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All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Integrating Wind Profiling Radars and Radiosonde Observations with Model Point Data to Develop a Decision Support Tool to Assess Upper-Level Winds for Space Launch

On the day-of-launch, the 45th Weather Squadron (45 WS) Launch Weather Officers (LWOs) monitor the upper-level winds for their launch customers to include NASA's Launch Services Program and NASA's Ground Systems Development and Operations Program. They currently do not have the capability to display and overlay profiles of upper-level observations and numerical weather prediction model forecasts. The LWOs requested the Applied Meteorology Unit (AMU) develop a tool in the form of a graphical user interface (GUI) that will allow them to plot upper-level wind speed and direction observations from the Kennedy Space Center (KSC) 50 MHz tropospheric wind profiling radar, KSC Shuttle Landing Facility 915 MHz boundary layer wind profiling radar and Cape Canaveral Air Force Station (CCAFS) Automated Meteorological Processing System (AMPS) radiosondes, and then overlay forecast wind profiles from the model point data including the North American Mesoscale (NAM) model, Rapid Refresh (RAP) model and Global Forecast System (GFS) model to assess the performance of these models. The AMU developed an Excel-based tool that provides an objective method for the LWOs to compare the model-forecast upper-level winds to the KSC wind profiling radars and CCAFS AMPS observations to assess the model potential to accurately forecast changes in the upperlevel profile through the launch count. The AMU wrote Excel Visual Basic for Applications (VBA) scripts to automatically retrieve model point data for CCAFS (XMR) from the Iowa State University Archive Data Server (http://mtarchive.qeol.iastate.edu) and the 50 MHz, 915 MHz and AMPS observations from the NASA/KSC Spaceport Weather Data Archive web site (http://trmm.ksc.nasa.gov). The AMU then developed code in Excel VBA to automatically ingest and format the observations and model point data in Excel to ready the data for generating Excel charts for the LWO's. The resulting charts allow the LWOs to independently initialize the three models 0-hour forecasts against the observations to determine which is the best performing model and then overlay the model forecasts on time-matched observations during the launch countdown to further assess the model performance and forecasts. This paper will demonstrate integration of observed and predicted atmospheric conditions into a decision support tool and demonstrate how the GUI is implemented in operations.

Bauman, William H., III↗
NASA NTRS2007

Oxygen production System Models for Lunar ISRU

In-Situ Resource Utilization (ISRU) seeks to make human space exploration feasible; by using available resources from a planet or the moon to produce consumables, parts, and structures that otherwise would be brought from Earth. Producing these in situ reduces the mass of such that must be launched and doing so allows more payload mass' for each mission. The production of oxygen from lunar regolith, for life support and propellant, is one of the tasks being studied under ISRU. NASA is currently funding three processes that have shown technical merit for the production of oxygen from regolith: Molten Salt Electrolysis, Hydrogen Reduction of Ilmenite, and Carbothermal Reduction. The ISRU program is currently developing system models of, the , abovementioned processes to: (1) help NASA in the evaluation process to select the most cost-effective and efficient process for further prototype development, (2) identify key parameters, (3) optimize the oxygen production process, (4) provide estimates on energy and power requirements, mass and volume.of the system, oxygen production rate, mass of regolith required, mass of consumables, and other important parameters, and (5) integrate into the overall end-to-end ISRU system model, which could be integrated with mission architecture models. The oxygen production system model is divided into modules that represent unit operations (e.g., reactor, water electrolyzer, heat exchanger). Each module is modeled theoretically using Excel and Visual Basic for Applications (VBA), and will be validated using experimental data from on-going laboratory work. This modularity (plug-n-play) feature of each unit operation allows the use of the same model on different oxygen production systems simulations resulting in comparable results. In this presentation, preliminary results for mass, power, volume will be presented along with brief description of the oxygen production system model.

Santiago-Maldonado, Edgardo↗
NASA NTRS2008

Statistical Short-Range Guidance for Peak Wind Speed Forecasts at Edwards Air Force Base, CA

The peak winds near the surface are an important forecast element for Space Shuttle landings. As defined in the Shuttle Flight Rules (FRs), there are peak wind thresholds that cannot be exceeded in order to ensure the safety of the shuttle during landing operations. The National Weather Service Spaceflight Meteorology Group (SMG) is responsible for weather forecasts for all shuttle landings. They indicate peak winds are a challenging parameter to forecast. To alleviate the difficulty in making such wind forecasts, the Applied Meteorology Unit (AMTJ) developed a personal computer based graphical user interface (GUI) for displaying peak wind climatology and probabilities of exceeding peak-wind thresholds for the Shuttle Landing Facility (SLF) at Kennedy Space Center. However, the shuttle must land at Edwards Air Force Base (EAFB) in southern California when weather conditions at Kennedy Space Center in Florida are not acceptable, so SMG forecasters requested that a similar tool be developed for EAFB. Marshall Space Flight Center (MSFC) personnel archived and performed quality control of 2-minute average and 10-minute peak wind speeds at each tower adjacent to the main runway at EAFB from 1997- 2004. They calculated wind climatologies and probabilities of average peak wind occurrence based on the average speed. The climatologies were calculated for each tower and month, and were stratified by hour, direction, and direction/hour. For the probabilities of peak wind occurrence, MSFC calculated empirical and modeled probabilities of meeting or exceeding specific 10-minute peak wind speeds using probability density functions. The AMU obtained and reformatted the data into Microsoft Excel PivotTables, which allows users to display different values with point-click-drag techniques. The GUT was then created from the PivotTables using Visual Basic for Applications code. The GUI is run through a macro within Microsoft Excel and allows forecasters to quickly display and interpret peak wind climatology and likelihoods in a fast-paced operational environment. A summary of how the peak wind climatologies and probabilities were created and an overview of the GUT will be presented.

Dreher, Joseph↗
NASA NTRS2014

Transition Marshall Space Flight Center Wind Profiler Splicing Algorithm to Launch Services Program Upper Winds Tool

NASAs LSP customers and the future SLS program rely on observations of upper-level winds for steering, loads, and trajectory calculations for the launch vehicles flight. On the day of launch, the 45th Weather Squadron (45 WS) Launch Weather Officers (LWOs) monitor the upper-level winds and provide forecasts to the launch team via the AMU-developed LSP Upper Winds tool for launches at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station. This tool displays wind speed and direction profiles from rawinsondes released during launch operations, the 45th Space Wing 915-MHz Doppler Radar Wind Profilers (DRWPs) and KSC 50-MHz DRWP, and output from numerical weather prediction models.The goal of this task was to splice the wind speed and direction profiles from the 45th Space Wing (45 SW) 915-MHz Doppler radar Wind Profilers (DRWPs) and KSC 50-MHz DRWP at altitudes where the wind profiles overlap to create a smooth profile. In the first version of the LSP Upper Winds tool, the top of the 915-MHz DRWP wind profile and the bottom of the 50-MHz DRWP were not spliced, sometimes creating a discontinuity in the profile. The Marshall Space Flight Center (MSFC) Natural Environments Branch (NE) created algorithms to splice the wind profiles from the two sensors to generate an archive of vertically complete wind profiles for the SLS program. The AMU worked with MSFC NE personnel to implement these algorithms in the LSP Upper Winds tool to provide a continuous spliced wind profile.The AMU transitioned the MSFC NE algorithms to interpolate and fill data gaps in the data, implement a Gaussian weighting function to produce 50-m altitude intervals in each sensor, and splice the data together from both DRWPs. They did so by porting the MSFC NE code written with MATLAB software into Microsoft Excel Visual Basic for Applications (VBA). After testing the new algorithms in stand-alone VBA modules, the AMU replaced the existing VBA code in the LSP Upper Winds tool with the new algorithms. They then tested the code in the LSP Upper Winds tool with archived data. The tool will be delivered to the 45 WS after the 50-MHz DRWP upgrade is complete and the tool is tested with real-time data. The 50-MHz DRWP upgrade is expected to be finished in October 2014.

Space launch↗
NASA NTRS2016

A Tool for Measuring NASA's Aeronautics Research Progress Toward Planned Strategic Community Outcomes

Description of a tool for portfolio analysis of NASA's Aeronautics research progress toward planned community strategic Outcomes is presented. For efficiency and speed, the tool takes advantage of a function developed in Excels Visual Basic for Applications. The strategic planning process for determining the community Outcomes is also briefly discussed. Stakeholder buy-in, partnership performance, progress of supporting Technical Challenges, and enablement forecast are used as the criteria for evaluating progress toward Outcomes. A few illustrative examples of using the tool are also presented.

Thrusts↗
NASA NTRS2015

[Determine and Implement Updates to Be Made to MODEAR (Mission Operations Data Enterprise Architecture Repository)]

My main project was to determine and implement updates to be made to MODEAR (Mission Operations Data Enterprise Architecture Repository) process definitions to be used for CST-100 (Crew Space Transportation-100) related missions. Emphasis was placed on the scheduling aspect of the processes. In addition, I was to complete other tasks as given. Some of the additional tasks were: to create pass-through command look-up tables for the flight controllers, finish one of the MDT (Mission Operations Directorate Display Tool) displays, gather data on what is included in the CST-100 public data, develop a VBA (Visual Basic for Applications) script to create a csv (Comma-Separated Values) file with specific information from spreadsheets containing command data, create a command script for the November MCC-ASIL (Mission Control Center-Avionics System Integration Laboratory) testing, and take notes for one of the TCVB (Terminal Configured Vehicle B-737) meetings. In order to make progress in my main project I scheduled meetings with the appropriate subject matter experts, prepared material for the meetings, and assisted in the discussions in order to understand the process or processes at hand. After such discussions I made updates to various MODEAR processes and process graphics. These meetings have resulted in significant updates to the processes that were discussed. In addition, the discussions have helped the departments responsible for these processes better understand the work ahead and provided material to help document how their products are created. I completed my other tasks utilizing resources available to me and, when necessary, consulting with the subject matter experts. Outputs resulting from my other tasks were: two completed and one partially completed pass through command look-up tables for the fight controllers, significant updates to one of the MDT displays, a spreadsheet containing data on what is included in the CST-100 public data, a tool to create a csv file with specific information from spreadsheets containing command data, a command script for the November MCC-ASIL testing which resulted in a successful test day identifying several potential issues, and notes from one of the TCVB meetings that was used to keep the teams up to date on what was discussed and decided. I have learned a great deal working at NASA these last four months. I was able to meet and work with amazing individuals, further develop my technical knowledge, expand my knowledge base regarding human spaceflight, and contribute to the CST-100 missions. My work at NASA has strengthened my desire to continue my education in order to make further contributions to the field, and has given me the opportunity to see the advantages of a career at NASA.

Fanourakis, Sofia↗
NASA NTRSDate not supplied

Rocket Propulsion Test Design and Analysis

As the small satellite launch vehicle (SSLV) market grows and NASA continues to prepare for upcoming missions in the Artemis program, John C. Stennis Space Center’s (SSC) testing facilities must adapt to the increasing demand in liquid rocket engine testing. The Rocket Propulsion Test Design and Analysis Project for this rotation focused in both of this areas through two separate subprojects, the creation of a Private Rocket Engine Database and the automatization of the A1-Set Point Prediction Model. To identify the testing requirements of future Stennis Space Center (SSC) costumers, an excel spread sheet was commissioned as a database to collect technical and non-technical features of over one hundred privately developed liquid and hybrid rocket engine. Similarly, The A-1 Set Point Sheet Model is used to satisfy Aerojet Rocketdyne’s testing requirements of Net Positive Suction Pressure (NPSP) for the RS-25 staged combustion cycle engine. To expand the capabilities of this prediction model, a feed back loop was programmed to automate the search of setpoints using Excel Visual Basic for Applications (VBA).

John Paul Ortiz↗
NASA NTRS2010

Astronaut Office Scheduling System Software

AOSS is a highly efficient scheduling application that uses various tools to schedule astronauts weekly appointment information. This program represents an integration of many technologies into a single application to facilitate schedule sharing and management. It is a Windows-based application developed in Visual Basic. Because the NASA standard office automation load environment is Microsoft-based, Visual Basic provides AO SS developers with the ability to interact with Windows collaboration components by accessing objects models from applications like Outlook and Excel. This also gives developers the ability to create newly customizable components that perform specialized tasks pertaining to scheduling reporting inside the application. With this capability, AOSS can perform various asynchronous tasks, such as gathering/ sending/ managing astronauts schedule information directly to their Outlook calendars at any time.

Brown, Estevancio↗
NASA NTRS2016

Station Program Note Pull Automation

Upon commencement of my internship, I was in charge of maintaining the CoFR (Certificate of Flight Readiness) Tool. The tool acquires data from existing Excel workbooks on NASA's and Boeing's databases to create a new spreadsheet listing out all the potential safety concerns for upcoming flights and software transitions. Since the application was written in Visual Basic, I had to learn a new programming language and prepare to handle any malfunctions within the program. Shortly afterwards, I was given the assignment to automate the Station Program Note (SPN) Pull process. I developed an application, in Python, that generated a GUI (Graphical User Interface) that will be used by the International Space Station Safety & Mission Assurance team here at Johnson Space Center. The application will allow its users to download online files with the click of a button, import SPN's based on three different pulls, instantly manipulate and filter spreadsheets, and compare the three sources to determine which active SPN's (Station Program Notes) must be reviewed for any upcoming flights, missions, and/or software transitions. Initially, to perform the NASA SPN pull (one of three), I had created the program to allow the user to login to a secure webpage that stores data, input specific parameters, and retrieve the desired SPN's based on their inputs. However, to avoid any conflicts with sustainment, I altered it so that the user may login and download the NASA file independently. After the user has downloaded the file with the click of a button, I defined the program to check for any outdated or pre-existing files, for successful downloads, to acquire the spreadsheet, convert it from a text file to a comma separated file and finally into an Excel spreadsheet to be filtered and later scrutinized for specific SPN numbers. Once this file has been automatically manipulated to provide only the SPN numbers that are desired, they are stored in a global variable, shown on the GUI, and transferred over to a new Excel worksheet for comparison. I managed to get my application to acquire the CSWG (Computer Safety Working Group) and the SPNWG (Space Station Working Group) SPN's with just two mouse clicks for each pull, as opposed to several from the original process. When all three pulls are performed, an Excel sheet containing all three different results will be generated for the user to compare and determine which SPN's will be presented or reviewed the following month. The experience from this internship has been spectacular. As a high school senior who will begin attending college in the fall, this internship has been both educationally and occupationally beneficial. The internship has allowed me the opportunities to learn new programming languages, effectively network with NASA personnel from a variety of departments at JSC, and allowed me to learn new professional skills and etiquette. My internship at NASA's Johnson Space Center has further motivated me to pursue a Master's degree in Software Engineering and strive for a prosperous career with NASA as a civil servant.

Delgado, Ivan↗
NASA NTRS2009

A General Tool for Evaluating High-Contrast Coronagraphic Telescope Performance Error Budgets

This paper describes a general purpose Coronagraph Performance Error Budget (CPEB) tool that we have developed under the NASA Exoplanet Exploration Program. The CPEB automates many of the key steps required to evaluate the scattered starlight contrast in the dark hole of a space-based coronagraph. It operates in 3 steps: first, a CodeV or Zemax prescription is converted into a MACOS optical prescription. Second, a Matlab program calls ray-trace code that generates linear beam-walk and aberration sensitivity matrices for motions of the optical elements and line-of-sight pointing, with and without controlled coarse and fine-steering mirrors. Third, the sensitivity matrices are imported by macros into Excel 2007 where the error budget is created. Once created, the user specifies the quality of each optic from a predefined set of PSDs. The spreadsheet creates a nominal set of thermal and jitter motions and combines them with the sensitivity matrices to generate an error budget for the system. The user can easily modify the motion allocations to perform trade studies.

Terrestrial Planet Finder↗
NASA NTRS2005

Reference Models for Structural Technology Assessment and Weight Estimation

Previously the Exploration Concepts Branch of NASA Langley Research Center has developed techniques for automating the preliminary design level of launch vehicle airframe structural analysis for purposes of enhancing historical regression based mass estimating relationships. This past work was useful and greatly reduced design time, however its application area was very narrow in terms of being able to handle a large variety in structural and vehicle general arrangement alternatives. Implementation of the analysis approach presented herein also incorporates some newly developed computer programs. Loft is a program developed to create analysis meshes and simultaneously define structural element design regions. A simple component defining ASCII file is read by Loft to begin the design process. HSLoad is a Visual Basic implementation of the HyperSizer Application Programming Interface, which automates the structural element design process. Details of these two programs and their use are explained in this paper. A feature which falls naturally out of the above analysis paradigm is the concept of "reference models". The flexibility of the FEA based JAVA processing procedures and associated process control classes coupled with the general utility of Loft and HSLoad make it possible to create generic program template files for analysis of components ranging from something as simple as a stiffened flat panel, to curved panels, fuselage and cryogenic tank components, flight control surfaces, wings, through full air and space vehicle general arrangements.

Cerro, Jeff↗
NASA NTRS2016

Using Remotely Sensed Data for Climate Change Mitigation and Adaptation: A Collaborative Effort Between the Climate Change Adaptation Science Investigators Workgroup (CASI), NASA Johnson Space Center, and Jacobs Technology

With ever changing landscapes and environmental conditions due to human induced climate change, adaptability is imperative for the long-term success of facilities and Federal agency missions. To mitigate the effects of climate change, indicators such as above-ground biomass change must be identified to establish a comprehensive monitoring effort. Researching the varying effects of climate change on ecosystems can provide a scientific framework that will help produce informative, strategic and tactical policies for environmental adaptation. As a proactive approach to climate change mitigation, NASA tasked the Climate Change Adaptation Science Investigators Workgroup (CASI) to provide climate change expertise and data to Center facility managers and planners in order to ensure sustainability based on predictive models and current research. Generation of historical datasets that will be used in an agency-wide effort to establish strategies for climate change mitigation and adaptation at NASA facilities is part of the CASI strategy. Using time series of historical remotely sensed data is well-established means of measuring change over time. CASI investigators have acquired multispectral and hyperspectral optical and LiDAR remotely sensed datasets from NASA Earth Observation Satellites (including the International Space Station), airborne sensors, and astronaut photography using hand held digital cameras to create a historical dataset for the Johnson Space Center, as well as the Houston and Galveston area. The raster imagery within each dataset has been georectified, and the multispectral and hyperspectral imagery has been atmospherically corrected. Using ArcGIS for Server, the CASI-Regional Remote Sensing data has been published as an image service, and can be visualized through a basic web mapping application. Future work will include a customized web mapping application created using a JavaScript Application Programming Interface (API), and inclusion of the CASI data for the NASA Johnson Space Center into a NASA-Wide GIS Institutional Portal.

Jagge, Amy↗
NASA NTRS1997

Numerical Flow Visualization in Basic- and Hyper-Cluster Spheres

Packed spherical particle beds have wide applications throughout the process industry and are usually analyzed using an appropriate combination of laminar and turbulent flows with empirically derived coefficients of which the Ergun (1952) relation is probably the best known. The 3-D complexity of the void distribution within the beds has precluded detailed studies of sphere clusters. Numerical modeling and flow vector visualization for the basic tetra- and hexa-sphere clusters and two hyper-sphere clusters are presented at two Reynolds numbers, 400 and 1200. Cutting planes are used to enable visualization of the complex flows generated within the sphere clusters and are discussed herein. The boundary conditions and flow fields for the simple clusters are also compared to the hyper-clusters with larger variations found for hexa-clusters.

Hendricks, R. C.↗
NASA NTRS1974

Application of the ERTS system to the study of Wyoming resources with emphasis on the use of basic data products

Many potential users of ERTS data products and other aircraft and satellite imagery are limited to visual methods of analyses of these products. Illustrations are presented from Wyoming studies that have employed these standard data products for a variety of geologic and related studies. Possible economic applications of these studies are summarized. Studies include regional geologic mapping for updating and correcting existing maps and to supplement incomplete regional mapping; illustrations of the value of seasonal images in geologic mapping; specialized mapping of such features as sand dunes, playa lakes, lineaments, glacial features, regional facies changes, and their possible economic value; and multilevel sensing as an aid in mineral exploration. Examples of cooperative studies involving botanists, plant scientists, and geologists for the preparation of maps of surface resources that can be used by planners and for environmental impact studies are given.

Houston, R. S.↗
NASA NTRS2016

Marshall Space Flight Center Telescience Resource Kit

Telescience Resource Kit (TReK) is a suite of software applications that can be used to monitor and control assets in space or on the ground. The Telescience Resource Kit was originally developed for the International Space Station program. Since then it has been used to support a variety of NASA programs and projects including the WB-57 Ascent Vehicle Experiment (WAVE) project, the Fast Affordable Science and Technology Satellite (FASTSAT) project, and the Constellation Program. The Payloads Operations Center (POC), also known as the Payload Operations Integration Center (POIC), provides the capability for payload users to operate their payloads at their home sites. In this environment, TReK provides local ground support system services and an interface to utilize remote services provided by the POC. TReK provides ground system services for local and remote payload user sites including International Partner sites, Telescience Support Centers, and U.S. Investigator sites in over 40 locations worldwide. General Capabilities: Support for various data interfaces such as User Datagram Protocol, Transmission Control Protocol, and Serial interfaces. Data Services - retrieve, process, record, playback, forward, and display data (ground based data or telemetry data). Command - create, modify, send, and track commands. Command Management - Configure one TReK system to serve as a command server/filter for other TReK systems. Database - databases are used to store telemetry and command definition information. Application Programming Interface (API) - ANSI C interface compatible with commercial products such as Visual C++, Visual Basic, LabVIEW, Borland C++, etc. The TReK API provides a bridge for users to develop software to access and extend TReK services. Environments - development, test, simulations, training, and flight. Includes standalone training simulators.

Wade, Gina↗
NASA NTRS2001

Implementation of a General Real-Time Visual Anomaly Detection System Via Soft Computing

The intelligent visual system detects anomalies or defects in real time under normal lighting operating conditions. The application is basically a learning machine that integrates fuzzy logic (FL), artificial neural network (ANN), and generic algorithm (GA) schemes to process the image, run the learning process, and finally detect the anomalies or defects. The system acquires the image, performs segmentation to separate the object being tested from the background, preprocesses the image using fuzzy reasoning, performs the final segmentation using fuzzy reasoning techniques to retrieve regions with potential anomalies or defects, and finally retrieves them using a learning model built via ANN and GA techniques. FL provides a powerful framework for knowledge representation and overcomes uncertainty and vagueness typically found in image analysis. ANN provides learning capabilities, and GA leads to robust learning results. An application prototype currently runs on a regular PC under Windows NT, and preliminary work has been performed to build an embedded version with multiple image processors. The application prototype is being tested at the Kennedy Space Center (KSC), Florida, to visually detect anomalies along slide basket cables utilized by the astronauts to evacuate the NASA Shuttle launch pad in an emergency. The potential applications of this anomaly detection system in an open environment are quite wide. Another current, potentially viable application at NASA is in detecting anomalies of the NASA Space Shuttle Orbiter's radiator panels.

Dominguez, Jesus A.↗
NASA NTRS2004

Thermodynamic Modeling of a Solid Oxide Fuel Cell to Couple with an Existing Gas Turbine Engine Model

The Controls and Dynamics Technology Branch at NASA Glenn Research Center are interested in combining a solid oxide fuel cell (SOFC) to operate in conjunction with a gas turbine engine. A detailed engine model currently exists in the Matlab/Simulink environment. The idea is to incorporate a SOFC model within the turbine engine simulation and observe the hybrid system's performance. The fuel cell will be heated to its appropriate operating condition by the engine s combustor. Once the fuel cell is operating at its steady-state temperature, the gas burner will back down slowly until the engine is fully operating on the hot gases exhausted from the SOFC. The SOFC code is based on a steady-state model developed by The U.S. Department of Energy (DOE). In its current form, the DOE SOFC model exists in Microsoft Excel and uses Visual Basics to create an I-V (current-voltage) profile. For the project's application, the main issue with this model is that the gas path flow and fuel flow temperatures are used as input parameters instead of outputs. The objective is to create a SOFC model based on the DOE model that inputs the fuel cells flow rates and outputs temperature of the flow streams; therefore, creating a temperature profile as a function of fuel flow rate. This will be done by applying the First Law of Thermodynamics for a flow system to the fuel cell. Validation of this model will be done in two procedures. First, for a given flow rate the exit stream temperature will be calculated and compared to DOE SOFC temperature as a point comparison. Next, an I-V curve and temperature curve will be generated where the I-V curve will be compared with the DOE SOFC I-V curve. Matching I-V curves will suggest validation of the temperature curve because voltage is a function of temperature. Once the temperature profile is created and validated, the model will then be placed into the turbine engine simulation for system analysis.

Brinson, Thomas E.↗
NASA NTRS2010

Introduction to Vector Field Visualization

Vector field visualization techniques are essential to help us understand the complex dynamics of flow fields. These can be found in a wide range of applications such as study of flows around an aircraft, the blood flow in our heart chambers, ocean circulation models, and severe weather predictions. The vector fields from these various applications can be visually depicted using a number of techniques such as particle traces and advecting textures. In this tutorial, we present several fundamental algorithms in flow visualization including particle integration, particle tracking in time-dependent flows, and seeding strategies. For flows near surfaces, a wide variety of synthetic texture-based algorithms have been developed to depict near-body flow features. The most common approach is based on the Line Integral Convolution (LIC) algorithm. There also exist extensions of LIC to support more flexible texture generations for 3D flow data. This tutorial reviews these algorithms. Tensor fields are found in several real-world applications and also require the aid of visualization to help users understand their data sets. Examples where one can find tensor fields include mechanics to see how material respond to external forces, civil engineering and geomechanics of roads and bridges, and the study of neural pathway via diffusion tensor imaging. This tutorial will provide an overview of the different tensor field visualization techniques, discuss basic tensor decompositions, and go into detail on glyph based methods, deformation based methods, and streamline based methods. Practical examples will be used when presenting the methods; and applications from some case studies will be used as part of the motivation.

Kao, David↗