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复杂流动中的智能颗粒游动策略研究进展

邱敬然 赵立豪

邱敬然, 赵立豪. 复杂流动中的智能颗粒游动策略研究进展. 力学学报, 2021, 53(10): 2630-2639 doi: 10.6052/0459-1879-21-402
引用本文: 邱敬然, 赵立豪. 复杂流动中的智能颗粒游动策略研究进展. 力学学报, 2021, 53(10): 2630-2639 doi: 10.6052/0459-1879-21-402
Qiu Jingran, Zhao Lihao. Progresses in swimming strategy of smart particles in complex flows. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(10): 2630-2639 doi: 10.6052/0459-1879-21-402
Citation: Qiu Jingran, Zhao Lihao. Progresses in swimming strategy of smart particles in complex flows. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(10): 2630-2639 doi: 10.6052/0459-1879-21-402

复杂流动中的智能颗粒游动策略研究进展

doi: 10.6052/0459-1879-21-402
基金项目: 国家自然科学基金(11911530141)和清华大学国强研究院(2019GQG1012)资助项目
详细信息
    作者简介:

    赵立豪, 教授, 主要研究方向: 颗粒湍流两相流, 智能体与强化学习. E-mail: zhaolihao@tsinghua.edu.cn

  • 中图分类号: V211.1+7

PROGRESSES IN SWIMMING STRATEGY OF SMART PARTICLES IN COMPLEX FLOWS

  • 摘要: 智能颗粒定义为可以根据环境的变化而主动调整自身在流场中运动的颗粒, 一般用于描述微小的游动物体, 如微生物、浮游生物和微纳机器人. 由于颗粒运动学特性和流场环境的复杂性, 探索智能颗粒的运动策略是一项具有现实意义与挑战性的研究. 近期强化学习算法被应用于智能颗粒的运动策略研究, 并取得一定进展. 本文将讨论强化学习在智能颗粒研究中的应用, 并介绍浮游生物运动策略的相关研究进展, 包括适用于海洋浮游生物的游动颗粒模型, 以及强化学习的基本原理和此类问题的研究框架. 海洋浮游生物的昼夜垂直迁移对其生存和繁衍至关重要, 生物学研究表明某些浮游生物可感知周围的流场信息, 但能否利用这些信息提高垂直方向游动效率仍是一个未知的问题. 基于这一背景, 相关研究考虑了重力沉降和颗粒形状对浮游生物垂直游动策略的影响. 研究发现细长的颗粒能够更加高效地垂直向上运动, 而重力沉降则导致智能游动策略发生较大变化. 在此基础上, 后续研究进一步考虑了局部流体信号的作用, 并讨论了基于局部信息在全局坐标系中定向运动的可能性. 当颗粒只能感受到局部流体信号时, 必须破坏动力学系统的对称性, 否则颗粒无法学到有效的垂向游动策略. 此外, 研究还发现强化学习能够利用流体信号背后的机制, 得到颗粒在二维定常流动和三维非定常湍流中高效垂直运动的策略. 这些智能游动策略依赖于本质的物理规律, 因此这些策略在更加复杂或真实的流动中也可能有优秀的表现.

     

  • 图  1  (a)智能颗粒示意图. x-y-z为全局坐标系, n-p-q为颗粒局部坐标系. (b)强化学习原理示意图[39]

    Figure  1.  (a) Sketch of smart particle. x-y-z defines the global frame of reference, and n-p-q defines the particle local frame of reference. (b) A diagram of reinforcement learning[39]

    图  2  智能颗粒垂直运动速度vz随形状和重力沉降作用的变化[22]. 颗粒速度以流动的特征速度u0无量纲化. RL: 强化学习得到的智能策略, naive: 简单策略, settle: 考虑沉降作用

    Figure  2.  Vertical velocity of smart particles vz as functions of the effects of gravitational settling and particle shape [22]. Particle velocity is normalized by velocity scale of the background flow u0. RL: smart strategy found by reinforcement learning. Naive: naive strategy. Settle: the cases considering settling effect

    图  3  (a)智能颗粒在二维Taylor-Green旋涡流动中的运动轨迹. 红点表示颗粒的初始位置, 背景为涡量云图. (b)智能颗粒与非智能颗粒当地流体垂直方向速度uy的概率密度分布(PDF)[23]

    Figure  3.  (a) Trajectories of smart particles in two-dimensional Taylor-Green vortex flow. Red dots represent the initial position of particles. Background contour shows the vorticity of fluid. (b) The probability distribution function (PDF) of the vertical velocity of local fluid uy[23]

    4  (a)颗粒在冻结流场(frozen)与随时间变化的流场(DNS)中的垂直方向速度vz, 以及游动速度nzvs, 当地流体速度uz和沉降速度vg,z的贡献. smart 2D: $ \mathrm{\Delta }{u}_{p} $流体信号下的智能策略; naive: 简单策略智能颗粒. (b)不同策略下颗粒在三维各向同性均匀湍流中的运动速度及各部分贡献. naive: 简单策略; 2D: Δup信号下的二维策略; 3D2S: Δup, Δuq信号下的三维策略; 3D4S: Snp, Snq, Δup, Δuq信号下的三维策略. (c)3D4S策略下的颗粒瞬时分布[24]

    4.  (a) Vertical velocity of particles vz in frozen flow (frozen) and time-dependent flow (DNS), with the contributions of swimming velocity nzvs, local fluid velocity uz, and settling velocity vg,z. Smart 2D: smart strategy with signal $ \mathrm{\Delta }{u}_{p} $. Naive: naive strategy. (b) Vertical velocity of particles under different swimming strategies in homogeneous isotropic turbulence. Naive: naive strategy. 2D: two-dimensional strategy with Δup signal. 3D2S: two-dimensional strategy with Δup, Δuq signals. 3D4S: two-dimensional strategy with Snp, Snq, Δup, Δuq signals. (c) Instanueous distribution of particles with 3D4S strategy[24]

    图  4  (a)颗粒在冻结流场(frozen)与随时间变化的流场(DNS)中的垂直方向速度vz, 以及游动速度nzvs, 当地流体速度uz和沉降速度vg,z的贡献. smart 2D: $ \mathrm{\Delta }{u}_{p} $流体信号下的智能策略; naive: 简单策略智能颗粒. (b)不同策略下颗粒在三维各向同性均匀湍流中的运动速度及各部分贡献. naive: 简单策略; 2D: Δup信号下的二维策略; 3D2S: Δup, Δuq信号下的三维策略; 3D4S: Snp, Snq, Δup, Δuq信号下的三维策略. (c)3D4S策略下的颗粒瞬时分布[24] (续)

    Figure  4.  (a) Vertical velocity of particles vz in frozen flow (frozen) and time-dependent flow (DNS), with the contributions of swimming velocity nzvs, local fluid velocity uz, and settling velocity vg,z. Smart 2D: smart strategy with signal $ \mathrm{\Delta }{u}_{p} $. Naive: naive strategy. (b) Vertical velocity of particles under different swimming strategies in homogeneous isotropic turbulence. Naive: naive strategy. 2D: two-dimensional strategy with Δup signal. 3D2S: two-dimensional strategy with Δup, Δuq signals. 3D4S: two-dimensional strategy with Snp, Snq, Δup, Δuq signals. (c) Instanueous distribution of particles with 3D4S strategy[24] (continued)

    表  1  二维与三维流场中独立流体信号分量

    Table  1.   Independent fluid signals in two-dimensional (2D) and three-dimensional (3D) flows

    Flow signals2D3D
    deformation rate${S_{nn}},\;\;{S_{np}}$${S_{nn}},\;{S_{np}},\;{S_{nq}},\;{S_{pp}},\;{S_{pq}}$
    relative angular velocity$\Delta {\varOmega _q}$$\Delta {\varOmega _n},\;\Delta {\varOmega _p},\;\Delta {\varOmega _q}$
    relative velocity$\Delta {u_n},\;\Delta {u_p}$$\Delta {u_n},\;\Delta {u_p},\;\Delta {u_q}$
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出版历程
  • 收稿日期:  2021-08-19
  • 录用日期:  2021-09-28
  • 网络出版日期:  2021-09-29
  • 刊出日期:  2021-10-26

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