---
license: cc-by-sa-4.0
size_categories:
- n<1K
task_categories:
- graph-ml
pretty_name: 2D external aero CFD RANS datasets, under geometrical variations
tags:
- physics learning
- geometry learning
configs:
- config_name: default
  data_files:
  - split: all_samples
    path: data/all_samples-*
dataset_info:
  description:
    legal:
      owner: Safran
      license: CC-by-SA 4.0
    data_production:
      type: simulation
      physics: 2D stationary RANS
      simulator: elsA
    split:
      train:
      - 0
      - 1
      - 2
      - 3
      - 4
      - 5
      - 6
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      test:
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    task: regression
    in_scalars_names: []
    out_scalars_names: []
    in_timeseries_names: []
    out_timeseries_names: []
    in_fields_names: []
    out_fields_names:
    - Mach
    - Pressure
    - Velocity-x
    - Velocity-y
    in_meshes_names:
    - /Base_2_2/Zone
    out_meshes_names: []
  features:
  - name: sample
    dtype: binary
  splits:
  - name: all_samples
    num_bytes: 1290091704
    num_examples: 400
  download_size: 813895818
  dataset_size: 1290091704
---

# Dataset Card
![image/png](https://i.ibb.co/fd5njm16/2-D-profile.png)

This dataset contains a single huggingface split, named 'all_samples'.

The samples contains a single huggingface feature, named called "sample".

Samples are instances of [plaid.containers.sample.Sample](https://plaid-lib.readthedocs.io/en/latest/autoapi/plaid/containers/sample/index.html#plaid.containers.sample.Sample).
Mesh objects included in samples follow the [CGNS](https://cgns.github.io/) standard, and can be converted in
[Muscat.Containers.Mesh.Mesh](https://muscat.readthedocs.io/en/latest/_source/Muscat.Containers.Mesh.html#Muscat.Containers.Mesh.Mesh).


Example of commands:
```python
import pickle
from datasets import load_dataset
from plaid.containers.sample import Sample

# Load the dataset
dataset = load_dataset("chanel/dataset", split="all_samples")

# Get the first sample of the first split
split_names = list(dataset.description["split"].keys())
ids_split_0 = dataset.description["split"][split_names[0]]
sample_0_split_0 = dataset[ids_split_0[0]]["sample"]
plaid_sample = Sample.model_validate(pickle.loads(sample_0_split_0))
print("type(plaid_sample) =", type(plaid_sample))

print("plaid_sample =", plaid_sample)

# Get a field from the sample
field_names = plaid_sample.get_field_names()
field = plaid_sample.get_field(field_names[0])
print("field_names[0] =", field_names[0])

print("field.shape =", field.shape)

# Get the mesh and convert it to Muscat
from Muscat.Bridges import CGNSBridge
CGNS_tree = plaid_sample.get_mesh()
mesh = CGNSBridge.CGNSToMesh(CGNS_tree)
print(mesh)
```

## Dataset Details

### Dataset Description


This dataset contains 2D external aero CFD RANS solutions, under geometrical variations (correspond to "large" in the Zenodo repository).

The variablity in the samples is the geometry (mesh). Outputs of interest are 4 fields. Each sample have been computed on large refined meshes, which have been cut close to the profil, and adapted (remeshed) using an anisotropic metric based on the output fields of interest.

Dataset created using the [PLAID](https://plaid-lib.readthedocs.io/) library and datamodel, version 0.1.
    
- **Language:** [PLAID](https://plaid-lib.readthedocs.io/)
- **License:** cc-by-sa-4.0
- **Owner:** Safran

### Dataset Sources

- **Repository:** [Zenodo](https://zenodo.org/records/15155119)