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Best Picture Award in Physical Science

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Long time Tecplot customer, Frank Muldoon, and his colleague, Hendrick Kuhlman, at the Vienna University of Technology, received the “Best Picture in Physical Science award” for this image of Invariant Streamtubes of a Hydrothermal Wave in a Thermocapillary Liquid Bridge.
Congratulations to you both!

Axial View

Invariant Streamtubes of a Hydrothermal Wave in a Thermocapillary Liquid Bridge

Abstract

In microgravity, large-size cylindrical liquid bridges can be established, because they do not break. Differential heating of the support disks creates a thermocapillary flow which is a gravity-independent driving mechanism. Typically, the flow becomes three-dimensional and a rotating hydrothermal wave arises. Surprisingly, there exist regions in this complicated three-dimensional flow such that fluid from one region does not mix with fluid from another region. The image shows an axial view of several of these regions, obtained numerically, which arise in closed tori and which rotate with the same azimuthal velocity as the hydrothermal wave.

Award

Best Image in Physical Sciences

Authors and affiliation: F. H. Muldoon and H. C. Kuhlmann (Vienna University of Technology), E-mail: h.kuhlmann@tuwien.ac.at

Frank Muldoon

Frank Muldoon has certainly taken Tecplot through its paces—over the years, he has carefully documented more than 70 issues! Congratulations again on the Award, and thank you from all of us here at Tecplot for your 15 years of support.

“Thanks for all your help with getting the best out of Tecplot!”
Frank Muldoon

Join us for a Webinar on Oct 10

Visualizing One Billion Cell Simulation Models on an Engineering Desktop with Tecplot 360 Reserve your Webinar Seat Today


Tecplot Tip: Report Tecplot Usage Trends with New RLM Tool

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Understanding Tecplot usage trends has been important for customers and prospects especially to help them justify new license purchases. The tools currently available on the market can be expensive and often have more features than needed to report usage trends.

Last weekend, I finally finished a new tool I’ve been working on (and off) since March. This tool accurately reads RLM usage log files. The RLM Log Reader tool will generate reports for each RLM usage log type.

  • The report log contains the most data, but is not enabled by default. Customers are encouraged to turn it on manually. The next Tecplot RLM release may see the report log default to on, but until then, here’s how you can turn it on: Turn on RLM report log.
  • The ISV usage log, teclmd.log, is turned on by default when installing RLM using the Tecplot installer (available from our Software Downloads website page).

Documentation is included with the tool that further explains how to use it. Sample logs are also included for you to try it out.

Steve RobinsonThis was an interesting hobby project that taught me some new aspects about programming. I hope you will find it useful. Here is a link to the tool: http://sourceforge.net/projects/rlmlogreader/

This Tecplot Tip was written by Steve Robinson, Software Development Engineer at Tecplot, Inc.

 

Post-Processing One Billion Cells

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By Dr. Durrell Rittenberg, Vice President of Product Management

Over the last decade, I have followed several technological advancements in numerical simulation post-processing. While there were improvements in the area of parallel rendering and client-server technology, these approaches were limited to those with large computational resources. The most notable breakthroughs available to most engineers came in the form of improved graphics cards – thanks in large part to the gaming community. Today, a majority of the engineering community uses a workstation for CFD post-processing visualization.

In 2008, Tecplot introduced “Load-On-Demand” technology which allowed users to load only the variables needed during their visual analysis. This significantly reduced the memory footprint for large data analysis. However, for a very large solution where loading a single variable into memory was not possible, performance suffered. Subsequently, a new approach was needed to support the requirements of the CFD community looking at very large computational meshes.

TrapWing300New Technology Solves Very Large Data Demand

Researchers at Tecplot have been working on new technology that would enable engineers to post-process very large data solutions on engineering workstations, desktop computers, or laptops. After almost 24 months of research and development, one of the key issues – that of memory usage – has been overcome. Tecplot now has a working Beta version of this new technology.

Tecplot Working Beta with New Technology

Early results are promising. Internal tests have shown improvements on the order of 40 to 100 times faster than Tecplot 360 2013 R1 for common post-processing tasks. The increase in speed is very exciting, but perhaps more exciting is the decrease in memory usage.

This morning I loaded a one billion cell model on my laptop and never used over 8GB of RAM. Loading this same data into Tecplot 360 2013 R1, by comparison, required close to 60 GB of RAM!

Join us for a Webinar on Oct 10

Tecplot’s Chief Technical Officer, Dr. Scott Imlay, and I invite you to join us as we demonstrate this new technology, and discuss the next generation of Tecplot 360 products.

Space is limited. Reserve your Webinar Seat Now. Register for the Webinar

Using MRI to Measure 3D Velocity and 3D Concentration in Engineering Flows

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This blog was contributed by Filippo Coletti, Postdoctoral Fellow at Stanford University. In January 2014, Coletti will join the faculty at the University of Minnesota as Assistant Professor in the Department of Aerospace Engineering and Mechanics.1
Magnetic Resonance Imaging (MRI) is a well‐established technique in the medical community, able to produce volumetric reconstructions of the body by applying a combination of magnetic field gradients and radiofrequency pulses. MRI can also be used to perform velocimetry in fluid flows, thanks to the phase‐sensitivity of its signal to motion.

Traditionally, MRI has been used to measure flow properties in “in vivo” applications. At Stanford, the group led by John Eaton has pioneered the use of MRI to measure 3D velocity (MRV) and 3D concentration (MRC) in engineering flows. Coletti, a postdoctoral fellow in Eaton’s lab, has been using and advancing this method in various applications.

Coletti is investigating transport and mixing in complex flows and turbulent flows. Because he deals with millions of experimental data points, visualizing large 3D data sets is critical to understanding the dynamics of the flows he studies. Coletti uses Tecplot 360 to visualize his results and to communicate his findings to others.

One example is the dispersion of a contaminant injected into a crossflow. In this case, Coletti collaborated with Honeywell to understand the flow physics of film cooling for gas turbine airfoils.

Video 1 shows isosurfaces of time-averaged concentration of a contaminant injected into the turbulent cross-flow. The animation displays decreasing concentration levels, which extend further downstream as the contaminant gets diluted by the crossflow.


Video 2 shows progressive slices of the 3D volume as they move downstream from the injection. Both concentration contours and in-plane velocity vectors are plotted.


A second example is the flow through a stack of porous fins. The random pore distribution produces a meandering of the flow through the solid matrix, leading to significant transverse mixing. In Video 3, isosurfaces of positive (red) and negative (blue) streamwise vorticity are shown, highlighting elongated structures that swirl in the direction of the flow.


To illustrate the mixing mechanism, a plume of contaminant was injected upstream of the fin stack. Figure 1 depicts an isosurface at 2.5% of concentration, demonstrating how the random structure of the fin contributes to the spreading of the contaminant.
Mixing mechanism of a contaminant

Figure 1. Isosurface at 2.5% of concentration demonstrates how the random structure of the fin contributes to the spreading of the contaminant.

A third example is the inspiratory flow in human airways. The X-ray scan of a subject was used to fabricate a 3D model by stereolithography which replicates the patient anatomy from the mouth to the eighth generation of bronchial branching. Figure 2 shows various sections of the flow field, at the first bifurcation, and at further generations. In-plane velocity vectors (superimposed onto color contours of flow speed) demonstrate that strong recirculation of the inspiratory flow persists deep down into the bronchial tree.

Sections of the flow field

Figure 2. Various sections of the flow field, in the first bifurcation, and at various stations at further generations.

1Filippo Coletti received B.S. and M.S. degrees in Mechanical Engineering from the University of Perugia (Italy) in 2003 and 2005, respectively. He completed the Diploma Course in Fluid Dynamics at the von Karman Institute (Belgium) in 2006, and obtained a Ph.D. in Aerospace Engineering from the University of Stuttgart (Germany) in 2010.

After receiving his Ph.D., Coletti worked as Senior Research Engineer at the von Karman Institute, and as Postdoctoral Fellow at Stanford University.

He has received several awards, including the Italian Ministry of Education Scholarship from the University of Perugia, the Prize for Excellence in Experimental Research from the von Karman Institute, and the Arthur Charles Main Prize from the Institution of Mechanical Engineers.

In January 2014, he will join the faculty at the University of Minnesota as Assistant Professor in the Department of Aerospace Engineering and Mechanics.

Coletti is an experimentalist, with expertise in a wide range of flow diagnostics, including particle image velocimetry, infrared thermography, magnetic resonance imaging, and X-ray computed tomography. At the von Karman Institute, he investigated the turbulent flow and heat transfer in internal cooling channels, with focus on solid-fluid thermal coupling and rotational effects. At Stanford he used medical imaging to explore a broad spectrum of transport problems, including the mixing of contaminant injected into a crossflow, the dispersion of fluid and heat through random porous materials, and the air flow inside the human lungs.

Coletti’s research interests lie in the area of transport and mixing in turbulent and/or multiphase flows. In Minnesota he will be investigating the aerosol transport in human airways and the interaction of hot inertial particles with turbulence. Website: http://www.aem.umn.edu/people/faculty/bio/coletti.shtml

The Egg Came First!

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A blog by Tecplot’s Chief Technical Officer, Scott Imlay.

Lately I’ve been struggling with the visualization version of the classic causality dilemma, and I finally have an answer! But first, a little background.

As many of you know, we have been working hard on a new technology—called subzone load-on-demand—that minimizes the amount of data loaded by Tecplot 360. The basic idea is to divide a large dataset into small pieces, or subzones, and load only those subzones needed for the desired plot.

For example, to make an isosurface of pressure for a large dataset, like the NASA trapezoidal wing shown in figure 1, only the subzones with a pressure range (pmin,pmax) that contain the desired isosurface value of pressure will be loaded. To generate the isosurface shown in figure 2, only 1.5% of the subzones need to be loaded. This makes isosurface generation in the new version of Tecplot 360, Tecplot 360 EX, orders of magnitude faster than previous versions (and faster than competitor codes) that load pressure, for the majority of the data points in the dataset.

 NASA Trapezoidal Wing Geometry

Figure 1. NASA Trapezoidal Wing (High-Lift Prediction Workshop) Geometry

 

Pressure isosurface for NASA Trapezoidal Wing

Figure 2. Pressure isosurface for NASA Trapezoidal Wing

 

But, you ask, what if pressure isn’t stored in the dataset? What if only the conservative variables (ρ, ρυ, ρu, ρω, Ε) are stored? This is a common scenario in PLOT3D format files generated by codes like Overflow. This is the classic “chicken or the egg” scenario. Ranges of pressure for each subzone (the egg) determine which subzones to load, but pressure (the chicken) doesn’t exist in the file.

For ideal gases, pressure can be computed from the conservative variables using the following formula:

One solution would be to load all five conservative variables, compute pressure at every point in the dataset, compute the pressure ranges, and proceed as before. Unfortunately, this would end up loading all the data—violating the subzone load-on-demand goal to minimize the amount of data loaded. It’s the chicken or the egg; the “Kobayashi Maru”; the no-win situation. Or is it?

It turns out that interval arithmetic can be used to compute bounds for the range of the pressure in each subzone without actually computing the pressure in the subzones.  Interval arithmetic allows you to estimate the range (actually bounds of the range) of the computed variable from the ranges of the input variables and the equation to be computed. With subzone load-on-demand, interval arithmetic is used to determine which subzones to load and only the new variable in the loaded subzones is computed. However, because interval arithmetic will overestimate the ranges of the computed variable for each subzone, it is possible that many unneeded subzones will also be loaded.

We used the NASA Trapezoidal Wing case (figure 1) to test the impact of this approximation when computing, on demand, the pressure from the conservative variables. We also pre-computed a new pressure variable so that we could compare the number of subzones loaded.

The Trapezoidal Wing dataset contains a total of 800,606 cell subzones. Using the pre-computed pressure directly, subzone load-on-demand loads 11,905 subzones (1.49% of the total). Using interval arithmetic with the ranges of (ρ, ρυ, ρu, ρω, Ε), subzone load-on-demand will load 13,349 subzones (1.67% of the total). That is only 12% more subzones than were loaded with the pre-computed pressure.

So, you don’t need the chicken, the egg can come first!

Be a Beta TesterYou are invited to participate in the Tecplot 360 Beta Program.

Join over one hundred of your colleagues who are using the Beta to quickly and efficiently load and analyze simulation results. Early benchmarks show a significant performance increase on very large files—using an 8 GB desktop workstation!

Join the Beta Program

Visualization Conference: Trendy Ideas or Priceless Gems?

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Last month, I attended the IEEE Visualization Conference in Atlanta. The Vis conference is the premier forum where researchers and academics in the fields of scientific visualization, information visualization, and visual analytics present their latest and greatest ideas. This year Durrell Rittenberg (Vice President of Product Management) joined me on the cross-country trek.

I’ve been attending the Vis Conference off-and-on since 2000—it helps me make sure Tecplot is aware of the newest techniques and trends in visualization research. Many clever ideas are presented each year, but most are not really applicable to our products—either because they don’t solve the problems our customers face or don’t add enough value to warrant the additional complexity they bring, or the ideas are just trendy and oversold.

On the flip side, some ideas are priceless gems that can improve the capability and usability of Tecplot software. Our grid coarsening techniques (decimation) were derived from ideas presented at the 2001 Vis conference in San Diego, and many subzone load-on-demand techniques can be traced back to out-of-core techniques presented at the 2005 Vis conference in Seattle.

This year, I did hear a couple of interesting themes (and thankfully, no “trendy” ideas). The rest of this blog is about one of the gems.

Theme:  Visualization software architectures must evolve to keep pace with changes in computing hardware.

This won’t be a surprise to those who have been reading our blogs or have attended our webinars. In fact, Tecplot has been aggressively responding to the changing hardware landscape for the last two years. Three primary drivers of the change are:

    1. Computing performance is improving much faster than I/O performance. In other words, it is taking more and more time to read or write the data we are capable of creating with high-performance computer (HPC) systems. The industry is responding to this in a variety of ways – from not writing the data at all (in Situ visualization) to faster storage systems and file formats (like exaHDF5).  Tecplot is responding to this change with subzone load-on-demand, a technology that allows our software to read only the data needed to perform the desired analysis or visualization.
    2. The number of cores per CPU or GPU is growing rapidly. This has huge ramifications for software that uses the message-passing-interface (MPI) to parallelize computations. As the number of cores increase, MPI becomes less efficient. In fact, Kenneth Moreland (a panelist in the “Challenges for Scientific Visualization Software” session) said that MPI is simply not usable for GPU-based computing. The solution is to use threading in combination with MPI. Fortunately, Tecplot is primarily parallelized through threading so we are not impacted as much as some other software packages.
    3. Memory per thread is declining. Memory for each CPU/GPU combination is actually increasing, but the number of threads required to efficiently utilize CPU’s or GPU’s is increasing faster than memory. This has major ramifications for in Situ visualization—a common idea for circumventing the first driver. Any memory utilized for visualization is memory that cannot be utilized for the simulation software. Existing visualization software tends to take too much memory for in Situ visualization on HPC systems.

To summarize, visualization products struggle to keep pace with changes in computing hardware,
but software packages that depend on massively parallel systems to visualize large data files are
affected the most.

Learn more about Tecplot’s large data solution.

Watch this webinar Durrell and I recently recorded:
Visualizing One Billion Cell Simulation Models on an Engineering Desktop with Tecplot 360.

Watch the Webinar

Gearing up for AIAA SciTech 2014

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Saving Time and Money at AIAA SciTech 2014

SciTech next weekLike many planning to attend AIAA SciTech next week, I’ve been going to the Aerospace Sciences Meetings for more than a decade. This year the organizers have changed the format to hold multiple conferences at the same time—including the MDAO/NDA and the Modeling and Simulation Technologies Conference. At first, this seemed like a great way to save my time and our company’s money.

One of the new features AIAA added was the ability to develop your own personal itinerary online so you can make the most of your time at the conference. I like the ability to select the talks that I’m most interested in.

I soon realized, however, that many of the talks I was planning on attending are being held at the same time on the same day! Although I like the convenience of having all these conferences in one venue, I now have the difficult decision of choosing which talks to attend and which to miss.

In case you’re interested, here are the talks I’ve selected: AIAA SciTech Planner (pdf) >>

Scott ImlayVisit Tecplot in Booth #407

We hope you’ll add the presentation given by Tecplot’s Chief Technical Officer, Dr. Scott Imlay, titled, “Compression of Finite-Element Node Maps with Subzone Load-On-Demand.” It’s on Monday, January 13 at 2:30 p.m. in Chesapeake conference room #9.

Tecplot will be unveiling our next version of Tecplot 360. Get a one-on-one demonstration. Find out more on our Product Preview page >>
Visit us in the exhibition hall booth #407.

Tecplot 360 EXYou Are Invited to Tecplot’s Reception

Join us on Tuesday evening between 6:30 p.m. and 10 p.m. for a Reception in our Suite. Relax and enjoy refreshments, talk with Tecplot experts, and pick up the latest Tecplot T-shirt (while supplies last). To reserve a T-shirt in your size, E-mail marcom@tecplot.com. Visit our booth #407 for directions to the suite.

Follow me on Twitter—I’ll be checking in throughout the week @durrellritt

 

Increasing the Speed of Discovery

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Contour plot of solar eruptionBy Durrell Rittenberg, VP Product Management, Tecplot, Inc.

Over the last 12 years, I’ve had the opportunity to work with some of the brightest engineers around the world. I’ve worked closely with groups designing the next generation of manned and unmanned flight vehicles, with scientists modeling solar flares, and with researchers investigating atmospheric conditions that lead to global warming.

Recently, many of these engineering groups have contracted in size. For some of them the contraction was due to sequestration, for others it was due to the breakup of large engineering groups into smaller and leaner project teams. A good friend of mine from a large aerospace company shared with me that right after sequestration his group decreased by 50%. It was a dark time, and many people looked for opportunities elsewhere while others decided to retire.

About eight months later, I ran into him at a conference and was surprised to hear that he was doing great. He said he had more work now than before sequestration, but his group had not hired additional engineers and were not planning to hire any in the near future. I asked him how he managed to get more work done with less people. “Painfully,” he said, “I end up looking for any way to automate anything that will help me get the work done.”

His group has adopted robust engineering principles to analyze their ever more challenging design problems. These principles emphasize evaluating multiple designs, which increases not only the size of each project, but also the number of potential designs being evaluated for each project. The net result has been fewer engineers to tackle larger, more complex engineering problems.

Feedback from other engineers over the last two years has confirmed the need for tools to help engineers tackle these modern design problems. Here at Tecplot, we’ve been improving both the performance and usability of our tools to help our customers speed discovery.  The upcoming webinar on June 12 will show you some of the work we’ve been doing. Register now: 4 Keys to Making Faster and More Accurate Design Decisions

Performance improves the speed of discovery

In 2012 we released Tecplot Chorus to address the needs of engineers involved in parametric analysis. Very soon we will release Tecplot 360 EX which is designed to speed up computational results analysis workflows. Tecplot 360 EX introduces our newest technology, subzone load-on-demand (SZL). Engineers using SZL to perform common simulation analysis tasks on large computation grids will see much faster than ever before. With this technology, large, high fidelity simulations can be processed with the same speed as low fidelity, small mesh simulations. Find out details about this technology in our white papers and recorded webinars.

Click reduction increases the speed of discovery

In addition to general performance improvements with large data, we’ve invested in improving the overall usability of Tecplot 360. To do this, we rebuilt Tecplot 360 from the ground up using new UI technology. We preserved common workflow elements to reduce switching cost for existing users. Tasks like extracting cutting planes that once took 15 clicks, now take only one. Other usability improvements include creating PowerPoint-like multipage layouts, and using context menus to set properties directly on a plot. See the new features in these quick videos.

The goal of these investments has been to address the needs of people like my friend who are trying to get more done with their available resources. Over 300 engineers and scientists worldwide, including my friend, have helped make this release possible through their participation in the Tecplot 360 EX Beta program.

I personally want to thank all of the Beta Testers for helping to make Tecplot 360 EX a reality!



Tecplot Tip: How to insert CAD drawing into 3D flow field data

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This Tip describes imposing a 3D wing model on 3D flow field in Tecplot 360. The 3D flow field data are obtained using Volumetric Three-Component Velocimetry (V3V) where the three components of the velocity vectors are measured in a 3D volume of 140X140X100 mm3. Figure 1 shows slices in x-z and y-z planes of the streamwise mean velocity over a periodically cambered plate at an angle of attack of 8 degrees at Reynolds number of 28,000. The data is shown in Tecplot 360.

This Tecplot Tip was contributed by: Dr. Redha Wahidi and Mr. Zheng Zhang (PhD candidate), Dept. of Aerospace Engineering, The University of Alabama

Streamwise mean velocity

Figure 1. Slices shown in x-z and y-z planes of the streamwise mean velocity over a periodically cambered plate at an angle of attack of 8 degrees at Reynolds number of 28,000.

Follow this procedure to include the test article geometry with the 3D flow field in Tecplot:

    1. Create a 3D drawing of the wing model using SolidWorks and save it in STL format. To facilitate easier alignment between the 3D drawing and 3D flow field data in Tecplot, we recommend matching the dimensions and coordinate system in the 3D drawing to the 3D flow field data. This means that the distances in the x, y and z directions should match. Also, the origin and orientation of coordinate system in the 3D drawing and flow field data should match. In the example shown (Figure 2), the origin is located at the midspan leading-edge location. Before saving the file in STL format, click the “option” dialog in SolidWorks and examine the setting and ensure that the “Do not translate STL output to positive space” option is checked (Figure 3).
      CAD midspan location

      Figure 2. The origin is located at the midspan leading-edge location.

      Do not translate STL output to positive space

      Figure 3. Make sure that the “Do not translate STL output to positive space” option is checked.

    2. Open a Tecplot file that contains the flowfield data.
    3. Go to “Create New Frame” in the “Frame” menu.
    4. Draw a “box” anywhere in the layout.
    5. Go to “File” menu and select “Load Data File”
    6. Select “3D System STL”
    7. Browse to find the CAD drawing.
    8. Select “3D Cartesian” from the “Initial Plot Type” drop down menu. Figure 4 shows the CAD drawing with the flow field data.
CAD drawing with 3D flow field data

Figure 4. CAD drawing with the flow field data.

  1. Select “Frame Linking” from the “Frame” menu.
  2. Check “Frame Size and Position” and “3D Plot View.”
  3. Select “Order Frame” from the “Frame” menu, and select “Activate” Frame 002.
  4. Select “Frame Linking” from the “Frame” menu.
  5. Check “Frame Size and Position” and “3D Plot View.”
  6. Pop Frame 001 and activate it.
  7. Select “Edit Active Frame” in the “Frame” menu and uncheck “Show Background.”
  8. Remove the axes from the current active frame, i.e., Frame 001.
  9. Activate Frame 002 and show the axes on this frame.
  10. Adjust the size and position as desired. Figures 5 and 6 show the flow field data with the wing model. Figure 5 shows the streamwise mean velocity with the wing model. Figure 6 shows iso-surfaces of the normal component of the vorticity vector with the wing model.
Streamwise mean velocity

Figure 5. Streamwise mean velocity with the wing model

Iso-surfaces

Figure 6. Iso-surfaces of the normal component of the vorticity vector with the wing model.

Do you have a Tecplot Tip to share? We would love to hear from you. Email your tip to marketing@tecplot.com.


Creating Tecplot Format Files From MATLAB Data

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Often, the hardest challenge to any project is getting your data into the correct file format and program. This script exemplifies how to create Tecplot format files from MATLAB data imported from simple text files. Included in the file are resources and links to where to find more information.

For this example, open source data from the University of Washington aeronautical senior design wind tunnel test was used as sample input data. The script then writes the data into two different Tecplot file formats, ASCII/.dat and binary/.plt. In addition, it creates a basic macro to load and save plots as .png images.

The MATLAB to Tecplot zipped files contain:

Download the MATLAB to Tecplot zipped files

 

Contributed by:
Devon Simpson
Intern at Tecplot, Inc.


Tecplot Tip: Tecplot 360’s Quick Macro Panel Streamlines Repetitive Operations

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Using the software for data analysis often requires algorithms with many repeated operations, and in some cases many different operations. Tecplot engineers have created a macro language to streamline these operations. Macros are recordable, easy to edit, and don’t require any development experience.

Quick Macro Panel

Some commonly used complex and repetitive algorithms (within the aerospace community) have been incorporated into a dynamic Tecplot 360 interface called the Quick Macro Panel (QMP). The QMP, available under Tecplot 360’s “Scripting…” main menu, offers single-click initiation of a macro sequence. By utilizing the QMP, the user interface becomes more robust for the analysis of solution quality and the development of animation.

Tecplot Quick Macro Panel Menu

Tecplot’s Quick Macro Panel (QMP), available under the main menu, offers single-click initiation of a macro sequence.

Tecplot XY Animation Macro Suite

Steve Alter, a long-time Tecplot customer, made this blog possible by sharing his Tecplot XY animation macro suite. The macro coding and the algorithms show what is possible within the Tecplot 360 macro framework. You can view his document below. It describes the operation and applicability of a QMP suite of macros.

View the PDF

A Quick Macro Panel Suite to Augment the X-Y Line Mapping Interface of Tecplot.

Contact information: Stephen J. Alter, Williamsburg, VA, altertalk at cox.net. Steve adds that he uses code developed by Bill Wood (William.A.Wood at NASA.Gov) that enables literate programming.
Thank you, Steve!


 

Quick Macro Panel

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Tecplot 360 Tutorial: Quick Macro Panel

Macros are useful for getting your work done faster whether you are streamlining repetitive operations, running multi-step tasks, or performing complex operations. The Quick Macro Panel in Tecplot 360 EX allows you to run a macro with a single click or to link to your most commonly used macros. You also have the ability to record macros interactively. The simple readable format requires no programming experience. Macros are completely customizable to help you automate a variet of tasks.

You can also download and play this video in .mp4 format

Quick Macro Panel Resources


Shadowgraph Technique in Tecplot 360 EX R2

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Using Tecplot 360 to look at pressure and density gradients with Shadowgraph. I’ve been asked lately about using Schlieren images in Tecplot 360. Schlieren is an optical technique looking for inhomogeneous density gradients typically in translucent media like air. In computational methods you can use the technique of the Shadowgraph.

 
Download in .mp4 format


Try Tecplot 360 EX 2014 Release 2 for Free >>

Converting Your Data Files to Tecplot Subzone Loadable Format

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As the years go by, more and more engineers and scientists are working with files containing hundreds of millions of cells, with billions on the horizon. Tecplot 360 EX 2014 introduces a new data file format called SZL or subzone loadable (filename extension .szplt) to address the needs of customers who are visualizing large data files, improving interactive performance, and simultaneously reducing memory requirements.

When faced with the task of making large data files load faster, two possible solutions come to mind:

  • Significantly increase the performance of the computer system, particularly its storage
  • Figure out a way to load a lot less data for most operations

The first approach relies on the giants of the computer industry to continue advancing the state of the art. Unfortunately, this approach only gets us so far; the ever-increasing size of simulation data is clearly swamping even the fastest of today’s processors, memory, and storage when it comes time to visualize the results. Next year’s hardware will certainly be faster, but next year’s data will just as surely be even bigger. And continually chasing the absolutely fastest hardware is very expensive.

Subzone loadable

Tecplot 360 EX 2014 reduces perceived loading times.

The subzone loadable format employs the second approach. In the past, Tecplot 360 has supported load-on-demand for several popular CFD file formats, which reduces perceived loading time by loading a given variable in a particular zone only when it is needed for a plot. If you never needed a particular variable, Tecplot 360 wouldn’t waste time or memory on it. If you did need all the data in the file displayed at once, you wouldn’t notice any benefit, but more common use cases saw a great improvement in performance.

Subzone loading is the next evolution of this concept. By dividing each zone into subzones and arranging the data for easy access by spatial location, we can substantially reduce the amount of data that needs to be read for any given operation and easily locate the data we do need. For example, rather than needing to read an entire variable into memory to slice into a volume zone, at a potential cost of several minutes, we can now read the only the cells the slice cuts through in a few seconds. As an added benefit, subzone loading requires a fraction of the memory required by traditional load-on-demand.

Unfortunately, it isn’t possible to use subzone loading with existing data files in industry-standard formats. Virtually without exception, these formats lay out zones and variables sequentially: first all the variables from zone 1 in order, then the variables from zone 2 in order, and so on. The data’s location in the file bears no real relation to where it is located spatially, and there is no way provided to easily locate data near given coordinates. Solving this problem necessitated the invention of the .szplt file format, which is the key to the performance and memory usage improvements of Tecplot 360 EX.

Creating Subzone Loadable Data Files

The simplest way to create subzone loadable data files is to upgrade your solver to a version that can write them natively. For developers, this is simple if their solver is already writing .plt files using Tecplot’s TecIO library. A version of TecIO that writes .szplt files is now available, and developers merely need to use that version instead of the one they’re using now and make a few minor changes.

If your solver does not output data in Tecplot’s formats, or if you already have large data files in other formats that Tecplot 360 can read, you can convert them to .szplt files to enjoy the benefits of subzone loading.

To do this:

  • Load the file in Tecplot 360 EX in the usual fashion.

Choose Load Data File(s) from the File menu. Then choose the existing file format of the data from the Files of Type menu in the Load Data Files dialog. Finally, select the file to be loaded and click Open.

If you wish to use options other than the defaults when loading the data file, mark the Advanced Options checkbox before clicking Open, then set the options in the dialog that appears next.

  • Write the data back out of Tecplot 360 EX in .szplt format.

Choose Write Data File from the File Menu. Choose Tecplot Subzone Data Writer from the Files of Type menu in the Write Data File dialog. Finally, enter a name for the file and click Save.

Automatic Conversion of Data Files

Converting a large number of files manually is cumbersome and time-consuming. You may want to add conversion to .szplt format to your post-processing workflow, so that after your solver finishes processing a case, it is automatically converted and ready to view interactively in Tecplot 360 EX.

Automating conversion of data files to .szplt format is best done by using Tecplot 360 EX in batch mode. In most cases, you will want to do the conversion on the cluster where the simulation is run, so you have enough memory to load the file (see “Memory Requirements” below).

There are three pieces to the solution:

  • A layout that specifies the options for loading the data. The data file specified in the layout will be ignored when we later pass the name of the file on the command line, so there is no need to actually load one of your typical data files. You can use a smaller file as long as it is your usual format and uses the same options.

To create the layout, simply load a data file that is similar to the ones you will be converting later, then save a layout file. Choose Load Data File(s) from the File menu. Next, choose the data file format from the Files of Type menu in the Load Data Files dialog and mark the Advanced Options checkbox. Select the file to be opened, then click Open. In the next dialog, set the loading options. Finally, choose Save Layout As from the File menu and save the layout as convert2szplt.lay.

  • A macro that contains the instruction to save the data in .szplt format. Paste the instructions below into a text editor and save the resulting file as convert2szplt.mcr:
#!MC 1410
$!EXTENDEDCOMMAND
COMMANDPROCESSORID = 'Tecplot Subzone Data Tools'
COMMAND = 'WRITEDATASET FILENAME="|DATASETFNAME|.szplt"'
  • A short batch file or shell script that ties it all together. On Windows, this is:
tec360 -b -p convert2szplt.mcr convert2szplt.lay %1

On Mac OS X or Linux, this should instead be:

#!/bin/sh
tec360 -b -p convert2szplt.mcr convert2szplt.lay $1

Save the file as convert2szplt.bat (Windows) or just convert2szplt (Mac/Linux) in a directory that is in your system path. On Mac OS X or Linux, also execute the following command to make the script executable:

chmod +x convert2szplt

To convert a single data file to .szplt format, you can now issue the following from a command prompt (or include it in the script that runs your solver, or configure it as a post-processing script in your solver):

convert2szplt /path/to/data/file.dat

The new file is named the same as the original file with .szplt added. You can create multiple sets of layout/macro/script files (under different names or in different directories) to handle multiple categories of files that you deal with on a regular basis.

Converting Multiple Files

If the data you are working with is in multiple files that need to be loaded separately, or uses a format that requires that multiple filenames be specified when loading the file, a different approach is required. An entire data set can be saved to a single .szplt file regardless of the number of files it was originally stored in. The only trick is getting Tecplot 360 EX to load the necessary files. The procedure we’ll use here is:

  • In Tecplot 360 EX, record a macro that loads the desired data and saves it as a .szplt file. To do this, begin recording (choose Scripting > Record Macro), name and save the macro file, record the loading of the data, and then click Stop Recording in the Macro Recorder window.
  • Convert this macro to a “template” by replacing the filenames with placeholders, which can later be replaced with the names of the files you want to convert and the name under which to save them. Open the macro in a text editor, find all the filenames resulting from loading and saving your file, and replace the different filenames in the script with {1}, {2}, {3}, and so on.
  • Create a Python script to accept filenames from the command line and plug them into a template, then tell Tecplot 360 EX to run the resulting script. Here’s the script:
#!/usr/bin/python
import sys, os
sys.argv.pop(0)
template = sys.argv[0]
macro = template.replace(".mcr", "_.mcr")
open(macro, "w").write(open(template).read().format(*sys.argv))
os.system("tec360 –b " + macro)

Save this script as macrotemplate.py and (on Linux and Mac OS X) perform chmod +x macrotemplate.py.

So if you have created a macro file template named convert3files.mcr, which loads three files and saves them as a single .szplt file, it should have  four placeholders in it: one each for the names of the files to load, and one for the name of the file to save. You can thus invoke it using macrotemplate.py as follows:

macrotemplate.py convert3files.mcr file1.dat file2.dat file3.dat outputfile.szplt

The first argument to the script is the name of the macro template file to be used. The script substitutes each of the listed files into the macro replacing the tokens {1}, {2}, and so on (in this case up to {4}). The resulting macro is then written to the file convert3files_.mcr (note the underscore in the filename, which is added to prevent the original file from being overwritten). Finally, Tecplot 360 EX is invoked in batch mode and executes the macro, loading the files and saving the data in the .szplt file.

Memory Requirements

It is difficult to predict exactly how much memory (RAM) will be required to convert data to the subzone loadable format, as it varies depending on the characteristics of the data. Generally, however, the process requires substantially less memory than would be required to load the entire file and work with it interactively. Only one data variable needs to be loaded at a time, and the memory used by each variable can be reused by the next. For finite-element data, the X, Y, and Z variables and the connectivity data, which may be substantial, must remain loaded throughout the conversion. There is also some scratch memory overhead.

Once the data is converted to .szplt format, for most operations only small a fraction of this memory is needed on the workstation being used to work with the data interactively.


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Streaklines, Streamlines, and Particle Paths


Shock vs Shadowgraph

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In this quick tutorial we use Tecplot 360 EX to evaluate the shock vs shadowgraph functions in a 2D airfoil. We calculate both functions and compare the results side by side.
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The Trillion Cell Grand Challenge

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By Dr. Scott Imlay, Tecplot CTO and Director of Research

Tecplot’s large-data initiative began several years ago. We’ve spent tens of thousands of developer-hours on removing barriers to large-data visualization: adding parallelization (threading) of performance critical code, optimizing algorithms, and optimizing I/O. Since then, we’ve improved the large-data capabilities of Tecplot 360 dramatically. For example, with Tecplot 360 EX 2014 Release 2, the creation of an iso-surface for a particular one billion cell data set is two orders-of-magnitude faster than it was with Tecplot 360 2013 Release 1. Still, we are not satisfied!

Dell Workstation

Figure 1. Tecplot intends to meeting the Trillion Cell Challenge using an engineering workstation like this.

Today I’d like to describe an internal Tecplot initiative called the Trillion Cell Grand Challenge. This blog describes the Challenge, and future blogs will explain why we chose this Challenge, the major obstacles to visualizing one trillion cells, our approach to the problem, and our results to date (spoiler: we’ve done 300 billion cells so far). But today, I just want to give you the details of the Challenge.

The Challenge: By the end of 2015, Tecplot will visualize a finite-element data set containing one trillion tetrahedral cells—using slices, iso-surface, and streamtraces. Furthermore, Tecplot intends to do this visualization using an engineering workstation like this one.

Figure one is actually a picture of my work computer, a Dell Precision T7610 containing dual 8-core Intel Xeon processors, an NVIDIA Quadro K4000 video card, 128GB of memory, and a 16TB Raid5 external hard-disk array. This system is probably a little more advanced than what is sitting beside your desk, but systems with these capabilities will be commonplace in the near future. All for less than $10,000.

Specifically, we are NOT requiring computers like this.

Stampede Advance Computing

This is the STAMPEDE system at the Texas Advanced Computing Center.

This is the STAMPEDE system at the Texas Advanced Computing Center. There is nothing wrong with this system—I’d love to have one here at Tecplot—but it is very expensive (no doubt many millions of dollars). Why use a multi-million dollar computer for visualization when it can be done on a computer costing less than $10,000?

Next blog: Why One Trillion Cells?

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Why One Trillion Cells?

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Blog #2 in the series The Trillion Cell Grand Challenge, by Dr. Scott Imlay

Wind tunnels won’t go away – we’ll need them to store the printouts from our CFD solutions!This is my second-hand memory of a quote attributed to Dean Chapman of NASA Ames as he gave the 1979 AIAA Dryden Lecture1.

He was joking, of course, but the lecture was a serious look at the future computer requirements for various types of CFD calculations. He also forecast the rapid growth of computer power through the 1990s. The highest fidelity CFD simulation that he described was a large-eddy simulation (LES) of a large full airplane configuration. To do this simulation requires a grid containing one trillion cells (or grid points).

Even Reynold’s averaged Navier-Stokes (RANS) solutions will require more grid points as the complexity of the problems and the geometry increase. For example, an airplane in landing configuration has a complex array of flaps and slats that allow it to land at slower speeds. In this flight regime, aerodynamicists use flow control devices, like the nacelle fence in the image below, to tailor the behavior of the flow on the wing. In this case the fence creates a vortex that allows the flow on the wing to remain attached to a higher angle-of-attack than it would have otherwise. However, they don’t want the fence vortex to be too effective or it will lead to wing-tip stall and poor low-speed behavior. So it is very important to accurately resolve the vortex creation and propagation, and that requires a lot of grid.

Nacelle Fence

Aerodynamicists use flow control devices, like the nacelle fence in this image, to tailor the behavior of the flow on the wing. In this case the fence creates a vortex that allows the flow on the wing to remain attached to a higher angle-of-attack than it would have otherwise.

Even in 1979 it was understood that the computer performance is, and will remain for some time, the primary limiter to what can be accomplished with CFD. The large full airplane CFD calculations being done today are in the 100’s of millions of cells, but they are not LES. Instead they solve the RANS equations, which employ turbulence models to account for sub-grid flow features. These models have limitations and must be applied with care. LES calculations have fewer assumptions and therefore should have broader applicability.

In CFD, the size of the grid (in cells or grid points) increases as the computer power increases. It grows with Moore’s law!

Dean Chapman was an early visionary in CFD, and his ideas on grid requirements have held up well. Recently, NASA released their CFD Vision 2030 Study. In its Technology Development Roadmap, under the category “Knowledge Extraction”, it has technology demonstrations for the following:

  1. In 2020: On demand analysis/visualization of a ten-billion point unsteady CFD simulation.
  2. In 2025: On demand analysis/visualization of a 100-billion point unsteady CFD simulation.
  3. In 2025: Creation of a real-time multi-fidelity database with 1000 unsteady CFD simulations, plus test data with complete uncertainty quantification of all data sources.By extrapolation of technology demonstrations 1 and 2, we would expect:
  4. In 2030: On-demand analysis/visualization of a one-trillion point unsteady CFD simulation.

In other words, in 2030 we should expect the first LES calculations for a large full airplane configuration, and that will require one trillion cells. But, you say, why does our challenge visualize one trillion cells in 2015 when the first real calculations won’t occur until 2030?

It is because we need to ensure our designs scale appropriately. The truth is we don’t expect the one-trillion cell visualization to be snappy in 2015. It is the equivalent of visualizing one billion cells on the workstation you had in 2000 (for me, two cores and 2 GB of memory). But we want to write our software to handle future growth. And as we’ll discuss in the next blog, this means sub-linear scaling for all algorithms!

Next blog – What Obstacles Stand Between Us and One Trillion Cells?

References:

1Chapman, D. R. “Computational Aerodynamics Development and Outlook,” AIAA Journal 17. 1293 (1979).

2Slotnick, J., Khodadoust, A., Alonso, J., Darmofal, D., Gropp, W., Lurie, E., and Mavriplis, D., “CFD Vision 2030 Study: A Path to Revolutionary Computational Aerosciences,” NASA/CR-2014-218178, http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20140003093.pdf.

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Join the Tecplot Team!

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By Tom Chan, Vice President of Customer Development

Join the Tecplot Team!

Customer Development team outing at Seacrest Park

Tecplot is hiring! And what better place to find a good fit than in our Tecplot and CFD communities?

We are looking for the right candidate to fill a new role as Strategic Account Manager in our Customer Development department. This position involves working closely with our top customers in the United States and Canada to understand their needs and to create new revenue opportunities.

We’d love to find someone familiar with Tecplot products, but the requirement is a science or engineering background.

Tecplot is a great place to work. In business for over 30 years, we’re a small, stable company located in Bellevue, Washington. We work hard, but we also find time to have fun together with Tecplot team and company events and outings.

Take a look at our Careers page, and the official Strategic Account Manager posting.

If you or someone you know might be interested in this position, or if you have questions, please let me know.

Tom
425-653-1200
tomchan@tecplot.com


Tecplot Bondalyzer

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Tecplot BondalyzerFor some years now, researchers at Tecplot have been collaborating with a group at the Colorado School of Mines on a project, led by Dr. Mark Eberhart, to develop predictive software that can compute the bulk properties of materials from the electronic structure.

The latest findings of the Tecplot Bondalyzer project have been published in the January issue of ChemPhysChem1.

Previously referred to as Tecplot ChemBond, the 2012 blog, New visualization software helps create stronger metals, refers to the initial paper, Better Alloys with Quantum Design, published through the American Physical Society.

A year later, Dr. Scott Imlay, Tecplot’s Chief Technical Officer, explained how this research fits into that President Obama’s Materials Genome Initiative in the blog titled From Atoms to Airplanes.

So the project once called Tecplot ChemBond has become the Tecplot Bondalyzer.


1Miorelli, J., Wilson, T., Morgenstern, A., Jones, T. and Eberhart, M. E. (2015), Back Cover: A Full Topological Analysis of Unstable and Metastable Bond Critical Points (ChemPhysChem 1/2015). ChemPhysChem, 16: 260. doi: 10.1002/cphc.201590005


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