利用动力系统极限环的强化学习优化流体混合
Optimization of fluid mixing by reinforcement learning using limit cycles of a dynamical system
中文总结 AI 辅助
提出一种无需详细流场测量、利用二维动力系统稳定极限环的强化学习优化方法，用于周期性旋转圆柱容器中的流体混合，能适应粘度变化并给出物理合理的优化运动。
中文摘要 AI 辅助
我们提出了一种方法，以克服将强化学习应用于流体混合过程时遇到的困难。该方法有两个主要特点：（i）它不需要对流动状态进行详细测量；（ii）通过有效利用二维动力系统（Li'enard系统）的稳定极限环，它可以在不对控制参数施加显式约束的情况下稳定地进行优化。作为示例，我们优化了一个过程，其中包含在圆柱形容器中的流体通过周期性旋转容器来混合。所得的最优容器运动在物理上是合理的：它在固体旋转状态建立之前反转旋转方向。此外，即使在混合过程中流体粘度随时间增加，该方法也能连续地使控制参数适应变化的粘度。
英文摘要
We propose a method to overcome the difficulties encountered when applying reinforcement learning to fluid mixing processes. The proposed method has two main features: (i) it does not require detailed measurements of the flow state, and (ii) by effectively exploiting a stable limit cycle of a two-dimensional dynamical system (the Li'enard system), it can stably perform optimization without imposing explicit constraints on the control parameters. As an illustrative example, we optimize a process in which a fluid contained in a cylindrical vessel is mixed by periodically rotating the vessel. The resulting optimal vessel motion is physically reasonable: it reverses its direction of rotation before a solid-body rotation state is established. Furthermore, even when the fluid viscosity increases with time during the mixing process, the method can continuously adapt the control parameters to the changing viscosity.
