PLAID-datasets/Rotor37

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.bridges.huggingface_bridge import huggingface_dataset_to_plaid

hf_dataset = load_dataset("PLAID-datasets/Rotor37", split="all_samples")

dataset, problem = huggingface_dataset_to_plaid(hf_dataset, processes_number = 4)

ids_train = problem.get_split('train_1000')
ids_test  = problem.get_split('test')

sample_train_0 = dataset[ids_train[0]]
sample_test_0 = dataset[ids_test[0]]

# inputs
nodes = sample_train_0.get_nodes()
elements = sample_train_0.get_elements()

for fn in ['NormalsX', 'NormalsY', 'NormalsZ']:
    field = sample_train_0.get_field(fn)

for sn in ['Omega', 'P']:
    scalar = sample_train_0.get_scalar(sn)

# outputs
for fn in ['Density', 'Pressure', 'Temperature']:
    field = sample_train_0.get_field(fn)

for sn in ['Massflow', 'Compression_ratio', 'Efficiency']:
    scalar = sample_train_0.get_scalar(sn)

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

• Repository: Zenodo
• Paper: arxiv