Datasets:
metadata
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:
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test:
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task: regression
in_scalars_names:
- camb
- rot
- t_le
- t_mx
- t_te
- u_mx
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: 328720977
num_examples: 399
download_size: 225335734
dataset_size: 328720977
Dataset Card
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. Mesh objects included in samples follow the CGNS standard, and can be converted in Muscat.Containers.Mesh.Mesh.
Example of commands:
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 are 6 input scalars and the geometry (mesh). Outputs of interest are 4 fields. Samples 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 library and datamodel, version 0.1.
- Language: PLAID
- License: cc-by-sa-4.0
- Owner: Safran
Dataset Sources
- Repository: Zenodo