Datasets:
metadata
license: cc-by-4.0
task_categories:
- graph-ml
pretty_name: 3D RANS simulations of the rotor37
tags:
- physics learning
- geometry learning
dataset_info:
features:
- name: Base_2_3/Zone/CellData/Density
list: float32
- name: Base_2_3/Zone/CellData/NormalsX
list: float32
- name: Base_2_3/Zone/CellData/NormalsY
list: float32
- name: Base_2_3/Zone/CellData/NormalsZ
list: float32
- name: Base_2_3/Zone/CellData/Pressure
list: float32
- name: Base_2_3/Zone/CellData/Temperature
list: float32
- name: Base_2_3/Zone/GridCoordinates/CoordinateX
list: float32
- name: Base_2_3/Zone/GridCoordinates/CoordinateY
list: float32
- name: Base_2_3/Zone/GridCoordinates/CoordinateZ
list: float32
- name: Base_2_3/Zone/PointData/Density
list: float32
- name: Base_2_3/Zone/PointData/NormalsX
list: float32
- name: Base_2_3/Zone/PointData/NormalsY
list: float32
- name: Base_2_3/Zone/PointData/NormalsZ
list: float32
- name: Base_2_3/Zone/PointData/Pressure
list: float32
- name: Base_2_3/Zone/PointData/Temperature
list: float32
- name: Global/Compression_ratio
list: float32
- name: Global/Efficiency
list: float32
- name: Global/Massflow
list: float32
- name: Global/Omega
list: float32
- name: Global/P
list: float32
splits:
- name: train
num_bytes: 1783864000
num_examples: 1000
- name: test
num_bytes: 214123400
num_examples: 200
download_size: 2203709865
dataset_size: 1997987400
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
data_production:
physics: 3D CFD RANS compressor blade
type: simulation
legal:
license: CC-BY-SA
owner: Safran
plaid:
version: 0.1.10.dev114+gcbd3fd46f.d20251014
Example of commands:
from datasets import load_dataset
from plaid.bridges import huggingface_bridge
repo_id = "chanel/dataset"
pb_def_name = "pb_def_name" #`pb_def_name` is to choose from the repo `problem_definitions` folder
# Load the dataset
hf_datasetdict = load_dataset(repo_id)
# Load addition required data
flat_cst, key_mappings = huggingface_bridge.load_tree_struct_from_hub(repo_id)
pb_def = huggingface_bridge.load_problem_definition_from_hub(repo_id, pb_def_name)
# Efficient reconstruction of plaid samples
for split_name, hf_dataset in hf_datasetdict.items():
for i in range(len(hf_dataset)):
sample = huggingface_bridge.to_plaid_sample(
hf_dataset,
i,
flat_cst[split_name],
key_mappings["cgns_types"],
)
# Extract input and output features from samples:
for t in sample.get_all_mesh_times():
for path in pb_def.get_in_features_identifiers():
sample.get_feature_by_path(path=path, time=t)
for path in pb_def.get_out_features_identifiers():
sample.get_feature_by_path(path=path, time=t)
This dataset was generated in PLAID, we refer to this documentation for additional details on how to extract data from sample objects.