Intelligent Light and FieldView

ScienceNode: Wind Farm CFD by University of Wyoming and Intelligent Light

In Situ processing enables full wind farm simulation by reducing file size from 234TB to under 1TB

Excerpted from an article originally published on ScienceNode. Read the complete article

Researchers at the University of Wyoming (UW) and Intelligent Light are working hard to improve the efficiency of wind energy. One aspect they're currently investigating is the use of computational fluid dynamics (CFD) to simulate, predict, and improve the performance of wind farms.

CFD uses supercomputers to model the behavior of fluids such as air or water over moving surfaces. In this instance, UW professor of mechanical engineering Dimitri Mavriplis and Earl Duque at Intelligent Light turned to CFD to study how wind currents transform after coming into contact with a moving turbine's blades in the context of a complete wind farm.

Using the Cheyenne supercomputer at the National Center for Atmospheric Research (NCAR)-Wyoming Supercomputer Alliance, Duque and UW researchers Mavriplis, Michael Brazell and Andrew Kirby were able to model wind farms in unique ways. 

What really makes their research unique is the use of in-situ processing. Unlike traditional visualizations produced from stored data, in-situ processing means that visualizations and analysis can be created as a simulation takes place, without writing to disk.

In-situ processing eliminates file transfer bottlenecks, allows increased fidelity, and faster turnaround. 

"In-situ has helped reduce the amount of 3D data that we need to store. Just the restart files take 10 TB on a 12-hour run. Automation has gone way up; we have scripts that go straight from simulation to animation. Also, in-situ has helped with debugging—I don't need to pull down 3D data, and I can just use slices, save those to a FieldView XDB file, and then view that on my local machine."

Michael Brazell, University of Wyoming

Excerpts trom an article was originally published on Read the original article.

CFD images courtesy of Prof. Dimitry Mavripilis and Andrew Kirby, University of Wyoming, an Intelligent Light University Partner.

Digital Engineering: CFD in Formula 1 Racing, highlights contributions by Intelligent Light

Seeking Aerodynamic Perfection in Pixels and Wind

F1 Teams Look for Aerodynamic Advantages in Wind Tunnel Tests and CFD Simulation

Intelligent Light and FieldView have long been delivering results for the demanding world of Formula 1 engineering. In the October issue of Digital Engineering, Senior Editor Kenneth Wong has two articles exploring the way that CAE is used in Formula 1. 

In the 1960s, F1 cars began to sprout wings. The so-called airfoils operate much like aircraft wings, but in reverse. Whereas airplanes use their wings to create lift, race cars use theirs to create negative lift, or downforce. "The most obvious aerodynamic devices on a Formula One car are the front and rear wings, which together account for around 60% of overall downforce (with the floor responsible for the majority of the rest)," FIA explains.

For most F1 engineers, the race began long before the car's tire touches the track. Their quest for aerodynamic advantages began in computational fluid dynamics (CFD) programs and wind tunnel tests.

"The role of FieldView is from when the solver run ends to when the engineer makes a decision. The software focuses on reducing the movement of results files which, in the case of F1 teams, could be massive. In minimizing data movement, it increases the CFD solution's performance and nimbleness. The software also emphasizes data management, allowing small packages of data to replace large results files while maintaining full numerical fidelity."

Torbjörn Larsson of Creo Dynamics AB is working on Formula 1 overtake simulations. FieldView images by Intelligent Light.

"Formula One puts more stress on CFD than anybody else we know. We work with NASA, Boeing, Lockheed, and the Department of Energy, but F1 demands daily turnaround on multi-million-cell models to understand the smallest design details or the impact of weather. They want both fidelity and

Steve Legensky, General Manager and Founder, Intelligent Light

NVIDIA and Intelligent Light Pushing Ahead with In Situ Rendering on Servers

NVIDA developer blog has a feature about server side rendering featuring our work with customers such as Daimler AG.
"Intelligent Light and Daimler AG use server-based pipelines to analyze the results of their large-scale vehicle simulations. To validate thermal operating bounds when designing new vehicles, Intelligent Light's FieldView can be used to visualize the results of highly-parallel simulations. Sifting through the 15 terabytes of data from this simulation is much more quickly done on the server that ran the simulation, after which the salient time steps can be extracted and used to visually communicate results.​"

Leading the Way With In Situ Extracts

At Intelligent Light, we continue to lead the charge for the adoption of in situ, a technique that can maximize insight from simulation runs while also avoiding the problems caused by saving, storing and moving massive amounts of data. Our work this year shows that using in situ allows the CFD practitioner to increase resolution by saving data at a higher frequency, while still saving far less data overall. This reduces disk space and time to read the data in the post-processing phase.

At the AIAA SciTech 2016 conference this month, I shared our in situ work with the AIAA community in two ways: I presented a paper to the MVCE technical committee titled, "In Situ Infrastructure Enhancements for Data Extract Generation", and I presented an in-booth talk about how to add in situ processing into a solver.

Many of the engineers I met at this year's SciTech are running codes at scale on high performance computers but find it impractical, often impossible, to save all of the data on such systems. In situ enables operations such as visualization and analysis, which have traditionally been performed as post-processing, to be executed in the solver itself as it runs. Instead of writing large amounts of volume data, in situ enables the creation of smaller data products such as images and FieldView XDB extract files. XDB files, for example, capture surfaces of interest as well as scalar and vector fields from the solver and write that data in a compact form orders of magnitude smaller than the standard results file.

GT Rotor visualization. Iso surfaces of Q, colored by Cp. Bottom left includes a cross plane of the mesh.

The paper I presented to the MVCE technical committee, "In Situ Infrastructure Enhancements for Data Extract Generation", describes enhancements made by Intelligent Light to VisIt/Libsim that improve its support for batch-creation of VisIt plots, which can then be exported as XDB extracts. Working with James Forsythe of the US Navy's NAVAIR, the CREATE-AVTM Kestrel solver was instrumented with the latest VisIt/Libsim enhancements for batch support and parallel data writing. Kestrel was run at scales up to 1024 cores using a workflow that produced XDB files every 5 solver iterations, an output frequency far higher than would be attempted with volume-based outputs. Even with writing extracts so often, the in situ production of extract files consumed less than 3% of the overall solver runtime. The set of extract files for a single time step is also 21 times smaller than the corresponding volume data, saving both disk space and time to read in large files for subsequent visualization. Several instances of FieldView operating concurrently processed the resulting XDB files into a movie showing helicopter rotor vortices. One strength of this workflow is that it is parallel from data extraction to extract I/O, all the way through XDB visualization. In addition, the workflow is flexible because XDB extracts provide both geometry and fields that can be visualized, enabling fast data analysis that skips the burden of large I/O using volume data.

Intelligent Light's recent VisIt/Libsim improvements make the process of instrumenting a simulation for in situ simpler than ever before. During the SciTech exhibition, I held a talk in the Intelligent Light booth about how to add in situ processing into a solver. The presentation was well attended by users and solver developers from the US, Japan and Israel. There was much interest in adding VisIt/Libsim and XDB data extraction to solvers and the workflow continues to prove its value.


FieldView 16 and XDBview 2 Now Available

3D PDF export generated in FieldView and seen in Adobe Acrobat Reader® (left) and on a tablet (right). (DrivAer geometry courtesy of TU München, Mesh and simulation by VINAS with Pointwise and Helyx).

3D PDF export, faster data read, reduced memory usage and many more improvements

Just a little less than a year has passed by since our release of FieldView 15 and I am happy to announce that our new version, FieldView 16, is ready for download. While software users sometimes feel like installing a new version is not going to impact them much, I am confident that every single FieldView user will benefit from this release.

You will find that FieldView 16 is faster to read your data than previous versions and will also use less memory. Data read can easily be made even faster and more memory efficient by selecting the right Data Input option. When you don't need to perform Dataset Sampling or to compute a lot of streamlines, select "Less" Grid Processing for faster data input. In cases when you do need that kind of performance, select "More" right from the Data Input panel and you will use more memory to get better performance during you session.

FieldView 16 is the first CFD post-processor to introduce a built-in 3D PDF export. This standard format is a great way to present and share your results, allowing interactive exploration via rotation, zooming, panning and commenting in Adobe Acrobat Reader and in 3D PDF viewer apps.

Other highlights of this release include:

  • New Vertices and Shaded Vertices display types for better insight and fast performance
  • The ability to sweep surfaces coming from an XDB extract and to synchronize this operation between multiple datasets for easy animations and side-by-side comparison
  • More control over the location of Surface Flows
  • A new Growing display type for pathlines animation
  • 10x faster read times for AcuSolve users, compared to FieldView 15
  • The ability to read single-file transient data as steady-state
  • Support for results from FLOW-3D v11

We're also introducing the second version of XDBview. Our free, sharable viewer for XDB extracts, now includes the same sweep capability as FieldView 16 and can now read your CAD geometries in STL format.

To learn more about these new versions, I encourage you to read our What's New in FieldView 16 document (Japanese version).

FieldView 16 is a major release. You will need to request new passwords from Intelligent Light. FieldView 16 passwords will work for both version 15 and 16, but as of this release FieldView version 14 and earlier are no longer supported. If you need to test FieldView 16 with your data or workflows before upgrading, feel free to request additional temporary passwords and we'll be happy to provide them free of charge.

Please contact our FieldView Support Team  or your account manager for more information.