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SURF-FluidSimulation/FluidSimulation

SURF: A Generalisation Benchmark for GNNs Predicting Fluid Dynamics SURF, is a benchmark designed to test the generalization of learned graph-based fluid simulators. The benchmark consists of seven independent datasets: Base Turned Topo Range Dynamic Full FullFiner Each dataset is available as separate *.zip file and consists of at least 1200 2D incompressible fluid flow simulations with 300 timesteps. The data structure is as follows: folder: dataset_name folders: dpx… See the full description on the dataset page: https://huggingface.co/datasets/SURF-FluidSimulation/FluidSimulation.

sourceHugging Facecc-by-nc-4.0updated 3y agoView on Hugging Face
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SURF: A Generalisation Benchmark for GNNs Predicting Fluid Dynamics

SURF, is a benchmark designed to test the generalization of learned graph-based fluid simulators. The benchmark consists of seven independent datasets:

  • —Base
  • —Turned
  • —Topo
  • —Range
  • —Dynamic
  • —Full
  • —FullFiner

Each dataset is available as separate *.zip file and consists of at least 1200 2D incompressible fluid flow simulations with 300 timesteps. The data structure is as follows:

  • —folder: dataset_name
  • —folders: dpx
  • —files: sim.npz, triangles.py, constrainedkmeans20.npy, Simulationdp1Timestep_50.png
  • —folder: Splits
  • —files: train.txt, test.txt, valid.txt

The file sim.npz (numpy archive) contains the result of the simulation for each timestep at each node:

  • —'pointcloud': x, y coordinates
  • —'VX': velocity in x-direction
  • —'VY': velocity in y-direction
  • —'PS': static pressure
  • —'PG': dynamic pressure
  • —'T': temperature
  • —'TC': thermal conductivity of fluid
  • —'HC': heat capacity of fluid

The results have the following shape: VX.shape=(#timesteps, #nodes, 1).

The file triangles.py contains the mesh connectivity. triangles.shape=(#timesteps, #elements, 3). Each triangle is defined by the node numbers in counter clockwise direction.