Analyzing Transient Turbulence in a Stenosed Carotid Artery by Proper Orthogonal Decomposition

Grinberg, Leopold; Yakhot, Alexander; Karniadakis, George Em

doi:10.1007/s10439-009-9769-z

Cited by 79 publications

(52 citation statements)

References 30 publications

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“…Hydrodynamic instability and transition into turbulence in arterial pulsatile flow has been studied extensively in the past (Nerem et al 1972;Steiger & Reulen 1986;Einav & Sokolov 1993;Blackburn & Sherwin 2007). More recently, high-resolution simulations were performed for stenosed carotids by Fischer et al (2007), Lee et al (2008) and Grinberg et al (2009). In all cases, turbulence or temporal fluctuations were observed in the decelerating systole or near the end of systole.…”

Section: Discussionmentioning

confidence: 99%

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Flow instability and wall shear stress variation in intracranial aneurysms

Baek

Jayaraman

Richardson

et al. 2009

J. R. Soc. Interface.

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We investigate the flow dynamics and oscillatory behaviour of wall shear stress (WSS) vectors in intracranial aneurysms using high resolution numerical simulations. We analyse three representative patient-specific internal carotid arteries laden with aneurysms of different characteristics: (i) a wide-necked saccular aneurysm, (ii) a narrower-necked saccular aneurysm, and (iii) a case with two adjacent saccular aneurysms. Our simulations show that the pulsatile flow in aneurysms can be subject to a hydrodynamic instability during the decelerating systolic phase resulting in a high-frequency oscillation in the range of 20-50 Hz, even when the blood flow rate in the parent vessel is as low as 150 and 250 ml min 21 for cases (iii) and (i), respectively. The flow returns to its original laminar pulsatile state near the end of diastole. When the aneurysmal flow becomes unstable, both the magnitude and the directions of WSS vectors fluctuate at the aforementioned high frequencies. In particular, the WSS vectors around the flow impingement region exhibit significant spatio-temporal changes in direction as well as in magnitude.

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Section: Discussionmentioning

confidence: 99%

“…CFD simulations in the carotid stenosis (Fischer et al 2007;Lee et al 2008;Grinberg et al 2009) were performed and intermittent turbulence during the decelerating systole was reported. However, the current work seems to be the first numerical study to report flow instability in aneurysms.…”

Section: Introductionmentioning

confidence: 99%

Flow instability and wall shear stress variation in intracranial aneurysms

Baek

Jayaraman

Richardson

et al. 2009

J. R. Soc. Interface.

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“…It is important to remind that, even when the number of degrees of freedom is drastically reduced (from about 10 5 to a few dozen in our case), the nonlinearities may compromise the reduction of computational time if they are not correctly handled. A number of works have addressed this issue, see, e.g., (Carlberg et al, 2011;Grepl et al, 2007;Baiges et al, 2012;Wang et al, 2012;Yano et al, 2012;Grinberg et al, 2009;Grinberg, 2012). With the current implementation of our method, we observed a reduction of 20% to 30% of the CPU time with respect to the full order model.…”

Section: Reduced Vs Full Simulationsmentioning

confidence: 75%

Group-wise construction of reduced models for understanding and characterization of pulmonary blood flows from medical images

Guibert

McLeod

Caiazzo

et al. 2014

Medical Image Analysis

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Abstract3D computational fluid dynamics (CFD) in patient-specific geometries provides complementary insights to clinical imaging, to better understand how heart disease, and the side effects of treating heart disease, affect and are affected by hemodynamics. This information can be useful in treatment planning for designing artificial devices that are subject to stress and pressure from blood flow. Yet, these simulations remain relatively costly within a clinical context. The aim of this work is to reduce the complexity of patient-specific simulations by combining image analysis, computational fluid dynamics and model order reduction techniques. The proposed method makes use of a reference geometry estimated as an average of the population, within an efficient statistical framework based on the currents representation of shapes. Snapshots of blood flow simulations performed in the reference geometry are used to build a POD (Proper Orthogonal Decomposition) basis, which can then be mapped on new patients to perform reduced order blood flow simulations with patient specific boundary conditions. This approach is applied to a data-set of 17 tetralogy of Fallot patients to simulate blood flow through the pulmonary artery under normal (healthy or synthetic valves with almost no backflow) and pathological (leaky or absent valve with backflow) conditions to better understand the impact of regurgitated blood on pressure and velocity at the outflow tracts.The model reduction approach is further tested by performing patient simulations under exercise and varying degrees of pathophysiological conditions based on reduction of reference solutions (rest and medium backflow conditions respectively).

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“…It was first introduced in [83] in the context of fluid dynamics as a method for discerning and analyzing coherent structures in experimental turbulent flows, and more recently in direct numerical simulations of turbulent flows in [53,126], where also the concept of space-time windowing of POD has been introduced, to identify turbulent effects in transitional flow that are highly localized both in space and time.…”

Section: Proper Orthogonal Decompositionmentioning

confidence: 99%

Model Order Reduction in Fluid Dynamics: Challenges and Perspectives

Lassila

Manzoni

Quarteroni

et al. 2014

Reduced Order Methods for Modeling and Computational Reduction

147

179

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This chapter reviews techniques of model reduction of fluid dynamics systems. Fluid systems are known to be difficult to reduce efficiently due to several reasons. First of all, they exhibit strong nonlinearities -which are mainly related either to nonlinear convection terms and/or some geometric variability -that often cannot be treated by simple linearization. Additional difficulties arise when attempting model reduction of unsteady flows, especially when long-term transient behavior needs to be accurately predicted using reduced order models and more complex features, such as turbulence or multiphysics phenomena, have to be taken into consideration. We first discuss some general principles that apply to many parametric model order reduction problems, then we apply them on steady and unsteady viscous flows modelled by the incompressible Navier-Stokes equations. We address questions of inf-sup stability, certification through error estimation, computational issues and -in the unsteady case -long-time stability of the reduced model. Moreover, we provide an extensive list of literature references.

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Analyzing Transient Turbulence in a Stenosed Carotid Artery by Proper Orthogonal Decomposition

Cited by 79 publications

References 30 publications

Flow instability and wall shear stress variation in intracranial aneurysms

Flow instability and wall shear stress variation in intracranial aneurysms

Group-wise construction of reduced models for understanding and characterization of pulmonary blood flows from medical images

Model Order Reduction in Fluid Dynamics: Challenges and Perspectives

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