Lagrangian mixing of pulsatile flows in constricted tubes

TL;DR

This study uses Lagrangian methods like FTLE, concentration, and residence time maps to analyze mixing in pulsatile flow through a constricted artery model, revealing flow structures and how mixing varies with Reynolds number.

Contribution

It introduces a comprehensive Lagrangian analysis combining multiple methods to characterize flow mixing and transport in pulsatile constricted flows, highlighting the role of coherent structures and flow parameters.

Findings

Identification of Lagrangian coherent structures (LCS) governing mixing.

Increased Reynolds number raises the probability of fluid leaving the region of interest.

Main mixing regions are linked to vortex dynamics and flow parameters.

Abstract

In this work several lagrangian methods were used to analyze the mixing processes in an experimental model of a constricted artery under a pulsatile flow. Upstream Reynolds number $Re$ was changed between 1187 and 1999, while the pulsatile period $T$ was kept fixed at 0.96s. Velocity fields were acquired using Digital Particle Image Velocimetry (DPIV) for a region of interest (ROI) located downstream of the constriction. The flow is composed of a central jet and a recirculation region near the wall where vortex forms and sheds. To study the mixing processes, finite time Lyapunov exponents (FTLE) fields and concentration maps were computed. Two lagrangian coherent structures (LCS) responsible for mixing and transporting fluid were found from FTLE ridges. A first LCS delimits the trailing edge of the vortex, separating the flow that enters the ROI between successive periods. A second LCS delimits the leading edge of the vortex. This LCS concentrates the highest particle agglomeration, as verified by the concentration maps. Moreover, from the particle residence time maps (RT) the probability for a fluid particle of leaving the ROI before one cycle was measured. As $Re$ increases, the probability of leaving the ROI increases from 0.6 to 0.95. Final position maps $r{_f}$ were introduced to evaluate the flow mixing between different subregions of the ROI. These maps allowed us to compute an exchange index between subregions, $\bar{\mathrm{EI}}$, which shows the main region responsible for the mixing increase with $Re$. Finally by integrating the results of the different lagrangian methods (FTLE, Concentration maps, RT and $r_f$ maps), a comprehensive description of the mixing and transport of the flow was provided.