Immersed boundary method predictions of shear stresses for different flow topologies occuring in cerebral aneurysms

Julia Olegivna Mikhal, J.C.F Pereira (Editor), A. Sequeira (Editor), D.J. Lopez Penha, Cornelis H. Slump, J.M.C. Pereira (Editor), J. Janela (Editor), Bernardus J. Geurts, L. Borges (Editor)

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    Abstract

    A volume-penalizing immersed boundary method is presented that facilitates the computation of incompressible fluid flow in complex flow domains. We apply this method to simulate the flow in cerebral aneurysms, and focus on the accuracy with which the flow field and the corresponding shear stress field are computed. The method is applied to laminar, incompressible flow in curved cylindrical vessels and in a model aneurysm. The time-dependent shear stress distributions over the vessel walls are visualized and interpreted in terms of the flow fields that develop. We compute shear stress levels at two different Reynolds numbers, corresponding to a steady and an unsteady flow. In the latter situation strong fluctuations in the shear stress are observed, that may be connected to raised risk-levels of aneurysm rupture.
    Original languageEnglish
    Title of host publicationProceedings of the V European Conference on Computational Fluid Dynamics, ECCOMAS CFD 2010
    EditorsJ.C.F. Pereira, A. Sequeira, J.M.C. Pereira
    Place of PublicationLisbon
    PublisherTechnical University of Lisbon
    Number of pages12
    ISBN (Print)978-989-96778-1-4
    Publication statusPublished - Jun 2010
    Event5th European Conference on Computational Fluid Dynamics, ECCOMAS ECFD 2010 - Lisbon, Portugal
    Duration: 14 Jun 201017 Jun 2010
    Conference number: 5

    Conference

    Conference5th European Conference on Computational Fluid Dynamics, ECCOMAS ECFD 2010
    Abbreviated titleECCOMAS ECFD 2010
    Country/TerritoryPortugal
    CityLisbon
    Period14/06/1017/06/10

    Keywords

    • Volume penalization
    • Shear stress
    • Immersed boundary method
    • Cerebral aneurysm
    • Incompressible flow

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