| { |
| "@context": { |
| "@language": "en", |
| "@vocab": "https://schema.org/", |
| "citeAs": "cr:citeAs", |
| "column": "cr:column", |
| "conformsTo": "dct:conformsTo", |
| "cr": "http://mlcommons.org/croissant/", |
| "rai": "http://mlcommons.org/croissant/RAI/", |
| "data": { |
| "@id": "cr:data", |
| "@type": "@json" |
| }, |
| "dataType": { |
| "@id": "cr:dataType", |
| "@type": "@vocab" |
| }, |
| "dct": "http://purl.org/dc/terms/", |
| "examples": { |
| "@id": "cr:examples", |
| "@type": "@json" |
| }, |
| "extract": "cr:extract", |
| "field": "cr:field", |
| "fileProperty": "cr:fileProperty", |
| "fileObject": "cr:fileObject", |
| "fileSet": "cr:fileSet", |
| "format": "cr:format", |
| "includes": "cr:includes", |
| "isLiveDataset": "cr:isLiveDataset", |
| "jsonPath": "cr:jsonPath", |
| "key": "cr:key", |
| "md5": "cr:md5", |
| "parentField": "cr:parentField", |
| "path": "cr:path", |
| "recordSet": "cr:recordSet", |
| "references": "cr:references", |
| "regex": "cr:regex", |
| "repeated": "cr:repeated", |
| "replace": "cr:replace", |
| "sc": "https://schema.org/", |
| "separator": "cr:separator", |
| "source": "cr:source", |
| "subField": "cr:subField", |
| "transform": "cr:transform" |
| }, |
| "@type": "sc:Dataset", |
| "name": "closure-challenge-v2", |
| "description": "Closure Challenge v2 — extended ML-RANS turbulence-modelling benchmark with composite scoring across 14 test cases (8 inherited from v1 plus 6 new engineering-relevant cases). The new cases bridge canonical 2D wall-bounded flows (NASA TMR: zero-pressure-gradient flat plate, NACA 0012 polar, axisymmetric subsonic jet) and 3D bluff-body separation (Ahmed body 25-degree slant, NASA Faith Hill, ERCOFTAC wing-body junction). For each case the dataset provides a baseline k-omega SST RANS prediction and a high-fidelity reference (DNS/LES, experimental PIV/LDA/PSP/FISF, or theoretical correlations).", |
| "conformsTo": "http://mlcommons.org/croissant/1.0", |
| "license": "https://huggingface.co/datasets/anon-closure-challenge-v2/closure-challenge-v2/blob/main/LICENSE", |
| "url": "https://huggingface.co/datasets/anon-closure-challenge-v2/closure-challenge-v2", |
| "version": "0.4.0", |
| "citeAs": "@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}}", |
| "datePublished": "2026-05-04", |
| "keywords": [ |
| "turbulence modeling", |
| "RANS", |
| "computational fluid dynamics", |
| "machine learning benchmark", |
| "DNS", |
| "LES", |
| "k-omega SST", |
| "NASA TMR", |
| "ERCOFTAC" |
| ], |
| "creator": { |
| "@type": "Organization", |
| "name": "Anonymous (under double-blind review)" |
| }, |
| "isLiveDataset": false, |
|
|
| "rai:dataCollection": "Curated extracts from public DNS/LES databases (NASA Turbulence Modeling Resource, ERCOFTAC kbwiki Classic Collection, Vinuesa-lab duct database, Xiao et al. parametric periodic-hill database) and original OpenFOAM k-omega SST baseline runs at the same evaluation points.", |
| "rai:dataPreprocessingProtocol": "Standardisation of NASA Tecplot zone .dat files to flat CSV with a 'zone' column. Extraction of baseline-RANS values at experimental measurement points via VTK probing of foamToVTK output. Per-case curation of which quantities of interest enter the composite score. The high-fidelity reference data is byte-identical to upstream sources; only filename conventions and column headers are normalised.", |
| "rai:dataUseCases": "Evaluation of machine-learning-augmented Reynolds-averaged Navier-Stokes (RANS) turbulence closure models. Validation of CFD solver implementations. Comparison of CFD-result extraction or interpolation methods. Educational resource for CFD validation methodology.", |
| "rai:dataLimitations": "(1) The 14 test cases focus on incompressible to low-Mach steady-state RANS-relevant flows; not applicable to transonic, hypersonic, or strongly unsteady regimes without re-curation. (2) The dataset is specifically chosen to be challenging for k-omega SST; models that score well here may still need separate validation for industrial-scale geometries. (3) DNS/LES references have finite statistical and numerical accuracy; the current score does not weight cases by reference uncertainty. (4) Some baseline-RANS quantities have known artifacts at body surfaces (e.g. Faith Hill PSP probe failures inside reverse-flow regions) and are excluded via NaN-aware scoring.", |
| "rai:dataSocialImpact": "Provides a community-standardised benchmark for evaluating machine-learning-based turbulence closures, enabling reproducible comparison across modeling approaches. Reduces duplicated evaluation effort across research groups and helps surface methods that generalise across Reynolds number and geometry.", |
| "rai:dataReleaseMaintenancePlan": "Versioned releases on Hugging Face. Errata tracked via GitHub issues on the public release repository. Significant corrections trigger a new patch version (v0.4.x); new test cases or scoring-protocol changes trigger a new minor or major version. Old versions remain accessible to preserve leaderboard reproducibility.", |
| "rai:dataBiases": "The benchmark deliberately selects canonical research geometries (parametric periodic hills, square and rectangular ducts, NASA TMR validation cases, simplified automotive bluff bodies, axisymmetric subsonic jet, smooth-body separation, wing-body junction). It is therefore biased toward research-grade flow configurations rather than industrial-scale geometries, and toward incompressible to low-Mach steady-state regimes rather than transonic, hypersonic, or strongly unsteady flows. The cases are also intentionally chosen to be challenging for the k-omega SST baseline, which biases the difficulty distribution toward separated and adverse-pressure-gradient flows. No human-subject, demographic, or socioeconomic bias is present because the dataset contains only fluid-mechanics measurements.", |
| "rai:personalSensitiveInformation": "None. The dataset contains computational fluid dynamics simulation outputs and laboratory experimental measurements (PIV, LDA, particle-image velocimetry, pressure-sensitive paint, fringe-imaging skin friction). No human subjects, no personally identifiable information, no health data, no sensitive personal categories.", |
| "rai:hasSyntheticData": "Partially. The high-fidelity references include both physics-based numerical simulations (DNS and LES, which are algorithm-generated and therefore synthetic in the RAI sense) and real-world laboratory measurements (PIV, LDA, PSP, FISF, hot-wire, surface pressure taps). The k-omega SST baseline predictions are simulation outputs. No data is generated by machine-learning models or generative AI; all simulation outputs come from numerical integration of the Navier-Stokes equations in established CFD solvers (OpenFOAM for the baselines; various academic / commercial solvers for the upstream high-fidelity references).", |
|
|
| "distribution": [ |
| { |
| "@type": "cr:FileObject", |
| "@id": "repo", |
| "name": "repo", |
| "description": "Hugging Face dataset repository hosting the lightweight integral-profile CSVs, scoring code, and metadata files.", |
| "contentUrl": "https://huggingface.co/datasets/anon-closure-challenge-v2/closure-challenge-v2", |
| "encodingFormat": "git+https", |
| "sha256": "main" |
| }, |
| { |
| "@type": "cr:FileSet", |
| "@id": "highfidelity-csv", |
| "name": "highfidelity-csv", |
| "description": "High-fidelity reference data per test case (DNS, LES, experimental PIV/LDA/PSP/FISF, theoretical/empirical correlations).", |
| "containedIn": {"@id": "repo"}, |
| "encodingFormat": "text/csv", |
| "includes": "data/*/highfidelity/*.csv" |
| }, |
| { |
| "@type": "cr:FileSet", |
| "@id": "highfidelity-dat", |
| "name": "highfidelity-dat", |
| "description": "Tecplot zone-format companion files for the high-fidelity references (byte-identical to upstream NASA TMR or ERCOFTAC archives).", |
| "containedIn": {"@id": "repo"}, |
| "encodingFormat": "application/x-tecplot-dat", |
| "includes": "data/*/highfidelity/*.dat" |
| }, |
| { |
| "@type": "cr:FileSet", |
| "@id": "baseline-csv", |
| "name": "baseline-csv", |
| "description": "k-omega SST RANS baseline predictions per test case, sampled at the high-fidelity reference points.", |
| "containedIn": {"@id": "repo"}, |
| "encodingFormat": "text/csv", |
| "includes": "data/*/baseline_komegasst/*.csv" |
| }, |
| { |
| "@type": "cr:FileSet", |
| "@id": "baseline-dat", |
| "name": "baseline-dat", |
| "description": "Tecplot zone-format companion files for the k-omega SST baseline.", |
| "containedIn": {"@id": "repo"}, |
| "encodingFormat": "application/x-tecplot-dat", |
| "includes": "data/*/baseline_komegasst/*.dat" |
| }, |
| { |
| "@type": "cr:FileSet", |
| "@id": "scalar-references", |
| "name": "scalar-references", |
| "description": "Per-case integrated scalar reference values (e.g. Ahmed body C_D = 0.285) and metadata.", |
| "containedIn": {"@id": "repo"}, |
| "encodingFormat": "application/json", |
| "includes": "data/*/baseline_komegasst/scalars.json" |
| }, |
| { |
| "@type": "cr:FileSet", |
| "@id": "evaluation-points", |
| "name": "evaluation-points", |
| "description": "Fixed evaluation-point grids for the v1 test cases (used by the upstream closure-challenge package).", |
| "containedIn": {"@id": "repo"}, |
| "encodingFormat": "text/csv", |
| "includes": "data/evaluation_points/*.csv" |
| } |
| ] |
| } |
|
|
