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.

中文翻译：

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湍流射流的人工智能控制

开发了一种人工智能 (AI) 控制系统来操纵湍流射流，以最大程度地混合。该系统由传感器（两条热线）、用于学习/进化的遗传编程和执行机制（6 个不稳定的径向微型喷气机）组成。混合性能由喷射中心线平均速度量化。AI 控制发现了迄今为止尚未探索过的扑动和螺旋强迫的组合。这种几种驱动机制的组合——如果不是创造新的——实际上是传统方法无法获得的，如系统参数分析和梯度搜索，并且大大优于由传统开环或闭环产生的优化的周期性轴对称、螺旋或扑动强迫控件。耐人寻味的是，AI 控制的学习过程会按照性能提高的顺序发现所有这些强制因素。AI 控制消除了传感器反馈和多频组件以进行优化。我们的研究是第一个非常成功的人工智能控制实验，用于湍流的非平凡空间分布驱动。结果表明，人工智能在征服许多执行器和传感器的控制律的巨大机会空间和操纵湍流方面具有巨大潜力。