Wall-PIV as a near wall flow validation tool for CFD: Application in a pathologic vessel enlargement (aneurysm) View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2009-09

AUTHORS

L. Goubergrits, S. Weber, Ch. Petz, H-Ch. Hege, A. Spuler, J. Poethke, A. Berthe, U. Kertzscher

ABSTRACT

Flow visualization of a near wall flow is of great importance in the field of biofluid mechanics in general and for studies of pathologic vessel enlargements (aneurysms) particularly. Wall shear stress (WSS) is one of the important hemodynamic parameters implicated in aneurysm growth and rupture. The WSS distributions in anatomically realistic vessel models are normally investigated by computational fluid dynamics (CFD). However, the results of CFD flow studies should be validated. The recently proposed Wall-PIV method was first applied in an enlarged transparent model of a cerebri anterior artery terminal aneurysm made of silicon rubber. This new method, called Wall-PIV, allows the investigation of a flow adjacent to transparent surfaces with two finite radii of curvature (vaulted walls). Using an optical method which allows the observation of particles up to a predefined depth enables the visualization solely of the boundary layer flow. This is accomplished by adding a specific molecular dye to the fluid which absorbs the monochromatic light used to illuminate the region of observation. The results of the Wall-PIV flow visualization were qualitatively compared with the results of the CFD flow simulation under steady flow conditions. The CFD study was performed using the program FLUENT®. The results of the CFD simulation were visualized using the line integral convolution (LIC) method with a visualization tool from AMIRA®. The comparison found a very good agreement between experimental and numerical results. More... »

PAGES

241-250

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf03181862

DOI

http://dx.doi.org/10.1007/bf03181862

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1025039386


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