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Conference Papers Year : 2020

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.

Dates and versions

hal-02398705 , version 1 (07-12-2019)



Dewei Fan, Yu Zhou, Bernd Noack, Ruiying Li. Artificial intelligence control of a turbulent jet. 21st Australasian Fluid Mechanics Conference, Dec 2018, Adelaide, Australia. pp.A27, ⟨10.1017/jfm.2020.392⟩. ⟨hal-02398705⟩
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