WAKESET: A Large-Scale, High-Reynolds Number Flow Dataset for Machine Learning of Turbulent Wake Dynamics

What this is

WAKESET is a large-scale CFD dataset of turbulent wake flow fields around a generalized XLUUV geometry, spanning forward speed and turning angle parameterizations.

Contents

• Volumes: CUBE_128 (regular 128×128×128 Cartesian grid; ML-friendly volumetric tensors)
• Planes: VERTPLN and HORZPLN (planar exports / slices on the CFD mesh or interpolated grids, depending on file type)
• Examples: Examples/Python/ (loader scripts and minimal usage examples)

File naming convention

Forward_[VVVV]_ms_Angle_[AA]_[TYPE]_[SUFFIX]

• VVVV: forward speed in mm/s (e.g., 5000 = 5.00 m/s)
• AA: yaw angle in degrees (e.g., 20)
• TYPE: CUBE_128, VERTPLN, HORZPLN (and/or your specific suffix variants)

Variables (units)

• Velocity components: u, v, w [m/s]
• Velocity magnitude: v_mag [m/s]
• Pressure: p_total, p_abs, p_dyn [Pa]
• Vorticity magnitude: omega_mag [1/s]
• Turbulence intensity: I [%]

License

• Dataset (CFD outputs / derived fields): released under CC BY 4.0 (license: cc-by-4.0).
  • You may share and adapt, including commercially, with attribution.
  • See LICENSE for full terms.

Attribution / Citation

If you use WAKESET, please:

1. Cite the associated paper
2. Include attribution: “WAKESET dataset by Zachary Cooper-Baldock et al., CC BY 4.0.”

BibTeX

@dataset{wakeset,
  title   = {WAKESET: A Large-Scale, High-Reynolds Number Flow Dataset for Machine Learning of Turbulent Wake Dynamics},
  author  = {Cooper-Baldock, Zachary and Santos, Paulo E. and Brinkworth, Russell S. A. and Sammut, Karl},
  year    = {2025},
  publisher = {Hugging Face},
  note    = {CC BY 4.0},
  url     = {https://huggingface.co/datasets/ZacharyCB99/WAKESET}
  doi     = {10.57967/hf/7698}
}

@article{wakeset_paper,
  title         = {WAKESET: A Large-Scale, High-Reynolds Number Flow Dataset for Machine Learning of Turbulent Wake Dynamics},
  author        = {Cooper-Baldock, Zachary and Santos, Paulo E. and Brinkworth, Russell S. A. and Sammut, Karl},
  year          = {2025},
  eprint        = {<ARXIV_ID>},
  archivePrefix = {arXiv},
  primaryClass  = {<class>} 
  doi           = {<PUBLISHED_DOI_IF_AVAILABLE>}
}