Dataset of a parameterized U-bend flow for Deep Learning Applications
| dc.contributor | {"last":"Wünsch","first":"Olaf Prof. Dr.-Ing.","role":"ProjectLeader","affiliation":"Fluid Dynamics, Universität Kassel","id":"orcid","id_value":"0000-0002-7295-8862"} | |
| dc.contributor | {"last":"Sick","first":"Bernhard Prof. Dr. rer. nat.","role":"ProjectLeader","affiliation":"Intelligent Embedded Systems, Universität Kassel","id":"orcid","id_value":"0000-0001-9467-656X"} | |
| dc.contributor.author | {"last":"Decke","first":"Jens","affiliation":"Intelligent Embedded Systems","id":"orcid","id_value":"0000-0002-7893-1564"} | |
| dc.date.accessioned | 2023-03-09T10:26:24Z | |
| dc.date.available | 2023-03-09T10:26:24Z | |
| dc.identifier.uri | https://daks.uni-kassel.de/handle/123456789/50 | |
| dc.identifier.uri | https://doi.org/10.48662/daks-17 | |
| dc.description | This dataset contains 10,000 U-bend shapes. Each of them is described by 28 design parameters, which are processed with the help of Computational Fluid Dynamics methods. The dataset provides a comprehensive benchmark for investigating various problems and methods from the field of design optimization. For these investigations supervised, semi-supervised and unsupervised deep learning approaches can be employed. One unique feature of this dataset is that each shape can be represented by three distinct data types including design parameter and objective combinations. Five different resolutions of 2D images from the geometry and the solution variables of the numerical simulation. As well as a representation using the cell values of the numerical mesh. This third representation enables considering the specific data structure of numerical simulations for deep learning approaches. The source code and the container which was used to generate the data can be found in the git and docker repositorys mentioned below. IMPORTANT: In case you use the data please cite our corresponding article mentioned below | de_DE |
| dc.description.sponsorship | {"funderName":"Andere","awardTitle":"BMWK - Bundesministerium für Wirtschaft und Klimaschutz"} | de_DE |
| dc.language.iso | eng | de_DE |
| dc.relation | {"relationType":"IsSupplementTo","relatedIdentifierType":"arXiv","id_value":"TODO Link einfügen sobald vorhanden!"} | |
| dc.relation | {"relationType":"IsSupplementedBy","relatedIdentifierType":"URL","id_value":"https://git.ies.uni-kassel.de/jdecke/dataset-of-a-parameterized-u-bend-flow-for-deep-learning-applications"} | |
| dc.relation | {"relationType":"IsSupplementedBy","relatedIdentifierType":"URL","id_value":"https://hub.docker.com/r/jdecke/foamy"} | |
| dc.rights | Creative Commons Attribution-NonCommercial 4.0 | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc/4.0/ | |
| dc.subject | Machine Learning | de_DE |
| dc.subject | Deep Learning | de_DE |
| dc.subject.classification | 404-03 Strömungsmechanik | de_DE |
| dc.subject.ddc | 620 | |
| dc.title | Dataset of a parameterized U-bend flow for Deep Learning Applications | de_DE |
| dc.type | Dataset | de_DE |
| dc.type | Image | de_DE |
| dc.type | Model | de_DE |
| dc.type | Workflow | de_DE |
| local.metadata.public | yes | |
| local.ka.faculty | FB16:Elektrotechnik/Informatik | de_DE |
| local.ka.department | Intelligent Embedded Systems | de_DE |
| dc.description.version | 1.0 | de_DE |
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