Two-particle dispersion is of central importance to a wide range of natural and industrial applications. It has been an active area of research since Richardson's (1926) seminal paper. This review emphasizes recent results from experiments, high-end direct numerical simulations, and modern theoretical discussions. Our approach is complementary to Sawford's (2001), whose review focused primarily on stochastic models of pair dispersion. We begin by reviewing the theoretical foundations of relative dispersion, followed by experimental and numerical findings for the dissipation subrange and inertial subrange. We discuss the findings in the context of the relevant theory for each regime. We conclude by providing a critical analysis of our current understanding and by suggesting paths toward further progress that take full advantage of exciting developments in modern experimental methods and peta-scale supercomputing.

This paper presents the first detailed comparisons between experiments and direct numerical simulations (DNS) of inertial particle clustering in nearly isotropic ‘box turbulence’. The experimental system consists of a box 38cm in each dimension with fans in the eight corners that sustain nearly isotropic turbulence in the centre of the box. We inject hollow glass spheres with a mean diameter of 6 μm and measure the locations of several hundred particles in a 1 cm3 volume in the centre of the box using three-dimensional digital holographic particle imaging. We observe particle concentration fluctuations that result from inertial clustering (sometimes called ‘preferential concentration’). The radial distribution function (RDF), a statistical measure of clustering, has been calculated from the particle position field. We select this measure because of its relevance to the collision kernel for particles. DNS of the equivalent system, with nearly perfect parameter overlap, have also been performed. We observe good agreement between the RDF predictions of the DNS and the experimental observations, despite some challenges in the interpretation of the experiments. The results provide important guidance on ways to improve the measurement.

Particles that are heavy compared to the fluid in which they are embedded (inertial particles) tend to cluster in turbulent flow, with the degree of clustering depending on the particle Stokes number. The phenomenon is relevant to a variety of multiphase flows, including atmospheric clouds; in most realistic systems, particles have a continuous distribution of sizes and therefore the clustering of 'polydisperse' particle populations is of special relevance. In this part of the study, measurements of spatial correlations of particles in high-Reynolds-number turbulence are compared with the results of a direct numerical simulation of particle-laden turbulence. The experimentally derived radial distribution functions (RDFs) exhibit a pronounced scale break at approximately 10-30 times the Kolmogorov scale, with large-scale clustering arising from 5 International Collaboration for Turbulence Research. 6 2 'scalar mixing' of the droplet field, and smaller-scale clustering depending on the particle Stokes numbers. A procedure is outlined for isolating the RDF due to inertial clustering from that resulting from large-scale mixing. Reasonable agreement between the experiment and the direct numerical simulations (DNS) is obtained for St 0.3 when particle Stokes number distributions in the DNS match those existing in the experiments. The experimental RDFs are consistent with the flattening or saturation scale appearing for bidisperse particles, but as in the companion paper, also support the 'saturation' effect in the asymmetric response of the power-law slope. The evidence for a universal scale break, as observed in both the DNS and the experiments, suggests that the pre-factor in the theoretical expression for the RDF is inherently tied to the power-law exponent, and an empirical form for this is given. Finally, no strong influence of the turbulence Reynolds number was observed for the clustering phenomenon. The consistency between the carefully analyzed DNS and experiments, in terms of St dependence, dissipation-range scale break and saturation of clustering for polydisperse particles, provides an indirect confirmation of the diffusion-drift theory of Chun et al (2005 J. Fluid Mech. 536 219-51).

Particles that are heavy compared to the fluid in which they are embedded (inertial particles) tend to cluster in turbulent flow, with the degree of clustering depending on the particle Stokes number. The phenomenon is relevant to a variety of systems, including atmospheric clouds; in most realistic systems particles have a continuous distribution of sizes and therefore the clustering of 'polydisperse' particle populations is of special relevance. In this work a theoretical expression for the radial distribution function (RDF) for mono-and bidisperse inertial particles in the low Stokes number limit (Chun et al 2005 J. Fluid Mech. 536 219-51) is compared with the results of a direct numerical simulation of particle-laden turbulence. The results confirm the power-law form of the RDF for monodisperse particles with St 0.3. The clustering signature occurs at scales 10-30 times the Kolmogorov scale, consistent with a dissipation-scale mechanism. The theory correctly predicts the decorrelation 5 International Collaboration for Turbulence Research 6

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In the present study, we investigate the scaling of relative velocity structure functions, of order two and higher, for inertial particles, both in the dissipation range and the inertial subrange using direct numerical simulations (DNS). Within the inertial subrange our findings show that contrary to the well-known attenuation in the tails of the one-point acceleration probability density function (p.d.f.) with increasing inertia (Bec et al., J. Fluid Mech., vol. 550, 2006, pp. 349–358), the opposite occurs with the velocity structure function at sufficiently large Stokes numbers. We observe reduced scaling exponents for the structure function when compared to those of the fluid, and correspondingly broader p.d.f.s, similar to what occurs with a passive scalar. DNS allows us to isolate the two effects of inertia, namely biased sampling of the velocity field, a result of preferential concentration, and filtering, i.e. the tendency for the inertial particle velocity to attenuate the velocity fluctuations in the fluid. By isolating these effects, we show that sampling is playing the dominant role for low-order moments of the structure function, whereas filtering accounts for most of the scaling behaviour observed with the higher-order structure functions in the inertial subrange. In the dissipation range, we see evidence of so-called ‘crossing trajectories’, the ‘sling effect’ or ‘caustics’, and find good agreement with the theory put forth by Wilkinson et al. (Phys. Rev. Lett., vol. 97, 2006, 048501) and Falkovich & Pumir (J. Atmos. Sci., vol. 64, 2007, 4497) for Stokes numbers greater than 0.5. We also look at the scaling exponents within the context of the model proposed by Bec et al. (J. Fluid Mech., vol. 646, 2010, pp. 527–536). Another interesting finding is that inertial particles at low Stokes numbers sample regions of higher kinetic energy than the fluid particle field, the converse occurring at high Stokes numbers. The trend at low Stokes numbers is predicted by the theory of Chun et al. (J. Fluid Mech., vol. 536, 2005, 219–251). This work is relevant to modelling the particle collision rate (Sundaram & Collins, J. Fluid Mech., vol. 335, 1997, pp. 75–109), and highlights the interesting array of phenomena induced by inertia.

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In this study, we investigate the effect of “biased sampling,” i.e., the clustering of inertial particles in regions of the flow with low vorticity, and “filtering,” i.e., the tendency of inertial particles to attenuate the fluid velocity fluctuations, on the probability density function of inertial particle accelerations. In particular, we find that the concept of “biased filtering” introduced by Ayyalasomayajula et al. [“Modeling inertial particle acceleration statistics in isotropic turbulence,” Phys. Fluids 20, 0945104 (2008)10.1063/1.2976174], in which particles filter stronger acceleration events more than weaker ones, is relevant to the higher order moments of acceleration. Flow topology and its connection to acceleration is explored through invariants of the velocity-gradient, strain-rate, and rotation-rate tensors. A semi-quantitative analysis is performed where we assess the contribution of specific flow topologies to acceleration moments. Our findings show that the contributions of regions of high vorticity and low strain decrease significantly with Stokes number, a non-dimensional measure of particle inertia. The contribution from regions of low vorticity and high strain exhibits a peak at a Stokes number of approximately 0.2. Following the methodology of Ooi et al. [“A study of the evolution and characteristics of the invariants of the velocity-gradient tensor in isotropic turbulence,” J. Fluid Mech. 381, 141 (1999)10.1017/S0022112098003681], we compute mean conditional trajectories in planes formed by pairs of tensor invariants in time. Among the interesting findings is the existence of a stable focus in the plane formed by the second invariants of the strain-rate and rotation-rate tensors. Contradicting the results of Ooi et al., we find a stable focus in the plane formed by the second and third invariants of the strain-rate tensor for fluid tracers. We confirm, at an even higher Reynolds number, the conjecture of Collins and Keswani [“Reynolds number scaling of particle clustering in turbulent aerosols,” New J. Phys. 6, 119 (2004)10.1088/1367-2630/6/1/119] that inertial particle clustering saturates at large Reynolds numbers. The result is supported by the theory presented in Chun et al. [“Clustering of aerosol particles in isotropic turbulence,” J. Fluid Mech. 536, 219 (2005)10.1017/S0022112005004568].