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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Compression After Impact Testing of Sandwich Structures Using the Four Point Bend Test

For many composite laminated structures, the design is driven by data obtained from Compression after Impact (CAI) testing. There currently is no standard for CAI testing of sandwich structures although there is one for solid laminates of a certain thickness and lay-up configuration. Most sandwich CAI testing has followed the basic technique of this standard where the loaded ends are precision machined and placed between two platens and compressed until failure. If little or no damage is present during the compression tests, the loaded ends may need to be potted to prevent end brooming. By putting a sandwich beam in a four point bend configuration, the region between the inner supports is put under a compressive load and a sandwich laminate with damage can be tested in this manner without the need for precision machining. Also, specimens with no damage can be taken to failure so direct comparisons between damaged and undamaged strength can be made. Data is presented that demonstrates the four point bend CAI test and is compared with end loaded compression tests of the same sandwich structure.

Nettles, Alan T.↗
DOE OSTI2025

What to Support When You’re Compressing

Over the last nearly 20 years, lossy compression has become an essential aspect of HPC applications’ data pipelines, allowing them to overcome limitations in storage capacity and bandwidth and, in some cases, increase computational throughput and capacity. However, with the adoption of lossy compression comes the requirement to assess and control the impact lossy compression has on scientific outcomes. In this work, we take a major step forward in describing the state of practice and by characterizing workloads. We examine applications’ needs and compressors’ capabilities across 9 different supercomputing application domains. We present 24 takeaways that provide best practices for applications, operational impacts for facilities achieving compressed data, and gaps in application needs not addressed by production compressors that point towards opportunities for future compression research.

Error-Bounded Lossy Compression↗
DOE OSTI2021

Equation-of-state, sound speed, and reshock of shock-compressed fluid carbon dioxide

Mechanical equation-of-state data of initially liquid and solid CO2 shock-compressed to terapascal conditions are reported. Diamond-sapphire anvil cells were used to vary the initial density and state of CO2 samples that were then further compressed with laser-driven shock waves, resulting in a data set from which precise derivative quantities, including Grüneisen parameter and sound speed, are determined. Reshock states are measured to 800 GPa and map the same pressure-density conditions as the single shock using different thermodynamic paths. The compressibility data reported here do not support current density-functional-theory calculations, but are better represented by tabular equation-of-state models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

StOKeDMD: Streaming Occupation kernel dynamic mode decomposition

Dynamic mode decomposition (DMD) has become a common technique for constructing surrogate models for dynamical systems from observed system states. The Occupation Kernel DMD (OKDMD) method proposed in (Rosenfeld et al., 2022) and (Rosenfeld et al., 2024) is a Liouville operator based method that builds surrogate models from system state trajectories. Here, this paper proposes an extension of OKDMD to the case when the system states are observed in a streaming fashion, i.e., only a small fraction of the state trajectory is available at a given time. The developed method, Streaming Occupation Kernel DMD (StOKeDMD), accommodates the streaming data input by leveraging properties of specific choices of kernel functions and occupation kernels. We apply the StoKeDMD method as a compression method for streaming data, analyze the memory complexity, and demonstrate the performance of StoKeDMD in the compression of streaming data generated from a Lorenz system and a fluid flow simulation.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1986

Micromechanics of composite laminate compression failure

The Dugdale analysis for metals loaded in tension was adapted to model the failure of notched composite laminates loaded in compression. Compression testing details, MTS alignment verification, and equipment needs were resolved. Thus far, only 2 ductile material systems, HST7 and F155, were selected for study. A Wild M8 Zoom Stereomicroscope and necessary attachments for video taping and 35 mm pictures were purchased. Currently, this compression test system is fully operational. A specimen is loaded in compression, and load vs shear-crippling zone size is monitored and recorded. Data from initial compression tests indicate that the Dugdale model does not accurately predict the load vs damage zone size relationship of notched composite specimens loaded in compression.

Guynn, E. Gail↗
NASA NTRS2012

Development of RGB Composite Imagery for Operational Weather Forecasting Applications

The NASA Short-term Prediction Research and Transition (SPoRT) Center, in collaboration with the Cooperative Institute for Research in the Atmosphere (CIRA), is providing red-green-blue (RGB) color composite imagery to several of NOAA s National Centers and National Weather Service forecast offices as a demonstration of future capabilities of the Advanced Baseline Imager (ABI) to be implemented aboard GOES-R. Forecasters rely upon geostationary satellite imagery to monitor conditions over their regions of responsibility. Since the ABI will provide nearly three times as many channels as the current GOES imager, the volume of data available for analysis will increase. RGB composite imagery can aid in the compression of large data volumes by combining information from multiple channels or paired channel differences into single products that communicate more information than provided by a single channel image. A standard suite of RGB imagery has been developed by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), based upon the Spinning Enhanced Visible and Infrared Imager (SEVIRI). The SEVIRI instrument currently provides visible and infrared wavelengths comparable to the future GOES-R ABI. In addition, the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard the NASA Terra and Aqua satellites can be used to demonstrate future capabilities of GOES-R. This presentation will demonstrate an overview of the products currently disseminated to SPoRT partners within the GOES-R Proving Ground, and other National Weather Service forecast offices, along with examples of their application. For example, CIRA has used the channels of the current GOES sounder to produce an "air mass" RGB originally designed for SEVIRI. This provides hourly imagery over CONUS for looping applications while demonstrating capabilities similar to the future ABI instrument. SPoRT has developed similar "air mass" RGB imagery from MODIS, and through a case study example, synoptic-scale features evident in single-channel water vapor imagery are shown in the context of the air mass product. Other products, such as the "nighttime microphysics" RGB, are useful in the detection of low clouds and fog. Nighttime microphysics products from MODIS offer some advantages over single-channel or spectral difference techniques and will be discussed in the context of a case study. Finally, other RGB products from SEVIRI are being demonstrated as precursors to GOES-R within the GOES-R Proving Ground. Examples of "natural color" and "dust" imagery will be shown with relevant applications.

Molthan, Andrew L.↗
DOE OSTI2025

Optimising the processing and storage of visibilities using lossy compression

The next-generation radio astronomy instruments are providing a massive increase in sensitivity and coverage, largely through increasing the number of stations in the array and the frequency span sampled. The two primary problems encountered when processing the resultant avalanche of data are the need for abundant storage and the constraints imposed by I/O, as I/O bandwidths drop significantly on cold storage. An example of this is the data deluge expected from the SKA Telescopes of more than 60 PB per day, all to be stored on the buffer filesystem. While compressing the data is an obvious solution, the impacts on the final data products are hard to predict. In this paper, we chose an error-controlled compressor – MGARD – and applied it to simulated SKA-Mid and real pathfinder visibility data, in noise-free and noise-dominated regimes. As the data have an implicit error level in the system temperature, using an error bound in compression provides a natural metric for compression. MGARD ensures the compression incurred errors adhere to the user-prescribed tolerance. To measure the degradation of images reconstructed using the lossy compressed data, we proposed a list of diagnostic measures, exploring the trade-off between these error bounds and the corresponding compression ratios, as well as the impact on science quality derived from the lossy compressed data products through a series of experiments. We studied the global and local impacts on the output images for continuum and spectral line examples. We found relative error bounds of as much as 10%, which provide compression ratios of about 20, have a limited impact on the continuum imaging as the increased noise is less than the image RMS, whereas a 1% error bound (compression ratio of 8) introduces an increase in noise of about an order of magnitude less than the image RMS. For extremely sensitive observations and for very precious data, we would recommend a 0.1% error bound with compression ratios of about 4. These have noise impacts two orders of magnitude less than the image RMS levels. At these levels, the limits are due to instabilities in the deconvolution methods. We compared the results to the alternative compression tool DYSCO, in both the impacts on the images and in the relative flexibility. MGARD provides better compression for similar error bounds and has a host of potentially powerful additional features.

Techniques: interferometric↗
NASA NTRS1994

Progressive Vector Quantization on a massively parallel SIMD machine with application to multispectral image data

A progressive vector quantization (VQ) compression approach is discussed which decomposes image data into a number of levels using full search VQ. The final level is losslessly compressed, enabling lossless reconstruction. The computational difficulties are addressed by implementation on a massively parallel SIMD machine. We demonstrate progressive VQ on multispectral imagery obtained from the Advanced Very High Resolution Radiometer instrument and other Earth observation image data, and investigate the trade-offs in selecting the number of decomposition levels and codebook training method.

Manohar, Mareboyana↗
NASA NTRS1960

Correlation of the Buckling Strength of Pressurized Cylinders in Compression or Bending with Structural Parameters

The data on nonpressurized cylinders in bending of NACA TN 3735 and the data on pressurized cylinders in compression and bending of NASA TN D-360 are correlated with structural parameters by using small-deflection buckling theory and reduced values for the extensional stiffness of the cylinder wall. The correlating procedure should prove useful in future shell-buckling investigations by reducing the number of tests required.

Peterson, James P.↗
DOE OSTI2023

HarDWR - Water Management Area (WMA) Shapefiles

For a detailed description of the database of which this record is only one part, please see the HarDWR meta-record. Borders of all Water Management Areas (WMAs) across the 11 Western-most states of the continental United States are available filtered through a single source. The legal name for this set of boundaries varies state-by-state. The data is provided as two compressed shapefiles. One, stateWMAs, contains data for all 11 states. For 10 of those states, Arizona being the exception, the polygons represent the legal management boundaries used by those states to manage their surface and groundwater resources respectively. Arizona is unique among this collection of states in that surface and groundwater resources are managed using two separate sets of boundaries. During our followup analysis, due to technical reasons we decided to focus on one set of boundaries, those for surface water. Due to this, the Arizona surface WMAs are included within stateWMAs. The Arizona groundwater WMAs are provided as the second file azGroundWMAs as a companion to the first file for completeness and general reference, although the boundaries were no longer referenced in the analysis (Grogan et al., in review) past a certain point. The columns for both shapefiles are: basinNum: the state provided unique numerical ID basinName: the state provided English name of the area, where applicable state: the state name uniID: a unique identifier we created by concatenating the state name, and underscore, and the state numerical ID

Economics↗
DOE OSTI2020

HarDWR - Water Management Area (WMA) Shapefiles

A dataset within the Harmonized Database of Western U.S. Water Rights (HarDWR). For a detailed description of the database, please see the meta-record v2.0. Changelog v2.0 - No changes v1.0 - Initial public release Description Borders of all Water Management Areas (WMAs) across the 11 western-most states of the coterminous United States are available filtered through a single source. The legal name for this set of boundaries varies state-by-state. The data is provided as two compressed shapefiles. One, stateWMAs, contains data for all 11 states. For 10 of those states, Arizona being the exception, the polygons represent the legal management boundaries used by those states to manage their surface and groundwater resources respectively. WMAs refer to the set of boundaries a particular state uses to manage its water resources. Each set of boundaries was collected from the states individually, and then merged into one spatial layer. The merging process included renaming some columns to enable merging with all other source layers, as well as removing columns deemed not required for followup analysis. The retained columns for each boundary are: basinNum - the state provided unique numerical ID; basinName - the state provided English name of the area, where applicable; state - the state name; and uniID - a unique identifier we created by concatenating the state name, and underscore, and the state numerical ID. Arizona is unique within this collection of states in that surface and groundwater resources are managed using two separate sets of boundaries. During our followup analysis (Grogan et al., in review) we decided to focus on one set of boundaries, those for surface water. This is due to the recommendation of our hydrologists that the surface water boundary set is a more realist representation of how water moves across the landscape, as a few of the groundwater boundaries are based on political and/or economic considerations. Therefore, the Arizona surface WMAs are included within stateWMAs. The Arizona groundwater WMAs are provided as a separate file, azGroundWMAs, as a companion to the first file for completeness and general reference. WMA spatial boundary data sources by state: Arizona: Arizona Surface Water Watersheds; Collected February, 2020; https://gisdata2016-11-18t150447874z-azwater.opendata.arcgis.com/datasets/surface-watershed/explore?location=34.158174%2C-111.970823%2C7.50 Arizona: Arizona Ground Water Basins; Collected February, 2020; https://gisdata2016-11-18t150447874z-azwater.opendata.arcgis.com/datasets/groundwater-basin-2/explore?location=34.158174%2C-111.970823%2C7.50 California: California CalWater 2.2.1; Collected February, 2020; https://www.mlml.calstate.edu/mpsl-mlml/data-center/data-entry-tools/data-tools/gis-shapefile-layers/ Colorado: Colorado Water District Boundaries; Collected February, 2020; https://www.colorado.gov/pacific/cdss/gis-data-category Idaho: Idaho Department of Water Resources (IDWR) Administrative Basins; Collected November, 2015; https://data-idwr.opendata.arcgis.com/datasets/fb0df7d688a04074bad92ca8ef74cc26_4/explore?location=45.018686%2C-113.862284%2C6.93 Montana: Collected June, 2019; Directly contacted Montana Department of Natural Resources and Conservation (DNRC) Office of Information Technology (OIT) Nevada: Nevada State Engineer Admin Basin Boundaries; Collected April, 2020 https://ndwr.maps.arcgis.com/apps/mapviewer/index.html?layers=1364d0c3a0284fa1bcd90f952b2b9f1c New Mexico: New Mexico Office of the State Engineer (OSE) Declared Groundwater Basins; Collected April, 2020 https://geospatialdata-ose.opendata.arcgis.com/datasets/ose-declared-groundwater-basins/explore?location=34.179783%2C-105.996542%2C7.51 Oregon: Oregon Water Resources Department (OWRD) Administrative Basins; Collected February, 2020; https://www.oregon.gov/OWRD/access_Data/Pages/Data.aspx Utah: Utah Adjudication Books; Collected April, 2020; https://opendata.gis.utah.gov/datasets/utahDNR::utah-adjudication-books/explore?location=39.497165%2C-111.587782%2C-1.00 Washington: Washington Water Resource Inventory Areas (WRIA); Collected June, 2017; https://ecology.wa.gov/Research-Data/Data-resources/Geographic-Information-Systems-GIS/Data Wyoming: Wyoming State Engineer's Office Board of Control Water Districts; Collected June, 2019; Directly contacted Wyoming State Engineer's Office

Economics↗
DOE OSTI2021

Data Science and Computation for Rapid and Dynamic Compression Experiment Workflows at Experimental Facilities (Workshop Overview) [Slides]

Motivation for this workshop stems from these items: Compression experiments are important to LANL's core mission. The scientific community is sometimes frustrated at the pace of discovery. Data analytics for other experimental regimes are advancing. Now is the time as facilities are upgraded and coming online. Advances in data analytics and computational resources could push compression experiment discoveries to a new level.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A High-Quality Workflow for Multi-Resolution Scientific Data Reduction and Visualization

Multi-resolution methods such as Adaptive Mesh Refinement (AMR) can enhance storage efficiency for HPC applications generating vast volumes of data. However, their applicability is limited and cannot be universally deployed across all applications. Furthermore, integrating lossy compression with multi-resolution techniques to further boost storage efficiency encounters significant barriers. To this end, we introduce an innovative workflow that facilitates high-quality multi-resolution data compression for both uniform and AMR simulations. Initially, to extend the usability of multi-resolution techniques, our workflow employs a compression-oriented Region of Interest (ROI) extraction method, transforming uniform data into a multi-resolution format. Subsequently, to bridge the gap between multi-resolution techniques and lossy compressors, we optimize three distinct compressors, ensuring their optimal performance on multi-resolution data. These optimizations can improve the compression ratio of SOTA approaches by up to 3.3× under the same data quality loss. Lastly, we incorporate an advanced uncertainty visualization method into our workflow to understand the potential impacts of lossy compression. Experimental evaluation demonstrates that our workflow achieves significant compression quality improvements.

Wang, Daoce↗
DOE OSTI2021

Data Science and Computation for Rapid and Dynamic Compression Experiment Workflows at Experimental Facilities, September 8-11, 2020. Workshop Report

The application of high pressure to materials has enabled discoveries in scientific fields such as planetary science, materials science, and materials synthesis. Recent advances in X-ray user light sources and other facilities, co-location and integration of user facilities with high-pressure drivers, availability of high-performance computing (HPC) platforms, and the development of new data science techniques have created opportunities for, and challenges in, advancing data analytics for rapid and dynamic compression experiments. To address these challenges, harness the emerging technology now available, and expedite scientific discovery, Los Alamos National Laboratory (LANL) hosted a virtual workshop entitled “Data Science and Computation for Rapid and Dynamic Compression Workflows at Experimental Facilities” from September 8 to 11, 2020. The workshop included 95 registered scientists and analytics experts from 15 universities, 9 United States (US) national laboratories, 5 US and European X-ray light sources, neutron sources such as the Los Alamos Neutron Science Center (LANSCE), other big science facilities such as the National Ignition Facility (NIF), and an industry representative. The workshop included 31 invited talks and 4 lightning talks by students and postdocs.

36 MATERIALS SCIENCE↗
NASA NTRS1993

A study of the compressible flow through a diffusing S-duct

Benchmark aerodynamic data are presented for compressible flow through a representative S-duct configuration. A numerical prediction of the S-duct flow field, obtained from a subsonic parabolized Navier-Stokes algorithm, is also shown. The experimental and numerical results are compared. Measurements of the three-dimensional velocity field, total pressures, and static pressures were obtained at five cross-sectional planes. Aerodynamic data were gathered with calibrated pneumatic probes. Surface static pressure and surface flow visualization data were also acquired. All reported tests were conducted with an inlet centerline Mach number of 0.6. The Reynolds number, based on the inlet centerline velocity and duct inlet diameter, was 2.6 x 10(exp 6). Thin inlet turbulent boundary layers existed. The collected data should be beneficial to aircraft inlet designers and the measurements are suitable for the validation of computational codes. The results show that a region of streamwise flow separation occurred within the duct. Details about the separated flow region, including mechanisms which drive this complicated flow phenomenon, are discussed. Results also indicate that the duct curvature induces strong pressure driven secondary flows. The cross flows evolve into counter-rotating vortices. These vortices convect low momentum fluid of the boundary layer toward the center of the duct, degrading both the uniformity and magnitude of the total pressure profile.

Wellborn, Steven R.↗
DOE OSTI2022

Ephemeral Learning - Augmenting Triggers with Online-Trained Normalizing Flows

The large data rates at the LHC require an online trigger system to select relevant collisions. Rather than compressing individual events, we propose to compress an entire data set at once. We use a normalizing flow as a deep generative model to learn the probability density of the data online. The events are then represented by the generative neural network and can be inspected offline for anomalies or used for other analysis purposes. We demonstrate our new approach for a toy model and a correlation-enhanced bump hunt.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1993

On the performance of Trellis coded modulation with octal phase shift keying over the TDRSS channel

As the National Aeronautics and Space Administration moves into the 21st century with programs like Space Station Freedom, a manned mission to Mars, and the new Landsat mission, transmission demands on the Tracking and Data Relay Satellite System (TDRSS) will very likely exceed the available bandwidth. The Manual Lujan, Jr. Center for Space Telemetering and Telecommunications Systems (CSTTS) at New Mexico State University (NMSU) is studying techniques for increasing the data rate capabilities of TDRSS. These techniques include the use of advanced bandwidth efficient modulation formats to increase the data rate that can be sustained in a TDRSS transponder and the use of lossless bandwidth compression of the data to be transmitted to lower the data rate required from the user spacecraft. Based upon current technology the most promising bandwidth efficient modulation technique is Trellis Coded Modulation (TCM) operating with Octal Phase shift Keying (8PSK). Trellis Coded Modulation coding with 8PSK carrier modulation has the capability to increase the data rate which can be transmitted through the TDRSS spacecraft by a factor of 2 to 2.5 times that available with todays coded QPSK systems with only a small penalty in link performance relative to the existing systems. However, before NASA can safely employ TCM coding it is necessary to prove that this complex format can perform on the real TDRSS link as it does in labs and simulation studies. This proof-of-concept test over a live satellite channel was the objective of the construction and testing performed under this task of the NMSU NASA grant referenced above. In conjunction with NASA, NMSU's CSTTS has constructed a system to test a new candidate TDRSS modulation scheme, TCM 8PSK, that can enhance the information throughput of the TDRSS spacecraft. The test system for this project which was constructed over a period of 18 months by NMSU consisted of two racks of commercial and univeristy-designed and -built equipment. This project has included modifications of an existing White Sands Ground Terminal (WSGT) High Rate QPSK Demodulator to demodulate 8PSK as well as the construction of other support hardware. Also, two TCM codecs (coder/decoders) have been constructed to implement two levels of bandwidth efficiency. One was designed and built by the research team at NMSU while the other was created by the University of Notre Dame with the University of South Australia. The NMSU codec achieves a 2-to-1 increase in data rate per unit bandwidth with a coding gain relative to QPSK of about 3dB. The Notre Dame/South Australia codec achieves a 2.5-to-1 increase in data rate per unit of occupied bandwidth and a coding gain of about 2dB.

Osborne, William P.↗
NASA NTRS1987

A multiprocessing architecture for real-time monitoring

A multiprocessing architecture for performing real time monitoring and analysis using knowledge-based problem solving techniques is discussed. To handle asynchronous inputs and perform in real time, the system consists of three or more separate processes which run concurrently on one or more processors and communicate via a message passing scheme. The Data Management Process gathers, compresses, scales and sends the incoming telemetry data to other tasks. The Inference Process consists of a proprietary high performance inference engine that runs at 1000 rules per second using telemetry data to perform real time analysis on the state and health of the Space Telescope. The multiprocessing architecture has been interfaced to a simulator and is able to process the incoming telemetry in real time.

Laffey, Thomas J.↗