| --- |
| license: cc-by-sa-4.0 |
| task_categories: |
| - other |
| tags: |
| - CFD |
| - aerodynamics |
| - point-cloud |
| - pytorch |
| - fluid-dynamics |
| - regression |
| size_categories: |
| - 100K<n<1M |
| splits: |
| train: |
| num_examples: 387 |
| validation: |
| num_examples: 48 |
| test: |
| num_examples: 49 |
| --- |
| |
| # DrivaerML-PCTR |
|
|
| Processed version of [neashton/drivaerml](https://huggingface.co/datasets/neashton/drivaerml) |
| for use with the PCT-R (Point Cloud Transformer for Regression) model. |
|
|
| ## Processing |
|
|
| Each `boundary_i.vtp` file (~500MB, ~8.8M cells) was processed as follows: |
|
|
| 1. **Random sampling:** 100,000 cells per run, fixed seed `seed = run_id + 42`. |
| 2. **Cell centres:** Coordinates are CFD cell centres — exact locations where OpenFOAM computed field values. |
| 3. **Coordinate normalisation:** Local per-run, zero mean and unit std per axis. Stored in each file for unnormalisation. |
| 4. **Target fields:** Raw, no normalisation. All 500 runs share identical CFD boundary conditions. |
|
|
| ## Dataset Splits |
|
|
| The dataset is split **80/10/10** (train/val/test) using a random shuffle with `seed=42`. |
|
|
| | Split | Runs | |
| |------------|------| |
| | Train | 387 | |
| | Validation | 48 | |
| | Test | 49 | |
| | **Total** | **484** | |
|
|
| > **Missing runs:** `167, 211, 218, 221, 248, 282, 291, 295, 316, 325, 329, 364, 370, 376, 403, 473` |
|
|
| ## File Structure |
|
|
| ``` |
| train/ |
| run_i/ |
| boundary_i.pt |
| val/ |
| run_i/ |
| boundary_i.pt |
| test/ |
| run_i/ |
| boundary_i.pt |
| ``` |
|
|
| Each `.pt` file contains: |
|
|
| ```python |
| { |
| 'coords': torch.float32, # [100000, 3] locally normalised x, y, z |
| 'targets': torch.float32, # [100000, 4] raw CFD field values |
| 'sample_idx': torch.int64, # [100000] indices into original VTP |
| 'coords_mean': torch.float32, # [3] per-axis mean |
| 'coords_std': torch.float32, # [3] per-axis std |
| 'run_id': int, |
| } |
| ``` |
|
|
| ## Target Field Order |
|
|
| | Index | Field | Units | Description | |
| |-------|------------------------|-----------|----------------------------------------------------------| |
| | 0 | `CpMeanTrim` | [-] | Time-averaged static pressure coefficient | |
| | 1 | `pMeanTrim` | [m²/s²] | Time-averaged kinematic pressure | |
| | 2 | `pPrime2MeanTrim` | [m⁴/s⁴] | Time-averaged square of pressure fluctuations | |
| | 3 | `wallShearStressMeanTrim` | [m²/s²] | Magnitude of time-averaged wall shear stress vector | |
|
|
| ## Loading the Dataset |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| import torch, os |
| |
| # Download the full dataset |
| local_dir = snapshot_download(repo_id="Jrhoss/DrivaerML-PCTR", repo_type="dataset") |
| |
| # Load a single run from the training split |
| data = torch.load(os.path.join(local_dir, "train", "run_1", "boundary_1.pt")) |
| print(data['coords'].shape) # [100000, 3] |
| print(data['targets'].shape) # [100000, 4] |
| ``` |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{ashton2024drivaer, |
| title = {DrivAerML: High-Fidelity Computational Fluid Dynamics Dataset for Road-Car External Aerodynamics}, |
| year = {2024}, |
| journal = {arxiv.org}, |
| url = {https://arxiv.org/abs/2408.11969}, |
| author = {Ashton, N., Mockett, C., Fuchs, M., Fliessbach, L., Hetmann, H., Knacke, T., |
| Schonwald, N., Skaperdas, V., Fotiadis, G., Walle, A., Hupertz, B., and Maddix, D} |
| } |
| ``` |
|
|
| ## License |
|
|
| CC-BY-SA 4.0 (inherited from original dataset) |
|
|
