Abstract

We report on the mobility and orientation of finite-size, neutrally buoyant, prolate ellipsoids (of aspect ratio $\varLambda =4$ ) in Taylor–Couette flow, using interface-resolved numerical simulations. The set-up consists of a particle-laden flow between a rotating inner and a stationary outer cylinder. The flow regimes explored are the well-known Taylor vortex, wavy vortex and turbulent Taylor vortex flow regimes. We simulate two particle sizes $\ell /d=0.1$ and $\ell /d=0.2$ , $\ell$ denoting the particle major axis and $d$ the gap width between the cylinders. The volume fractions are $0.01\,\%$ and $0.07\,\%$ , respectively. The particles, which are initially randomly positioned, ultimately display characteristic spatial distributions which can be categorised into four modes. Modes (i) to (iii) are observed in the Taylor vortex flow regime, while mode (iv) encompasses both the wavy vortex and turbulent Taylor vortex flow regimes. Mode (i) corresponds to stable orbits away from the vortex cores. Remarkably, in a narrow $\textit {Ta}$ range, particles get trapped in the Taylor vortex cores (mode (ii)). Mode (iii) is the transition when both modes (i) and (ii) are observed. For mode (iv), particles distribute throughout the domain due to flow instabilities. All four modes show characteristic orientational statistics. The focus of the present study is on mode (ii). We find the particle clustering for this mode to be size-dependent, with two main observations. Firstly, particle agglomeration at the core is much higher for $\ell /d=0.2$ compared with $\ell /d=0.1$ . Secondly, the $\textit {Ta}$ range for which clustering is observed depends on the particle size. For this mode (ii) we observe particles to align strongly with the local cylinder tangent. The most pronounced particle alignment is observed for $\ell /d=0.2$ at around $\textit {Ta}=4.2\times 10^5$ . This observation is found to closely correspond to a minimum of axial vorticity at the Taylor vortex core ( $\textit {Ta}=6\times 10^5$ ) and we explain why.

Highlights

  • Particle-laden flows are ubiquitous in both nature and industrial applications

  • We emphasise that in the range of Ta = 3.2 × to Ta = 1.9 × the orbit of few spheres is close to the vortex core, which results in a p.d.f. that is comparable with the one observed in figure 3(b) where most particles spiral to the edge of the vortex

  • We aim to answer the question: Why do the particles align at this Ta value? In the following, we show that the preferential alignment is linked to the TC flow state which exhibits a minimum in the shear gradient at the Taylor vortex core

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Summary

Introduction

Particle-laden flows are ubiquitous in both nature and industrial applications. For example, in rivers, where the deposition of large grains can influence the solutal and nutrient exchange processes (Ferdowsi et al 2017). In industrial applications the accumulation of particles in turbo-machineries can reduce the efficiency and even damage rotor or stator blades (Hamed, Tabakoff & Wenglarz 2006) Another example is in the paper-making industry, where the orientation of the fibres in the pulp suspension determines the mechanical strength of the final product (Lundell, Söderberg & Alfredsson 2011). The second category focuses on explaining the dynamics of particles in these flows themselves We want to work out the underlying physics The answers to these questions can provide valuable insight into the underlying mechanism for particle-/bubble-induced drag reduction in wall-bounded turbulent flows, where particle geometry might affect momentum transport.

Taylor–Couette set-up in the Taylor vortex flow regime
Carrier phase
Particles
Observed spatial modes
The transition from stable orbits to clustering at the core is size-dependent
A comparison with volume-equivalent spheres
Angular dynamics
Angular statistics corresponding to the observed spatial distributions
The most pronounced alignment of particles
The link between strong alignment and minimum axial vorticity
Lagrangian statistics of particle rotational energy
The clustering mechanism for ellipsoids
Conclusion
Newton–Euler equations
Short-range collisions: forces and torques
Collision of ellipsoids with the inner and outer cylinder
Collision validations

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