Machine Learning flow control in the few sensors limit

A comparative assessment of machine learning (ML) methods for closed-loop wake control in configurations with a limited number of sensors is presented. The baseline flow field is a two-dimensional simulation of the Kármán vortex street past a cylinder at moderate Reynolds number (Re=100). The actuation is performed by two jets on the sides of a cylinder. The flow is monitored with several sensor probe arrangements including 5, 11 and 151 velocity signals and one case with lift and drag sensors. Two popular alternatives are evaluated: Deep Reinforcement Learning (DRL) as pioneered by Rabault et al. (2019, JFM) and Genetic Programming control (GPC) for tree-based, linear and gradient-enriched realizations as pursued by the authors. All machine learning control methods successfully stabilize the vortex shedding and effectively reduce drag while using small mass flow rates for the actuation. DRL and GPC have complementary strengths. DRL yields higher drag reductions for large numbers of probes and short training periods. In contrast, GPC performs better for cases with fewer sensors and longer training periods. The results hint at combinations of DRL and GPC for further performance improvements.