Settling velocity of small inertial particles in homogeneous isotropic turbulence from high-resolution DNS

Rosa, Bogdan; Parishani, Hossein; Ayala, Orlando; Wang, Lian‐Ping

doi:10.1016/j.ijmultiphaseflow.2016.04.005

Cited by 57 publications

(68 citation statements)

References 56 publications

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“…We only plot cases in which the mean vertical fluid velocity is smaller than 0.25W s , and in fact in most cases it is 0.01 − 0.05W s . The first observation from the plot is that the vast majority of cases display strong settling enhancement, especially for St η ≈ 1, which is consistent with most previous numerical (Wang & Maxey 1993;Bosse & Kleiser 2006;Dejoan & Monchaux 2013;Bec et al 2014a;Ireland et al 2016b;Rosa et al 2016) and experimental studies (Aliseda et al 2002;Yang & Shy 2003Good et al 2014). The amount of such increase is more remarkable, with the settling velocity being enhanced by a factor 2.6 for St η ≈ 1 − 2.…”

Section: Mean Settling Velocitysupporting

confidence: 88%

Experimental study of inertial particles clustering and settling in homogeneous turbulence

2019

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We study experimentally the spatial distribution, settling, and interaction of sub-Kolmogorov inertial particles with homogeneous turbulence. Utilizing a zero-mean-flow air turbulence chamber, we drop size-selected solid particles and study their dynamics with particle imaging and tracking velocimetry at multiple resolutions. The carrier flow is simultaneously measured by particle image velocimetry of suspended tracers, allowing the characterization of the interplay between both the dispersed and continuous phases. The turbulence Reynolds number based on the Taylor microscale ranges from Re λ ≈ 200 -500, while the particle Stokes number based on the Kolmogorov scale varies between St η = O(1) and O(10). Clustering is confirmed to be most intense for St η ≈ 1, but it extends over larger scales for heavier particles. Individual clusters form a hierarchy of self-similar, fractal-like objects, preferentially aligned with gravity and sizes that can reach the integral scale of the turbulence. Remarkably, the settling velocity of St η ≈ 1 particles can be several times larger than the still-air terminal velocity, and the clusters can fall even faster. This is caused by downward fluid fluctuations preferentially sweeping the particles, and we propose that this mechanism is influenced by both large and small scales of the turbulence. The particle-fluid slip velocities show large variance, and both the instantaneous particle Reynolds number and drag coefficient can greatly differ from their nominal values. Finally, for sufficient loadings, the particles generally augment the small-scale fluid velocity fluctuations, which however may account for a limited fraction of the turbulent kinetic energy.

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Section: Mean Settling Velocitysupporting

confidence: 88%

Experimental study of inertial particles clustering and settling in homogeneous turbulence

2019

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“…Good et al (2014) were only able to observe loitering in their DNS when either the horizontal motion of the particles was artificially eliminated, or else when a non-linear drag force was used for the particles. However, as discussed earlier, other studies such as Rosa et al (2016) have found only a small effect of non-linear drag on the particle settling speeds, and they did not observe loitering.…”

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confidence: 53%

“…Nevertheless, DNS studies have also come to conflicting conclusions concerning the role of non-linear drag on particle settling speeds in turbulence. Wang & Maxey (1993) studied the influence of a non-linear drag coefficient using DNS and found that its effect was to only slightly reduce the particle settling speeds compared to the linear drag case, with similar results in Rosa et al (2016). However, Good et al (2014) observed a significant reduction in the particle settling velocity compared with the linear drag case, in certain regimes of St, Sv.…”

mentioning

confidence: 78%

“…They also confirmed the physical argument of Maxey (1987) that settling inertial particles preferentially accumulate in low vorticity regions of the flow where fluid velocity aligns with the direction of gravity. They referred to this effect as the "preferential sweeping mechanism" or "fast-tracking", and it has since been observed in several DNS (Bec et al 2014;Ireland et al 2016b;Rosa et al 2016;Monchaux & Dejoan 2017) and experimental studies (Aliseda et al 2002;Good et al 2014;Petersen et al 2019).…”

mentioning

confidence: 99%

“…Their argument is that the preferential sweeping effect depends on the small scale clustering of the particles, and hence τ η , while the drag force on the particles depends mainly on the large scales, and hence u ′ . The recent study of Rosa et al (2016) also argued that the relevant fluid velocity scale determining the settling enhancement is u ′ .…”

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Multiscale preferential sweeping of particles settling in turbulence

Tom

Bragg

2019

J. Fluid Mech.

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In a seminal article, Maxey (1987, J. Fluid Mech., 174:441-465) presented a theoretical analysis showing that enhanced particle settling speeds in turbulence occur through the preferential sweeping mechanism, which depends on the preferential sampling of the fluid velocity gradient field by the inertial particles. However, recent Direct Numerical Simulation (DNS) results in Ireland et al. (2016b, J. Fluid Mech., 796:659-711) show that even in a portion of the parameter space where this preferential sampling is absent, the particles nevertheless exhibit enhanced settling velocities. Further, there are several outstanding questions concerning the role of different turbulent flow scales on the enhanced settling, and the role of the Taylor Reynolds number R λ . The analysis of Maxey does not explain these issues, partly since it was restricted to particle Stokes numbers St ≪ 1. To address these issues, we have developed a new theoretical result, valid for arbitrary St, that reveals the multiscale nature of the mechanism generating the enhanced settling speeds. In particular, it shows how the range of scales at which the preferential sweeping mechanism operates depends on St. This analysis is complemented by results from DNS where we examine the role of different flow scales on the particle settling speeds by coarse-graining the underlying flow. The results show how the flow scales that contribute to the enhanced settling depend on St, and that contrary to previous claims, there can be no single turbulent velocity scale that characterizes the enhanced settling speed. The results explain the dependence of the particle settling speeds on R λ , and show how the saturation of this dependence at sufficiently large R λ depends upon St.The results also show that as the Stokes settling velocity of the particles is increased, the flow scales of the turbulence responsible for enhancing the particle settling speed become larger. Finally, we explored the multiscale nature of the preferential sweeping mechanism by considering how particles preferentially sample the fluid velocity gradients coarsegrained at various scales. The results show that while rapidly settling particles do not preferentially sample the fluid velocity gradients, they do preferentially sample the fluid velocity gradients coarse-grained at scales outside of the dissipation range. This explains the findings of Ireland et al., and further illustrates the truly multiscale nature of the mechanism generating enhanced particle settling speeds in turbulence.

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Size Distribution and Dispersion of Droplets Generated by Impingement of Breaking Waves on Oil Slicks

et al. 2017

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This laboratory experimental study investigates the temporal evolution of the size distribution of subsurface oil droplets generated as breaking waves entrain oil slicks. The measurements are performed for varying wave energy, as well as large variations in oil viscosity and oil‐water interfacial tension, the latter achieved by premixing the oil with dispersant. In situ measurements using digital inline holography at two magnifications are applied for measuring the droplet sizes and Particle Image Velocimetry (PIV) for determining the temporal evolution of turbulence after wave breaking. All early (2–10 s) size distributions have two distinct size ranges with different slopes. For low dispersant to oil ratios (DOR), the transition between them could be predicted based on a turbulent Weber (We) number in the 2–4 range, suggesting that turbulence plays an important role. For smaller droplets, all the number size distributions have power of about −2.1, and for larger droplets, the power decreases well below −3. The measured steepening of the size distribution over time is predicted by a simple model involving buoyant rise and turbulence dispersion. Conversely, for DOR 1:100 and 1:25 oils, the diameter of slope transition decreases from ∼1 mm to 46 and 14 µm, respectively, much faster than the We‐based prediction, and the size distribution steepens with increasing DOR. Furthermore, the concentration of micron‐sized droplets of DOR 1:25 oil increases for the first 10 min after entrainment. These phenomena are presumably caused by the observed formation and breakup oil microthreads associated with tip streaming.

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Settling velocity of small inertial particles in homogeneous isotropic turbulence from high-resolution DNS

Cited by 57 publications

References 56 publications

Experimental study of inertial particles clustering and settling in homogeneous turbulence

Experimental study of inertial particles clustering and settling in homogeneous turbulence

Multiscale preferential sweeping of particles settling in turbulence

Size Distribution and Dispersion of Droplets Generated by Impingement of Breaking Waves on Oil Slicks

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