Efficient control of chaotic turbulent flow with reinforcement learning

Chaotic systems often exhibit extreme events in which quantities-of-interest significantly deviate from the mean value, arbitrarily, for finite periods of time. Common examples include oceanic rogue waves, shocks in power grids, earthquakes, and turbulence. The detrimental effects of these systems make their control of utmost importance, however, finding the cause of these events and mitigating them remains a challenge. This work focuses on controlling the behavior of sinusoidally-driven turbulent flow, which exhibits extreme energy dissipation events due to non-linear energy transfers at different scales. Specifically, a model-free, low-dimensional reinforcement learning agent acts on selected energy modes to manipulate the system to achieve a desired behavior. The control goal of this work is two-fold: first, we demonstrate that mixing can be enhanced at low Reynolds numbers, and secondly, we illustrate empirically that the extreme events can be stabilized ahead of time.