lrwei/bladenet

Blade3DNO Structured CFD WebDataset

This repository provides an ML-ready WebDataset release associated with Blade3DNO, a geometry-consistent spectral operator learning framework for three-dimensional transonic compressor flows.

The data contain reconstructed structured tensors derived from steady RANS solutions of multi-circular-arc compressor blade configurations under different operating conditions. Each sample combines geometry, operating-condition features, grid metrics, and three-dimensional flow fields on a logical grid of 256 × 64 × 32.

The paper associated with this release is:

Liangrui Wei, Yuxin Zhao, Zhou Du, Quanyong Xu, and Feng Zhou. Blade3DNO: Geometry-consistent spectral operator learning for transonic 3D compressor flows. 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

.
├── 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:

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:

pip install numpy webdataset

The public archives can then be streamed directly:

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:

@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). Redistribution and adaptation are permitted with appropriate attribution.