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人工智能控制湍流进展: 系统、算法、成就、数据分析方法

吴智 范德威 周裕

吴智, 范德威, 周裕. 人工智能控制湍流进展: 系统、算法、成就、数据分析方法. 力学进展, 2023, 53(2): 273-307 doi: 10.6052/1000-0992-22-045
引用本文: 吴智, 范德威, 周裕. 人工智能控制湍流进展: 系统、算法、成就、数据分析方法. 力学进展, 2023, 53(2): 273-307 doi: 10.6052/1000-0992-22-045
Wu Z, Fan D W, Zhou Y. Advances in control of turbulence by artificial intelligence: Systems, algorithms, achievements and data analysis methods. Advances in Mechanics, 2023, 53(2): 273-307 doi: 10.6052/1000-0992-22-045
Citation: Wu Z, Fan D W, Zhou Y. Advances in control of turbulence by artificial intelligence: Systems, algorithms, achievements and data analysis methods. Advances in Mechanics, 2023, 53(2): 273-307 doi: 10.6052/1000-0992-22-045

人工智能控制湍流进展: 系统、算法、成就、数据分析方法

doi: 10.6052/1000-0992-22-045
基金项目: 作者感谢自然科学基金委通过项目编号91952204、12202123和深圳科创委通过项目编号JCYJ20190806143611025、JCYJ20210324132816040和GXWD20220811152110003所给予的支持
详细信息
    作者简介:

    吴智, 2019年1月获得哈尔滨工业大学博士学位. 2019年6月至2021年10月, 在韩国首尔大学Haecheon Choi 教授团队从事博士后研究. 2021年10月加入哈尔滨工业大学 (深圳) , 任助理教授, 获“鹏城孔雀特聘岗”. 已发表国际主流(SCI)期刊十余篇, 包括《Journal of Fluid Mechanics》《Experiments in Fluids》《Physics of Fluids》等. 主持国家自然科学基金青年项目一项和深圳市高校稳定支持项目一项. 目前研究方向包括: 机翼流动分离主动控制、钝体扰流主动控制、湍流边界层主动控制、人工智能湍流控制等

    通讯作者:

    yuzhou@hit.edu.cn

  • 中图分类号: O351

Advances in control of turbulence by artificial intelligence: Systems, algorithms, achievements and data analysis methods

More Information
  • 摘要: 湍流控制涉及流体力学和控制理论, 对航空航天、运载工具、风力发电等众多领域具有重要的科学意义和应用价值. 由于湍流的复杂性, 传统控制方法在湍流控制领域面临很多瓶颈, 人工智能技术的发展为突破这些瓶颈提供了工具. 本文简要综述了文献报道的有模型和无模型的人工智能控制湍流的进展, 总结了研究中采用的典型的人工智能控制系统、算法、在不同湍流控制应用中取得的突出成果. 作者所在团队在国际上首次尝试对人工智能控制系统产生的海量数据进行分析, 从而挖掘出重要的信息乃至发现控制相似律. 对面临的挑战和未来的展望进行了分析.

     

  • 图  1  人工智能在湍流控制中的应用分类

    图  2  典型的人工智能控制湍流系统

    图  3  神经网络结构示意图

    图  4  槽道湍流减阻控制中的神经网络结构图(Lee et al. 1997)

    图  5  遗传算法基本流程图

    图  6  x-z平面上在y+ = 7时在无控制、传统开环控制 (COC) 和人工智能 (AI) 控制下典型的烟线流动显示图像, 红框区域所示为稳定条带结构(Yu et al. 2021)

    图  7  强化学习(a)和简化的强化学习算法(b)的控制框图(Lai et al. 2021)

    图  8  (a)探索梯度法运行框图, (b)无控制下三圆柱涡量图和(c) EGM搜索到的最优控制参数下的涡量图, 箭头表示旋转方向, 黑色扇区大小与转速成正比(Li et al. 2021)

    图  9  利用Okubo-Weiss参数法得到的Ahmed车模绕流的在Q = 20000时的等值面(Li et al. 2021)

    图  10  基于改进蚁群加权抽样算法的汽车模型减阻控制系统逻辑框图 (沈子凡2021)

    图  11  基于蚁群算法下的学习曲线. 正方形符号表示第n代中最佳个体An (Zhang et al. 2022)

    图  12  (a)树形图表示个体, (b)矩阵表示个体

    图  13  最优开环控制(fa,dc)w(HS)和最优闭环控制律$ {\mathcal{F}}_{W\left(HS\right)}^{GP} $分别在设计工况 (高速环境HS) 和非设计工况 (低速环境LS) 下对比测试. 第一行是不同控制条件下的JW和对应的质量流率系数Cq, 第二行是每三个热线探针信号的频率谱密度函数 (PSD) , 第三行是t = 0.25 s范围内流场的伪可视化结果, 灰度表示−1.5 ~ 1.5 m/s范围内的脉动速度u' (Parezanović et al. 2015)

    图  14  GP得到的最佳控制律对平均流场的影响. (a)无控制下和(b)最佳控制下的平均流线图. 颜色表示速度的模$ \left\| {\boldsymbol{u}} \right\| = \sqrt {({{\overline u }^2} + {{\overline w }^2})} $(Li et al. 2017)

    图  15  (a)基于LGP的系统控制框图, (b)无控制与LGP最优控制律下的流动显示图 (Wu et al. 2018a)

    图  16  遗传编程算法在射流混合控制中的学习曲线及特定控制律下的流动显示图 (Zhou et al. 2020)

    图  17  (a)DRL在一次训练过程中的运行过程示意图, (b)训练500次后圆柱表面压力分布, (c)展向平均涡量的时间平均以及上方小圆柱附近速度分布(Fan et al. 2020a)

    图  18  (a) 径向基函数神经网络自适应控制系统程序框图及(b)其在来流马赫数M和攻角α连续改变时控制系统后缘襟翼角度β和升力系数Cl的响应(Ren et al. 2020b)

    图  19  (a)混合人工智能控制系统的结构示意图, (b)不同雷诺数下混合人工智能控制系统优化得到的最佳控制参数(Perumal et al. 2022)

    图  20  第 1, 2, 5 和 11 代 (共 400 个控制律) 的近邻图. 每个符号代表一个单独的控制律. 背景颜色对应于控制律的目标函数值J (Zhou et al. 2020). (a) ~ (d) 中的空心圆、三角、方和钻石形符号分别代表第 1, 2, 5 和 11 代的最优控制律

    图  21  图20(a)中提取的与控制律 A ~ F 相关联的控制模式(Zhou et al. 2020). 每个扇形部分对应激励周期的六分之一, 而六个扇区的位置则代表六个微射流. 箭头表示微射流处于开启状态, 蓝色阴影的径向深度与微射流喷射持续时间成正比

    图  22  雷诺数5800 ~ 40000范围内变量ζ = (Cm/α)(D/d)1−n(1/Re)(fe/fe,opt)mKe/K0的影响. 人工智能实验数据选取了占空比α = 0.07 ~ 0.9, fe/f0 = 0.1 ~ 1.5范围内的测试点. 红线是实验数据根据最小二乘法得到的多项式拟合曲线. 蓝色点对应给定雷诺数的最大混合速率Ke,max/K0 (Perumal et al. 2022)

    表  1  基于模型的人工智能湍流控制应用统计

    文献控制方法控制对象激励方式传感器研究手段
    Lee et al. (1997)NN & 反向控制槽道湍流减阻动态吹吸气壁面剪切应力传感器CFD
    Lorang et al. (2008)NN & 反向控制槽道湍流减阻动态吹吸气壁面剪切应力传感器CFD
    Kaiser et al. (2017)基于聚类的控制斜坡流动分离脉冲吹吸气速度传感器CFD
    Han和Huang (2020)CNN & 反向控制槽道湍流减阻动态吹吸气壁面剪切应力传感器CFD
    Park和Choi (2020)CNN & 反向控制槽道湍流减阻动态吹吸气壁面剪切应力传感器、
    压力传感器
    CFD
    Bieker et al. (2020)RNN & MPC流体弹球
    尾流控制
    三个独立
    旋转的圆柱
    测力传感器CFD
    RNN: recurrent neural network; MPC: model predictive control; NN: neural network; CNN: convolutional neural networks; CFD: computational fluid dynamics.
    下载: 导出CSV

    表  2  控制参数优化在无模型人工智能流动控制中的应用

    文献控制方法控制对象激励方式传感器研究手段
    Koumoutsakos et al. (2001)ES射流混合正弦狭缝射流速度传感器CFD
    Hilgers和Boersma (2001)ES射流混合螺旋激励和轴向激励速度传感器CFD
    Benard et al. (2016)GA后向台阶流动分离等离子体激励压力传感器Exp.
    Minelli et al. (2020)GA方柱体减阻前缘吹吸气多频率扰动测力传感器CFD
    Qiao et al. (2021)GA俯仰方柱体减阻前缘吹吸气多频率扰动测力传感器Exp.
    Zigunov et al. (2021)GA倾斜圆柱体减阻定常微射流阵列组合粒子图像测速仪Exp.
    Yu et al. (2021)GA湍流边界层减阻独立控制合成射流阵列热线传感器Exp.
    Fan et al. (2020b)EGMAhmed 模型减阻脉冲狭缝射流测力传感器Exp.
    Maceda et al. (2021)EGM三圆柱组合体尾流控制三个独立旋转的圆柱速度传感器CFD
    Li et al. (2021)EGM三圆柱组合体尾流控制三个独立旋转的圆柱测力传感器CFD
    Ahmed模型减阻五个独立定常狭缝射流测力传感器
    Lai et al. (2021)SDRL圆柱减阻旋转圆柱测力传感器CFD
    沈子凡 (2021) ACOAhmed模型减阻五个角度可调节定常
    狭缝射流
    测力传感器Exp.
    Zhang et al. (2022)ACOAhmed模型减阻五组定常或非定常狭
    缝射流激励
    测力传感器Exp.
    ES: evolution strategies; GA: genetic algorithm; EGM: explorative gradient method; SDRL: simplified deep reinforcement learning; ACO: ant colony optimization; CFD: computational fluid dynamics; Exp: experiments.
    下载: 导出CSV

    表  3  无模型控制律优化在流动控制中的应用

    文献控制方法控制对象激励方式传感器研究手段
    Gautier et al. (2015)GP后台阶流动分离动态狭缝射流粒子图像测速仪Exp.
    Parezanović et al. (2015, 2016)GP混合层脉冲微射流阵列热线传感器Exp.
    Debien et al. (2016)GP斜坡流动分离脉冲主动涡流发生器阵列热膜传感器Exp.
    Chovet et al. (2017)GP后台阶流动分离脉冲微射流阵列压力传感器Exp.
    Li et al. (2017)GPAhmed模型减阻模型尾部脉冲微射流阵列测力传感器Exp.
    Wu et al. (2018a)GP射流掺混单个径向脉冲微射流热线传感器Exp.
    Ren et al. (2019)GP圆柱涡激振动动态吹吸气位移传感器CFD
    Li et al. (2020)GP混合层动态体积力扰动速度传感器CFD
    Raibaudo和Martinuzzi (2021), Raibaudo et al. (2020)GP三圆柱组合体尾流三个旋转的圆柱速度传感器Exp.
    Zhou et al. (2020)GP射流掺混六支径向脉冲微射流热线传感器Exp.
    Maceda et al. (2021)GP三圆柱组合体尾流三个旋转的圆柱速度传感器CFD
    Nair et al. (2019)CBC翼型绕流动态吹吸气测力传感器CFD
    Rabault et al. (2019a),
    Rabault和Kuhnle (2019b)
    DRL圆柱绕流动态吹吸气速度、测力
    传感器
    CFD
    Fan et al. (2020a)DRL圆柱绕流两个旋转圆柱体测力传感器Exp
    Rabault et al. (2020)DRL圆柱绕流动态吹吸气速度、测力
    传感器
    CFD
    Ren et al. (2020b)DRL翼型跨音速抖振后缘襟翼角度测力传感器CFD
    Shimomura et al. (2020)DRL翼型绕流等离子激励器压力传感器Exp.
    Tang et al. (2020)DRL圆柱绕流四个合成射流激励器速度、测力
    传感器
    CFD
    Tokarev et al. (2020)DRL圆柱绕流旋转圆柱速度、测力
    传感器
    CFD
    Ghraieb et al. (2021)DRL翼型绕流; 翼型攻角; 角度、测力
    传感器
    CFD
    Ghraieb et al. (2021)DRL两串联分布圆柱
    绕流;
    两圆柱间距测力传感器CFD
    Ghraieb et al. (2021)DRL三圆柱组合体尾流三个圆柱转速测力传感器CFD
    Mei et al. (2021)DRL圆柱涡激振动三个合成射流的质量流率速度、测力
    传感器
    CFD
    Mei et al. (2022)DRL钝体减阻多个合成射流速度、测力
    传感器
    CFD
    Paris et al. (2021)DRL圆柱绕流两个合成射流速度、测力
    传感器
    CFD
    Zeng和Graham (2021)DRLKSE流动控制
    问题
    均匀分布的激励器速度传感器CFD
    Zheng et al. (2021)DRL圆柱涡激振动两支动态射流速度、测力
    传感器
    CFD
    Ren et al. (2021a)DRL圆柱绕流一对反相射流速度、测力
    传感器
    CFD
    Ren et al. (2021b)DRL圆柱尾迹前缘吸气后缘吹气速度传感器CFD
    Perumal et al. (2022)DRL射流增混一支径向微射流热线传感器Exp.
    Wang et al. (2022)DRL翼型绕流合成射流速度、测力
    传感器
    CFD
    GP: genetic programming; CBC: cluster-based control; DRL: deep reinforcement learning.
    下载: 导出CSV

    表  4  典型湍流控制方法的优缺点及适用范围

    控制方法优点缺点适用范围
    基于神经网络的反向控制对槽道湍流控制效果好要求传感器数量多、适用
    范围小
    仅适用于反向控制的应用
    基于聚类降阶模型的控制可以处理高维、非线性系统模型具有不准确性、要求传感器数量多系统状态易测量的控制应用
    RNN和MPC对传感器数量要求低需要提供系统期望状态轨迹使系统输出达到预设目标的应用
    参数优化方法: ES, GA, EGM, ACO, SDRL不依赖模型、对传感器数量要求低、鲁棒性强、简单
    通用
    参数维数高时不易收敛, 仅可以用于 参数优化形式固定的控制律中参数的优化
    GP对传感器数量要求低、不依赖模型、鲁棒性强、简单
    通用
    优化反馈控制律时抗噪声干扰能力差优化开环、闭环控制律的
    应用
    DRL不依赖模型、适用范围广对传感器数量要求高、
    对计算资源要求高、
    优化闭环控制律的应用
    基于聚类的无模型反馈控制对传感器数量要求低、不依赖模型、收敛时间短实验研究还需要验证实时反馈控制应用
    基于LGP的混合模式人工智能控制不依赖模型、可同时实现对控制律和控制参数的优化优化反馈控制律时抗
    噪声干扰能力差
    优化开环、闭环控制律或
    控制参数的应用
    下载: 导出CSV
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  • 收稿日期:  2022-10-24
  • 录用日期:  2023-01-28
  • 网络出版日期:  2023-02-06
  • 刊出日期:  2023-06-25

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