SIDA / README.md
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metadata
license: cc-by-nc-4.0
language:
  - en
tags:
  - cfd
  - sciml
pretty_name: Shock-Induced Droplet Aerobreakup (SIDA)
size_categories:
  - 10B<n<100B

Description:

This Dataset includes 128 trajectories of time-dependent Shock-Induced Droplet Aerobreakup (SIDA). Using a Finite Volume solver, we solve the two-dimensional (2D) axisymmetric compressible Euler equations for this multiphase problem.

  • dataset_name: Shock-induced Droplet Aero-breakup,
  • PDE: 2D axisymmetric compressible Euler equations,
  • created: 08-2025,
  • time_dependent: true,
  • include_initial_state: true,

Spatiotemporal Information:

  • num_trajectories: 128,
  • num_time_steps: 61,
  • num_channels: 6,
  • channel_names: ["density", "pressure", "velocity_x", "velocity_y", "schlieren", "vorticity"],
  • spatial_dimensions: 2,
  • spatial_grid_size: [128, 256],
  • dx=dy: 1.171875000e-05

Boundary conditions:

  • west: Dirichlet,
  • east: ZeroGradient,
  • south: Axisymmetric,
  • north: ZeroGradient

Two separet files, "train.h5" and "test.h5", are provided. The former includes 96 trajectories for training and validation; the latter covers the 32 remaining trajectories for inference only.

Refer to the "metadat_SIDA.json" file for more details on the dataset.

A sample Out-of-Distribution, "OOD.h5", is added as well, which covers a higher shock Mach number across 32 trajectories. For more details, refer to "metadata_SIDA_OOD.json".

Download:

The dataset can be downloaded, e.g., via huggingface-cli download.

huggingface-cli download FluidVerse/SIDA --repo-type dataset --local-dir .

Extra Data Generation:

Use the instructions inside the generation script, "generator.py", for creating larger datasets. This script runs the solver specified in the "metadata_SIDA.json" file.

Strict Licensing Notice:

This dataset is released under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0) and is exclusively for non-commercial research and educational purposes. Any commercial use—including, but not limited to, training machine learning models, developing generative AI tools, creating software products, or other commercial R&D applications—is strictly prohibited.