We report experimental evidence of spatial clustering of dense particles in homogenous, isotropic turbulence at high Reynolds numbers. The dissipation-scale clustering becomes stronger as the Stokes number increases and is found to exhibit similarity with respect to the droplet Stokes number over a range of experimental conditions (particle diameter and turbulent energy dissipation rate). These findings are in qualitative agreement with recent theoretical and computational studies of inertial particle clustering in turbulence. Because of the large Reynolds numbers a broad scaling range of particle clustering due to turbulent mixing is present, and the inertial clustering can clearly be distinguished from that due to mixing of fluid particles.

A new helicopter payload designed to overcome rotor downwash allows better than 10 cm resolution for turbulence and microphysical observations in boundary layer clouds.

Conducting accurate cloud microphysical measurements from airborne platforms poses a number of challenges. The technique of phase Doppler interferometry (PDI) confers numerous advantages relative to traditional light-scattering techniques for measurement of the cloud drop size distribution, and, in addition, yields drop velocity information. Here, we describe PDI for the purposes of aiding atmospheric scientists in understanding the technique fundamentals, advantages, and limitations in measuring cloud microphysical properties. The performance of the Artium Flight PDI, an instrument specifically designed for airborne cloud measurements, is studied. Drop size distributions, liquid water content, and velocity distributions are compared with those measured by other airborne instruments.

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).

Holographic measurements of the clustering of electrically charged, inertial particles in homogenous and isotropic turbulent flow reveal novel particle dynamics. When particles are identically charged, Coulomb repulsion introduces a length scale below which inertial clustering is suppressed such that the radial distribution function (RDF) mimics that of a nonideal gas. The result is described with a Fokker-Planck framework modeling inertial clustering as a diffusion-drift process modified to include Coulomb interaction. The peak in the RDF is well predicted by the balance between the particle terminal velocity under Coulomb repulsion and a time-averaged "drift" velocity obtained from the nonuniform sampling of fluid strain and rotation due to finite particle inertia. The resulting functional form of the RDF matches the measurements closely, providing support for the drift-diffusion description of particle clustering.

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

The three-dimensional incompressible Navier–Stokes equations, which describe the motion of many fluids, are the cornerstones of many physical and engineering sciences. However, it is still unclear whether they are mathematically well posed, that is, whether their solutions remain regular over time or develop singularities. Even though it was shown that singularities, if exist, could only be rare events, they may induce additional energy dissipation by inertial means. Here, using measurements at the dissipative scale of an axisymmetric turbulent flow, we report estimates of such inertial energy dissipation and identify local events of extreme values. We characterize the topology of these extreme events and identify several main types. Most of them appear as fronts separating regions of distinct velocities, whereas events corresponding to focusing spirals, jets and cusps are also found. Our results highlight the non-triviality of turbulent flows at sub-Kolmogorov scales as possible footprints of singularities of the Navier–Stokes equation.

When cloud particles are small enough, they move with the turbulent air in the cloud. On the other hand, as particles become larger their inertia affects their motions, and they move differently than the air. These inertial dynamics impact cloud evolution and ultimately climate prediction, since clouds govern the Earth's energy balances. However, we lack a simple description of the dynamics. Falkovich et al describe theoretically a new dynamical mechanism called the 'sling effect' by which extreme events in the turbulent air cause idealized inertial cloud particles to break free from the airflow (Falkovich et al 2002 Nature 419 151). The sling effect thereafter causes particle trajectories to cross each other within isolated pockets in the flow, which increases the chance of collisions that forms larger particles. We combined experimental techniques that allow for precise control of a turbulent flow with three-dimensional tracking of multiple particles at unprecedented resolution. In this way, we could observe both the sling effect and crossing trajectories between real particles. We isolated the inertial sling dynamics from those caused by turbulent advection by conditionally averaging the data. We found the dynamics to be universal in terms of a local Stokes number that quantifies the local particle velocity gradients. We measured the probability density of this quantity, which shows that sharp