---
license: cc-by-4.0
task_categories:
  - other
language:
  - en
tags:
  - turbulence
  - rans
  - cfd
  - dns
  - les
  - benchmark
  - machine-learning-for-physics
pretty_name: "Closure Challenge v2 — extended ML-RANS turbulence benchmark"
size_categories:
  - 100K<n<1M
viewer: false
---

# Closure Challenge v2

Extended ML-RANS turbulence-modelling benchmark with composite scoring across 14 test cases.

This dataset is the **lightweight integral-profile portion** used by the scoring protocol — CSV reference data and k-ω SST baseline predictions sampled at the canonical evaluation points for each case. The full OpenFOAM cases (mesh, fields, run scripts) and DNS-native partitioned VTUs are hosted on a separate Hugging Face dataset linked from the paper.

NeurIPS 2026 Evaluations & Datasets track submission. Authors anonymised for double-blind review.

## What's in the test set

8 inherited v1 cases:

- 4 parametric periodic-hill geometries at Re=5600
- 3 square / rectangular duct configurations (AR=1, 3, 14.4 at Re_τ=180/360)
- NASA wall-mounted hump

6 new v2 cases:

| Case | Reference type | Quantities of interest |
|------|-----------------|--------------------------|
| `NASA_2DZP` | NASA TMR theory | C_f(x), u⁺(log y⁺) |
| `NASA_2DN00` | Ladson NASA TM 4074 + Gregory NPL R&M 3726 | C_L(α), C_D(α), C_p(x/c), C_f(x/c) |
| `NASA_ASJ` | Bridges-Wernet ARN consensus PIV | U/U_jet centerline + 5 stations, ⟨u'v'⟩/U_jet² at 5 stations |
| `ERCOFTAC_AhmedBody25` | LDA wake + pressure taps (case082) | rear-surface C_p, integrated C_D vs canonical 0.285 |
| `NASA_FaithHill` | PIV centerline + PSP + FISF | mean velocity, TKE, surface C_p, surface C_f |
| `ERCOFTAC_WingBodyJunction` | DNS 1-6 | symmetry-plane velocity / TKE / R_xx + bottom-wall and wing-root C_p |

## Layout

```
data/
├── alpha_15_13929_4048/   v1 PHLL
├── alpha_15_13929_2024/
├── alpha_05_4071_4048/
├── alpha_05_4071_2024/
├── AR_1_Ret_360/          v1 DUCT
├── AR_3_Ret_360/
├── AR_14_Ret_180/
├── NASA_2DWMH/            v1 hump
├── NASA_2DZP/             v2 new
├── NASA_2DN00/
├── NASA_ASJ/
├── ERCOFTAC_AhmedBody25/
├── NASA_FaithHill/
├── ERCOFTAC_WingBodyJunction/
└── evaluation_points/     v1 case grids
```

For each v2-new case:

```
<case>/
├── baseline_komegasst/   k-ω SST baseline (.dat + .csv pairs)
├── highfidelity/         Reference data (.dat + .csv pairs)
└── plots/                Pre-rendered comparison figures
```

## Quick start

```python
import pandas as pd
df = pd.read_csv("data/NASA_2DZP/highfidelity/cf_as_function_of_x.csv")
print(df.columns.tolist())
# ['zone', 'x', 'skinfr', '5percenterror']
```

For end-to-end scoring, use the `closure-challenge-v2` Python package and the `notebooks/sample_eval.ipynb` example, both available in the linked code repository.

## License

- **Code**: MIT (see `LICENSE-CODE` in the code repository)
- **Curated data**: CC-BY-4.0 for all cases derived from public-domain or CC-BY upstream sources (NASA TMR, Vinuesa duct database, Xiao parametric PHLL, NASA wall-mounted hump, Faith Hill, NACA 0012)
- **ERCOFTAC kbwiki extracts**: CC-BY-NC-SA-4.0 (Ahmed body case082, wing-body junction DNS 1-6) — inherited from upstream

Per-case attribution is in `SOURCES.md` of the code repository.

## Citation

```bibtex
@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}
}
```

## Croissant metadata

A Croissant 1.0 + RAI metadata file is provided as `croissant.json` in this repository, suitable for the JoaquinVanschoren/croissant-checker validator.