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Closure Challenge v2 — CFD case files (heavy)

Companion dataset to anon-closure-challenge-v2/closure-challenge-v2.

This repository hosts the full OpenFOAM cases (mesh, fields, run scripts), VTK volume snapshots, and DNS-native partitioned VTUs for the 14 test cases of the Closure Challenge v2 benchmark.

The lightweight integral-profile portion needed for reproducing the scoring protocol is in the companion dataset. Most reviewers and submitters do not need this heavy dataset — it is only required for re-running the CFD simulations from scratch or for extended analyses.

Status

Heavy CFD assets will be uploaded after acceptance. The 14-case OpenFOAM tree is approximately 15-20 GB. We are deferring the upload to the camera-ready stage to allow time for thorough PII scrubbing of run scripts, comments, and cluster-specific paths.

Planned contents

For each of the 14 test cases:

<case>/
├── 0/                  OpenFOAM initial conditions
├── <converged_time>/   Converged solution + diagnostic fields
├── constant/           polyMesh, turbulenceProperties, thermophysicalProperties
├── system/             controlDict, fvSchemes, fvSolution, post-processing dicts
├── postProcessing/     residuals, line probes, surface samples
├── VTK/                Volume + surface snapshots for ParaView
└── *.foam              Empty file for ParaView OpenFOAM reader

Plus extraction utilities:

extract_baseline_from_vtk.py    Probe baseline at high-fidelity reference points
compute_forces.py               Integrate p over body surface to produce CD/CL
extract_wbj_surface_lines.py    Wing-body junction surface line extraction
faith_hill_to_csv.py            FAITH hill PIV/PSP/FISF re-formatter

License

  • OpenFOAM cases (this work): MIT
  • Curated extracts of upstream data: CC-BY-4.0 for NASA TMR, Vinuesa duct, Xiao parametric PHLL; CC-BY-NC-SA-4.0 for ERCOFTAC kbwiki extracts (Ahmed body case082, wing-body junction DNS 1-6).

Citation

@inproceedings{closure_challenge_v2_neurips26,
  title={The Closure Challenge: A Benchmark Task for Machine Learning in Turbulence Modeling},
  author={Anonymous},
  booktitle={NeurIPS Datasets and Benchmarks Track (under review)},
  year={2026}
}
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