--- license: cc-by-4.0 pretty_name: Blade3DNO Structured CFD WebDataset size_categories: - 1K Liangrui Wei, Yuxin Zhao, Zhou Du, Quanyong Xu, and Feng Zhou. > [Blade3DNO: Geometry-consistent spectral operator learning for transonic 3D > compressor flows](https://doi.org/10.1016/j.ast.2026.112334). > *Aerospace Science and Technology*, 177:112334, 2026. ## Dataset summary The public release contains 1,828 samples: | Split | Archive | Samples | Design-ID manifest | |---|---:|---:|---| | Train | `train.tar` | 1,279 | `train_design_ids.txt` | | Validation | `val.tar` | 274 | `val_design_ids.txt` | | Test | `test.tar` | 275 | `test_design_ids.txt` | | **Total** | | **1,828** | | The split manifests define membership in each split. Archive member names such as `000795.npz` are packaging keys rather than design identifiers. Always use the `design_id` stored inside each NPZ sample, or join against `coefficients.csv`. > **Release note:** the article reports 1,827 samples in its experiment > snapshot, whereas this public package contains 1,828 samples. The archives > and manifests above are authoritative for this release. ## Repository contents ```text . ├── train.tar ├── val.tar ├── test.tar ├── coefficients.csv ├── train_design_ids.txt ├── val_design_ids.txt └── test_design_ids.txt ``` - The three TAR files are WebDataset archives. - Every TAR member is a compressed NumPy archive with an `.npz` extension. - `coefficients.csv` contains one `design_id` column and 30 raw scalar operating/boundary-condition columns. - The three text files contain one `design_id` per line. ## Sample schema Unless noted otherwise, arrays are stored as `float32`. | Field | Shape / type | Description | |---|---|---| | `design_id` | scalar string | Canonical identifier used by the manifests and `coefficients.csv`. | | `features` | scalar Python object containing a dictionary | Min-max-normalized operating and boundary-condition features. | | `coordinates` | `(256, 64, 32, 3)` | Physical-grid coordinates, with Cartesian coordinates in the last dimension. | | `coordinates_centered` | `(256, 64, 32, 3)` | Centered coordinate representation used during preprocessing. | | `density` | `(256, 64, 32)` | Density field. | | `velocity` | `(256, 64, 32)` | Velocity-magnitude field. | | `mach` | `(256, 64, 32)` | Mach-number field. | | `temperature` | `(256, 64, 32)` | Temperature field. | | `pressure` | `(256, 64, 32)` | Pressure field. | | `density_std` | `(256, 64, 32)` | Precomputed normalized density field; the `_std` suffix is retained for compatibility. | | `mach_std` | `(256, 64, 32)` | Precomputed normalized Mach-number field; the `_std` suffix is retained for compatibility. | | `temperature_std` | `(256, 64, 32)` | Precomputed normalized temperature field; the `_std` suffix is retained for compatibility. | | `pressure_std` | `(256, 64, 32)` | Precomputed normalized pressure field; the `_std` suffix is retained for compatibility. | | `sdf` | `(256, 64, 32)` | Signed-distance-function geometry representation. | | `normals_x`, `normals_y`, `normals_z` | `(256, 64, 32)` each | Cartesian components of the stored geometry-normal representation. | | `wall_mask` | `(256, 64, 32)` | Binary wall-region mask stored as `float32`. | | `metrics_dxi_dx`, `metrics_dxi_dy`, `metrics_dxi_dz` | `(256, 64, 32)` each | Stored derivatives of the logical coordinate `xi`. | | `metrics_deta_dx`, `metrics_deta_dy`, `metrics_deta_dz` | `(256, 64, 32)` each | Stored derivatives of the logical coordinate `eta`. | | `metrics_dzeta_dx`, `metrics_dzeta_dy`, `metrics_dzeta_dz` | `(256, 64, 32)` each | Stored derivatives of the logical coordinate `zeta`. | The directory name used during preprocessing contains `256_32_32`, but the serialized arrays have the verified shape `256 × 64 × 32`. Downstream code should use the array shapes stored in the NPZ files. ### Scalar coefficients The `features` dictionary contains normalized forms of the following 30 columns from `coefficients.csv`: ```text inlet_static_pressure inlet_static_temperature inlet_velocity_x inlet_velocity_y inlet_velocity_z inlet_velocity_magnitude inlet_mach_number inlet_total_pressure inlet_total_temperature inlet_dynamic_pressure inlet_density inlet_mass_flow_rate outlet_static_pressure outlet_static_temperature outlet_velocity_x outlet_velocity_y outlet_velocity_z outlet_velocity_magnitude outlet_mach_number outlet_total_pressure outlet_total_temperature outlet_dynamic_pressure outlet_density outlet_mass_flow_rate inlet_total_pressure_p01 inlet_static_pressure_p1 inlet_temperature_t1 velocity_y velocity_z outlet_static_pressure_p2 ``` Use `coefficients.csv` when the original, non-normalized scalar values are required. ## Loading the WebDataset Install the two lightweight reader dependencies: ```bash pip install numpy webdataset ``` The public archives can then be streamed directly: ```python import io import numpy as np import webdataset as wds url = ( "https://huggingface.co/datasets/lrwei/bladenet/" "resolve/main/train.tar" ) dataset = wds.WebDataset(url, shardshuffle=False) sample = next(iter(dataset)) with np.load(io.BytesIO(sample["npz"]), allow_pickle=True) as data: design_id = str(data["design_id"].item()) coordinates = data["coordinates"] # (256, 64, 32, 3) sdf = data["sdf"] # (256, 64, 32) pressure = data["pressure"] # (256, 64, 32) temperature = data["temperature"] # (256, 64, 32) density = data["density"] # (256, 64, 32) mach = data["mach"] # (256, 64, 32) features = data["features"].item() # dict[str, np.float32] print(design_id) print(features) ``` `allow_pickle=True` is required because the scalar `features` field stores a Python dictionary. Enable it only for dataset artifacts obtained from a trusted source. To read another split, replace `train.tar` with `val.tar` or `test.tar`. Because the archives are large, streaming or copying them to fast local storage is recommended. ## Data generation and processing The Blade3DNO study constructs a parametric multi-circular-arc compressor-blade design space and samples geometry and operating conditions before running steady RANS simulations. Solver-native multi-block flow fields are then mapped to globally continuous structured tensors suitable for convolutional models and neural operators. The packaged representation includes: - blade geometry encoded by coordinates, SDF values, and normal components; - operating and boundary conditions encoded by scalar features; - raw and normalized flow variables; - wall masks and logical-to-physical grid metric components. Refer to the paper for the CFD setup, geometric parameterization, filtering criteria, reconstruction method, and model experiments. ## Intended uses This release is intended for research on: - three-dimensional compressor-flow surrogate modeling; - neural operators and structured-grid learning; - geometry-aware scientific machine learning; - full-field prediction of pressure, temperature, density, and Mach number; - comparisons with 3D CNN and point-cloud baselines. The data are numerical RANS results rather than experimental measurements. Models trained on this release should be validated independently before use in safety-critical or production engineering workflows. ## Limitations - This is a processed, grid-aligned ML representation rather than the original solver-native multi-block archive. - Interpolation and reconstruction may smooth or alter local flow features. - The release contains one large TAR archive per split, so random access is less efficient than with many smaller shards. - Exact reproduction of paper results also depends on the preprocessing, training code, random seeds, and experiment configuration used in the study. ## Citation If you use this dataset, please cite the Blade3DNO paper: ```bibtex @article{wei2026blade3dno, title = {Blade3DNO: Geometry-consistent spectral operator learning for transonic 3D compressor flows}, author = {Wei, Liangrui and Zhao, Yuxin and Du, Zhou and Xu, Quanyong and Zhou, Feng}, journal = {Aerospace Science and Technology}, volume = {177}, pages = {112334}, year = {2026}, doi = {10.1016/j.ast.2026.112334}, url = {https://doi.org/10.1016/j.ast.2026.112334} } ``` When referring specifically to this packaged release, also include the repository URL: `https://huggingface.co/datasets/lrwei/bladenet`. ## License This dataset is released under the [Creative Commons Attribution 4.0 International license (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/). Redistribution and adaptation are permitted with appropriate attribution.