The ParaView files of flow simulations using 1, 5, 20 and 82 million particles are available from a research data repository under a plain CC BY license. I imagined that the community of ParaView developers and users may find pre-computed datasets with 82M particles useful for benchmarking the performance/robustness of their visualization and data-analysis algorithms.
The flow is a so-called dam break: a block of still water is allowed to slide and hits a wall at some distance downstream. Interestingly, the flow features a first stage where the trajectories are smooth and orderly, and a second when the motion is definitely chaotic after countless rebounds and splashes. (The simulations have been carried out with a particle-based method called SPH for Smoothed Particle Hydrodynamics.)
Please have a look at the YouTube playlist for a first visual appreciation: The 2D dam-break benchmark dataset • SPH solutions - YouTube. Note that, for your convenience, each video has a detailed explanation of its content, context and purpose. (Of course: all has been rendered in ParaView!)
The datasets contain, for each particle count, the 201 vtk files showed in the playlist animations. These files are available from the certified repository 4TU.ResearchData in the Netherlands. You can visit it via the link below:
If you are interested, the following navigation tips can be handy.
- The collection where you land groups five datasets.
- One ‘gateway’ dataset contains all information needed to decide what to do with it next. In particular a Commentary.pdf document explains the origin and architecture of the collection.
- Then, the other data sets contain the files at the different particle counts (1, 5, 20, 82 millions): there you find a compressed vtk file for each frame with a consistent naming WaterParticle_???.vtk.xz
- The total size of the collection is 735GB and consists of 1650 files. You can download either entire datasets or individual files.
I recommend to read the Commentary first.
I hope that sharing these large datasets can be useful also from the perspective of post-processing.