anisotropy, currently used numerical solutions are based on the integro-differential form of the linearized Boltzmann transport equation. This paper, departs from classical thought and presents an alternative numerical approach based on application of the integral form of the transport equation. Use of the integral formalism facilitates the following steps: a reduction in dimensionality of the system prior to discretization, the use of symbolic manipulation to augment the computational procedure, and the direct determination of key physical quantities which are derivable through the various Legendre moments of the intensity. The approach is developed in the context of radiative heat transfer in a plane-parallel geometry, and results are presented and compared with existing benchmark solutions. Encouraging results are presented to illustrate the potential of the integral formalism for computation. The integral formalism appears to possess several computational attributes which are well-suited to radiative and neutron transport calculations.

Frankel, J. I.↗
NASA NTRS2012

Blocking Losses With a Photon Counter

It was not known how to assess accurately losses in a communications link due to photodetector blocking, a phenomenon wherein a detector is rendered inactive for a short time after the detection of a photon. When used to detect a communications signal, blocking leads to losses relative to an ideal detector, which may be measured as a reduction in the communications rate for a given received signal power, or an increase in the signal power required to support the same communications rate. This work involved characterizing blocking losses for single detectors and arrays of detectors. Blocking may be mitigated by spreading the signal intensity over an array of detectors, reducing the count rate on any one detector. A simple approximation was made to the blocking loss as a function of the probability that a detector is unblocked at a given time, essentially treating the blocking probability as a scaling of the detection efficiency. An exact statistical characterization was derived for a single detector, and an approximation for multiple detectors. This allowed derivation of several accurate approximations to the loss. Methods were also derived to account for a rise time in recovery, and non-uniform illumination due to diffraction and atmospheric distortion of the phase front. It was assumed that the communications signal is intensity modulated and received by an array of photon-counting photodetectors. For the purpose of this analysis, it was assumed that the detectors are ideal, in that they produce a signal that allows one to reproduce the arrival times of electrons, produced either as photoelectrons or from dark noise, exactly. For single detectors, the performance of the maximum-likelihood (ML) receiver in blocking is illustrated, as well as a maximum-count (MC) receiver, that, when receiving a pulse-position-modulated (PPM) signal, selects the symbol corresponding to the slot with the largest electron count. Whereas the MC receiver saturates at high count rates, the ML receiver may not. The loss in capacity, symbol-error-rate (SER), and count-rate were numerically computed. It was shown that the capacity and symbol-error-rate losses track, whereas the count-rate loss does not generally reflect the SER or capacity loss, as the slot-statistics at the detector output are no longer Poisson. It is also shown that the MC receiver loss may be accurately predicted for dead times on the order of a slot.

Moision, Burce E.↗
NASA NTRS1978

Image synthesis for SAR system, calibration and processor design

The Point Scattering Method of simulating radar imagery rigorously models all aspects of the imaging radar phenomena. Its computational algorithms operate on a symbolic representation of the terrain test site to calculate such parameters as range, angle of incidence, resolution cell size, etc. Empirical backscatter data and elevation data are utilized to model the terrain. Additionally, the important geometrical/propagation effects such as shadow, foreshortening, layover, and local angle of incidence are rigorously treated. Applications of radar image simulation to a proposed calibrated SAR system are highlighted: soil moisture detection and vegetation discrimination.

Holtzman, J. C.↗
NASA NTRS2010

Modeling Regular Replacement for String Constraint Solving

Bugs in user input sanitation of software systems often lead to vulnerabilities. Among them many are caused by improper use of regular replacement. This paper presents a precise modeling of various semantics of regular substitution, such as the declarative, finite, greedy, and reluctant, using finite state transducers (FST). By projecting an FST to its input/output tapes, we are able to solve atomic string constraints, which can be applied to both the forward and backward image computation in model checking and symbolic execution of text processing programs. We report several interesting discoveries, e.g., certain fragments of the general problem can be handled using less expressive deterministic FST. A compact representation of FST is implemented in SUSHI, a string constraint solver. It is applied to detecting vulnerabilities in web applications

Fu, Xiang↗
NASA NTRS2009

Symbolic Processing Combined with Model-Based Reasoning

A computer program for the detection of present and prediction of future discrete states of a complex, real-time engineering system utilizes a combination of symbolic processing and numerical model-based reasoning. One of the biggest weaknesses of a purely symbolic approach is that it enables prediction of only future discrete states while missing all unmodeled states or leading to incorrect identification of an unmodeled state as a modeled one. A purely numerical approach is based on a combination of statistical methods and mathematical models of the applicable physics and necessitates development of a complete model to the level of fidelity required for prediction. In addition, a purely numerical approach does not afford the ability to qualify its results without some form of symbolic processing. The present software implements numerical algorithms to detect unmodeled events and symbolic algorithms to predict expected behavior, correlate the expected behavior with the unmodeled events, and interpret the results in order to predict future discrete states. The approach embodied in this software differs from that of the BEAM methodology (aspects of which have been discussed in several prior NASA Tech Briefs articles), which provides for prediction of future measurements in the continuous-data domain.

James, Mark↗