论文标题
通过增强学习调整的混合流控制的生物启发的可变状态襟翼
Bio-inspired variable-stiffness flaps for hybrid flow control, tuned via reinforcement learning
论文作者
论文摘要
由固定刚度的扭转弹簧固定在翼型上的生物风格的，被动的可被部署的皮瓣，可以在后置攻击角度提供显着的提升改进。在这项工作中，我们描述了这种纯粹的被动流控制范式的杂种活性量变种，其中铰链的刚度在时间上积极变化，以产生与固定态度案例更大的空气动力学益处的被动流体结构相互作用（FSI）。与主动规定皮瓣运动相比，这种杂种活跃的流动控制策略可以使用可变的刚度执行器来实施。铰链刚度通过加强学习（RL）训练的闭环反馈控制器变化。引入了基于物理学的惩罚和长期工作的培训策略，以实现混合控制器的快速培训。证明混合控制器可提供高达136 \％和85 \％的提升改进，分别相对于无瓣机翼和最佳的固定状态案例。由于刚度在四个数量级上变化，因此由于大量振幅瓣振荡而实现了这些提升的改进，该刚度详细分析了与流量的相互作用。
A bio-inspired, passively deployable flap attached to an airfoil by a torsional spring of fixed stiffness can provide significant lift improvements at post-stall angles of attack. In this work, we describe a hybrid active-passive variant to this purely passive flow control paradigm, where the stiffness of the hinge is actively varied in time to yield passive fluid-structure interaction (FSI) of greater aerodynamic benefit than the fixed-stiffness case. This hybrid active-passive flow control strategy could potentially be implemented using variable stiffness actuators with less expense compared with actively prescribing the flap motion. The hinge stiffness is varied via a reinforcement learning (RL)-trained closed-loop feedback controller. A physics-based penalty and a long-short-term training strategy for enabling fast training of the hybrid controller are introduced. The hybrid controller is shown to provide lift improvements as high as 136\% and 85\% with respect to the flap-less airfoil and the best fixed-stiffness case, respectively. These lift improvements are achieved due to large-amplitude flap oscillations as the stiffness varies over four orders of magnitude, whose interplay with the flow is analyzed in detail.
