Intelligent Light and FieldView

Uncertainty Quantification (UQ) at ASME V&V Symposium

Uncertainty Quantification (UQ) of CFD data.

Earlier this month at the ASME V&V symposium, Seth Lawrence, a graduate student at our University Partner Northern Arizona University, presented his Master's thesis work on "Verification, Validation and Uncertainty Quantification of Turbulent Twin Jets". Seth was advised by our own Dr. Duque who maintains an adjunct Faculty position @ NAU. This event was Seth's first outing at a major international technical symposium. He did a great job of presenting (and defending) his work to the leaders in the field of V&V/UQ, such as Oberkampf, Roy, Celik and Eca. The work was a Challenge Problem sponsored by the ASME V&V 30 Committee.  GREAT JOB SETH!    

In Uncertainty Quantification (UQ), engineers utilize standardized procedures such as the ASME V&V 20 and V&V 30 guidelines to account for the effects of probabilistic inputs to a CFD simulation to arrive at a non-deterministic answer. Through UQ, an engineer could state with 95% certainty answers to their design question while justifying and documenting how they arrived at their answer.

This challenge problem was the only one at the symposium to focus on UQ. It is a key area of interest for those seeking to capitalize on information gleaned from verification & validation work in new design studies. 

To combine CFD and UQ analysis, Mr. Lawrence created an automated workflow using FieldView to post-process the results of Fluent solutions and pass data to Dakota (Sandia National Lab) and then pass data from Dakota as input to Fluent in an iterative process. FieldView was also used to visualize the CFD data to create images for the presentation and 3D PDF to share results.

"Seth did a great job presenting to the leaders in the VVUQ community. His work was well received and cited by other presenters later in the symposium.  It was gratifying to hear statements among veteran symposium participants including 'This is the first time I've seen error bars on a CFD result, very impressive.'"

Earl P. N. Duque, PhD Manager of Applied Research at Intelligent Light

Mr. Lawrence noted that he enjoyed the chance to see how the experts in this field approached the benchmark ASME turbulent twin jet numeric model validation problem.

Professor Tom L. Acker from NAU and Intelligent LIght's Earl P.N. Duque served as advisers on the project.

​Mr Lawrence used the 3D PDF export capability in FieldView throughout the development of the CFD model, allowing him to easily share results of his grid convergence study (CGI) and in the observed order of the solver (p-obs). 3D PDF files are downloadable below.

"Throughout the development of the CFD model, I made good use of the 3D PDF generator that is available in the new FieldView 16.1 package. This was very helpful in the presentation of model results, and provided the ability to easily send detailed model results of large CFD datasets in the form of a small file via email, and the recipient does not need any special software to view the 3D PDF results - fantastic!"

Seth Lawrence, Northern Arizona University
Download 3D PDF. Numerical uncertainty in y-velocity. Adobe Acrobat Reader recommended for viewing. 3D PDF content is not supported within web browswers.
Download 3D PDF. Observed y-velocity. Adobe Acrobat Reader recommended for viewing. 3D PDF content is not supported within web browsers.

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CFD at Wilson Labs highlighted on Golf Channel’s Driver vs. Driver

Click to view the show segment on YouTube

"When you're a challenger, you have to think differently...Golf sometimes has been slow to adapt to new ideas, so anytime you're in the position of being a challenger, you have to be willing to break the mold."

Michael Vrska, Director of Innovation at Wilson Golf

Wilson Labs uses Altair's Virtual Wind Tunnel software to test aerodynamic performance empowering them to create revolutionary new products faster. The CFD suite includes AcuFieldView, a version of FieldView for Altair CFD users designed to support engineering driven organizations like Wilson Labs.

See FieldView in action as Wilson Labs explains how they use CFD to design sporting goods, and in particular golf clubs, as featured on the 7-part Golf Channel series DRIVER vs. DRIVER.

It is "...about the expertise of the team of engineers at Wilson Labs...a good idea for a golf club is merely a start. Its execution to a finished product requires technological horsepower."

by Golf Digest

Wilson, Wilson Golf and Wilson Labs are trademarks of Wilson Sporting Goods Co.
Golf Digest and Golf Channels are trademarks of their respective owners.

Live Webinar - Get to know FieldView 16.1

Send your data straight from FieldView to Youtube or PowerPoint with FieldView Update 16.1.

LIVE Webinar - Concluded

  • Tuesday, January 31, 2017
    2:00 pm EST (19:00 GMT)

  • Wednesday, February 1, 2017
    9:00 am EST (14:00 GMT)

Please join Yves-Marie Lefebvre, FieldView Product Chief for a live web event "Get to know FieldView 16.1"

We will demonstrate powerful new features including:

    • MP4 Video Export with Frame Rate Control: Straight from FieldView to YouTube and PowerPoint. Highest quality with reduced file size and maximum portability 
    • Logarithmic Colormap Scaling with Powers of 10 Annotation 
    • Improved Defined Views for easy Up Axis 
    • Surface Sampling for faster datasets comparison
    ​Plus dozens of helpful fixes and features as requested by our users. Including, new licensing options for today's HPC environments and enhanced security, Tkinter to enable Python GUI panels and VTK readers to support existing Catalyst extract workflows. 

    The 40 minute presentation will be followed by a live Q&A.

    Yves-Marie Lefebvre, FieldView Product Chief

    Data Handling and Visualization for Large CFD Simulations

    Hear it for yourself!  

    LIVE Webinar
    October 6th, 2016
    Register now!

    Large scale CFD run on HPC systems generate huge volumes of results requiring a workflow that allows users to manage the large data and extract useful information efficiently. Workflows that do not keep pace with the rate of simulation prevent people from making the most of their simulations. The negative outcomes can include not sharing data effectively or perhaps even avoiding the use of HPC resources to begin with.

    We've made it simple to use the best resources available for your CFD. We'll show you how we are helping our customers achieve the promise of HPC for large CFD by reducing the data bottleneck and lowering costs. Cray and Intelligent Light invite you to join this live discussion and learn how some of the most productive CFD users in the world are working with their large data.

    We'll also discuss an industry collaboration aimed at improving the quality of CFD simulation of the physics of helicopter flight. The AIAA Hover Prediction Workshop uses data from multiple organizations submitting varied solutions for a helicopter in hover. The results are brought together on an shared HPC resource where standardized, automated post-processing is performed. Results are directly compared, reports are generated and extracts are delivered that can be shared, explored interactively and archived.

    Join Cray and Intelligent Light for a live discussion
    October 6th 11 am or 9 pm ET
    Event details and registration

    See what HPC Technology, Teamwork, and Trust can do for your CFD programs!

    Register now!


    ISC Workshop on In Situ Visualization

    Time step from the evolution of temporal mixing layer from initial to vortex breakdown. AVF/Leslie simulation performed using up to 131,072 cores on DOE's TITAN supercomputer. In situ processing using VisIt software delivered the images related plots and XDB files for the researchers. Time step 150,000 out of 200,000.

    This summer I presented a talk called In Situ Production of Extract Databases for Visualization to the Workshop on In Situ Visualization at the ISC Conference held in Frankfurt, Germany. In situ takes workflows that have been created post hoc and executes them directly on simulation data in memory while the simulation is running. The workshop was attended by visualization experts mainly from Europe and the US and talks focused on using in situ software to address the challenges of being able to save sufficient simulation data on supercomputers.

    Hank Childs from the University of Oregon, a prominent visualization expert, gave a keynote address emphasizing the importance of in situ against the backdrop of upcoming exascale machines with their diminishing memory per core and lower relative I/O bandwidth compared to today's machines. Jens Henrik Goebbert from Aachen University presented an abstraction library that simplifies in situ integration with multiple in situ infrastructures, including Libsim. Roberto Sisneros from NCSA presented work on a parameter study highlighting the importance of providing good default application settings and showed that performance for VisIt's streamline plot could be enhanced by simply improving the default settings.

    My talk summarized Intelligent Light's in situ efforts with VisIt, Libsim and our extract database files (XDB). Specifically, we instrument a simulation for in situ using Libsim, which brings VisIt's capabilities into the simulation. We developed a library that efficiently writes the FieldView XDB files and added it as an export option to VisIt. The simulation uses VisIt to create surface geometry extracts, typically without making any copies of simulation data, and exports the extract as a FieldView XDB file. Tight coupling of simulation to visualization and analysis provides opportunities to perform data reductions that result in smaller, concentrated, more useful results being written out more frequently, avoiding the costs of writing and later reading large volume datasets.

    ​I presented results from running the AVF-LESLIE combustion code on the Titan machine at Oak Ridge National Laboratory using an in situ rendering workflow and our extract database workflow. For the rendering workflow we were able to run the code up to 131K cores and render images of slices and isosurfaces from the simulation every 5 time steps to produce a visualization of a turbulent mixing layer of 2 fluids. In another of our experiments, we extracted surface-based results, saving the geometry plus field data to our XDB format for later post-processing within FieldView. We saved XDB extracts from every 5th time step, taking around 2% of the simulation runtime. Each XDB file was over 200x smaller than the corresponding volume data file. We wrote 20 XDB files for every volume output file and the combined size of 20 XDB files was still 10x smaller than a single volume output file.

    This work is supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research under Award Number DE-SC0012449.