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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.accessioned2023-03-09T10:26:24Z
dc.date.available2023-03-09T10:26:24Z
dc.identifier.urihttps://daks.uni-kassel.de/handle/123456789/50
dc.identifier.urihttps://doi.org/10.48662/daks-17
dc.descriptionThis dataset contains 10,000 fluid flow and heat transfer simulations in 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 used to generate the data are published as part of this work. IMPORTANT: In case you use the data please cite our corresponding article mentioned belowde_DE
dc.description.sponsorship{"funderName":"Andere","awardTitle":"BMWK - Bundesministerium für Wirtschaft und Klimaschutz"}de_DE
dc.language.isoengde_DE
dc.relation{"relationType":"IsSupplementTo","relatedIdentifierType":"DOI","id_value":"https://doi.org/10.1016/j.dib.2023.109477"}
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.rightsCreative Commons Attribution-NonCommercial 4.0
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.subjectMachine Learningde_DE
dc.subjectDeep Learningde_DE
dc.subject.classification404-03 Strömungsmechanikde_DE
dc.subject.ddc620
dc.titleDataset of a parameterized U-bend flow for Deep Learning Applicationsde_DE
dc.typeDatasetde_DE
dc.typeImagede_DE
dc.typeModelde_DE
dc.typeWorkflowde_DE
local.metadata.publicyes
local.ka.facultyFB16:Elektrotechnik/Informatikde_DE
local.ka.departmentIntelligent Embedded Systemsde_DE
dc.description.version1.0de_DE


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Creative Commons Attribution-NonCommercial 4.0
Except where otherwise noted, this item's license is described as Creative Commons Attribution-NonCommercial 4.0