Topological flow structures and stir mixing for steady flow in a peripheral bypass graft with uncertainty

Gambaruto, Alberto; Moura, Alexandra; Sequeira, Adélia

doi:10.1002/cnm.1393

Cited by 13 publications

(9 citation statements)

References 53 publications

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“…It is evident that the average geometries exhibit reduced pressure drops compared to the direct average of the cases; due to the simplified form of the average and not alteration in the nasal valve that are well preserved (as shown in Table 1). The mixing is given as % of the maximum achievable mixing ( [36]). The average wall shear stress is calculated for the middle cavity region (see Figure 1).…”

Section: Average Geometrymentioning

confidence: 99%

“…An entropic measure of mixing, discussed in greater depth in [36] and references therein, is used to quantify these differences. In this work this measure is obtained by seeding approximately 40,000 equi-spaced passive particles, with an associated species attribute based on their position at the naris inlet that is given by the distance from the wall.…”

Section: Flow Simulationmentioning

confidence: 99%

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Decomposition and Description of the Nasal Cavity Form

2011

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Section: Average Geometrymentioning

confidence: 99%

Section: Flow Simulationmentioning

confidence: 99%

Decomposition and Description of the Nasal Cavity Form

2011

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“…The non-planarity and tortuousity of vessels play a determining role in the human arterial system, resulting in a strong influence of the local vessel topology on the flow field [1,2,3,4,5,6]. An abnormal flow field, usually described as complex and disturbed, is often related to the diseased states.…”

Section: Introductionmentioning

confidence: 99%

Flow structures in cerebral aneurysms

Gambaruto

João

2012

Computers & Fluids

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General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/pure/about/ebr-terms AbstractMechanical properties of blood flow are commonly correlated to a wide range of cardiovascular diseases. In this work means to describe and characterise the flow field in the free-slip and no-slip domains are discussed in the context of cerebral aneurysms, reconstructed from in-vivo medical imaging. The approaches rely on a Taylor series expansion of the velocity field to first order terms that leads to a system of ODEs, the solution to which locally describes the motion of the flow. On performing the expansion on the vessel wall using the wall shear stress, the critical points can be identified and the near-wall flow field parallel to the wall can be concisely described and visualised. Furthermore the near-wall expansion can be expressed in terms of relative motion, and the near-wall convective transport normal and parallel to the wall can be accurately derived on the no-slip domain. Together, these approaches give a viable and robust means to identify and describe fluid mechanic phenomena both qualitatively and quantitatively, leading to feasible practical use in biomedical applications. From analysis of steady and unsteady flow simulations in two anatomically accurate cerebral saccular aneurysm cases, a set of measures can be readily obtained at all time intervals, including the impingement region, separation lines, convective transport near the wall and vortex core lines or structures, which have all been related to diseased states. Other fluid mechanic measures are also discussed in order to give further detail and insight during postprocessing, and may play an important role in the growth and rupture of the aneurysm.

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“…Equation 17 has also been used in quantifying mixing of bypass grafts with an Eulerian approach (advection-diffusion equation) [50]. …”

Section: Mixingmentioning

confidence: 99%

Lagrangian Postprocessing of Computational Hemodynamics

Shadden

Arzani

2014

Ann Biomed Eng

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Recent advances in imaging, modeling and computing have rapidly expanded our capabilities to model hemodynamics in the large vessels (heart, arteries and veins). This data encodes a wealth of information that is often under-utilized. Modeling (and measuring) blood flow in the large vessels typically amounts to solving for the time-varying velocity field in a region of interest. Flow in the heart and larger arteries is often complex, and velocity field data provides a starting point for investigating the hemodynamics. This data can be used to perform Lagrangian particle tracking, and other Lagrangian-based postprocessing. As described herein, Lagrangian methods are necessary to understand inherently transient hemodynamic conditions from the fluid mechanics perspective, and to properly understand the biomechanical factors that lead to acute and gradual changes of vascular function and health. The goal of the present paper is to review Lagrangian methods that have been used in post-processing velocity data of cardiovascular flows.

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Topological flow structures and stir mixing for steady flow in a peripheral bypass graft with uncertainty

Cited by 13 publications

References 53 publications

Decomposition and Description of the Nasal Cavity Form

Decomposition and Description of the Nasal Cavity Form

Flow structures in cerebral aneurysms

Lagrangian Postprocessing of Computational Hemodynamics

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