Preferential concentration of heavy particles in turbulence

Obligado, Martín; Teitelbaum, Tomas; Cartellier, Alain H.; Mininni, Pablo D.; Bourgoin, Mickaël

doi:10.1080/14685248.2014.897710

Cited by 83 publications

(132 citation statements)

References 19 publications

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“…The results show that cluster sizes are distributed over a wide range of scales, from the dissipation up to integral range scales. In agreement with previous studies ( [20,27,30,32,52]), we find that the right hand side of the PDF is well described as a power law with exponent −2, which implies self-similarity of the clusters, in line with results in [20,55]. This slope was found to be ∼ −5/3 in the experimental study of [28].…”

Section: Local Analysis Of Particle Accelerationssupporting

confidence: 92%

“…These points are often used to define thresholds for detecting clusters and voids, with volumes less than ϑ C denoting clustered regions, and volumes greater than ϑ v denoting void regions. In view of these definitions, it is then apparent that the R λ dependence of the s. In a number of previous studies it has been reported that the PDF of Voronoï volumes follows a log-normal distribution [20,25,27,28,31]. In figure 4 the centered and normalized…”

Section: A Voronoï Volume Distributionsmentioning

confidence: 97%

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Local analysis of the clustering, velocities, and accelerations of particles settling in turbulence

Momenifar

Bragg

2020

Phys. Rev. Fluids

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Using 3D Voronoï analysis, we explore the local dynamics of small, settling, inertial particles in isotropic turbulence using Direct Numerical Simulations (DNS). We independently vary the Taylor Reynolds number R λ ∈ [90, 398], Froude number F r ≡ a η /g ∈ [0.052, ∞] (where a η is the Kolmogorov acceleration, and g is the acceleration due to gravity), and Kolmogorov scale Stokes number St ≡ τ p /τ η ∈ [0, 3]. In agreement with previous results using global measures of particle clustering, such as the Radial Distribution Function (RDF), we find that for small Voronoï volumes (corresponding to the most clustered particles), the behavior is strongly dependent upon St and F r, but only weakly dependent upon R λ , unless St > 1. However, larger Voronoï volumes (void regions) exhibit a much stronger dependence on R λ , even when St ≤ 1, and we show that this, rather than the behavior at small volumes, is the cause of the sensitivity of the standard deviation of the Voronoï volumes that has been previously reported. We also show that the largest contribution to the particle settling velocities is associated with increasingly larger Voronoï volumes as the settling parameter Sv ≡ St/F r is increased.Our local analysis of the acceleration statistics of settling inertial particles shows that clustered particles experience a net acceleration in the direction of gravity, while particles in void regions experience the opposite. The particle acceleration variance, however, is a convex function of the Voronoï volumes, with or without gravity, which seems to indicate a non-trivial relationship between the Voronoï volumes and the sizes of the turbulent flow scales. Results for the variance of the fluid acceleration at the inertial particle positions are of the order of the square of the Kolmogorov acceleration and depend only weakly on Voronoï volumes. These results call into question the "sweep-stick" mechanism for particle clustering in turbulence which would lead one to expect that clustered particles reside in the special regions where the fluid acceleration is zero (or at least small).We then consider the properties of particles in clusters, which are regions of connected Voronoï cells whose volume is less than a certain threshold. The results show self-similarity of the clusters, and that the statistics of the cluster volumes depends only weakly on St, with a stronger dependance on F r and R λ . Finally, we compare the average settling velocities of all particles in the flow with those in clusters, and show that those in the clusters settle much faster, in agreement with previous work. However, we also find that this difference grows significantly with increasing R λ and exhibits a non-monotonic dependence on F r. The kinetic energy of the particles, however, are almost the

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Section: Local Analysis Of Particle Accelerationssupporting

confidence: 92%

Section: A Voronoï Volume Distributionsmentioning

confidence: 97%

Local analysis of the clustering, velocities, and accelerations of particles settling in turbulence

Momenifar

Bragg

2020

Phys. Rev. Fluids

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“…This recurrent question has been considered so far by randomly removing particles from arXiv:1907.07607v1 [physics.flu-dyn] 17 Jul 2019 the collected sets, either from experiments or from numerical data sets. It was shown in [6,7,9] that the Voronoï analysis of clustering is weakly sensitive to sub-sampling. More specifically, relative Voronoï area PDFs and cluster area PDFs are nearly unchanged even if half of the particles present in the original set is randomly removed [6].…”

Section: Introductionmentioning

confidence: 99%

Study on preferential concentration of inertial particles in homogeneous isotropic turbulence via big-data techniques

et al. 2020

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We present an experimental study of the preferential concentration of sub-Kolmogorov inertial particles in active-grid-generated homogeneous and isotropic turbulence, characterized via Voronoï tessellations.We show that the detection and quantification of clusters and voids is influenced by the intensity of the laser and high values of particles volume fraction φ v . Different biases on the statistics of Voronoï cells are analyzed to improve the reliability of the detection and the robustness in the characterization of clusters and voids. We do this by adapting Big-Data techniques that allow to process the particle images up to 10 times faster than standard algorithms.Finally, as preferential concentration is known to depend on multiple parameters, we performed experiments where one parameter was varied and all others were kept constant (φ v , Reynolds number based on the Taylor length scale Re λ , and residence time of the particles interacting with the turbulence). Our results confirm, in agreement with published work, that clustering increases with both φ v and Re λ . On the other hand, we find new evidence that the mean size of clusters increases with Re λ but decreases with φ v and that the cluster settling velocity is strongly affected by Re λ up to the maximum value studied here, Re

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“…For particles having St < 1 clustering is reduced compared to no-gravity case whereas for particles having St > 1 clustering is significantly increased compared to no-gravity case. In a real experiment, gravity is always present hence, these results can be used to understand the experimental observation of particle clustering in turbulent flows [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Statistics of relative velocity for particles settling under gravity in a turbulent flow

Bhatnagar

2020

Phys. Rev. E

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We study the joint probability distributions of separation, R, and radial component of the relative velocity, VR, of particles settling under gravity in a turbulent flow. We also obtain the moments of these distributions and analyze their anisotropy using spherical harmonics. We find that the qualitative nature of the joint distributions remains the same as no gravity case. Distributions of VR for fixed values of R show a power-law dependence on VR for a range of VR, exponent of the power-law depends on the gravity. Effects of gravity are also manifested in the following ways: (a) moments of the distributions are anisotropic; degree of anisotropy depends on particle's Stokes number, but does not depend on R for small values of R. (b) mean velocity of collision between two particles is decreased for particles having equal Stokes numbers but increased for particles having different Stokes numbers. For the later, collision velocity is set by the difference in their settling velocities.

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Preferential concentration of heavy particles in turbulence

Cited by 83 publications

References 19 publications

Local analysis of the clustering, velocities, and accelerations of particles settling in turbulence

Local analysis of the clustering, velocities, and accelerations of particles settling in turbulence

Study on preferential concentration of inertial particles in homogeneous isotropic turbulence via big-data techniques

Statistics of relative velocity for particles settling under gravity in a turbulent flow

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