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Secondary flow structures are vortical flow patterns that occur in internal flow geometries with curvatures such as curved pipes and channels. These vortical structures arise due to the combined action of centrifugal forces, adverse pressure gradients and inflow characteristics. In general, secondary flow structures appear in planar cross-sections of curved pipes as symmetric Dean-type vortices under steady inflow and, symmetric Dean- and Lyne-type vortices under oscillatory inflow conditions 1 - 3. Secondary flow morphologies are greatly affected by pulsatility and multiple harmonics of pulsatile, physiological inflow conditions. These structures acquire markedly different size-strength-shape characteristics compared to non-physiological (steady and oscillatory) flows 1 - 6. Atherosclerotic lesion development in arteries is affected by the existence of high frequency shear oscillations in regions experiencing low mean shear 27, 28. Secondary flow structures may influence the progress of diseases such as atherosclerosis and possibly, mediate the endothelial response due to pulsatile blood flow by altering wall shear stresses and exposure times of blood-borne particles.
A common treatment to atherosclerosis, a complication resulting in narrowing of arteries by obstructive lesions, is the implantation of stents. Stent fractures are structural failures of implanted stents that lead to further medical complications such as in-stent restenosis (ISR), stent thrombosis and aneurysm formation 9 - 13. Stent fractures have been categorized into various failure "Types I-to-IV", wherein "Type IV" characterizes the highest clinical severity and is defined as the complete transverse fracture of stent struts along with linear displacements of the stent fragments 14. The protocol presented in this study describes an experimental method of visualization of secondary flow structures downstream of an idealized "Type IV" stent fracture in a curved artery model.
The suggested protocol has the following four essential features:
Design and fabrication of laboratory-scale stent models: Geometric description of stents can be associated with a set of self-expandable spirals (springs or helices) intertwined using Nitinol (an alloy of nickel and titanium) wires 29. The length of the stent and its strut diameter depend on the length scale of arterial lesions encountered during clinical implantation 5. Parametric variation of strut diameter and the rising of the winding (or pitch) leads to stents of various geometric configurations. A summary of stent design parameters chosen for 3D printing are presented in Table 1.
Preparation of a blood analog working fluid matched with kinematic viscosity of blood and refractive index of the test section: Optical access to the curved artery test section is required in order to make non-invasive velocity measurements. Accordingly, a Newtonian blood-mimicking working fluid with the refractive index of the vascular model and ideally, a dynamic viscosity, matching human blood is used to obtain accurate blood flow measurements 16 - 18, 30. The working fluid used in this study was reported by Deutsch et al. (2006), that comprised of 79% saturated aqueous sodium iodide (NaI), 20% pure glycerol, and 1% water (by volume)16.
Experimental arrangement for the detection of coherent secondary flow structures using a two-component, two-dimensional particle image velocimetry (2C-2D PIV): Experiments were designed to acquire phase-averaged secondary flow velocity data at various planar cross-sectional locations downstream of a combination of straight and curved stent sections embodying an idealized "Type IV" stent fracture 5, 6, 9, 14. The protocol-steps pertaining to the acquisition of secondary flow velocity fields using particle image velocimetry (PIV) technique involves a PIV system that comprises of a laser (light sheet) source, optics to focus and illuminate the regions of flow, a special cross-correlation charge coupled device (CCD-sensor or camera) and tracer particles to be illuminated by the light sheet within a short time interval (Δt; see Table 4) 31, 32.
The steps in the protocol assume the following: First, a calibrated, experimental set-up of a two-component, two-dimensional (2C-2D) PIV system that evaluates images by double-frame, single-exposure recordings. Second, the 2C-2D PIV system calculates the mean displacements of tracer particles by performing cross-correlation between two image frames acquired during each recording. A brief summary of PIV specifications and image acquisition software is presented in the materials and equipment table. Third, all safety precautions needed to operate the laser are followed by trained laboratory personnel according to the guidelines provided by the host institution. The authors suggest Refs. 31 and 32 for a holistic understanding of the implementation, functionality and application of PIV technique in aero-, hydro- and microfluid dynamics, correlation peak detection and displacement estimation, material and density of tracer particles and, measurement noise and accuracy. Also note that the laser and camera can be controlled by the PIV data acquisition computer (Figure 3A) and data processing software.
Data acquisition and post-processing for coherent structure detection: Phase-averaged secondary flow velocity measurements using a 2C-2D PIV were generated using the protocol description that follows. Post-processing of the data involved coherent secondary flow structure detection using the following three methods: continuous wavelet transforms,
5, 6, 19 - 24, 26.
The authors note that the velocity gradient tensor is essentially, a 3 x 3 matrix,
.
The protocol presents a method of acquiring two-dimensional experimental measurements (from 2C-2D PIV technique). Therefore, full experimental access to the velocity gradient tensor will not be attainable using this method. The velocity gradient tensor for each pixel
of the PIV image
should be a 2 x 2 matrix,
. The z-component vorticity
for each pixel
is computed using the anti-symmetric part of the velocity gradient tensor
. The result will be a 2D array of vorticity
that can be visualized in a contour plot. The authors strongly suggest Ref. 25 for an eloquent discussion experimental access to the velocity gradient tensor toward enhancing the knowledge of vorticity dissipation, strain rates and coherent structure detection. Furthermore, the authors do not attempt to explore the inter-relationships between the aforementioned coherent structure detection methods and suggest Ref. 23, 24 for a comprehensive discussion on that subject.
The focus of the steps in the protocol is the quantitative identification of secondary flow (vortical) structures (also known as coherent structures). Three methods of coherent structure detection viz.,
and wavelet transformed vorticity
are applied to velocity field data toward detection of multi-scale, multi-strength occurrences of secondary flow structures downstream of the idealized "Type IV" stent fracture.
The
, defines a vortex as a spatial region where the Euclidean norm of the vorticity tensor dominates that of the rate of strain 19, 23, 24.The velocity gradient matrix is decomposed into symmetric (strain rate) and anti-symmetric (rotation) parts. Eigenvalues of strain rate matrix are computed;
. Norm of the strain rate is then calculated;
. Vorticity is computed from the anti- symmetric part. Enstrophy or square of z-component vorticity,
) is then computed. The
is finally computed;
. A contour plot of the entire set of
with iso-regions of
, will indicate secondary flow structures 19.
The
, also known as 'swirling strength' is a vortex identification method performed by critical-point analysis of the local velocity gradient tensor and its corresponding eigenvalues 20 - 24. Eigenvalues of the velocity gradient tensor at each pixel
are computed. The eigenvalues should be of the form,
. A contour plot of
with iso-regions of
will indicate secondary flow structures 20 - 22.
Wavelet transform method utilizes an analyzing function (or wavelet) that has smoothness in physical and spectral spaces, is admissible (or has zero mean) and has a finite
5, 6, 26. By convolving a dilated or contracted wavelet with a 2D vorticity field, wavelet transformed vorticity
field is generated comprising of coherent structures with a wide range of scales and strengths 5, 6, 26. Shannon entropy of the 2D wavelet-transformed vorticity field is computed to estimate the optimal wavelet scale at which all the coherent structures are adequately resolved. This entropy estimation involves a set of probabilities
for each pixel
such that
, the normalized square modulus of the vorticity associated with the pixel at location m, n 5, 6. The procedural steps are presented graphically in Figure 6. The restrictions placed on the choice of the wavelet are presented in detail in Ref. 26. This protocol step describes the procedure for coherent structure detection using a 2D Ricker wavelet. The justification for the use of this wavelet for vortical pattern matching is presented in Ref. 5, 6 and the pertinent references cited therein.