THESIS
2021
1 online resource (xxii, 195 pages) : illustrations (some color)
Abstract
Particle-laden turbulent flow is a ubiquitous type of multiphase flow in numerous natural and
industrial processes. It depicts a large number of dispersed particles, which could be solid
particles, liquid droplets, or gas bubbles with density different from the carrier fluid, suspending
or settling in turbulence. Many of our activities and living quality are related to particle-laden
turbulent flow, such as weather prediction, pollutant transportation in the atmosphere and ocean,
fuel combustion in engines, CO
2 sequestration for mitigation of greenhouse effect, etc. Usually,
the carrier fluid is a turbulent flow and due to the multi-scale property of turbulence the
interaction between dispersed particles and turbulence is not solely limited to the small scale
eddies but also include eff...[
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Particle-laden turbulent flow is a ubiquitous type of multiphase flow in numerous natural and
industrial processes. It depicts a large number of dispersed particles, which could be solid
particles, liquid droplets, or gas bubbles with density different from the carrier fluid, suspending
or settling in turbulence. Many of our activities and living quality are related to particle-laden
turbulent flow, such as weather prediction, pollutant transportation in the atmosphere and ocean,
fuel combustion in engines, CO
2 sequestration for mitigation of greenhouse effect, etc. Usually,
the carrier fluid is a turbulent flow and due to the multi-scale property of turbulence the
interaction between dispersed particles and turbulence is not solely limited to the small scale
eddies but also include effect of the large scales. Furthermore, the multiplicity of physical
properties of the dispersed particles should also be considered in order to fully understand the
mechanisms of their interaction occurring in various natural and industrial processes.
In this study, experimental and numerical techniques are developed to investigate the clustering
behaviour and settling velocity of inertial particles in both isotropic and anisotropic turbulence.
In the experimental investigation, an optical technique making of different light wavelengths
emitted from seeding and inertial particles is adopted to separate the fluid and particle phases.
Simultaneous particle image velocimetry (PIV) and particle tracking velocimetry (PTV)
measurement technique is used to obtain instantaneous velocities of the two phases on a
measurement plane. In the numerical investigation, direct numerical simulation (DNS) is used
to solve the Navier-Stokes equation for fluid phase and resolve all turbulent scales down to
Kolmogorov scales. For the particle phase, the Maxey-Riley equation is used in the Lagrangian
framework.
The experimental results show that the reduction of settling velocity of inertial particles in
turbulence is a joint consequence of effects of small and large turbulence scales and gravity.
The Stokes number representing solely the small scale effect can only partially determine
whether settling velocity of inertial particles is enhanced or reduced. From the experimental
results, a multiscale parameter
Sv
ηRe
L1/2 including all the effect of small and large turbulence
scales and gravity is proposed to represent the multiscale interaction between the two phases.
In addition, the results also show that the clustering of bidisperse inertial particles is weaker
than the corresponding monodisperse inertial particles and the normalized settling velocity of
the mixed situation is larger.
The simulation results show that clustering and settling velocity of bidisperse inertial particles
in a turbulent channel flow is determined by the effective volume fraction ratio of small and
large inertial particles when the interaction between two-phase is included (i.e. two-way
coupling simulation). When increasing the initial particle number ratio, i.e., decreasing the
effective volume fraction ratio, clustering of small and large particles in the bidisperse cases
are both enhanced, while their settling velocities are both reduced. Regardless of the different
effective volume fraction ratio, weaker clustering of bidisperse inertial particles is still observed
in the logarithmic layer, which is consistent with experimental results even though the Stokes
number in simulations is higher. Using three different vortex identification methods, it is
confirmed that Rortex is a better indicator of vortices in turbulence to represent the mechanism
of preferential concentration.
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