dc.contributor.author
Katz, Sarah
dc.date.accessioned
2025-04-14T13:25:18Z
dc.date.available
2025-04-14T13:25:18Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/46634
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-46348
dc.description.abstract
Turbulence is an important feature of blood flow near the heart. A discretization of moderate resolution must therefore model the interactions between "coarse" resolvable flow behaviors and "fine" subgrid-scale features. We compare several choices of turbulence model in terms of their impact on clinically interesting flow statistics. We also present an investigation of a novel method for augmenting classical turbulence models with machine learning algorithms trained on coarse-fine simulation pairs.
en
dc.format.extent
107 Seiten
dc.rights.uri
https://creativecommons.org/licenses/by-nc/4.0/
dc.subject
Hemodynamics
en
dc.subject
Fluid dynamics
en
dc.subject.ddc
500 Natural sciences and mathematics::510 Mathematics::518 Numerical analysis
dc.title
Turbulence modeling and blood flow: impacts and alternatives
dc.contributor.gender
female
dc.contributor.firstReferee
John, Volker
dc.contributor.furtherReferee
Richter, Thomas
dc.date.accepted
2025-02-11
dc.identifier.urn
urn:nbn:de:kobv:188-refubium-46634-1
refubium.affiliation
Mathematik und Informatik
dcterms.accessRights.dnb
free
dcterms.accessRights.openaire
open access
dcterms.accessRights.proquest
accept
