论文标题
布拉夫身体使用深入的学习学习训练的主动流控制来实现流体动力隐身
Bluff body uses deep-reinforcement-learning trained active flow control to achieve hydrodynamic stealth
论文作者
论文摘要
我们提出了一种新型的活动流量控制（AFC）策略，以使其对捕食者隐藏其流体动力痕迹。采用了一组迎风 - 乳液吹动（WSLB）执行器，以控制在均匀流中浸入圆形圆柱体的唤醒。在近唤醒中部署了一系列速度传感器，以提供反馈信号。通过数据驱动的深入增强学习（DRL），有效的控制策略进行了WSLB驱动的培训，以减轻圆柱体的流体动力特征，即强的剪切和周期性脱落涡流。仅检测到流速速度的0.29％的赤字，与未控制值相比，它降低了99.5％。当气缸经历横向涡旋诱导的振动（VIV）时，发现相同的控制策略也是有效的。这项研究的发现可以为水下结构和机器人技术的设计和操作提供一些灯光，以实现流体动力隐身。
We propose a novel active-flow-control (AFC) strategy for bluff bodies to hide their hydrodynamic traces from predators. A group of windward-suction-leeward-blowing (WSLB) actuators are adopted to control the wake of a circular cylinder submerged in a uniform flow. An array of velocity sensors are deployed in the near wake to provide feedback signals. Through the data-driven deep reinforcement learning (DRL), effective control strategies are trained for the WSLB actuation to mitigate the cylinder's hydrodynamic signatures, i.e., strong shears and periodically shed vortices. Only a 0.29% deficit in streamwise velocity is detected, which is a 99.5% reduction from the uncontrolled value. The same control strategy is found to be also effective when the cylinder undergoes transverse vortex-induced vibration (VIV). The findings from this study can shed some lights on the design and operation of underwater structures and robotics to achieve hydrodynamic stealth.
