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ZENODO
Dataset . 2025
License: CC BY SA
Data sources: ZENODO
ZENODO
Dataset . 2025
License: CC BY SA
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY SA
Data sources: Datacite
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VKI-LS59: a 2D internal aero CFD RANS dataset, under geometrical variations

Authors: Bucci, Michele Alessandro; Saverio, Luca; Casenave, Fabien;

VKI-LS59: a 2D internal aero CFD RANS dataset, under geometrical variations

Abstract

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"))

Keywords

AI, Physics, Machine learning, CFD, Geometrical variations, Simulation

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average