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BlidReview/steady-rans-generalization

Steady-RANS cross-family generalization dataset Data for the paper "Towards generalized flow field prediction: one model across unseen object families" (under double blind review; this account is anonymous for that reason). Trained checkpoints and evaluation code are in the companion model repo: steady-rans-surrogates. Steady incompressible k-omega SST (OpenFOAM simpleFoam) external flow around 855 distinct shapes (17 scripted parametric families plus 40 ModelNet object… See the full description on the dataset page: https://huggingface.co/datasets/BlidReview/steady-rans-generalization.

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Steady-RANS cross-family generalization dataset

Data for the paper "Towards generalized flow field prediction: one model across unseen object families" (under double blind review; this account is anonymous for that reason). Trained checkpoints and evaluation code are in the companion model repo: steady-rans-surrogates.

Steady incompressible k-omega SST (OpenFOAM simpleFoam) external flow around 855 distinct shapes (17 scripted parametric families plus 40 ModelNet object categories), three log-uniform Reynolds numbers in [500, 1e5] per shape, random yaw 0 to 20 degrees. All solves are nondimensional (U_inf = rho = L = 1).

Contents

FolderSizeWhat
cache_v3/~13 GB2546 processed PyG graphs (.pt), one per converged case: ~10^4 sampled nodes with 12 node features, kNN edges (k=8, symmetrized), and the 7 target fields (u, v, w, p, and transformed k, omega, nu_t) at every node. This is the exact training and evaluation input; the deterministic train / val / OOD split is derived from it in code.
ext_dataset_of/~0.5 GBThe 9 zero shot external geometry families (AhmedML, WindsorML, DrivAerML, DARPA SUBOFF, NASA-CRM, motorbike, swept wings, slotted plates, CAARC buildings), re-simulated through the same pipeline at matched conditions: per case meta.json plus VTK exports (boundary and internal fields), the zero shot ground truth used in the paper.
dataset_stls/~0.4 GBThe 855 watertight training shape STLs, for re-meshing or extending the corpus.
dataset_of/~35 GBRaw OpenFOAM cases for the training corpus (meta.json + exported fields per case), for full pipeline reproducibility.

Splits

The split is deterministic and byte identical across architectures and seeds; it is computed by split() in code/ezflow_v3/gnn/train_v5.py of the model repo. Held out: six whole ModelNet categories (car, airplane, bottle, cone, chair, lamp; 267 cases, 89 shapes) on the geometry axis, and the Reynolds band [20000, 50000] (375 cases) on the flow axis, checked in that order; a random 10 percent of the remainder is validation (198 cases); 1706 cases train.

Use

bash
huggingface-cli download BlidReview/steady-rans-generalization --local-dir ./data --repo-type dataset
# then, from the model repo:
python easy_eval.py --cache ./data/cache_v3 --weights ./weights

Graph field layout is defined in code/ezflow_v3/gnn/etl.py (features) and code/ezflow_v3/gnn/features.py (target transforms) of the model repo.

Provenance and license

All cases were generated by the authors with OpenFOAM. The external zero shot geometries are derived from public benchmark shapes (AhmedML, WindsorML, DrivAerML, DARPA SUBOFF, NASA-CRM, the OpenFOAM motorbike tutorial, CAARC); we release only our own simulations of them at our conditions, not the original benchmark data. License: CC BY-NC 4.0 (non-commercial, attribution). Surrogates trained on this data are approximations; do not use them as the sole basis for safety critical decisions.

BlidReview/steady-rans-generalization · Team Ai