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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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Pretreatment technique accelerates compression set of O-rings: seal is pressure loaded; seal and pressure mechanisms are heated to 160 F; load is applied to heated seal, causing material to flow; parts are cooled to room temperature; and load is removed.
The results presented represent the first successful attempt at the measurement of the driving capabilities of coaxial gaseous propellant rocket injectors. The required data have been obtained by employing the modified impedance tube technique with compressed air as the oxidizer and acetylene gas as the fuel. The data describe the frequency dependence of the injector admittances, from which the frequency dependence of the injection response factors can be calculated. The measured injector admittances have been compared with the predictions of the Feiler and Heidmann (1967) analytical model assuming different values for the characteristic combustion time. The values of combustion time which result in a best fit between the measured and predicted data are indicated for different equivalence ratios. It is shown that for the coaxial injector system investigated in this study the characteristic combustion times vary between .7 and 1.2 msec for equivalence ratios in the range of .57 to 1.31. The experimental data clearly show that the tested injector system could indeed drive combustion instabilities over a frequency range that is in qualitative agreement with the predictions of the Feiler and Heidmann model.
The paper describes the results of an experimental study of the quantitative determination of the capabilities of the combustion processes associated with coaxial gaseous propellant rocket injectors to drive combustor pressure oscillations. The data, obtained by employing the modified impedance tube technique with compressed air as the oxidizer and acetylene gas as the fuel, describe the frequency dependence of the admittance of the combined injector-combustion process. The measured data are compared with the predictions of the Feiler and Heidmann analytical model utilizing different values for the characteristic combustion time tau sub b. The values of tau sub b which result in a best fit between the measured and predicted data are indicated for different equivalence ratios. It is shown that for the coaxial injector investigated in this study the tau sub b varies between 0.7 and 1.2 msec for equivalence ratios in the range of 0.57 to 1.31. In addition, the experimental data indicate that the tested injector system could drive combustion instabilities over a frequency range that is in qualitative agreement with the predictions of the Feiler and Heidmann model.
An indentation test technique for compressively loading the ends of individual fibers to produce debonding has been applied to metal, glass, and glass-ceramic matrix composites; bond strength values at debond initiation are calculated using a finite-element model. Results are correlated with composite longitudinal and interlaminar shear behavior for carbon and Nicalon fiber-reinforced glasses and glass-ceramics including the effects of matrix modifications, processing conditions, and high-temperature oxidation embrittlement. The data indicate that significant bonding to improve off-axis and shear properties can be tolerated before the longitudinal behavior becomes brittle. Residual stress and other mechanical bonding effects are important, but improved analyses and multiaxial interfacial failure criteria are needed to adequately interpret bond strength data in terms of composite performance.
Code excited linear prediction coding is an efficient technique for compressing speech sequences. Communications quality of speech can be obtained at bit rates below 8 Kb/s. However, relatively large coding delays are necessary to buffer the input speech in order to perform the LPC analysis. A low delay 8 Kb/s CELP coder is introduced in which the short term predictor is based on past synthesized speech. A new distortion measure that improves the tracking of the formant filter is discussed. Formal listening tests showed that the performance of the backward adaptive coder is almost as good as the conventional CELP coder.
The following question about Huffman coding, which is an important technique for compressing data from a discrete source, is considered. If p is the smallest source probability, how long, in terms of p, can the longest Huffman codeword be? It is shown that if p is in the range 0 less than p less than or equal to 1/2, and if K is the unique index such that 1/F(sub K+3) less than p less than or equal to 1/F(sub K+2), where F(sub K) denotes the Kth Fibonacci number, then the longest Huffman codeword for a source whose least probability is p is at most K, and no better bound is possible. Asymptotically, this implies the surprising fact that for small values of p, a Huffman code's longest codeword can be as much as 44 percent larger than that of the corresponding Shannon code.
The advent of digital information storage and retrieval has led to explosive growth in data transmission techniques, data compression alternatives, and the need for high capacity random access data storage. Advances in data storage technologies are limiting the utilization of digitally based systems. New storage technologies will be required which can provide higher data capacities and faster transfer rates in a more compact format. Magnetic disk/tape and current optical data storage technologies do not provide these higher performance requirements for all digital data applications. A new technology developed at the Optex Corporation out-performs all other existing data storage technologies. The Electron Trapping Optical Memory (ETOM) media is capable of storing as much as 14 gigabytes of uncompressed data on a single, double-sided 54 inch disk with a data transfer rate of up to 12 megabits per second. The disk is removable, compact, lightweight, environmentally stable, and robust. Since the Write/Read/Erase (W/R/E) processes are carried out 100 percent photonically, no heating of the recording media is required. Therefore, the storage media suffers no deleterious effects from repeated Write/Read/Erase cycling.
Feedfoward neural networks with backpropagation learning have been used as function approximators for modeling the space shuttle main engine (SSME) sensor signals. The modeling of these sensor signals is aimed at the development of a sensor fault detection system that can be used during ground test firings. The generalization capability of a neural network based function approximator depends on the training vectors which in this application may be derived from a number of SSME ground test-firings. This yields a large number of training vectors. Large training sets can cause the time required to train the network to be very large. Also, the network may not be able to generalize for large training sets. To reduce the size of the training sets, the SSME test-firing data is reduced using the learning vector quantization (LVQ) based technique. Different compression ratios were used to obtain compressed data in training the neural network model. The performance of the neural model trained using reduced sets of training patterns is presented and compared with the performance of the model trained using complete data. The LVQ can also be used as a function approximator. The performance of the LVQ as a function approximator using reduced training sets is presented and compared with the performance of the backpropagation network.
Report discusses noiseless data-compression-coding algorithms, performance characteristics and practical consideration in implementation of algorithms in coding modules composed of very-large-scale integrated circuits. Report also has value as tutorial document on data-compression-coding concepts. Coding techniques and concepts in question "universal" in sense that, in principle, applicable to streams of data from variety of sources. However, discussion oriented toward compression of high-rate data generated by spaceborne sensors for lower-rate transmission back to earth.
The results of a study of techniques for spatial compression of synthetic-aperture-radar (SAR) imagery are summarized. Emphasis is on image-data volume reduction for archive and online storage applications while preserving the image resolution and radiometric fidelity. A quantitative analysis of various techniques, including vector quantization (VQ) and adaptive discrete cosine transform (ADCT), is presented. Various factors such as compression ratio, algorithm complexity, and image quality are considered in determining the optimal algorithm. The compression system requirements are established for electronic access of an online archive system based on the results of a survey of the science community. The various algorithms are presented and their results evaluated considering the effects of speckle noise and the wide dynamic range inherent in SAR imagery. The conclusion is that although the ADCT produces the best signal-to-distortion-noise ratio for a given compression ratio, the two-level tree-searched VQ technique is preferred due to its simplicity of decoding and near-optimal performance.
This report describes the results of a seven day effort to assist subject matter experts address a problem related to COVID-19. In the course of this effort, we analyzed the 29K documents provided as part of the White House's call to action. This involved applying a variety of natural language processing techniques and compression-based analytics in combination with visualization techniques and assessment with subject matter experts to pursue answers to a specific question. In this paper, we will describe the algorithms, the software, the study performed, and availability of the software developed during the effort.
The techniques of Galilean-Lorentz transformation and matched asymptotic expansions are used to simplify the procedure of calculating the lift and pressure distribution induced on an infinite-span thin wing interacting with an oblique sinusoidal gust in subsonic flow. This technique requires that the product of the flow Mach number and the reduced frequency be small. Under this condition, the inner region of the transformed space behaves as an incompressible flow, so that existing incompressible flow theories can be used as a basis to construct closed-form solutions for the airload induced on the wing. This approach is an extension of the GASP approximation developed by Amiet and Sears (1970). Results are obtained for both the magnitude and the phase of the unsteady lift due to interaction with gust. These results are compared with available numerical results. Some discrepancies are noted and discussed.
An in-house developed proprietary advanced computational fluid dynamics code called SHARP (Trademark) is a primary tool for many flow simulations and design analyses. The SHARP code is a time dependent, two dimensional (2-D) axisymmetric numerical solution technique for the compressible Navier-Stokes equations. The solution technique in SHARP uses a vectorizable implicit, second order accurate in time and space, finite volume scheme based on an upwind flux-difference splitting of a Roe-type approximated Riemann solver, Van Leer's flux vector splitting, and a fourth order artificial dissipation scheme with a preconditioning to accelerate the flow solution. Turbulence is simulated by an algebraic model, and ultimately the kappa-epsilon model. Some other capabilities of the code are 2-D two-phase Lagrangian particle tracking and cell blockages. Extensive development and testing has been conducted on the 3-D version of the code with flow, combustion, and turbulence interactions. The emphasis here is on the specific applications of SHARP in Solid Rocket Motor design. Information is given in viewgraph form.
Compressive sensing is a novel mathematical technique for simultaneous data acquisition and compression. This technique is particularly apt for time-series photometric measurements. In this work we apply compressive sensing to time-series photometric measurements specifically obtained due to gravitational microlensing events. We show through simulation modelling the error sensitivity for detecting microlensing event parameters. Particularly, we show the relation of the amount of error and its impact on the microlensing parameters of interest. We derive statistical error bounds to apply those as a baseline for analyzing the effectiveness of compressive sensing application. Our results conclude that for single and binary microlensing events we can obtain error less than 1% over a 3-pixel radius of the center of the microlensing star by using 25% Nyquist rate measurements.
This paper describes a new technique which jointly applies clustering and source encoding concepts to obtain data compression. The cluster compression technique basically uses clustering to extract features from the measurement data set which are used to describe characteristics of the entire data set. In addition, the features may be used to approximate each individual measurement vector by forming a sequence of scalar numbers which define each measurement vector in terms of the cluster features. This sequence, called the feature map, is then efficiently represented by using source encoding concepts. A description of a practical cluster compression algorithm is given and experimental results are presented to show trade-offs and characteristics of various implementations. Examples are provided which demonstrate the application of cluster compression to multispectral image data of the Earth Resources Technology Satellite.
Here, we present a fully discrete approximation technique for the compressible Navier–Stokes equations that is second-order accurate in time and space, semi-implicit, and guaranteed to be invariant domain preserving. The restriction on the time step is the standard hyperbolic CFL condition, i.e. τ ≲ O ( h ) / V where V is some reference velocity scale and h the typical meshsize.
Analysis of straight line segment interpolation techniques for data compression, noting application of method to any polynomial interpolation with any probability model
Compressive Sensing is a technique for simultaneous acquisition and compression of data that is sparse or can be made sparse in some domain. It is currently under intense development and has been profitably employed for industrial and medical applications. We here describe the use of this technique for the processing of astronomical data. We outline the procedure as applied to exoplanet gravitational microlensing and analyze measurement results and uncertainty values. We describe implications for on-spacecraft data processing for space observatories. Our findings suggest that application of these techniques may yield significant, enabling benefits especially for power and volume-limited space applications such as miniaturized or micro-constellation satellites.