
This dataset contains 2D internal aero CFD RANS solutions, under geometrical variations, based on the VKI-LS59 blade. The files format is PLAID, see the plaid documentation. The variablity in the samples are 2 input scalars and the geometry (mesh) - the signed distance function is also provided and can be used as an input field. Outputs of interest are 7 fields (6 2D-fields and 1 1D-field) and 6 scalars. Eight nested training sets of sizes 8 to 671 are provided, with complete input-output data. A testing set of size 168 is provided, for which outputs are not provided. Tips to access the data: After decompressing the downloaded file: from plaid.containers.dataset import Datasetfrom plaid.problem_definition import ProblemDefinition dataset = Dataset()problem = ProblemDefinition() problem._load_from_dir_(os.path.join(/path/to/data,'problem_definition'))dataset._load_from_dir_(os.path.join(/path/to/data,'dataset'), verbose = True) print("problem =", problem)print("dataset =", dataset) sample = dataset[0]print("sample =", sample) for fn in ["sdf", "ro", "rou", "rov", "roe", "nut", "mach"]: print(f"{fn} =", sample.get_field(fn, base_name="Base_2_2"))print("M_iso =", sample.get_field("M_iso", base_name="Base_1_2"))for sn in sample.get_scalar_names(): print(f"{sn} =", sample.get_scalar(sn)) print("nodes 2D (flow) =", sample.get_nodes(base_name="Base_2_2"))print("nodes 1D (blade surface) =", sample.get_nodes(base_name="Base_1_2"))print("elements 2D (flow) =", sample.get_elements(base_name="Base_2_2"))print("elements 1D (blade surface) =", sample.get_elements(base_name="Base_1_2"))print("nodal_tags 2D (flow) =", sample.get_nodal_tags(base_name="Base_2_2"))
AI, Physics, Machine learning, CFD, Geometrical variations, Simulation
AI, Physics, Machine learning, CFD, Geometrical variations, Simulation
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