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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS1970

Optical data processing

Book on optical data processing covering light characteristics, Fourier transforms, spectrum analysis, photographic film, filtering, holography, etc

Shulman, A. R.↗
NASA NTRS1971

Holographic data processing

Holography utilization to data processing, discussing factors affecting recording and matched filters creation for pattern recognition

Jenkins, R. W.↗
NASA NTRS2014

SpaceCube v2.0 Space Flight Hybrid Reconfigurable Data Processing System

This paper details the design architecture, design methodology, and the advantages of the SpaceCube v2.0 high performance data processing system for space applications. The purpose in building the SpaceCube v2.0 system is to create a superior high performance, reconfigurable, hybrid data processing system that can be used in a multitude of applications including those that require a radiation hardened and reliable solution. The SpaceCube v2.0 system leverages seven years of board design, avionics systems design, and space flight application experiences. This paper shows how SpaceCube v2.0 solves the increasing computing demands of space data processing applications that cannot be attained with a standalone processor approach.The main objective during the design stage is to find a good system balance between power, size, reliability, cost, and data processing capability. These design variables directly impact each other, and it is important to understand how to achieve a suitable balance. This paper will detail how these critical design factors were managed including the construction of an Engineering Model for an experiment on the International Space Station to test out design concepts. We will describe the designs for the processor card, power card, backplane, and a mission unique interface card. The mechanical design for the box will also be detailed since it is critical in meeting the stringent thermal and structural requirements imposed by the processing system. In addition, the mechanical design uses advanced thermal conduction techniques to solve the internal thermal challenges.The SpaceCube v2.0 processing system is based on an extended version of the 3U cPCI standard form factor where each card is 190mm x 100mm in size The typical power draw of the processor card is 8 to 10W and scales with application complexity. The SpaceCube v2.0 data processing card features two Xilinx Virtex-5 QV Field Programmable Gate Arrays (FPGA), eight memory modules, a monitor FPGA with analog monitoring, Ethernet, configurable interconnect to the Xilinx FPGAs including gigabit transceivers, and the necessary voltage regulation. The processor board uses a back-to-back design methodology for common parts that maximizes the board real estate available. This paper will show how to meet the IPC 6012B Class 3A standard with a 22-layer board that has two column grid array devices with 1.0mm pitch. All layout trades such as stack-up options, via selection, and FPGA signal breakout will be discussed with feature size results. The overall board design process will be discussed including parts selection, circuit design, proper signal termination, layout placement and route planning, signal integrity design and verification, and power integrity results. The radiation mitigation techniques will also be detailed including configuration scrubbing options, Xilinx circuit mitigation and FPGA functional monitoring, and memory protection.Finally, this paper will describe how this system is being used to solve the extreme challenges of a robotic satellite servicing mission where typical space-rated processors are not sufficient enough to meet the intensive data processing requirements. The SpaceCube v2.0 is the main payload control computer and is required to control critical subsystems such as autonomous rendezvous and docking using a suite of vision sensors and object avoidance when controlling two robotic arms.

Xilinx FPGA↗
NASA NTRS2000

Landsat 7 Science Data Processing: An Overview

The Landsat 7 Science Data Processing System, developed by NASA for the Landsat 7 Project, provides the science data handling infrastructure used at the Earth Resources Observation Systems (EROS) Data Center (EDC) Landsat Data Handling Facility (DHF) of the United States Department of Interior, United States Geological Survey (USGS) located in Sioux Falls, South Dakota. This paper presents an overview of the Landsat 7 Science Data Processing System and details of the design, architecture, concept of operation, and management aspects of systems used in the processing of the Landsat 7 Science Data.

Schweiss, Robert J.↗
NASA NTRS1979

Meteorological data-processing package

METPAK, meteorological data-processing package of satellite data used to develop cloud-tracking maps, is given. Data can develop and enhance numerical prediction models for mesoscale phenomena and improve ability to detect and predict storms.

Billingsly, J. B.↗
DOE OSTI2023

Streaming Data from Experimental Facilities to Supercomputers for Real-Time Data Processing

In this paper we demonstrate direct data streaming from instruments and detectors at a large-scale experimental facility to a supercomputer for real-time data processing and feedback. Streaming data to supercomputers introduces the potential for novel scientific applications and workflow models, including the ability to provide real-time feedback from very large datasets during an experiment and the integration of real-time ML training and inference at scale. We discuss a successful demonstration for real-time processing of data from the Advanced Photon Source (APS) on the Polaris supercomputer using an EPICS-based streaming framework. We describe the capabilities of the streaming framework itself, and outline the architecture that allows us to process experimentally derived data on a supercomputer without file-based data transfers. We present throughput measurements that are indicative of system performance capable of sustaining the expected data production rates of the facility, as well as discuss some outstanding challenges and our future directions.

real-time processing↗
NASA NTRS2005

Extending the LWS Data Environment: Distributed Data Processing and Analysis

The final stages of this work saw changes to the original framework, as well as the completion and integration of several data processing services. Initially, it was thought that a peer-to-peer architecture was necessary to make this work possible. The peer-to-peer architecture provided many benefits including the dynamic discovery of new services that would be continually added. A prototype example was built and while it showed promise, a major disadvantage was seen in that it was not easily integrated into the existing data environment. While the peer-to-peer system worked well for finding and accessing distributed data processing services, it was found that its use was limited by the difficulty in calling it from existing tools and services. After collaborations with members of the data community, it was determined that our data processing system was of high value and that a new interface should be pursued in order for the community to take full advantage of it. As such; the framework was modified from a peer-to-peer architecture to a more traditional web service approach. Following this change multiple data processing services were added. These services include such things as coordinate transformations and sub setting of data. Observatory (VHO), assisted with integrating the new architecture into the VHO. This allows anyone using the VHO to search for data, to then pass that data through our processing services prior to downloading it. As a second attempt at demonstrating the new system, a collaboration was established with the Collaborative Sun Earth Connector (CoSEC) group at Lockheed Martin. This group is working on a graphical user interface to the Virtual Observatories and data processing software. The intent is to provide a high-level easy-to-use graphical interface that will allow access to the existing Virtual Observatories and data processing services from one convenient application. Working with the CoSEC group we provided access to our data processing tools from within their software. This now allows the CoSEC community to take advantage of our services and also demonstrates another means of accessing our system.

Narock, Thomas↗
DOE OSTI2017

The problem analysis of data processing performance in nuclear power plant computerized information and control system - 288

This paper is based on the data processing performance problems of operating command delay and system display not updating real-time for nuclear power plant computerized information and control system. The plant computerized information and control system is responsible for monitoring and controlling the plant operation, and it is a key part in the plant DCS system. Its data processing performance will affect the operation stability of nuclear power plant. The architecture, hardware configuration and data communication for plant computerized information and control system among the different levels in DCS are described. The main reasons affecting data processing performance in different signal channels are analyzed according to the plant load test results. The CPU load curves for real time server and central processing server are displayed, although the detailed cause for operating command delay and system display not updating realtime are not found, central processing server load is heavier than real time server which remind us to analyze the data processing of software resource scheduling deeply. So the resource distribution problem of software execution task is also analyzed according to CPU load test results. This paper provides the optimization analysis for data processing performance, and it is available and effective according to the latest test results. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2011

RACORO aerosol data processing

The RACORO aerosol data (cloud condensation nuclei (CCN), condensation nuclei (CN) and aerosol size distributions) need further processing to be useful for model evaluation (e.g., GCM droplet nucleation parameterizations) and other investigations. These tasks include: (1) Identification and flagging of 'splash' contaminated Twin Otter aerosol data. (2) Calculation of actual supersaturation (SS) values in the two CCN columns flown on the Twin Otter. (3) Interpolation of CCN spectra from SGP and Twin Otter to 0.2% SS. (4) Process data for spatial variability studies. (5) Provide calculated light scattering from measured aerosol size distributions. Below we first briefly describe the measurements and then describe the results of several data processing tasks that which have been completed, paving the way for the scientific analyses for which the campaign was designed. The end result of this research will be several aerosol data sets which can be used to achieve some of the goals of the RACORO mission including the enhanced understanding of cloud-aerosol interactions and improved cloud simulations in climate models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2015

High-Speed On-Board Data Processing for Science Instruments: HOPS

The project called High-Speed On-Board Data Processing for Science Instruments (HOPS) has been funded by NASA Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST) program during April, 2012 â€" April, 2015. HOPS is an enabler for science missions with extremely high data processing rates. In this three-year effort of HOPS, Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) and 3-D Winds were of interest in particular. As for ASCENDS, HOPS replaces time domain data processing with frequency domain processing while making the real-time on-board data processing possible. As for 3-D Winds, HOPS offers real-time high-resolution wind profiling with 4,096-point fast Fourier transform (FFT). HOPS is adaptable with quick turn-around time. Since HOPS offers reusable user-friendly computational elements, its FPGA IP Core can be modified for a shorter development period if the algorithm changes. The FPGA and memory bandwidth of HOPS is 20 GB/sec while the typical maximum processor-to-SDRAM bandwidth of the commercial radiation tolerant high-end processors is about 130-150 MB/sec. The inter-board communication bandwidth of HOPS is 4 GB/sec while the effective processor-to-cPCI bandwidth of commercial radiation tolerant high-end boards is about 50-75 MB/sec. Also, HOPS offers VHDL cores for the easy and efficient implementation of ASCENDS and 3-D Winds, and other similar algorithms. A general overview of the 3-year development of HOPS is the goal of this presentation.

Beyon, Jeffrey↗
NASA NTRS1974

Apollo experience report: Apollo lunar surface experiments package data processing system

Apollo Program experience in the processing of scientific data from the Apollo lunar surface experiments package, in which computers and associated hardware and software were used, is summarized. The facility developed for the preprocessing of the lunar science data is described, as are several computer facilities and programs used by the Principal Investigators. The handling, processing, and analyzing of lunar science data and the interface with the Principal Investigators are discussed. Pertinent problems that arose in the development of the data processing schemes are discussed so that future programs may benefit from the solutions to the problems. The evolution of the data processing techniques for lunar science data related to recommendations for future programs of this type.

Eason, R. L.↗