Using wind to disperse microfliers that fall like seeds and leaves can help automate large-scale sensor deployments. Here, we present battery-free microfliers that can change shape in mid-air to vary their dispersal distance. We designed origami microfliers using bistable leaf-out structures and uncovered an important property: A simple change in the shape of these origami structures causes two dramatically different falling behaviors. When unfolded and flat, the microfliers exhibit a tumbling behavior that increases lateral displacement in the wind. When folded inward, their orientation is stabilized, resulting in a downward descent that is less influenced by wind. To electronically transition between these two shapes, we designed a low-power electromagnetic actuator that produces peak forces of up to 200 millinewtons within 25 milliseconds while powered by solar cells. We fabricated a circuit directly on the folded origami structure that includes a programmable microcontroller, a Bluetooth radio, a solar power–harvesting circuit, a pressure sensor to estimate altitude, and a temperature sensor. Outdoor evaluations show that our 414-milligram origami microfliers were able to electronically change their shape mid-air, travel up to 98 meters in a light breeze, and wirelessly transmit data via Bluetooth up to 60 meters away, using only power collected from the sun.

We propose an experimental study on the gravitational settling velocity of dense, sub-Kolmogorov inertial particles under different background turbulent flows. We report phase Doppler particle analyser measurements in a low-speed wind tunnel uniformly seeded with micrometre scale water droplets. Turbulence is generated with three different grids (two consisting of different active-grid protocols while the third is a regular static grid), allowing us to cover a very wide range of turbulence conditions in terms of Taylor-scale-based Reynolds numbers ( $Re_\lambda \in [30\unicode{x2013}520]$ ), Rouse numbers ( $Ro \in [0\unicode{x2013}5]$ ) and volume fractions ( $\phi _v \in [0.5\times 10^{-5}\unicode{x2013}2.0\times 10^{-5}]$ ). We find, in agreement with previous works, that enhancement of the settling velocity occurs at low Rouse number, while hindering of the settling occurs at higher Rouse number for decreasing turbulence energy levels. The wide range of flow parameters explored allowed us to observe that enhancement decreases significantly with the Taylor–Reynolds number and is significantly affected by the volume fraction $\phi _v$ . We also studied the effect of large-scale forcing on settling velocity modification. The possibility of changing the inflow conditions by using different grids allowed us to test cases with fixed $Re_\lambda$ and turbulent intensity but with different integral length scale. Finally, we assess the existence of secondary flows in the wind tunnel and their role on particle settling. This is achieved by characterising the settling velocity at two different positions, the centreline and close to the wall, with the same streamwise coordinate.

We study the three-dimensional clustering of velocity stagnation points, of nulls of the vorticity and of the Lagrangian acceleration, and of inertial particles in turbulent flows at fixed Reynolds numbers, but under different large-scale flow geometries. To this end, we combine direct numerical simulations of homogeneous and isotropic turbulence and of the Taylor–Green flow, with particle tracking velocimetry in a von Kármán experiment. While flows have different topologies (as nulls cluster differently), particles behave similarly in all cases, indicating that Taylor-scale neutrally buoyant particles cluster as inertial particles.

We show that the unsteadiness of turbulence has a drastic effect on turbulence parameters and in particle cluster formation. To this end we use direct numerical simulations of particle laden flows with a steady forcing that generates an unsteady large-scale flow. Particle clustering correlates with the instantaneous Taylor-based flow Reynolds number, and anticorrelates with its instantaneous turbulent energy dissipation constant. A dimensional argument for these correlations is presented. In natural flows, unsteadiness can result in extreme particle clustering, which is stronger than the clustering expected from averaged inertial turbulence effects.

It has been shown that, for dense, sub-Kolmogorov particles advected in a turbulent flow, carrier phase properties can be reconstructed from the particles’ velocity field. For that, the instantaneous particles’ velocity field can be used to detect the stagnation points of the carrier phase. The Rice theorem can therefore be used, implying that the Taylor length is proportional to the mean distance between such stagnation points. As this model has been only tested for one-dimensional time signals, this work discusses if it can be applied to two-phase, three-dimensional flows. We use direct numerical simulations with turbulent Reynolds numbers $R{e}_{\lambda }$ between 40 and 520 and study particle-laden flows with a Stokes number of $St=0.5$. We confirm that for the carrier phase, the Taylor length is proportional to the mean distance between stagnation points with a proportionality coefficient that depends weakly on $R{e}_{\lambda }$. Then, we propose an interpolation scheme to reconstruct the stagnation points of the particles’ velocity field. The results indicate that the Rice theorem cannot be applied in practice to two-phase three-dimensional turbulent flows, as the clustering of stagnation points forms very dense structures that require a very large number of particles to accurately sample the flow stagnation points.