Stabilization of the fluidic pinball with gradient-enriched machine learning control

We stabilize the flow past a cluster of three rotating cylinders - the fluidic pinball - with automated gradient-enriched machine learning algorithms. The control laws command the rotation speed of each cylinder in an open- and closed-loop manner. These laws are optimized with respect to the average distance from the target steady solution in three successively richer search spaces. First, stabilization is pursued with steady symmetric forcing. Second, we allow for asymmetric steady forcing. And third, we determine an optimal feedback controller employing nine velocity probes downstream. As expected, the control performance increases with every generalization of the search space. Surprisingly, both open- and closed-loop optimal controllers include an asymmetric forcing, which surpasses symmetric forcing. Intriguingly, the best performance is achieved by a combination of phasor control and asymmetric steady forcing. We hypothesize that asymmetric forcing is typical for pitchfork bifurcated dynamics of nominally symmetric configurations. Key enablers are automated machine learning algorithms augmented with gradient search: explorative gradient method for the open-loop parameter optimization and a gradient-enriched machine learning control (gMLC) for the feedback optimization. Gradient-enriched machine learning control learns the control law significantly faster thanpreviously employed genetic programming control. The gMLC source code is freely available online.

Automated summary

We stabilize flow past a cluster of three rotating cylinders, known as the fluidic pinball, using automated gradient-enriched machine learning algorithms to optimize control laws and achieve better performance through increasingly richer search spaces.