We introduce a reinforcement learning (RL) environment to design and benchmark control
strategies aimed at reducing drag in turbulent fluid flows enclosed in a channel. The …

In recent years the fluid mechanics community has been intensely focused on pursuing
solutions to its long-standing open problems by exploiting the new machine learning (ML) …

The dynamics of droplet fragmentation in turbulence is described by the Kolmogorov-Hinze
framework. Yet, a quantitative theory is lacking at higher concentrations when strong …

Intermittency refers to the broken self-similarity of turbulent flows caused by anomalous
spatiotemporal fluctuations. In this Letter, we ask how intermittency is affected by a …

The perturbation response of small-scale shear layers in turbulence is investigated with
direct numerical simulations (DNS). The analysis of shear layers in isotropic turbulence …

The formation of small droplets and bubbles in turbulent flows is a crucial process in
geophysics and engineering, whose underlying physical mechanism remains a puzzle. In …

We consider three-dimensional turbulence from which vortex stretching is removed. The
resulting system conserves enstrophy, but does not conserve kinetic energy. Using spectral …

Small bubbles in fluids rise to the surface due to Archimede's force. Remarkably, in turbulent
flows this process is severely hindered by the presence of vortex filaments, which act as …

Three-dimensional turbulence is usually studied experimentally by using a spatially
localized forcing at large scales (eg, via rotating blades or oscillating grids), often in a …

In the present thesis, the application of deep learning and deep reinforcement learning to
turbulent-flow simulations is investigated. Deep-learning models are trained to perform …