Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “DATA PROCESSOR”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,603 records · Page 89
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Passive method to measure strength of turbulence

Disclosed is a method to passively measure and calculate the strength of turbulence via the index of refraction structure constant C.sub.n.sup.2 from video imagery gathered by an imaging device, such as a video camera. Processing may occur with any type computing device utilizing a processor executing machine executable code stored on memory. This method significantly simplifies instrumentation requirements, reduces cost, and provides rapid data output. This method combines an angle of arrival methodology, which provides scale factors, with a new spatial/temporal frequency domain method. As part of the development process, video imagery from high speed cameras was collected and analyzed. The data was decimated to video rates such that statistics could be computed and used to confirm that this passive method accurately characterizes the atmospheric turbulence. C.sub.n.sup.2 accuracy from this method compared well with scintillometer data through two full orders of magnitude and more capability is expected beyond this verification.

O'Neill, Mary Morabito↗
DOE OSTI2021

Passive method to measure strength of turbulence

Disclosed is a method to passively measure and calculate the strength of turbulence via the index of refraction structure constant Cn2 from video imagery gathered by an imaging device, such as a video camera. Processing may occur with any type computing device utilizing a processor executing machine executable code stored on memory. This method significantly simplifies instrumentation requirements, reduces cost, and provides rapid data output. This method combines an angle of arrival methodology, which provides scale factors, with a new spatial/temporal frequency domain method. As part of the development process, video imagery from high speed cameras was collected and analyzed. The data was decimated to video rates such that statistics could be computed and used to confirm that this passive method accurately characterizes the atmospheric turbulence. Cn2 accuracy from this method compared well with scintillometer data through two full orders of magnitude and more capability is expected beyond this verification.

O'Neill, Mary Morabito↗
DOE OSTI2022

Differentiable, Learnable, Regionalized Process-Based Models With Multiphysical Outputs can Approach State-Of-The-Art Hydrologic Prediction Accuracy

Predictions of hydrologic variables across the entire water cycle have significant value for water resources management as well as downstream applications such as ecosystem and water quality modeling. Recently, purely data-driven deep learning models like long short-term memory (LSTM) showed seemingly insurmountable performance in modeling rainfall runoff and other geoscientific variables, yet they cannot predict untrained physical variables and remain challenging to interpret. Here, we show that differentiable, learnable, process-based models (called δ models here) can approach the performance level of LSTM for the intensively observed variable (streamflow) with regionalized parameterization. We use a simple hydrologic model HBV as the backbone and use embedded neural networks, which can only be trained in a differentiable programming framework, to parameterize, enhance, or replace the process-based model's modules. Without using an ensemble or post-processor, δ models can obtain a median Nash-Sutcliffe efficiency of 0.732 for 671 basins across the USA for the Daymet forcing data set, compared to 0.748 from a state-of-the-art LSTM model with the same setup. For another forcing data set, the difference is even smaller: 0.715 versus 0.722. Meanwhile, the resulting learnable process-based models can output a full set of untrained variables, for example, soil and groundwater storage, snowpack, evapotranspiration, and baseflow, and can later be constrained by their observations. Both simulated evapotranspiration and fraction of discharge from baseflow agreed decently with alternative estimates. The general framework can work with models with various process complexity and opens up the path for learning physics from big data.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1993

Clementine Sensor Processing System

The design of the DSPSE Satellite Controller (DSC) is baselined as a single-string satellite controller. The DSC performs two main functions: health and maintenance of the spacecraft; and image capture, storage, and playback. The DSC contains two processors: a radiation-hardened Mil-Std-1750, and a commercial R3000. The Mil-Std-1750 processor performs all housekeeping operations, while the R3000 is mainly used to perform the image processing functions associated with the navigation functions, as well as performing various experiments. The DSC also contains a data handling unit (DHU) used to interface to various spacecraft imaging sensors and to capture, compress, and store selected images onto the solid-state data recorder. The development of the DSC evolved from several key requirements; the DSPSE satellite was to do the following: (1) have a radiation-hardened spacecraft control system and be immune to single-event upsets (SEU's); (2) use an R3000-based processor to run the star tracker software that was developed by SDIO (due to schedule and cost constraints, there was no time to port the software to a radiation-hardened processor); and (3) fly a commercial processor to verify its suitability for use in a space environment. In order to enhance the DSC reliability, the system was designed with multiple processing paths. These multiple processing paths provide for greater tolerance to various component failures. The DSC was designed so that all housekeeping processing functions are performed by either the Mil-Std-1750 processor or the R3000 processor. The image capture and storage is performed either by the DHU or the R3000 processor.

Feldstein, A. A.↗
DOE OSTI2014

Multiple-Input Data Acquisition System (MIDAS) for Measuring the Carbon Content in Soil Using Inelastic Neutron Scattering

This report describes work funder under STTR grants Phase I and II and carried out jointly by XIA LLC and Brookhaven National Laboratory (BNL). The project goal was to develop a mobile nuclear activation analysis instrument that could be towed behind a tractor to document soil carbon levels in agricultural lands for carbon credit certification. XIA developed large NaI(Tl) detectors with integrated digital pulse processors controlled over USB 2.0 and delivered 16 of these units to BNL for integration into the prototype instrument, together with the necessary software to calibrate them and collect data. For reasons that are unknown to XIA, the BNL participants never completed the prototype vehicle, performed system integration, or carried out the proposed qualification and field tests, leaving the project incomplete.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1973

Recorder/processor apparatus

An apparatus is described for recording a data input on, a thermally processible storage medium. A light source, whose intensity is modulated in response to the incoming data input, generates a raster in conformance with incoming timing/control signals so as to expose a latent image of the input information on the storage medium. A rotating drum in conjunction with an incrementally driven lens carriage associated with the laser optical system provides the raster generation. The drum is automatically loaded with the storage medium from a supply means and automatically unloaded to a thermal processor upon completion of recording. The latent image is processed by the controlled application of heat so as to produce an actual displayable image corresponding to the data input at the output of the apparatus.

Shim, I. H.↗
NASA NTRS1998

Design and Performance Analysis of a Massively Parallel Atmospheric General Circulation Model

In the 1990's computer manufacturers are increasingly turning to the development of parallel processor machines to meet the high performance needs of their customers. Simultaneously, atmospheric scientists study weather and climate phenomena ranging from hurricanes to El Nino to global warming that require increasingly fine resolution models. Here, implementation of a parallel atmospheric general circulation model (GCM) which exploits the power of massively parallel machines is described. Using the horizontal data domain decomposition methodology, this FORTRAN 90 model is able to integrate a 0.6 deg. longitude by 0.5 deg. latitude problem at a rate of 19 Gigaflops on 512 processors of a Cray T3E 600; corresponding to 280 seconds of wall-clock time per simulated model day. At this resolution, the model has 64 times as many degrees of freedom and performs 400 times as many floating point operations per simulated day as the model it replaces.

Schaffer, Daniel S.↗
NASA NTRS1979

Multibeam single frequency synthetic aperture radar processor for imaging separate range swaths

A method and apparatus are described for single frequency multibeam imaging of multiple strips of range swath at high range intervals for those applications where it is desirable to cover a range swath much greater than is possible for a given interpulse interval. Data from a single frequency synthetic aperture radar (in which beam parameters are adjusted so that the return from each successive swath is received during successive interpulse periods) are separated in Dopple frequency for the return from each beam at the frequency plane of the processor. Alternatively, the image formed by each beam may be spatially separated in the azimuth direction and successively selected by positioning an appropriate slit in the recording plane of the processor.

Jain, A.↗
NASA NTRS1989

The Alaska SAR processor - Operations and control

The Alaska SAR (synthetic-aperture radar) Facility (ASF) will be capable of receiving, processing, archiving, and producing a variety of SAR image products from three satellite-borne SARs: E-ERS-1 (ESA), J-ERS-1 (NASDA) and Radarsat (Canada). Crucial to the success of the ASF is the Alaska SAR processor (ASP), which will be capable of processing over 200 100-km x 100-km (Seasat-like) frames per day from the raw SAR data, at a ground resolution of about 30 m x 30 m. The processed imagery is of high geometric and radiometric accuracy, and is geolocated to within 500 m. Special-purpose hardware has been designed to execute a SAR processing algorithm to achieve this performance. This hardware is currently undergoing acceptance testing for delivery to the University of Alaska. Particular attention has been devoted to making the operations semi-automated and to providing a friendly operator interface via a computer workstation. The operations and control of the Alaska SAR processor are described.

Carande, Richard E.↗
DOE OSTI2022

Memory access optimization for particle operations in computational fluid dynamics-discrete element method simulations

Computational Fluid Dynamics - Discrete Element Method is used to model gas-solid systems in several applications in energy, pharmaceutical and petrochemical industries. Computational performance bottlenecks often limit the problem sizes that can be simulated at industrial scale. The data structures used to store several millions of particles in such large-scale simulations have a large memory footprint that does not fit into the processor cache hierarchies on current high-performance-computing platforms, leading to reduced computational performance. This paper specifically addresses this aspect of memory access bottlenecks in industrial scale simulations. The use of space-filling curves to improve memory access patterns is described and their impact on computational performance is quantified in both shared and distributed memory parallelization paradigms. The Morton space filling curve applied to uniform grids and k-dimensional tree partitions are used to reorder the particle data-structure thus improving spatial and temporal locality in memory. The performance impact of these techniques when applied to two benchmark problems, namely the homogeneous-cooling-system and a fluidized-bed, are presented. We report these optimization techniques lead to approximately two-fold performance improvement in particle focused operations such as neighbor-list creation and data-exchange, with ~ 1.5 times overall improvement in a fluidization simulation with 1.27 million particles.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Cloud-Based Demodulation and Data Distribution of a Satellite Downlink

Ground station networks connected to the cloud allow space missions to have global communications coverage without operating their own infrastructure. In this work, we describe the communications architecture for the TechEdSat-13 mission, which performed the first in-space characterization of a neuromorphic processor. The mission utilizes a commercial provider for S-band downlinks. A suite of cloud services and open-source software such as GNU Radio are leveraged to demodulate signals received by an AWS ground station during passes with TechEdSat-13 and store recovered data. Once a pass is scheduled, the entire process takes place without human intervention. On-orbit results the past year of operations are presented, demonstrating the advantages of this approach over traditional operator-owned ground stations. Use of software-defined radio makes possible custom signal processing. The homogeneity of apertures and their interfaces to the cloud simplifies scaling across many sites. This abundance of candidate links lays the groundwork for intelligent scheduling agents to optimize pass selection across several factors, automatically recover from failed contacts, and gather metrics to learn from past performance.

cloud demodulation↗
DOE OSTI2013

Automated Cache Performance Analysis And Optimization

While there is no lack of performance counter tools for coarse-grained measurement of cache activity, there is a critical lack of tools for relating data layout to cache behavior to application performance. Generally, any nontrivial optimizations are either not done at all, or are done ”by hand” requiring significant time and expertise. To the best of our knowledge no tool available to users measures the latency of memory reference instructions for particular addresses and makes this information available to users in an easy-to-use and intuitive way. In this project, we worked to enable the Open|SpeedShop performance analysis tool to gather memory reference latency information for specific instructions and memory addresses, and to gather and display this information in an easy-to-use and intuitive way to aid performance analysts in identifying problematic data structures in their codes. This tool was primarily designed for use in the supercomputer domain as well as grid, cluster, cloud-based parallel e-commerce, and engineering systems and middleware. Ultimately, we envision a tool to automate optimization of application cache layout and utilization in the Open|SpeedShop performance analysis tool. To commercialize this software, we worked to develop core capabilities for gathering enhanced memory usage performance data from applications and create and apply novel methods for automatic data structure layout optimizations, tailoring the overall approach to support existing supercomputer and cluster programming models and constraints. In this Phase I project, we focused on infrastructure necessary to gather performance data and present it in an intuitive way to users. With the advent of enhanced Precise Event-Based Sampling (PEBS) counters on recent Intel processor architectures and equivalent technology on AMD processors, we are now in a position to access memory reference information for particular addresses. Prior to the introduction of PEBS counters, cache behavior could only be measured reliably in the aggregate across tens or hundreds of thousands of instructions. With the newest iteration of PEBS technology, cache events can be tied to a tuple of instruction pointer, target address (for both loads and stores), memory hierarchy, and observed latency. With this information we can now begin asking questions regarding the efficiency of not only regions of code, but how these regions interact with particular data structures and how these interactions evolve over time. In the short term, this information will be vital for performance analysts understanding and optimizing the behavior of their codes for the memory hierarchy. In the future, we can begin to ask how data layouts might be changed to improve performance and, for a particular application, what the theoretical optimal performance might be. The overall benefit to be produced by this effort was a commercial quality easy-to- use and scalable performance tool that will allow both beginner and experienced parallel programmers to automatically tune their applications for optimal cache usage. Effective use of such a tool can literally save weeks of performance tuning effort. Easy to use. With the proposed innovations, finding and fixing memory performance issues would be more automated and hide most to all of the performance engineer expertise ”under the hood” of the Open|SpeedShop performance tool. One of the biggest public benefits from the proposed innovations is that it makes performance analysis more usable to a larger group of application developers. Intuitive reporting of results. The Open|SpeedShop performance analysis tool has a rich set of intuitive, yet detailed reports for presenting performance results to application developers. Our goal was to leverage this existing technology to present the results from our memory performance addition to Open|SpeedShop. Suitable for experts as well as novices. Application performance is getting more difficult to measure as the hardware platforms they run on become more complicated. This makes life difficult for the application developer, in that they need to know more about the hardware platform, including the memory system hierarchy, in order to understand the performance of their application. Some application developers are comfortable in that sceario, while others want to do their scientific research and not have to understand all the nuances in the hardware platform they are running their application on. Our proposed innovations were aimed to support both experts and novice performance analysts. Useful in many markets. The enhancement to Open|SpeedShop would appeal to a broader market space, as it will be useful in scientific, commercial, and cloud computing environments. Our goal was to use technology developed initially at the and Lawrence Livermore National Laboratory combined with the development and commercial software experience of the Argo Navis Technologies, LLC (ANT) to form a powerful combination to delivery these objectives.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1995

Sensor Systems for Space Life Sciences

Sensors 2000! (S2K!) is a NASA Ames Research Center engineering initiative designed to provide biosensor and bio-instrumentation systems technology expertise to NASA's life sciences spaceflight programs. S2K! covers the full spectrum of sensor technology applications, ranging from spaceflight hardware design and fabrication to advanced technology development, transfer and commercialization. S2K! is currently developing sensor systems for space biomedical applications on BION (a Russian biosatellite focused on Rhesus Monkey physiology) and NEUROLAB (a Space Shuttle flight devoted to neuroscience). It's Advanced Technology Development-Biosensors (ATD-B) project focuses efforts in five principle areas: biotelemetry Systems, chemical and biological sensors, physiological sensors, advanced instrumentation architectures, and data and information management. Technologies already developed and tested included, application-specific sensors, preamplifier hybrids, modular programmable signal conditioners, power conditioning and distribution systems, and a fully implantable dual channel biotelemeter. Systems currently under development include a portable receiver system compatible with an off-the-shelf analog biotelemeter, a 4 channel digital biotelemetry system which monitors pH, a multichannel, g-processor based PCM biotelemetry system, and hand-held personal monitoring systems. S2K! technology easily lends itself to telescience and telemedicine applications as a front-end measurement and data acquisition device, suitable for obtaining and configuring physiological information, and processing that information under control from a remote location.

Somps, Chris J.↗
DOE OSTI2016

SU-F-T-468: Efficient Scanning Data Analysis for TomoTherapy

Purpose: The standard TomoTherapy 2D scanning system requires manual analysis using spreadsheets or similar software. Manual analysis is time consuming and does not allow efficient use of the medical physicist or timely results that may indicate issues during the acquisition of data. The purpose of this study is to determine if an in-house solution can provide timely analysis of TomoTherapy scan data. Methods: A custom MATLAB program utilizing a streamlined graphical interface was developed to interpret the native scanning data acquired by the TomoTherapy Electrometer Measurement System (TEMS). The gold beam data provided by the manufacturer was used as a baseline for the acquired data. Two separate sets of scan data were used including longitudinal, transverse, and percent depth dose (PDD). The scan data were compared visually in an overlay and analytically for each beam width using the full width half max (FWHM) for longitudinal profiles and difference inside the field core for the transverse profiles as recommended by TG-148. A plot of the PDD percent error at each point along the curve was generated to show agreement. The time to analysis was measured on a laptop running an Intel i5 processor with 8GB of RAM. Results: The software program required on average less than 5 seconds to load and analyze the results. No conversion or reformatting of the data was required. This allowed real-time visualization of the comparison to the gold beam data as well as the parameters recommended by TG-148. The average percent difference was 0.3% for the longitudinal profiles and 0.1% for the transverse profiles. All PDD data was less than 1% different beyond dmax. Conclusion: We have developed a tool to analyze TomoTherapy scan data in real-time. This allows an efficient method to perform beam commissioning, annual QA, and adjust the beam parameters if necessary.

61 RADIATION PROTECTION AND DOSIMETRY↗
NASA NTRS1994

Mobile Computing for Aerospace Applications

The use of commercial computer technology in specific aerospace mission applications can reduce the cost and project cycle time required for the development of special-purpose computer systems. Additionally, the pace of technological innovation in the commercial market has made new computer capabilities available for demonstrations and flight tests. Three areas of research and development being explored by the Portable Computer Technology Project at NASA Ames Research Center are the application of commercial client/server network computing solutions to crew support and payload operations, the analysis of requirements for portable computing devices, and testing of wireless data communication links as extensions to the wired network. This paper will present computer architectural solutions to portable workstation design including the use of standard interfaces, advanced flat-panel displays and network configurations incorporating both wired and wireless transmission media. It will describe the design tradeoffs used in selecting high-performance processors and memories, interfaces for communication and peripheral control, and high resolution displays. The packaging issues for safe and reliable operation aboard spacecraft and aircraft are presented. The current status of wireless data links for portable computers is discussed from a system design perspective. An end-to-end data flow model for payload science operations from the experiment flight rack to the principal investigator is analyzed using capabilities provided by the new generation of computer products. A future flight experiment on-board the Russian MIR space station will be described in detail including system configuration and function, the characteristics of the spacecraft operating environment, the flight qualification measures needed for safety review, and the specifications of the computing devices to be used in the experiment. The software architecture chosen shall be presented. An analysis of the performance characteristics of wireless data links in the spacecraft environment will be discussed. Network performance and operation will be modeled and preliminary test results presented. A crew support application will be demonstrated in conjunction with the network metrics experiment.

Alena, Richard↗
DOE OSTI2019

Parallel Distributed Hydrology Soil Vegetation Model (DHSVM) using global arrays

The Distributed Hydrology Soil Vegetation Model (DHSVM) code was parallelized for distributed memory computers using Global Arrays (GA). To analyze parallel performance, DHSVM was used to simulate two significantly sized river basins located in northwest continental United States and southwest Canada at 90~m resolution: (1) Clearwater (25,000~km2) and (2) Columbia (668,000~km2). Meteorological forcing applied to both basins was dynamically down-scaled from a global circulation model using the Weather Research and Forecasting (WRF) model and read into DHSVM as 2D maps each time step. We report parallel code speed up was significant. Run times for 1-year simulations were reduced by an order of magnitude for both test basins. A maximum speed up of 105 was attained with 480 processors while simulating the Columbia basin. Speed up was limited by input dominated tasks, particularly the input of meteorological forcing data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Algorithmic Improvements to MCNP5 for High-Resolution Fusion Neutronics Analyses

Neutronics analyses of the ITER experimental fusion reactor rely on increasingly complex geometry models and estimates of energy-dependent neutron flux and radiation dose-rate distributions generated at ever higher resolutions. There are significant practical challenges with applying the Monte Carlo N-Particle (MCNP) continuous-energy transport code to high-resolution analyses. For models consisting of more than 100 000 surfaces and cells, geometry initialization can take several hours, thus slowing down model integration and transport analysis efforts. In multithreaded simulations, the amount of memory consumed by superimposed mesh tally data increases in proportion to the number of threads. This behavior limits either the tally resolution or the number of processor cores that can be utilized in the simulation. This paper describes algorithmic improvements that were implemented in a modified version of MCNP5 to overcome these limitations. These improvements are referred to as the Oak Ridge National Laboratory Transformative Neutronics (ORNL-TN) upgrade. Here, a comparison of the performance and memory usage of both MCNP5 and ORNL-TN on several relevant fusion neutronics models is presented. In these tests and in actual high-resolution neutronics analyses, ORNL-TN reduces geometry processing times from hours to a few seconds and increases in-memory mesh tally capacity from the order of 10 8 to 10 10 space-energy bins.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Adaptive pruning-based optimization of parameterized quantum circuits

Abstract Variational hybrid quantum–classical algorithms are powerful tools to maximize the use of noisy intermediate-scale quantum devices. While past studies have developed powerful and expressive ansatze, their near-term applications have been limited by the difficulty of optimizing in the vast parameter space. In this work, we propose a heuristic optimization strategy for such ansatze used in variational quantum algorithms, which we call ‘parameter-efficient circuit training (PECT)’. Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms, in which each iteration of the algorithm activates and optimizes a subset of the total parameter set. To update the parameter subset between iterations, we adapt the Dynamic Sparse Reparameterization scheme which was originally proposed for training deep convolutional neural networks. We demonstrate PECT for the Variational Quantum Eigensolver, in which we benchmark unitary coupled-cluster ansatze including UCCSD and k -UpCCGSD, as well as the Low-Depth Circuit Ansatz (LDCA), to estimate ground state energies of molecular systems. We additionally use a layerwise variant of PECT to optimize a hardware-efficient circuit for the Sycamore processor to estimate the ground state energy densities of the one-dimensional Fermi-Hubbard model. From our numerical data, we find that PECT can enable optimizations of certain ansatze that were previously difficult to converge and more generally can improve the performance of variational algorithms by reducing the optimization runtime and/or the depth of circuits that encode the solution candidate(s).

Physics↗