Datasets:
The dataset viewer is not available for this split.
Error code: TooBigContentError
Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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:
from datasets import load_dataset
from plaid.containers.sample import Sample
import pickle
# Load the dataset
hf_dataset = load_dataset("PLAID-datasets/Rotor37", split="all_samples")
# Get split ids
ids_train = hf_dataset.description["split"]['train_1000']
ids_test = hf_dataset.description["split"]['test']
# Get inputs/outputs names
in_scalars_names = hf_dataset.description["in_scalars_names"]
out_fields_names = hf_dataset.description["out_fields_names"]
# Get samples
sample = Sample.model_validate(pickle.loads(hf_dataset[ids_train[0]]["sample"]))
sample_2 = Sample.model_validate(pickle.loads(hf_dataset[ids_test[0]]["sample"]))
# Examples data retrievals
# inputs
nodes = sample.get_nodes()
elements = sample.get_elements()
for fn in ['NormalsX', 'NormalsY', 'NormalsZ']:
field = sample.get_field(fn)
for sn in ['Omega', 'P']:
scalar = sample.get_scalar(sn)
# outputs
for fn in ['Density', 'Pressure', 'Temperature']:
field = sample.get_field(fn)
for sn in ['Massflow', 'Compression_ratio', 'Efficiency']:
scalar = sample.get_scalar(sn)
# Get the mesh and convert it to Muscat
from Muscat.Bridges import CGNSBridge
CGNS_tree = sample.get_mesh()
mesh = CGNSBridge.CGNSToMesh(CGNS_tree)
print(mesh)
Dataset Details
Dataset Description
This dataset contains 3D CFD RANS solutions, under geometrical variations of a compressor blade.
A description is provided in the MMGP paper Sections 4.1 and Appendix A.1.
The variablity in the samples are 2 input scalars and the geometry (mesh). Outputs of interest are 3 scalars and 3 fields.
Eight nested training sets of sizes 8 to 1000 are provided, with complete input-output data. A testing set of size 200 is provided, for which outputs are not provided.
Dataset created using the PLAID library and datamodel, version: 0.1.
- Language: PLAID
- License: cc-by-sa-4.0
- Owner: Safran
Dataset Sources
- Downloads last month
- 120