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基于离散单元法和人工神经网络的近壁颗粒动力学特征研究

赵云华 段总样 徐璋

赵云华, 段总样, 徐璋. 基于离散单元法和人工神经网络的近壁颗粒动力学特征研究. 力学学报, 2021, 53(10): 1-11 doi: 10.6052/0459-1879-21-313
引用本文: 赵云华, 段总样, 徐璋. 基于离散单元法和人工神经网络的近壁颗粒动力学特征研究. 力学学报, 2021, 53(10): 1-11 doi: 10.6052/0459-1879-21-313
ZHAO Yunhua, DUAN Zongyang, XU Zhang. Characterization of near-wall particle dynamics based on discrete element method andartificial neural network. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(10): 1-11 doi: 10.6052/0459-1879-21-313
Citation: ZHAO Yunhua, DUAN Zongyang, XU Zhang. Characterization of near-wall particle dynamics based on discrete element method andartificial neural network. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(10): 1-11 doi: 10.6052/0459-1879-21-313

基于离散单元法和人工神经网络的近壁颗粒动力学特征研究

doi: 10.6052/0459-1879-21-313
基金项目: 国家自然科学基金资助项目(51976194)
详细信息
    作者简介:

    赵云华, 副教授, 主要研究方向: 气固两相流. E-mail: zhaoyh@zjut.edu.cn

  • 中图分类号: TK05

CHARACTERIZATION OF NEAR-WALL PARTICLE DYNAMICS BASED ON DISCRETE ELEMENT METHOD ANDARTIFICIAL NEURAL NETWORK

  • 摘要: 颗粒与壁面的相互作用往往对颗粒流动具有显著影响. 为研究颗粒与壁面作用机理, 对滚筒内颗粒流动过程进行离散单元法(DEM)数值模拟. 基于模拟结果统计分析靠近壁面处颗粒的运动特征, 结果表明小摩擦系数时颗粒平动和旋转速度均近似满足正态分布, 但由于壁面影响, 摩擦系数增大时颗粒沿滚筒轴向的旋转速度偏离正态分布, 颗粒动力学理论推导壁面边界条件时应考虑速度正态分布的修正及速度脉动的各向异性. 采用人工神经网络(ANN)构建了颗粒无因次旋转温度、滑移速度和平动温度之间的函数模型, 进而可以在常规双流模型壁面边界条件中考虑颗粒旋转的影响. 基于DEM模拟及结果分析可以为壁面边界条件的理论构造和半经验修正提供基础数据和封闭模型.

     

  • 图  1  滚筒内的颗粒分布

    Figure  1.  Particle distribution in the drum

    图  2  切向速度与实验数据的比较

    Figure  2.  Comparison of the tangential velocity with experiment data

    图  3  网格大小与平均颗粒变量的关系

    Figure  3.  Relationship between mesh size and averaged particle variables

    图  4  近壁颗粒平动速度分布

    Figure  4.  Translational velocity distribution of near-wall particles

    图  5  近壁颗粒旋转速度分布

    Figure  5.  Rotational velocity distribution of near-wall particles

    图  6  不同摩擦系数下的近壁颗粒轴向旋转速度分布

    Figure  6.  Axial rotational velocity distribution of near-wall particles under different friction coefficients

    图  7  本文使用的神经网络结构

    Figure  7.  Structure of neural network used in present work

    图  8  无因次旋转温度的模型预测与DEM统计结果对比

    Figure  8.  Comparison of predicted dimensionless rotational temperature with DEM results

    图  9  不同摩擦系数下壁面切向与法向应力比

    Figure  9.  Wall stress ratio under different friction

    表  1  模拟参数

    Table  1.   Simulation parameters

    ParametersValue
    Drum diameter, D (m) 0.1
    Drum length, L (m) 0.65
    Drum fill level 35%
    Total number of particles 72000
    Particle diameter, d (m) 0.003
    Particles density, ρ (kg/m3) 2500
    Young modulus, E (N/m) 1.0 × 107
    Poisson ratio, $ \nu $ 0.29
    Restitution coefficient, e 0.9
    Sliding friction coefficient, μ 0.7
    Rolling friction coefficient, μr 0.01
    下载: 导出CSV

    表  2  平动速度分布的标准差和决定系数

    Table  2.   Standard deviation and determination coefficient of translational velocity distribution

    μ0.30.50.70.9
    SD切向0.023770.030480.031220.02929
    SD径向0.013120.020870.021570.02168
    SD轴向0.01890.020230.022330.02215
    R2切向0.935130.963930.988870.99515
    R2径向0.926830.931840.961790.97895
    R2轴向0.985240.996930.995750.99548
    下载: 导出CSV

    表  3  旋转速度分布的标准差和决定系数

    Table  3.   Standard deviation and determination coefficient of rotational velocity distribution

    μ0.30.50.70.9
    SDX11.9036818.6548221.3064321.80627
    SDY15.8622922.5867622.5665221.88801
    SDZ41.2514957.3378651.1331343.71098
    R2X0.986820.992910.995690.99535
    R2Y0.990720.984690.985890.98491
    R2Z0.977120.870480.767090.72841
    下载: 导出CSV

    表  4  人工神经网络模型的细节

    Table  4.   Details of the artificial neural network model

    No.ParticularsSpecifications
    1Network typeBP neural network
    2Activation functionSigmoid and Linear
    3Error calculationMSE
    4Number of input layer unit2
    5Input parametersTVsilp
    6Number of output layer unit1
    7output parametersλ
    8Number of hidden layer unit8
    9learning rate0.0002
    10Training times600
    11Training set size2060
    12Test set size560
    下载: 导出CSV

    表  5  神经网络预测模型

    Table  5.   Prediction model from the neural network

    $\lambda = W\dfrac{1}{ {1 + {e^{ - ({W_x}x + {b_1})} } } } + {b_2}$
    $x = \left[ T \right.\;\left. {{V_{{\text{slip}}}}} \right]$
    ${b_1} = [0.028\;820\;17\;0.028\;820\;17\;0.028\;820\;17\;0.028\;820\;17\;0.028\;820\;17\;0.028\;820\;17\;0.028\;820\;17\;0.028\;820\;17] $
    ${b_2} = 0.718\;393\;26$
    ${W_x} = [\begin{array}{*{20}{c}}{{W_1}}&{{W_2}{]^T}} \end{array}$
    ${W_1} = [ - {0.522\;647\;14} - {0.522\;647\;14} - {0.522\;647\;14} - {0.522\;647\;14} - {0.522\;647\;14} - {0.522\;647\;14} - {0.522\;647\;14} - 0.522\;647\;14]$
    ${W_2} = [0.087\;477\;71\;0.087\;477\;71\;0.087\;477\;71\;0.087\;477\;71\;0.087\;477\;71\;0.087\;477\;71\;0.087\;477\;71\;0.087\;477\;71]$
    $W = {[0.613\;576\;85\; 0.613\;576\;85\; 0.613\;576\;85\; 0.613\;576\;85\; 0.613\;576\;85\; 0.613\;576\;85\; 0.613\;576\;85\; 0.613\;576\;85]^{\text{T}}}$
    下载: 导出CSV
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