The Potential of Machine Learning Methods for Separated Turbulent Flow Simulations: Classical Versus Dynamic Methods

Simple item page
dc.contributor.authorStefan Heinz
dc.date.accessioned2025-04-16T12:52:49Z
dc.date.available2025-04-16T12:52:49Z
dc.identifier.urihttps://tustorage.ulb.tu-darmstadt.de/handle/tustorage/18670
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.ddc530
dc.titleThe Potential of Machine Learning Methods for Separated Turbulent Flow Simulations: Classical Versus Dynamic Methods
dc.typesupplierxml
dspace.entity.typeDistribution
relation.isDatasetOfDistribution67a1dfe0-8635-4353-9e01-887fae02371b
relation.isDatasetOfDistribution.latestForDiscovery67a1dfe0-8635-4353-9e01-887fae02371b

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
fluids-09-12-00278.xml
Size:
288.9 KB
Format:
Extensible Markup Language
Download

Collections

2024