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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

A Generalized Eulerian-Lagrangian Analysis, with Application to Liquid Flows with Vapor Bubbles

Under a NASA MSFC SBIR Phase 2 effort an analysis has been developed for liquid flows with vapor bubbles such as those in liquid rocket engine components. The analysis is based on a combined Eulerian-Lagrangian technique, in which Eulerian conservation equations are solved for the liquid phase, while Lagrangian equations of motion are integrated in computational coordinates for the vapor phase. The novel aspect of the Lagrangian analysis developed under this effort is that it combines features of the so-called particle distribution approach with those of the so-called particle trajectory approach and can, in fact, be considered as a generalization of both of those traditional methods. The result of this generalization is a reduction in CPU time and memory requirements. Particle time step (stability) limitations have been eliminated by semi-implicit integration of the particle equations of motion (and, for certain applications, the particle temperature equation), although practical limitations remain in effect for reasons of accuracy. The analysis has been applied to the simulation of cavitating flow through a single-bladed section of a labyrinth seal. Models for the simulation of bubble formation and growth have been included, as well as models for bubble drag and heat transfer. The results indicate that bubble formation is more or less 'explosive'. for a given flow field, the number density of bubble nucleation sites is very sensitive to the vapor properties and the surface tension. The bubble motion, on the other hand, is much less sensitive to the properties, but is affected strongly by the local pressure gradients in the flow field. In situations where either the material properties or the flow field are not known with sufficient accuracy, parametric studies can be carried out rapidly to assess the effect of the important variables. Future work will include application of the analysis to cavitation in inducer flow fields.

Dejong, Frederik J.↗
NASA NTRS1996

Shape-Memory-Alloy-Based Deicing System Developed

Ice buildup on aircraft leading edge surfaces has historically been a problem. Most conventional deicing systems rely either on surface heating to melt the accreted ice or pneumatic surface inflation to mechanically debond the ice. Deicers that rely solely on surface heating require large amounts of power. Pneumatic deicers usually cannot remove thin layers of ice and lack durability. Thus, there is a need for an advanced, low-power ice protection system. As part of the NASA Small Business and Innovation Research (SBIR) program, Innovative Dynamics, Inc., developed an aircraft deicing system that utilizes the properties of Shape Memory Alloys (SMA). The SMA-based system has achieved promising improvements in energy efficiency and durability over more conventional deicers. When they are thermally activated, SMA materials change shape; this is analogous to a conventional thermal expansion. The thermal input is currently applied via conventional technology, but there are plans to implement a passive thermal input that is supplied from the energy transfer due to the formation of the ice itself. The actively powered deicer was tested in the NASA Lewis Icing Research Tunnel on a powered rotating rig in early 1995. The system showed promise, deicing both rime and glaze ice shapes as thin as 1/8 in. The first prototype SMA deicer reduced power usage by 45 percent over existing electrothermal systems. This prototype system was targeted for rotorcraft system development. However, there are current plans underway to develop a fixed-wing version of the deicer.

Source record↗
NASA NTRS1974

The aurora and the magnetosphere - The Chapman Memorial Lecture

Review of recent progress in magnetospheric physics, in particular, in understanding the magnetospheric substorm. It is shown that a number of magnetospheric phenomena can now be understood by viewing the solar wind-magnetosphere interaction as an MHD dynamo; auroral phenomena are powered by the dynamo. Also, magnetospheric responses to variations of the north-south and east-west components of the interplanetary magnetic field have been identified. The magnetospheric substorm is entirely different from the responses of the magnetosphere to the southward component of the interplanetary magnetic field. It may be associated with the formation of a neutral line within the plasma sheet and with an enhanced reconnection along the line. A number of substorm-associated phenomena can be understood by noting that the new neutral line formation is caused by a short-circuiting of a part of the magnetotail current.

Akasofu, S.-I.↗
NASA NTRSDate not supplied

BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite

As space missions continue to become more ambitious, complex, and distant to Earth, the need for advanced on-board intelligent decision making to guide everything from mission operations to fault detection and recovery has become a major front of space research. While the prevalence of research on such Artificial Intelligence / Machine Learning (AI/ML) modules has exploded, the capacity to experimentally validate such modules in space in a rapid and inexpensive format has not. To this end, the Nano Orbital Workshop (NOW) group at NASA Ames Research Center has been at the forefront of performing initial flight evaluation tests of ‘commercially’ available AI/ML computational platforms via the TechEdSat (TES-n) flight series as part of what is programmatically referred to as the BRAINSTACK. BRAINSTACK will provide an orbital AI/ML evaluation laboratory where computational experiments are pre-loaded into memory prior to launch, and then executed as desired during the mission, with results reported back and program tweaks or new data sets uploaded as needed. Processors selected as part of the BRAINSTACK are of ideal size, packaging, and power consumption for easy integration into a cube satellite structure. These experiments have included the evaluation of small, high-performance GPUs and more recently, neuromorphic processors in LEO operations. Neuromorphic processors are of particular interest due to their superior computational power efficiency over GPUs. The first TES-n flight test of an Intel first-generation Loihi neuromorphic processor launched on January 13, 2022 and continues to operate in orbit despite almost no space environment modifications. The Intel Loihi Gen-1 is characterized by a 14nm 128-core Spiking Neural Network (SNN) able to support on-chip training. This experiment utilized a Loihi packaged in the ‘Kapoho Bay’ USB module, providing a relatively straight-forward interface to the bus avionics system. The Kapoho Bay was in turn managed by a host Intel Pentium single-board computer to handle scheduling of the AI/ML application payloads, and communications with the satellite vehicle manager. The recently released Intel Loihi Gen-2, able to support integer-valued spike payloads and produced using 7nm process, will form part of the basis of the evolving BRAINSTACK in the upcoming three TES-n/NOW flights. Additionally, it is planned to measure the radiation environment these processors experience to understand any degradation or computational artifacts caused by long term space radiation exposure on these novel architectures. This evolving flexible and collaborative environment involving various research teams across NASA and other organizations is intended to be a convenient orbital test platform from which many anticipated future space AI/ML applications may be initially tested.

Artificial Intelligence↗
NASA NTRSDate not supplied

BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite

As space missions continue to become more ambitious, complex, and distant to Earth, the need for advanced on-board intelligent decision making to guide everything from mission operations to fault detection and recovery has become a major front of space research. While the prevalence of research on such Artificial Intelligence / Machine Learning (AI/ML) modules has exploded, the capacity to experimentally validate such modules in space in a rapid and inexpensive format has not. To this end, the Nano Orbital Workshop (NOW) group at NASA Ames Research Center has been at the forefront of performing initial flight evaluation tests of ‘commercially’ available AI/ML computational platforms via the TechEdSat (TES-n) flight series as part of what is programmatically referred to as the BRAINSTACK. BRAINSTACK will provide an orbital AI/ML evaluation laboratory where computational experiments are pre-loaded into memory prior to launch, and then executed as desired during the mission, with results reported back and program tweaks or new data sets uploaded as needed. Processors selected as part of the BRAINSTACK are of ideal size, packaging, and power consumption for easy integration into a cube satellite structure. These experiments have included the evaluation of small, high-performance GPUs and more recently, neuromorphic processors in LEO operations. Neuromorphic processors are of particular interest due to their superior computational power efficiency over GPUs. The first TES-n flight test of an Intel first-generation Loihi neuromorphic processor launched on January 13, 2022 and continues to operate in orbit despite almost no space environment modifications. The Intel Loihi Gen-1 is characterized by a 14nm 128-core Spiking Neural Network (SNN) able to support on-chip training. This experiment utilized a Loihi packaged in the ‘Kapoho Bay’ USB module, providing a relatively straight-forward interface to the bus avionics system. The Kapoho Bay was in turn managed by a host Intel Pentium single-board computer to handle scheduling of the AI/ML application payloads, and communications with the satellite vehicle manager. The recently released Intel Loihi Gen-2, able to support integer-valued spike payloads and produced using 7nm process, will form part of the basis of the evolving BRAINSTACK in the upcoming three TES-n/NOW flights. Additionally, it is planned to measure the radiation environment these processors experience to understand any degradation or computational artifacts caused by long term space radiation exposure on these novel architectures. This evolving flexible and collaborative environment involving various research teams across NASA and other organizations is intended to be a convenient orbital test platform from which many anticipated future space AI/ML applications may be initially tested.

Artificial Intelligence↗
NASA NTRS1972

The Mariner Venus Mercury flight data subsystem.

The flight data subsystem (FDS) discussed handles both the engineering and scientific measurements performed on the MVM'73. It formats the data into serial data streams, and sends it to the modulation/demodulation subsystem for transmission to earth or to the data storage subsystem for storage on a digital tape recorder. The FDS is controlled by serial digital words, called coded commands, received from the central computer sequencer of from the ground via the modulation/demodulation subsystem. The eight major blocks of the FDS are: power converter, timing and control, engineering data, memory, memory input/output and control, nonimaging data, imaging data, and data output. The FDS incorporates some 4000 components, weighs 17 kg, and uses 35 W of power. General data on the mission and spacecraft are given.

Whitehead, P. B.↗
NASA NTRS1990

Practical applications of remote sensing technology

Land managers increasingly are becoming dependent upon remote sensing and automated analysis techniques for information gathering and synthesis. Remote sensing and geographic information system (GIS) techniques provide quick and economical information gathering for large areas. The outputs of remote sensing classification and analysis are most effective when combined with a total natural resources data base within the capabilities of a computerized GIS. Some examples are presented of the successes, as well as the problems, in integrating remote sensing and geographic information systems. The need to exploit remotely sensed data and the potential that geographic information systems offer for managing and analyzing such data continues to grow. New microcomputers with vastly enlarged memory, multi-fold increases in operating speed and storage capacity that was previously available only on mainframe computers are a reality. Improved raster GIS software systems have been developed for these high performance microcomputers. Vector GIS systems previously reserved for mini and mainframe systems are available to operate on these enhanced microcomputers. One of the more exciting areas that is beginning to emerge is the integration of both raster and vector formats on a single computer screen. This technology will allow satellite imagery or digital aerial photography to be presented as a background to a vector display.

Whitmore, Roy A., Jr.↗
NASA NTRS2023

BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite

As the space economy continues to expand through increasingly easy access to advanced and inexpensive technology, space missions themselves have become more ambitious with exploration targets growing ever distant while simultaneously requiring larger guidance and communication budgets. These conflicting desires of distance and control drive the need for advanced on-board intelligent decision making to reduce communication and control limitations by automating as many mission functions as possible in-situ. While the amount of research on such Artificial Intelligence and Machine Learning (AI/ML) software modules has grown exponentially, the capacity to experimentally validate such software modules in space in a rapid and inexpensive format has not. To this end, the Nano Orbital Workshop (NOW) group at NASA Ames Research Center has been at the forefront of performing initial flight evaluation tests of ‘commercially’ available bleeding-edge computational platforms via what is programmatically referred to as the BrainStack on the TechEdSat (TES-n) flight series. This on-orbit computational platform provides an evaluation laboratory where advanced software experiments are pre-loaded into memory prior to launch, then executed as payloads during mission operations with results reported back and program tweaks or new training sets uploaded as needed. Processors selected as part of the BrainStack are of ideal size, packaging, and power consumption for easy integration into a cube satellite structure. These experiments have included the evaluation of small, high-performance GPUs and, more recently, neuromorphic processors, in LEO operations. Neuromorphic processors are of particular interest due to their superior power efficiency over GPUs in intelligent automation applications. The first TES-n flight test of an Intel first-generation Loihi neuromorphic processor launched on TES-13, January 13, 2022, and continues to operate in orbit despite no significant modifications to harden the processor against the space environment. The Intel Loihi Gen-1 on TES-13 is characterized by a 14nm 128-core Spiking Neural Network (SNN) able to support on-chip training. The processor is packaged in the Kapoho Bay USB module, providing a relatively straight-forward interface to the bus avionics system. The Kapoho Bay was in turn managed by an Intel Pentium single-board computer to handle scheduling of the software application payloads and communications with the satellite’s primary computer. The recently released Intel Loihi Gen-2, able to support integer-valued spike payloads and produced using 7nm process, will form part of the continually evolving BrainStack in the upcoming three TES-n/NOW flights. The Kapoho Point unit will incorporate eight Loihi-2 processors, enabling neural networks of up to one million neurons and one billion synapsis. Additionally, it is planned to measure the radiation environment these processors experience to understand any degradation or computational artifacts caused by long term space radiation exposure on these novel architectures. This evolving flexible and collaborative environment involving various research teams across NASA and other organizations is intended to be a convenient orbital test platform from which many anticipated future space automation applications may be initially tested.

Artificial Intelligence↗
NASA NTRS1993

Spacecraft optical disk recorder memory buffer control

This paper discusses the research completed under the NASA-ASEE summer faculty fellowship program. The project involves development of an Application Specific Integrated Circuit (ASIC) to be used as a Memory Buffer Controller (MBC) in the Spacecraft Optical Disk System (SODR). The SODR system has demanding capacity and data rate specifications requiring specialized electronics to meet processing demands. The system is being designed to support Gigabit transfer rates with Terabit storage capability. The complete SODR system is designed to exceed the capability of all existing mass storage systems today. The ASIC development for SODR consist of developing a 144 pin CMOS device to perform format conversion and data buffering. The final simulations of the MBC were completed during this summer's NASA-ASEE fellowship along with design preparations for fabrication to be performed by an ASIC manufacturer.

Hodson, Robert F.↗
NASA NTRS2023

Development of Level of Detail System and First-Person Camera for the GCAS Visualization Suite

The use of data-driven simulations has become standard practice as part of planning for future space missions. These simulations allow visualizing the data interactively to show what the data represents, as well as the importance of the data in the context of the mission. Using this visualized data can enhance users’ understanding of it and accelerate analysis efforts related to missions planned around it. Three-dimensional (3D) visualization software was developed to allow creating 3D representations of various communication systems, as well as the physical terrain of the Moon, for upcoming missions. The goal of this software development effort was to create interactive visualization capabilities in the Glenn Research Center Communication Analysis Suite (GCAS) using data exported from MATLAB® (MathWorks, Inc.) scripts. This software had the functionality to visualize the line of sight and dynamic link margins of the communication satellites orbiting the Earth and the Moon. One important addition to this was the visualization of the terrain data located within the GeoTIFF files, which were produced in an effort to understand the Moon’s terrain. Proper displacement values of this data have to be visualized to showcase where craters are located and how the shadow casting works with said craters at different points of the day, as well as analysis of possible landing sites for future lunar expeditions. The graphics library coded in JavaScript, three.js, had been previously selected for developing this visualization software. The software was revised to conform to modern standards, then further developed to convert the MATLAB® data into JavaScript 3D objects and Blender GL Transmission Format Binary file (GLB) objects, which were to be imported into the scene. In the process, a variety of other testing projects were created to be combined with this project at a later point; these included the first-person camera movements around spherical objects to portray human movement around the Moon, GeoTIFF loading methods, data transfer methods for incorporating the elevation data into the scene, and level of detail (LOD) capabilities to decrease memory usage and rendering time.

Visualization↗
NASA NTRS2003

Software Aids Visualization of Computed Unsteady Flow

Unsteady Flow Analysis Toolkit (UFAT) is a computer program that synthesizes motions of time-dependent flows represented by very large sets of data generated in computational fluid dynamics simulations. Prior to the development of UFAT, it was necessary to rely on static, single-snapshot depictions of time-dependent flows generated by flow-visualization software designed for steady flows. Whereas it typically takes weeks to analyze the results of a largescale unsteady-flow simulation by use of steady-flow visualization software, the analysis time is reduced to hours when UFAT is used. UFAT can be used to generate graphical objects of flow visualization results using multi-block curvilinear grids in the format of a previously developed NASA data-visualization program, PLOT3D. These graphical objects can be rendered using FAST, another popular flow visualization software developed at NASA. Flow-visualization techniques that can be exploited by use of UFAT include time-dependent tracking of particles, detection of vortex cores, extractions of stream ribbons and surfaces, and tetrahedral decomposition for optimal particle tracking. Unique computational features of UFAT include capabilities for automatic (batch) processing, restart, memory mapping, and parallel processing. These capabilities significantly reduce analysis time and storage requirements, relative to those of prior flow-visualization software. UFAT can be executed on a variety of supercomputers.

Kao, David↗
NASA NTRS2013

Scalable Adaptive Graphics Environment (SAGE) Software for the Visualization of Large Data Sets on a Video Wall

The use of collaborative scientific visualization systems for the analysis, visualization, and sharing of "big data" available from new high resolution remote sensing satellite sensors or four‐dimensional numerical model simulations is propelling the wider adoption of ultra‐resolution tiled display walls interconnected by high speed networks. These systems require a globally connected and well‐integrated operating environment that provides persistent visualization and collaboration services. This abstract and subsequent presentation describes a new collaborative visualization system installed for NASA's Shortterm Prediction Research and Transition (SPoRT) program at Marshall Space Flight Center and its use for Earth science applications. The system consists of a 3 x 4 array of 1920 x 1080 pixel thin bezel video monitors mounted on a wall in a scientific collaboration lab. The monitors are physically and virtually integrated into a 14' x 7' for video display. The display of scientific data on the video wall is controlled by a single Alienware Aurora PC with a 2nd Generation Intel Core 4.1 GHz processor, 32 GB memory, and an AMD Fire Pro W600 video card with 6 mini display port connections. Six mini display‐to‐dual DVI cables are used to connect the 12 individual video monitors. The open source Scalable Adaptive Graphics Environment (SAGE) windowing and media control framework, running on top of the Ubuntu 12 Linux operating system, allows several users to simultaneously control the display and storage of high resolution still and moving graphics in a variety of formats, on tiled display walls of any size. The Ubuntu operating system supports the open source Scalable Adaptive Graphics Environment (SAGE) software which provides a common environment, or framework, enabling its users to access, display and share a variety of data‐intensive information. This information can be digital‐cinema animations, high‐resolution images, high‐definition video‐teleconferences, presentation slides, documents, spreadsheets or laptop screens. SAGE is cross‐platform, community‐driven, open‐source visualization and collaboration middleware that utilizes shared national and international cyberinfrastructure for the advancement of scientific research and education.

Jedlovec, Gary↗
NASA NTRSDate not supplied

Exploring the Capabilities of a Machine Learning Algorithm to Detect Space Weather-Significant Emerging Active Regions

Active regions are a source of various phenomena responsible for Space Weather disturbances; therefore, developing a technology for early warning about upcoming magnetic activity is crucial to mitigate its impact. However, observational limitations and the high nonlinearity of processes associated with the accumulation of magnetic flux and its interaction with the surrounding plasma during the emergence through the convection zone make early activity detection a challenging problem. To address these challenges, we developed a physics-driven machine learning model that allows us to detect active regions (ARs) before they become visible on the solar surface by analyzing the power spectra of acoustic oscillations observed by the SDO/HMI instrument. This study is based on a time series of Doppler shift maps of 31x31-degree areas tracked with the Carrington rotation rate for four days before and after the emergence. The Doppler shift time series are processed into the oscillation power maps for four frequency ranges and accompanied by line-of-sight magnetograms and the continuum intensity maps from SDO/HMI. The resulting data are converted into a 1D time series representing the mean temporal variations of these quantities. The redacted time series are used as input to predict AR emergence using the Long Short Term Memory (LSTM) method. The training of the LSTM model is based on 40 ARs, which includes an independent analysis for each sub region that exhibits AR emergence or remains quiet. The emergence of magnetic flux (defined as a decrease of the continuum intensity) was detected with the developed LSTM algorithm from 5 to 48 hours before the reported time by NOAA. The developed model is capable of pointing to the time and location of active region formation. In this presentation, we discuss reasons that impact how early in advance the model can identify the upcoming activity and the possibility of improving the current predictive skills and steps to transition to the operational forecast.

Heliophysics↗
NASA NTRS2015

AMO EXPRESS: A Command and Control Experiment for Crew Autonomy Onboard the International Space Station

NASA is investigating a range of future human spaceflight missions, including both Mars-distance and Near Earth Object (NEO) targets. Of significant importance for these missions is the balance between crew autonomy and vehicle automation. As distance from Earth results in increasing communication delays, future crews need both the capability and authority to independently make decisions. However, small crews cannot take on all functions performed by ground today, and so vehicles must be more automated to reduce the crew workload for such missions. NASA's Advanced Exploration Systems Program funded Autonomous Mission Operations (AMO) project conducted an autonomous command and control experiment on-board the International Space Station that demonstrated single action intelligent procedures for crew command and control. The target problem was to enable crew initialization of a facility class rack with power and thermal interfaces, and involving core and payload command and telemetry processing, without support from ground controllers. This autonomous operations capability is enabling in scenarios such as initialization of a medical facility to respond to a crew medical emergency, and representative of other spacecraft autonomy challenges. The experiment was conducted using the Expedite the Processing of Experiments for Space Station (EXPRESS) rack 7, which was located in the Port 2 location within the U.S Laboratory onboard the International Space Station (ISS). Activation and deactivation of this facility is time consuming and operationally intensive, requiring coordination of three flight control positions, 47 nominal steps, 57 commands, 276 telemetry checks, and coordination of multiple ISS systems (both core and payload). Utilization of Draper Laboratory's Timeliner software, deployed on-board the ISS within the Command and Control (C&C) computers and the Payload computers, allowed development of the automated procedures specific to ISS without having to certify and employ novel software for procedure development and execution. The procedures contained the ground procedure logic and actions as possible to include fault detection and recovery capabilities. The autonomous operations concept includes a reduction of the amount of data a crew operator is required to verify during activation or de-activation, as well as integration of procedure execution status and relevant data in a single integrated display. During execution, the auto-procedures (via Timerliner) provide a step-by-step messaging paradigm and a high-level status upon termination. This messaging and high-level status is the only data generated for operator display. To enhance situational awareness of the operator, the Web-based Procedure Display (WebPD) provides a novel approach to the issues of procedure display and execution tracking. WebPD is a web based application that serves as the user interface for electronic procedure execution. It incorporates several aspects of the HTML5 standard. Procedures are written in a dialect of XML called Procedure Representation Language (PRL). WebPD tracks execution status in the procedure or procedures being displayed. WebPD aggregates and simplifies the auto-sequence execution status information, and formatted to be easily followed and understood by an operator who is not dedicated to actively monitoring the task. WebPD also provides an integrated data and control interface to pause or halt the execution in order to provide a check point of operation and to examine progress before starting the next sequence of activities. For this demonstration, the procedure was initiated and monitored from the ground. As the Timeliner sequences executed, their high-level execution status was written to PLMDM memory. This memory is read and downlinked via Ku-Band at a 1 Hz rate. The data containing the high-level execution status is de-commutated on the ground, and rebroadcast for WebPD consumption. A future demonstration will be performed onboard, with ISS astronauts initiating the operations instead of ground controllers. The AMO EXPRESS experiment demonstrated activation and de-activation of EXPRESS rack 7, providing the capability of future single button activations and deactivations of facility class racks. The experiment achieved numerous technical and operations 'firsts' for the ISS

Stetson, Howard K.↗
NASA NTRS1980

Mark 3 real-time fringe detection system

A RAM memory built into the Mark 3 decoder module allows the capture of 1 Megabit of data. Data may be collected either in real time or from a pre-recorded tape. Once collected, the data may be retrieved using a standard EIA serial data link. The data may be transmitted to a remote computer for cross correlation processing with similar data from other stations to verify fringes in real time. The data may also be analyzed by a local computer to verify phase calibration, bandpass, format, etc., during a Mark 3 observing session.

Levine, J. I.↗
NASA NTRS2013

Software Method for Computed Tomography Cylinder Data Unwrapping, Re-slicing, and Analysis

A software method has been developed that is applicable for analyzing cylindrical and partially cylindrical objects inspected using computed tomography (CT). This method involves unwrapping and re-slicing data so that the CT data from the cylindrical object can be viewed as a series of 2D sheets (or flattened onion skins ) in addition to a series of top view slices and 3D volume rendering. The advantages of viewing the data in this fashion are as follows: (1) the use of standard and specialized image processing and analysis methods is facilitated having 2D array data versus a volume rendering; (2) accurate lateral dimensional analysis of flaws is possible in the unwrapped sheets versus volume rendering; (3) flaws in the part jump out at the inspector with the proper contrast expansion settings in the unwrapped sheets; and (4) it is much easier for the inspector to locate flaws in the unwrapped sheets versus top view slices for very thin cylinders. The method is fully automated and requires no input from the user except proper voxel dimension from the CT experiment and wall thickness of the part. The software is available in 32-bit and 64-bit versions, and can be used with binary data (8- and 16-bit) and BMP type CT image sets. The software has memory (RAM) and hard-drive based modes. The advantage of the (64-bit) RAM-based mode is speed (and is very practical for users of 64-bit Windows operating systems and computers having 16 GB or more RAM). The advantage of the hard-drive based analysis is one can work with essentially unlimited-sized data sets. Separate windows are spawned for the unwrapped/re-sliced data view and any image processing interactive capability. Individual unwrapped images and un -wrapped image series can be saved in common image formats. More information is available at http://www.grc.nasa.gov/WWW/OptInstr/ NDE_CT_CylinderUnwrapper.html.

Roth, Don J.↗
NASA NTRS1994

Automated Instrumentation, Monitoring and Visualization of PVM Programs Using AIMS

We present views and analysis of the execution of several PVM codes for Computational Fluid Dynamics on a network of Sparcstations, including (a) NAS Parallel benchmarks CG and MG (White, Alund and Sunderam 1993); (b) a multi-partitioning algorithm for NAS Parallel Benchmark SP (Wijngaart 1993); and (c) an overset grid flowsolver (Smith 1993). These views and analysis were obtained using our Automated Instrumentation and Monitoring System (AIMS) version 3.0, a toolkit for debugging the performance of PVM programs. We will describe the architecture, operation and application of AIMS. The AIMS toolkit contains (a) Xinstrument, which can automatically instrument various computational and communication constructs in message-passing parallel programs; (b) Monitor, a library of run-time trace-collection routines; (c) VK (Visual Kernel), an execution-animation tool with source-code clickback; and (d) Tally, a tool for statistical analysis of execution profiles. Currently, Xinstrument can handle C and Fortran77 programs using PVM 3.2.x; Monitor has been implemented and tested on Sun 4 systems running SunOS 4.1.2; and VK uses X11R5 and Motif 1.2. Data and views obtained using AIMS clearly illustrate several characteristic features of executing parallel programs on networked workstations: (a) the impact of long message latencies; (b) the impact of multiprogramming overheads and associated load imbalance; (c) cache and virtual-memory effects; and (4significant skews between workstation clocks. Interestingly, AIMS can compensate for constant skew (zero drift) by calibrating the skew between a parent and its spawned children. In addition, AIMS' skew-compensation algorithm can adjust timestamps in a way that eliminates physically impossible communications (e.g., messages going backwards in time). Our current efforts are directed toward creating new views to explain the observed performance of PVM programs. Some of the features planned for the near future include: (a) ConfigView, showing the physical topology of the virtual machine, inferred using specially formatted IP (Internet Protocol) packets; and (b) LoadView, synchronous animation of PVM-program execution and resource-utilization patterns.

Mehra, Pankaj↗
NASA NTRS1994

Automated Instrumentation, Monitoring and Visualization of PVM Programs Using AIMS

We present views and analysis of the execution of several PVM (Parallel Virtual Machine) codes for Computational Fluid Dynamics on a networks of Sparcstations, including: (1) NAS Parallel Benchmarks CG and MG; (2) a multi-partitioning algorithm for NAS Parallel Benchmark SP; and (3) an overset grid flowsolver. These views and analysis were obtained using our Automated Instrumentation and Monitoring System (AIMS) version 3.0, a toolkit for debugging the performance of PVM programs. We will describe the architecture, operation and application of AIMS. The AIMS toolkit contains: (1) Xinstrument, which can automatically instrument various computational and communication constructs in message-passing parallel programs; (2) Monitor, a library of runtime trace-collection routines; (3) VK (Visual Kernel), an execution-animation tool with source-code clickback; and (4) Tally, a tool for statistical analysis of execution profiles. Currently, Xinstrument can handle C and Fortran 77 programs using PVM 3.2.x; Monitor has been implemented and tested on Sun 4 systems running SunOS 4.1.2; and VK uses XIIR5 and Motif 1.2. Data and views obtained using AIMS clearly illustrate several characteristic features of executing parallel programs on networked workstations: (1) the impact of long message latencies; (2) the impact of multiprogramming overheads and associated load imbalance; (3) cache and virtual-memory effects; and (4) significant skews between workstation clocks. Interestingly, AIMS can compensate for constant skew (zero drift) by calibrating the skew between a parent and its spawned children. In addition, AIMS' skew-compensation algorithm can adjust timestamps in a way that eliminates physically impossible communications (e.g., messages going backwards in time). Our current efforts are directed toward creating new views to explain the observed performance of PVM programs. Some of the features planned for the near future include: (1) ConfigView, showing the physical topology of the virtual machine, inferred using specially formatted IP (Internet Protocol) packets: and (2) LoadView, synchronous animation of PVM-program execution and resource-utilization patterns.

Mehra, Pankaj↗