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Results for “COMPRESSION RATIO”

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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS1974

Outer planet Pioneer imaging communications system study

The effects of different types of imaging data compression on the elements of the Pioneer end-to-end data system were studied for three imaging transmission methods. These were: no data compression, moderate data compression, and the advanced imaging communications system. It is concluded that: (1) the value of data compression is inversely related to the downlink telemetry bit rate; (2) the rolling characteristics of the spacecraft limit the selection of data compression ratios; and (3) data compression might be used to perform acceptable outer planet mission at reduced downlink telemetry bit rates.

Source record↗
NASA NTRS1932

Combustion Velocity of Benzine-Benzol-Air Mixtures in High-Speed Internal-Combustion Engines

The present paper describes a device whereby rapid flame movement within an internal-combustion engine cylinder may be recorded and determined. By the aid of a simple cylindrical contact and an oscillograph the rate of combustion within the cylinder of an airplane engine during its normal operation may be measured for gas intake velocities of from 30 to 35 m/s and for velocities within the cylinder of from 20 to 25 m/s. With it the influence of mixture ratios, of turbulence, of compression ratio and kind of fuel on combustion velocity may be determined. Besides the determination of the influence of the above factors on combustion velocity, the degree of turbulence may also be determined. As a unit of reference in estimating the degree of turbulence, the intake velocity of the charge is chosen.

Schnauffer, Kurt↗
NASA NTRS1988

Spatial compression of Seasat SAR imagery

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.

Chang, C. Y.↗
DOE OSTI2018

DuoModel: Leveraging Reduced Model for Data Reduction and Re-Computation on HPC Storage

High-performance computing (HPC) applications generate large amounts of floating-point data that need to be stored and analyzed efficiently to extract the insights and advance knowledge discovery. With the growing disparities between compute and I/O, optimizing the storage stack alone may not suffice to cure the I/O problem. There has been a strong push in the HPC communities to perform data reduction before data is transmitted to storage in order to lower the I/O cost. However, as of now, neither lossless nor lossy compressors can achieve the adequate reduction ratio that is desired by applications. This paper proposes DuoModel, a new approach that leverages the similarity between the full and reduced application models, and further improve the data reduction ratio. DouModel further improves the compression ratio of state-of-the-art compressors via compressing the differences (termed as delta) between the data products of the two models. For data analytics, the high fidelity data can be re-computed by launching the reduced model and applying the compressed delta. Our evaluations confirm that DuoModel can further push the limit of data reduction while the high fidelity of data is maintained.

Luo, Huizhang (ORCID:0000000323920267)↗
NASA NTRS1994

Learning random networks for compression of still and moving images

Image compression for both still and moving images is an extremely important area of investigation, with numerous applications to videoconferencing, interactive education, home entertainment, and potential applications to earth observations, medical imaging, digital libraries, and many other areas. We describe work on a neural network methodology to compress/decompress still and moving images. We use the 'point-process' type neural network model which is closer to biophysical reality than standard models, and yet is mathematically much more tractable. We currently achieve compression ratios of the order of 120:1 for moving grey-level images, based on a combination of motion detection and compression. The observed signal-to-noise ratio varies from values above 25 to more than 35. The method is computationally fast so that compression and decompression can be carried out in real-time. It uses the adaptive capabilities of a set of neural networks so as to select varying compression ratios in real-time as a function of quality achieved. It also uses a motion detector which will avoid retransmitting portions of the image which have varied little from the previous frame. Further improvements can be achieved by using on-line learning during compression, and by appropriate compensation of nonlinearities in the compression/decompression scheme. We expect to go well beyond the 250:1 compression level for color images with good quality levels.

Gelenbe, Erol↗
DOE OSTI2018

Unsupervised classification-based hyperspectral data processing: lossy compression

The existing lossy compression algorithms play an important role in reducing the cost of storage equipment and bandwidth for hyperspectral (HS) application. However, none of the lossy compression algorithms considers the real-time classification of HS data. In this paper, we present a new lossy compression method for HS data that aims to optimally compress in both spatial and spectral domains and simultaneously maximize classification performance. For this target, Harsanyi–Farrand–Chang (HFC) and k-means++ algorithms are applied to achieve a spectral library and an index matrix for HS image. Spectral angle mapping and Euclidean distance are used to update the spectral library and the index matrix. The experiment results indicate that the proposed method has a good classification performance. The results also reveal that the proposed method works well in real-time classification and compression of HS data with a large volume and achieves a high compression ratio. It is noteworthy to mention that the superiority of our method in compression becomes more apparent as the volume of HS data grows. Consequently, the proposed method has a strong advantage in HS applications that require both compression and classification.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1993

LVQ and backpropagation neural networks applied to NASA SSME data

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.

Doniere, Timothy F.↗
DOE OSTI2020

waveSZ: A Hardware-Algorithm Co-Design of Efficient Lossy Compression for Scientific Data

Error-bounded lossy compression is critical to the success of extreme-scale scientific research because of ever-increasing volumes of data produced by today's high-performance computing (HPC) applications. Not only can error-controlled lossy compressors significantly reduce the I/O and storage burden but they can retain high data fidelity for post analysis. Existing state-of-the-art lossy compressors, however, generally suffer from relatively low compression and decompression throughput (up to hundreds of megabytes per second on a single CPU core), which considerably restrict the adoption of lossy compression by many HPC applications especially those with a fairly high data production rate. In this paper, we propose a highly efficient lossy compression approach based on field programmable gate arrays (FPGAs) under the state-of-the-art lossy compression model SZ. Our contributions are fourfold. (1) We adopt a wavefront memory layout to alleviate the data dependency during the prediction for higher-dimensional predictors, such as the Lorenzo predictor. (2) We propose a co-design framework named WAVESZ based on the wavefront memory layout and the characteristics of SZ algorithm and carefully implement it by using high-level synthesis. (3) We propose a hardware-algorithm co-optimization method to improve the performance. (4) We evaluate our proposed WAVESZ on three real-world HPC simulation datasets from the Scientific Data Reduction Benchmarks and compare it with other state-of-the-art methods on both CPUs and FPGAs. Experiments show that our WAVESZ can improve SZ's compression throughput by 6.9x similar to 8.7x over the production version running on a state-of-the-art CPU and improve the compression ratio and throughput by 2.1x and 5.8x on average, respectively, respectively, compared with the state-of-the-art FPGA design.

Compression Ratio↗
NASA NTRS1970

Entropy and data compression

Optimum compression ratio defined for Markov sources for compression algorithm efficiency and data compression schemes

Farr, E. H.↗
NASA NTRS1920

Recent Efforts and Experiments in the Construction of Aviation Engines

It became evident during World War I that ever-increasing demands were being placed on the mean power of aircraft engines as a result of the increased on board equipment and the demands of aerial combat. The need was for increased climbing efficiency and climbing speed. The response to these demands has been in terms of lightweight construction and the adaptation of the aircraft engine to the requirements of its use. Discussed here are specific efforts to increase flying efficiency, such as reduction of the number of revolutions of the propeller from 1400 to about 900 r.p.m. through the use of a reduction gear, increasing piston velocity, locating two crankshafts in one gear box, and using the two-cycle stroke. Also discussed are improvements in the transformation of fuel energy into engine power, the raising of compression ratios, the use of super-compression with carburetors constructed for high altitudes, the use of turbo-compressors, rotary engines, and the use of variable pitch propellers.

SCHWAGER↗
NASA NTRS1993

Applications of wavelet-based compression to multidimensional Earth science data

A data compression algorithm involving vector quantization (VQ) and the discrete wavelet transform (DWT) is applied to two different types of multidimensional digital earth-science data. The algorithms (WVQ) is optimized for each particular application through an optimization procedure that assigns VQ parameters to the wavelet transform subbands subject to constraints on compression ratio and encoding complexity. Preliminary results of compressing global ocean model data generated on a Thinking Machines CM-200 supercomputer are presented. The WVQ scheme is used in both a predictive and nonpredictive mode. Parameters generated by the optimization algorithm are reported, as are signal-to-noise (SNR) measurements of actual quantized data. The problem of extrapolating hydrodynamic variables across the continental landmasses in order to compute the DWT on a rectangular grid is discussed. Results are also presented for compressing Landsat TM 7-band data using the WVQ scheme. The formulation of the optimization problem is presented along with SNR measurements of actual quantized data. Postprocessing applications are considered in which the seven spectral bands are clustered into 256 clusters using a k-means algorithm and analyzed using the Los Alamos multispectral data analysis program, SPECTRUM, both before and after being compressed using the WVQ program.

Bradley, Jonathan N.↗
NASA NTRS1996

Multimode Data-Compression System

Data-compression system developed to satisfy need for high-speed, high-performance compression of data from sources as diverse as medical images, high-definition television images, audio signals, readouts from scientific instruments, and binary data files. Maximum data-transmission capability of communication channel or storage capacity of storage device multiplied by approximately compression ratio. Various combinations of lossless and lossy compression chosen to suit various data streams.

Fang, Wai-Chi↗
DOE OSTI2019

Numerical Simulation of Thermodynamic Parameters of High-Porosity Copper

We propose that a modified equation of state be used for calculating the thermodynamically equilibrium TEC2 model for improving the reliability of description of thermodynamic parameters of shock loading of pure materials and heterogeneous mixtures of various porosities. For copper, the parameters of a thermally consistent equation of state have been determined. A reliable description of shock-wave loading of copper has been obtained using a small number of fitting parameters determined from the concordance with experimental data. Thermodynamic parameters have been simulated for copper with various porosities; the compression ratio and the temperature along the shock adiabat have been determined, and the value of heat capacity along the normal isobar has been calculated. The results of calculations are compared with available experimental results obtained by different authors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

LP CO 2 Compressor Final Technical Report

This report summarizes work performed by Dresser-Rand, A Siemens Business and subcontractors, under DOE award number DE-FE0026727, in pursuit of the design, construction and test of a 10:1 pressure ratio “Low Pressure” (LP) supersonic CO 2 compressor suitable for use as a first stage in a two stage 100:1 compression system. This LP compressor, in conjunction with a corresponding 10:1 high pressure compressor, provide the overall 100:1 compression ratio required to compress CO 2 from 22 psia (as generated by a CO 2 capture system) to the 2215 psia required to transport CO 2 via a pipeline to a suitable sequestration site. The compressor demonstrates application of Dresser-Rand’s supersonic compression technology at an industrial scale using CO 2 in a closed-loop testing facility. This scope of this technical report is to summarize all phases of the program executed from August 5, 2015 to the June 30, 2018. This program included the final testing of a “high pressure” (HP) compressor that had previously been built by Dresser-Rand and the design, fabrication, assembly and test of a corresponding LP compressor. In summary, the LP compressor exceeded the target pressure ratio and efficiency goals for the program at CO 2 flowrates suitable for those expected in coal fired power plant carbon capture systems. Initial and Final Techno-Economic Analysis reports were generated to show the value this compression solution brings to carbon capture and sequestration (CC&S) systems for the Case B12B coal fired power plant model. Integration of the DATUM S compression technology into a carbon capture and sequestration system was shown to result in a 27% reduction in the Cost of Electricity for the compression system, as well as a 21,500 gallon per hour reduction in required cooling water flow for the plant.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS1991

Channel coding for digital HDTV terrestrial broadcasting

The Federal Communications Commission of the United States has ruled that high-definition television (HDTV) will occupy no more than 6 MHz of the VHF and UHF bands now used for conventional TV. In order to transmit the HDTV signal in 6 MHz, the four United States digital HDTV proponents, the DigiCipher, DSC-HDTV, ADTV, and ATVA-P systems, are reducing the video data rate of HDTV to 15-17 Mb/s, a compression ratio of approximately 60-70 times. The high compression dictates that channel coding be used to avoid block errors and multiframe error propagation. High efficiency in channel utilization required by the 6-MHz limitation means that the channel must be properly equalized and that the multipath and interfering signals must be severely limited. The channel coding techniques used for error reduction include data interleaving, error detection and replacement, and error correction at different levels of protection for bits and blocks of unequal importance.

Beakley, Guy W.↗
NASA NTRS1995

KRESKA: A compression system for small and very large images

An effective lossless compression system for grayscale images is presented using finite context variable order Markov models. A new method to accurately estimate the probability of the escape symbol is proposed. The choice of the best model order and rules for selecting context pixels are discussed. Two context precision and two symbol precision techniques to handle noisy image data with Markov models are introduced. Results indicate that finite context variable order Markov models lead to effective lossless compression systems for small and very large images. The system achieves higher compression ratios than some of the better known image compression techniques such as lossless JPEG, JBIG, or FELICS.

Ohnesorge, Krystyna W.↗
DOE OSTI2026

An analysis of alcohol compression ignition strategies on different engine platforms

Strategies that enable ignition of the low cetane alcohol fuels (methanol and ethanol) include intake heating (direct or via hot residual trapping), increased compression ratio (CR), multi-injection strategies, ignition enhancers, and active prechambers. The literature consistently shows alcohol compression ignition (CI) is more efficient than diesel at high loads irrespective of ignition strategy but shows contradictory results at low loads. To understand this, this work combines data from four different engine platforms ranging from a displaced volume of 0.4 to 2.1 L/cyl. using different alcohol CI ignition strategies. Using modeling to support experimental analysis, alcohol-specific efficiency penalties are highlighted to inform alcohol combustion system development. The alcohols’ lower energy density means more fuel must be injected near top dead center to achieve a given load, increasing the relative penalty of sensible/latent heating. Pilot injections help reduce this penalty. Intake heating incurs both a heat transfer penalty and a thermodynamic penalty due to a reduced ratio of specific heats during compression. A strategy that combines elevated CR, large pilot injections, and ignition enhancers can avoid this penalty. Alternatively, a prechamber strategy can achieve this without changing the CR, nor needing ignition enhancers in the fuel. At low loads, diesel burns in a partially premixed mode characterized by high burn rates and low heat losses, reducing alcohol-specific combustion benefits. Alcohol CI on the light-duty platform showed abnormally high heat transfer over diesel compared to other platforms, favoring a more premixed/partially premixed strategy despite a combustion efficiency tradeoff.

33 ADVANCED PROPULSION SYSTEMS↗