Kristian Gustavsson

TL;DR: This research direction promises to help disentangle the complexity of active matter and gain fundamental insights for instance in collective behaviour of systems at many length scales from colonies of bacteria to animal flocks.

TL;DR: The potential of reinforcement learning algorithms to model adaptive behavior in complex flows is illustrated and paves the way towards the engineering of smart microswimmers that solve difficult navigation problems.

TL;DR: The dynamics of heavy particles suspended in turbulent flows is of fundamental importance for a wide range of questions in astrophysics, atmospheric physics, oceanography, and technology as discussed by the authors, and it is known that heavy particles respond in intricate ways to turbulent fluctuations of the carrying fluid: noninteracting particles may cluster together and form spatial patterns even though the fluid is incompressible.

TL;DR: This work computes the distribution of relative velocities for a one-dimensional model of heavy particles suspended in a turbulent flow, quantifying the caustic contribution to the moments of relativeVelocities using the phase-space correlation dimension.

TL;DR: In this paper, the rotation of neutrally buoyant axisymmetric particles suspended in isotropic turbulence is investigated using laboratory experiments as well as numerical and analytical calculations, and it is shown that shape strongly affects orientational trajectories, but that it has negligible effect on the variance of the particle angular velocity.