Novati Guido, Verma Siddhartha, Alexeev Dmitry, Rossinelli Diego, van Rees Wim M, Koumoutsakos Petros
Computational Science and Engineering Laboratory, Clausiusstrasse 33, ETH Zürich, CH-8092, Switzerland. Wallace Visiting Professor, Massachusetts Institute of Technology, MA, United States of America.
Bioinspir Biomim. 2017 Mar 29;12(3):036001. doi: 10.1088/1748-3190/aa6311.
The coordinated motion by multiple swimmers is a fundamental component in fish schooling. The flow field induced by the motion of each self-propelled swimmer implies non-linear hydrodynamic interactions among the members of a group. How do swimmers compensate for such hydrodynamic interactions in coordinated patterns? We provide an answer to this riddle though simulations of two, self-propelled, fish-like bodies that employ a learning algorithm to synchronise their swimming patterns. We distinguish between learned motion patterns and the commonly used a-priori specified movements, that are imposed on the swimmers without feedback from their hydrodynamic interactions. First, we demonstrate that two rigid bodies executing pre-specified motions, with an alternating leader and follower, can result in substantial drag-reduction and intermittent thrust generation. In turn, we study two self-propelled swimmers arranged in a leader-follower configuration, with a-priori specified body-deformations. These two self-propelled swimmers do not sustain their tandem configuration. The follower experiences either an increase or decrease in swimming speed, depending on the initial conditions, while the swimming of the leader remains largely unaffected. This indicates that a-priori specified patterns are not sufficient to sustain synchronised swimming. We then examine a tandem of swimmers where the leader has a steady gait and the follower learns to synchronize its motion, to overcome the forces induced by the leader's vortex wake. The follower employs reinforcement learning to adapt its swimming-kinematics so as to minimize its lateral deviations from the leader's path. Swimming in such a sustained synchronised tandem yields up to [Formula: see text] reduction in energy expenditure for the follower, in addition to a [Formula: see text] increase in its swimming-efficiency. The present results show that two self-propelled swimmers can be synchronised by adapting their motion patterns to compensate for flow-structure interactions. Moreover, swimmers can exploit the vortical structures of their flow field so that synchronised swimming is energetically beneficial.
多个游泳者的协同运动是鱼群行为的一个基本组成部分。每个自主推进的游泳者的运动所诱导的流场意味着群体成员之间存在非线性流体动力相互作用。游泳者如何以协调的模式补偿这种流体动力相互作用呢？我们通过对两个自主推进的、类似鱼的物体进行模拟来回答这个谜题，这些物体采用一种学习算法来同步它们的游泳模式。我们区分了学习到的运动模式和常用的先验指定运动，后者是在没有来自流体动力相互作用反馈的情况下强加给游泳者的。首先，我们证明了两个执行预先指定运动的刚体，一个交替充当领导者和跟随者，可以显著降低阻力并间歇性地产生推力。相应地，我们研究了两个以领导者 - 跟随者配置排列的自主推进游泳者，它们具有先验指定的身体变形。这两个自主推进游泳者无法维持它们的串联配置。跟随者的游泳速度根据初始条件会增加或减少，而领导者的游泳情况基本不受影响。这表明先验指定的模式不足以维持同步游泳。然后，我们研究了一组游泳者，其中领导者有稳定的步态，跟随者学习同步其运动，以克服由领导者的尾涡诱导的力。跟随者采用强化学习来调整其游泳运动学，以便将其与领导者路径方向上的横向偏差最小化。以这种持续同步的串联方式游泳，除了使跟随者的游泳效率提高[公式：见原文]之外，还能使其能量消耗降低[公式：见原文]。目前的结果表明，两个自主推进的游泳者可以通过调整它们的运动模式来同步，以补偿流 - 结构相互作用。此外，游泳者可以利用其流场的涡旋结构，从而使同步游泳在能量方面是有益的。
