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: []
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
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 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 library and datamodel, version 0.1.
- Language: PLAID
- License: cc-by-sa-4.0
- Owner: Safran
Dataset Sources
- Repository: Zenodo