| --- |
| license: cc-by-nc-4.0 |
| tags: |
| - physics |
| - computational-fluid-dynamics |
| - rans |
| - graph-neural-networks |
| pretty_name: Steady-RANS cross-family generalization dataset |
| --- |
| |
| # 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](https://huggingface.co/BlidReview/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 |
| |
| | Folder | Size | What | |
| |---|--:|---| |
| | `cache_v3/` | ~13 GB | 2546 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 GB | The 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 GB | The 855 watertight training shape STLs, for re-meshing or extending the corpus. | |
| | `dataset_of/` | ~35 GB | Raw 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. |
|
|
