Tomographic particle image velocimetry investigation of the flow in a modeled human carotid artery bifurcation

Buchmann, Nicolas; Atkinson, Callum; Jeremy, Mark; Soria, Julio

doi:10.1007/s00348-011-1042-1

Cited by 64 publications

(44 citation statements)

References 43 publications

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“…Flexibility makes silicone rubbers particularly useful for compliant models of flows through flexible structures or membrane-like tissues, e.g., in RIM models for bloodflow experiments and, as a result, have been frequently employed in such systems (Duncan et al 1990;Perktold et al 1997;Bale-Glickman et al 2003;Burgmann et al 2009;Shuib et al 2010;Yousif et al 2010;Gülan et al 2012;Pielhop et al 2012;Geoghegan et al 2012;Im et al 2013;Kefayati and Poepping 2013). Sylgard 184, manufactured by Dow Corning, has been identified as a silicone rubber of particularly interest (Duncan et al 1990;Perktold et al 1997;Hopkins et al 2000;Yousif et al 2010;Shuib et al 2010;Buchmann et al 2010Buchmann et al , 2011Geoghegan et al 2012 andKefayati andPoepping 2013). Although a common choice, Hopkins et al (2000) cautioned that the effects of mixing and curing on Sylgard 184 can result in RI variations between models, and the care must, therefore, be taken in matching liquid RIs to individual models.…”

Section: Silicone and Urethane Rubbersmentioning

confidence: 99%

A review of solid–fluid selection options for optical-based measurements in single-phase liquid, two-phase liquid–liquid and multiphase solid–liquid flows

2017

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Experimental techniques based on optical measurement principles have experienced significant growth in recent decades. They are able to provide detailed information with high-spatiotemporal resolution on important scalar (e.g., temperature, concentration, and phase) and vector (e.g., velocity) fields in single-phase or multiphase flows, as well as interfacial characteristics in the latter, which has been instrumental to step-changes in our fundamental understanding of these flows, and the development and validation of advanced models with ever-improving predictive accuracy and reliability. Relevant techniques rely upon well-established optical methods such as direct photography, laser-induced fluorescence, laser Doppler velocimetry/phase Doppler anemometry, particle image/tracking velocimetry, and variants thereof. The accuracy of the resulting data depends on numerous factors including, importantly, the refractive indices of the solids and liquids used. The best results are obtained when the observational materials have closely matched refractive indices, including test-section walls, liquid phases, and any suspended particles. This paper reviews solid–liquid and solid–liquid–liquid refractive-index-matched systems employed in different fields, e.g., multiphase flows, turbomachinery, bio-fluid flows, with an emphasis on liquid–liquid systems. The refractive indices of various aqueous and organic phases found in the literature span the range 1.330–1.620 and 1.251–1.637, respectively, allowing the identification of appropriate combinations to match selected transparent or translucent plastics/polymers, glasses, or custom materials in single-phase liquid or multiphase liquid–liquid flow systems. In addition, the refractive indices of fluids can be further tuned with the use of additives, which also allows for the matching of important flow similarity parameters such as density and viscosity

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Section: Silicone and Urethane Rubbersmentioning

confidence: 99%

A review of solid–fluid selection options for optical-based measurements in single-phase liquid, two-phase liquid–liquid and multiphase solid–liquid flows

2017

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“…This is evident from the PTV analysis that does not show any motion away from the wall. The theoretical flow profile in a rectangular channel is given by a series expansion of trigonometric functions (Bruus 2008). To estimate the wall gradient, the profiles were fitted by a sixth-order polynomial resulting in a wallshear stress of s w = 0.0131 ± 0.0002 N/m 2 for singlepixel ensemble-correlation and s w = 0.0147 ± 0.0002 N/m 2 for particle tracking, while the theoretical value is s w = 0.0158 ± 0.0001 N/m 2 , where the uncertainty is estimated from the 95 % confidence level of the fit parameters.…”

Section: Microscopic Dpivmentioning

confidence: 99%

“…In biological flows for instance, the determination of the flow field around cells is very important as the behavior of cells can often be directly related to different magnitudes of the wall-shear stress. In medical flows, the wall-shear stress also plays a major role for the mechanical response of endothelial cells responsible for cardiovascular diseases (Buchmann et al 2011;Rossi et al 2009) or for the deposition of aerosols in lungs (Theunissen et al 2006). In the field of process engineering, the manipulation of particles or droplets (Burdick et al 2001) and the fluid droplet/bubble interaction (Champagne et al 2010) requires a deep understanding of the near-wall flow phenomena.…”

Section: Introductionmentioning

confidence: 99%

On the uncertainty of digital PIV and PTV near walls

2012

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The reliable measurement of mean flow properties near walls and interfaces between different fluids or fluid and gas phases is a very important task, as well as a challenging problem, in many fields of science and technology. Due to the decreasing concentration of tracer particles and the strong flow gradients, these velocity measurements are usually biased. To investigate the reason and the effect of the bias errors systematically, a detailed theoretical analysis was performed using window-correlation, singe-pixel ensemble-correlation and particle tracking evaluation methods. The different findings were validated experimentally for microscopic, long-range microscopic and large field imaging conditions. It is shown that for constant flow gradients and homogeneous particle image density, the bias errors are usually averaged out. This legitimates the use of these techniques far away from walls or interfaces. However, for inhomogeneous seeding and/or nonconstant flow gradients, only PTV image analysis techniques give reliable results. This implies that for wall distances below half an interrogation window dimension, the singe-pixel ensemble-correlation or PTV evaluation should always be applied. For distances smaller than the particle image diameter, only PTV yields reliable results.

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“…Particle Image Velocimetry (PIV) provides good temporal and spatial velocity distribution over the entire flow domain for quantification and visualisation. It has recently been used for studies in idealised and physiologically realistic CA models (18), (28), (29) and also in other physiological flow applications (12), (30), (31) . In most experimental approaches including PIV, however, important haemodynamic parameters such as fully three-dimensional velocity maps and WSS gradients are not yet readily available.…”

Section: Introductionmentioning

confidence: 99%

Particle Image Velocimetry (PIV) and Computational Fluid Dynamics (CFD) Modelling of Carotid Artery Haemodynamics under Steady Flow: A Validation Study

Buchmann

Yamamoto

Jermy

et al. 2010

JBSE

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Particle image velocimetry (PIV) and computational fluid dynamics (CFD) modelling of blood flow through a carotid artery bifurcation has been carried out in order to assess the role of haemodynamics in atherosclerosis and validate a novel wall shear stress (WSS) measurement method via detailed quantitative data analysis. Velocity and WSS data, obtained by PIV was compared against CFD-predicted data obtained for the same geometry and boundary conditions. The results demonstrate that both methods are capable of capturing essential flow characteristics, and are in good agreement. The axial velocity data clearly show the important flow features such as skewed velocity profiles and a low-momentum region near the sinus outer wall, although with slight errors between the two techniques in the order of 6.1-13.7%. The secondary velocity plots and three-dimensional particle traces reveal complex flow features within the sinus, such as Dean vortices and helicoidal flow. WSS data obtained by both techniques reveal the presence of a low-momentum and reversed-flow regions, and show agreement with errors in the order of 1% in the common carotid artery and 0.9-9.8% in the sinus. The error sources in both the experimental and numerical methodology are discussed and recommendations for future applications are made.

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Tomographic particle image velocimetry investigation of the flow in a modeled human carotid artery bifurcation

Cited by 64 publications

References 43 publications

A review of solid–fluid selection options for optical-based measurements in single-phase liquid, two-phase liquid–liquid and multiphase solid–liquid flows

A review of solid–fluid selection options for optical-based measurements in single-phase liquid, two-phase liquid–liquid and multiphase solid–liquid flows

On the uncertainty of digital PIV and PTV near walls

Particle Image Velocimetry (PIV) and Computational Fluid Dynamics (CFD) Modelling of Carotid Artery Haemodynamics under Steady Flow: A Validation Study

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