Artificial intelligence control of a turbulent jet

An artificial intelligence (AI) control system is developed to manipulate a turbulent jet with a view to maximizing its mixing. The system consists of sensors (two hot-wires), genetic programming for learning/ evolving and execution mechanism (6 unsteady radial minijets). Mixing performance is quantified by the jet centerline mean velocity. AI control discovers a hitherto unexplored combination of flapping and helical forcings. Such a combination of several actuation mechanisms—if not creating new ones—is practically inaccessible to conventional methods like a systematic parametric analysis and gradient search, and vastly outperforms the optimized periodic axisymmetric, helical or flapping forcing produced from conventional open- or closed-loop controls. Intriguingly, the learning process of AI control discovers all these forcings in the order of increased performance. The AI control has dismissed sensor feedback and multi-frequency components for optimization. Our study is the first highly successful AI control experiment for a non-trivial spatially distributed actuation of a turbulent flow. The results show the great potential of AI in conquering the vast opportunity space of control laws for many actuators and sensors and manipulating turbulence.