Particle Radial Distribution Function and Relative Velocity Measurement in Turbulence at Small Particle-Pair Separations

Abstract

The collision rate of particles suspended in turbulent flow is critical to particle agglomeration and droplet coalescence. The collision kernel can be evaluated by the radial distribution function (RDF) and radial relative velocity (RV) between particles at small separations $r$. Previously, the smallest $r$ was limited to roughly the Kolmogorov length $\eta$ due to particle position uncertainty and image overlap. We report a new approach to measure RDF and RV near contact ($r/a\approx$ 2.07, $a$ particle radius) overcoming these limitations. Three-dimensional particle tracking velocimetry using four-pulse Shake-the-Box algorithm recorded short particle tracks with the interpolated midpoints registered as particle positions to avoid image overlap. This strategy further allows removal of mismatched tracks using their characteristic false RV. We measured RDF and RV in a one-meter-diameter isotropic turbulence chamber with Taylor Reynolds number $R{e}_{\lambda }=324$ with particles of 12-16 $\mu$m radius and Stokes number $\approx$ 0.7. While at large $r$ the measured RV agrees with the literature, when $r<20\eta$ the first moment of negative RV is 10 times higher than direct numerical simulations of non-interacting particles. Likewise, when $r>\eta$, RDF scales as $r^{-0.39}$ reflecting RDF scaling for polydisperse particles in the literature , but when $r⪅\eta$ RDF scales as $r^{-6}$, yielding 1000 times higher near-contact RDF than simulations. Such extreme clustering and relative velocity enhancement can be attributed to particle-particle interactions. Uncertainty analysis substantiates the observed trends. This first-ever simultaneous RDF and RV measurement at small separations provides a clear glimpse into the clustering and relative velocities of particles in turbulence near-contact.

arXiv comment16 pages, 5 figures, 1 table. Under review in the Journal of Fluid Mechanics

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Cited by in corpus (2)

• Statistical models for the dynamics of heavy particles in turbulence
• Hydrodynamic interactions and extreme particle clustering in turbulence