Measurement of inertial particle clustering and relative velocity statistics in isotropic turbulence using holographic imaging

Jong, Jeremy de; Salazar, Juan P. L. C.; Woodward, Scott H.; Collins, Lance R.; Meng, Hui

doi:10.1016/j.ijmultiphaseflow.2009.11.008

Cited by 40 publications

(39 citation statements)

References 53 publications

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“…Yang and Shy (2005) also show that, for 0.3 ≤ St ≤ 1.9, clustering is greatest for St ∼ 1. Salazar et al (2008) andde Jong et al (2010) study (experimentally) the full 3-D RDF for 0.21 ≤ St ≤ 0.6 and the former also provide excellent confirmation of their clustering measurements at the small scales using DNS. All of these box turbulence measurements were made for R λ of the order of a few hundred.…”

Section: Laboratory Experimentsmentioning

confidence: 52%

Droplet growth in warm turbulent clouds

Devenish

Bartello

Brenguier

et al. 2012

Quart J Royal Meteoro Soc

243

281

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In this survey we consider the impact of turbulence on cloud formation from the cloud scale to the droplet scale. We assess progress in understanding the effect of turbulence on the condensational and collisional growth of droplets and the effect of entrainment and mixing on the droplet spectrum. The increasing power of computers and better experimental and observational techniques allow for a much more detailed study of these processes than was hitherto possible. However, much of the research necessarily remains idealized and we argue that it is those studies which include such fundamental characteristics of clouds as droplet sedimentation and latent heating that are most relevant to clouds. Nevertheless, the large body of research over the last decade is beginning to allow tentative conclusions to be made. For example, it is unlikely that small-scale turbulent eddies (i.e. not the energy-containing eddies) alone are responsible for broadening the droplet size spectrum during the initial stage of droplet growth due to condensation. It is likely, though, that small-scale turbulence plays a significant role in the growth of droplets through collisions and coalescence. Moreover, it has been possible through detailed numerical simulations to assess the relative importance of different processes to the turbulent collision kernel and how this varies in the parameter space that is important to clouds. The focus of research on the role of turbulence in condensational and collisional growth has tended to ignore the effect of entrainment and mixing and it is arguable that they play at least as important a role in the evolution of the droplet spectrum. We consider the role of turbulence in the mixing of dry and cloudy air, methods of quantifying this mixing and the effect that it has on the droplet spectrum. Copyright

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Section: Laboratory Experimentsmentioning

confidence: 52%

Droplet growth in warm turbulent clouds

Devenish

Bartello

Brenguier

et al. 2012

Quart J Royal Meteoro Soc

243

281

View full text Add to dashboard Cite

show abstract

“…We instead mirror the particle field across the image boundaries, so that the same number of annuli is used for each particle location. The maximum separation then equals the full image size, introducing only small biases near the boundaries (Salazar et al 2008;de Jong et al 2010;Petersen et al 2019). To avoid projection biases at separations below the illuminated volume thickness (Holtzer & Collins 2002), we only present g(r) for r > 1.1 mm.…”

Section: Two-point Statisticsmentioning

confidence: 99%

Velocity and spatial distribution of inertial particles in a turbulent channel flow

2019

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We present experimental observations of the velocity and spatial distribution of inertial particles dispersed in the turbulent downward flow through a vertical channel at friction Reynolds numbers Re τ = 235 and 335. The working fluid is air laden with size-selected glass micro-spheres, having Stokes numbers St = O(10) and O(100) when based on the Kolmogorov and viscous time scales, respectively. Cases at solid volume fractions φ v = 3×10 −6 and 5×10 −5 are considered. In the more dilute regime, the particle concentration profile shows near-wall and centerline maxima compatible with a turbophoretic drift down the gradient of turbulence intensity; the particles travel at similar speed as the unladen flow except in the near-wall region; and their velocity fluctuations generally follow the unladen flow level over the channel core, exceeding it in the near-wall region. The denser regime presents substantial differences in all measured statistics: the near-wall concentration peak is much more pronounced, while the centerline maximum is absent; the mean particle velocity decreases over the logarithmic and buffer layers; and particle velocity fluctuations and deposition velocities are enhanced. An analysis of the spatial distributions of particle positions and velocities reveals different behaviors in the core and near-wall regions. In the channel core, dense clusters form which are somewhat elongated, tend to be preferentially aligned with the vertical/streamwise direction, and travel faster than the less concentrated particles. In the near-wall region, the particles arrange in highly elongated streaks associated to negative streamwise velocity fluctuations, several channel height in length and spaced by O(100) wall units, supporting the view that these are coupled to fluid low-speed streaks typical of wall turbulence. The particle velocity fields contain a significant component of random uncorrelated motion, more prominent for higher St and in the near-wall region.

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“…The effect of inertia on the radial relative velocity statistics has been investigated in the context of predicting the collision kernel (Wang, Wexler & Zhou 2000;de Jong et al 2010;Bec et al 2010) and also for modelling the particle motion that leads to clustering (Zaichik & Alipchenkov 2003;Chun et al 2005;Zaichik & Alipchenkov 2009). Recently, it has been found that inertial particle relative velocities can be multi-valued in the limit of zero separation due to the formation of caustics (Wilkinson, Mehlig & Bezuglyy 2006;Falkovich & Pumir 2007;Salazar & Collins 2011), which tends to enhance the collision rate.…”

Section: Introductionmentioning

confidence: 99%