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三七种植土壤离散元仿真模型参数标定

王法安, 曾悦, 张兆国, 解开婷, 李东昊, 何忠平

王法安, 曾悦, 张兆国, 等. 三七种植土壤离散元仿真模型参数标定[J]. 华南农业大学学报, 2024, 45(4): 588-597. DOI: 10.7671/j.issn.1001-411X.202309042
引用本文: 王法安, 曾悦, 张兆国, 等. 三七种植土壤离散元仿真模型参数标定[J]. 华南农业大学学报, 2024, 45(4): 588-597. DOI: 10.7671/j.issn.1001-411X.202309042
WANG Fa’an, ZENG Yue, ZHANG Zhaoguo, et al. Parameter calibration of discrete element simulation model for Panax notoginseng planting soil[J]. Journal of South China Agricultural University, 2024, 45(4): 588-597. DOI: 10.7671/j.issn.1001-411X.202309042
Citation: WANG Fa’an, ZENG Yue, ZHANG Zhaoguo, et al. Parameter calibration of discrete element simulation model for Panax notoginseng planting soil[J]. Journal of South China Agricultural University, 2024, 45(4): 588-597. DOI: 10.7671/j.issn.1001-411X.202309042

三七种植土壤离散元仿真模型参数标定

基金项目: 国家重点研发计划(2022YFD2002004);云南省教育厅科学研究基础项目(2023J0151)
详细信息
    作者简介:

    王法安,讲师,博士,主要从事智能农业机械装备设计研究,E-mail: wfa@kust.edu.cn

    通讯作者:

    张兆国,教授,博士,主要从事农业机械装备设计研究,E-mail: zzg@kust.edu.cn

  • 中图分类号: S220.1;S225;R282.71

Parameter calibration of discrete element simulation model for Panax notoginseng planting soil

  • 摘要:
    目的 

    获取三七Panax notoginseng种植土壤与触土部件相互作用的离散元仿真模型参数。

    方法 

    基于Hertz-Mindlin with JKR接触模型建立三七种植土壤离散元模型并进行参数标定。首先,以土壤颗粒间及土壤−65Mn钢板间的JKR表面能、恢复系数、静摩擦系数、动摩擦系数为试验因素,以土壤堆积角、土壤在65Mn板上的滚动距离为评价指标。其次,采用基于Box-Behnken的响应面优化方法建立土壤堆积角、滚动距离回归模型。

    结果 

    对回归模型进行寻优,得到仿真标定的土壤颗粒间JKR表面能、恢复系数、静摩擦系数和动摩擦系数的最优值分别为14.88 J/m2、0.53、0.46和0.150,标定的土壤−65Mn板间JKR表面能、恢复系数、静摩擦系数和动摩擦系数的最优值分别为7.02 J/m2、0.59、0.57和0.058。通过三七挖掘铲仿真试验与土槽试验对比分析得到,挖掘铲受XY轴方向平均阻力仿真值与实测值相对误差分别为9.91%、8.78%。

    结论 

    标定的离散元土壤模型参数准确度高,研究可为三七收获机触土部件及装备优化提供理论参考。

    Abstract:
    Objective 

    To obtain the parameters of the discrete element simulation model for the interaction between Panax notoginseng planting soil and soil-engaging components.

    Method 

    This paper established a discrete element model of P. notoginseng planting soil based on the Hertz-Mindlin with JKR contact model, and calibrated parameters. Firstly, the JKR surface energy, restitution coefficient, static friction coefficient and rolling friction coefficient between soil particles and soil-65Mn steel plate were used as experiments factors, and the soil repose angle and the rolling distance of soil on the 65Mn plate were used as evaluation indexes. Secondly, the regression model for soil repose angle and rolling distance was established through the response surface optimization method based on Box-Behnken.

    Result 

    The regression model was optimized, and the optimal values of JKR surface energy, restitution coefficient, static friction coefficient and rolling friction coefficient between soil particles calibrated by simulation were 14.88 J/m2, 0.53, 0.46 and 0.150, respectively. The calibrated optimal values of JKR surface energy, restitution coefficient, static friction coefficient and rolling friction coefficient between soil-65Mn steel plate were 7.02 J/m2, 0.59, 0.57 and 0.058, respectively. Through the comparative analysis of the simulation test and the soil-bin test, the relative errors of the simulated and measured average resistance of the excavating shovel in the X and Y axis directions were 9.91% and 8.78%, respectively.

    Conclusion 

    The calibrated discrete element soil model parameters have high accuracy, and the research can provide a theoretical reference for the optimization of the soil-engaging components and equipment of the P. notoginseng harvester.

  • 图  1   土壤压缩试验

    Figure  1.   Soil compression test

    图  2   土壤堆积试验

    Figure  2.   Soil repose test

    图  3   静摩擦试验

    Figure  3.   Static friction test

    图  4   滚动摩擦试验

    Figure  4.   Rolling friction test

    图  5   堆积角仿真试验

    Figure  5.   Simulation test of repose angle

    图  6   土壤堆积角仿真与实际试验对比

    Figure  6.   Comparison between simulation and physical tests for soil repose angle

    图  7   斜面仿真试验

    Figure  7.   Slope simulation test

    图  8   三七挖掘铲仿真试验

    Figure  8.   Simulation test of Panax notoginseng excavating shovel

    图  9   三七挖掘铲土槽试验

    1:镇压辊,2:液压泵站,3:控制系统,4:三七挖掘铲,5:传感器模组

    Figure  9.   Soil-bin test of Panax notoginseng excavating shovel

    1: Pressure roll, 2: Hydraulic power pack, 3: Control system, 4: Panax notoginseng excavating shovel, 5: Sensor module

    图  10   仿真与试验过程挖掘铲阻力对比

    Figure  10.   Comparison of shovel resistance in simulation and test processes

    图  11   三七挖掘铲壅土

    Figure  11.   Soil blockage of Panax notoginseng excavating shovel

    表  1   土壤堆积角仿真试验因素及水平1)

    Table  1   Factors and levels of simulation test for soil repose angle

    水平
    Level
    ${x_1}$/(J∙m−2)${x_2}$${x_3}$${x_4}$
    低水平 Low level40.150.200.050
    中心水平 Central level100.450.620.125
    高水平 High level160.751.040.200
     1)${x_1}$:土壤颗粒间JKR表面能,${x_2}$:土壤颗粒间恢复系数,${x_3}$:土壤颗粒间静摩擦系数,${x_4}$:土壤颗粒间滚动摩擦系数
     1)${x_1}$: JKR surface energy between soil particles, ${x_2}$: Restitution coefficient between soil particles, ${x_3}$: Static friction coefficient between soil particles, ${x_4}$: Rolling friction coefficient between soil particles
    下载: 导出CSV

    表  2   土壤堆积角仿真试验设计及结果1)

    Table  2   Design and results of soil repose angle simulation test

    试验号
    Test
    No.
    ${x_1}$/(J∙m−2) ${x_2}$ ${x_3}$ ${x_4}$ 堆积角/(°)
    Repose
    angle
    1 4 0.75 0.62 0.125 18.00
    2 10 0.75 0.62 0.200 37.80
    3 4 0.15 0.62 0.125 25.36
    4 16 0.15 0.62 0.125 37.48
    5 16 0.45 0.62 0.200 44.13
    6 4 0.45 0.62 0.050 10.05
    7 10 0.75 1.04 0.125 29.78
    8 10 0.45 0.62 0.125 36.78
    9 10 0.15 0.62 0.200 40.57
    10 10 0.15 0.62 0.050 27.48
    11 10 0.75 0.20 0.125 31.62
    12 4 0.45 0.20 0.125 8.10
    13 10 0.15 0.20 0.125 39.95
    14 16 0.45 0.20 0.125 37.42
    15 4 0.45 1.04 0.125 23.09
    16 10 0.75 0.62 0.050 21.11
    17 10 0.45 1.04 0.050 22.59
    18 16 0.45 1.04 0.125 51.84
    19 10 0.45 0.20 0.200 42.32
    20 10 0.45 0.62 0.125 37.89
    21 10 0.45 1.04 0.200 39.42
    22 16 0.45 0.62 0.050 31.58
    23 16 0.75 0.62 0.125 36.87
    24 10 0.15 1.04 0.125 35.02
    25 10 0.45 0.62 0.125 35.53
    26 10 0.45 0.20 0.050 29.01
    27 10 0.45 0.62 0.125 37.49
    28 4 0.45 0.62 0.200 29.04
    29 10 0.45 0.62 0.125 35.68
     1)${x_1}$:土壤颗粒间JKR表面能,${x_2}$:土壤颗粒间恢复系数,${x_3}$:土壤颗粒间静摩擦系数,${x_4}$:土壤颗粒间滚动摩擦系数
     1)${x_1}$: JKR surface energy between soil particles, ${x_2}$: Restitution coefficient between soil particles, ${x_3}$: Static friction coefficient between soil particles, ${x_4}$: Rolling friction coefficient between soil particles
    下载: 导出CSV

    表  3   土壤堆积角回归模型方差分析1)

    Table  3   Variance analysis of soil repose angle regression model

    方差源
    Variance
    source
    平方和
    Sum of
    squares
    自由度
    Degree of
    freedom
    均方
    Mean
    square
    FP
    模型
    Model
    2 696.2324112.34100.930.000 2**
    ${x_1}$335.261335.26301.18<0.000 1**
    ${x_2}$20.88120.8818.760.012 3*
    ${x_3}$21.72121.7219.510.011 5*
    ${x_4}$227.101227.10204.020.000 1**
    ${x_1}{x_2}$11.39111.3910.230.032 9*
    ${x_1}{x_3}$0.0810.080.070.800 4
    ${x_1}{x_4}$10.37110.379.310.038 0*
    ${x_2}{x_3}$2.3912.392.140.216 9
    ${x_2}{x_4}$3.2413.242.910.163 2
    ${x_3}{x_4}$3.1013.102.780.170 6
    $x_1^2$53.95153.9548.460.002 2**
    $x_2^2$18.36118.3616.500.015 3*
    $x_3^2$1.0211.020.920.391 7
    $x_4^2$34.10134.1030.630.005 2**
    $x_1^2{x_2}$0.1710.170.150.715 0
    $x_1^2{x_3}$187.501187.50168.450.000 2**
    $x_1^2{x_4}$0.2510.250.220.663 4
    ${x_1}x_2^2$3.9613.963.560.132 3
    ${x_1}x_3^2$57.51157.5151.670.002 0**
    $x_2^2{x_3}$0.8110.810.730.441 0
    $x_2^2{x_4}$0.0210.020.020.909 8
    ${x_2}x_3^2$2.4512.452.200.211 8
    $x_1^2x_2^2$0.0010.000.000.978 7
    $x_1^2x_3^2$0.8810.880.790.423 3
    纯误差
    Pure error
    4.4541.11
    总和
    Sum
    2 700.6928
     1)${x_1}$:土壤颗粒间JKR表面能,${x_2}$:土壤颗粒间恢复系数,${x_3}$:土壤颗粒间静摩擦系数,${x_4}$:土壤颗粒间滚动摩擦系数;“*”“**”分别表示在P<0.05和P<0.01水平影响显著(P值检验法)
     1)${x_1}$: JKR surface energy between soil particles, ${x_2}$: Restitution coefficient between soil particles, ${x_3}$: Static friction coefficient between soil particles, ${x_4}$: Rolling friction coefficient between soil particles; “*” and “**” indicate signifcant effects at P < 0.05 and P < 0.01 levels respectively (P-value test method)
    下载: 导出CSV

    表  4   斜面仿真试验因素及水平1)

    Table  4   Factors and levels of slope simulation test

    水平
    Level
    ${x_5}$/(J∙m−2) ${x_6}$ ${x_7}$ ${x_8}$
    低水平 Low level 2 0.20 0.20 0.030
    中心水平 Central level 7 0.45 0.45 0.055
    高水平 High level 12 0.70 0.70 0.080
     1)${x_5}$:土壤−65Mn板间JKR表面能,${x_6}$:土壤−65Mn板间恢复系数,${x_7}$:土壤−65Mn板间静摩擦系数,${x_8}$:土壤−65Mn板间滚动摩擦系数
     1)${x_5}$: JKR surface energy between soil-65Mn plate, ${x_6}$: Restitution coefficient between soil-65Mn plate, ${x_7}$: Static friction coefficient between soil-65Mn plate, ${x_8}$: Rolling friction coefficient between soil-65Mn plate
    下载: 导出CSV

    表  5   斜面仿真试验设计及结果1)

    Table  5   Design and results of slope simulation test

    试验号
    Test No.
    ${x_5}$/(J∙m−2) ${x_6}$ ${x_7}$ ${x_8}$ 滚动距离/mm
    Rolling
    distance
    1 7 0.20 0.45 0.030 882.53
    2 7 0.70 0.70 0.055 402.56
    3 2 0.45 0.70 0.055 463.21
    4 7 0.70 0.45 0.030 854.41
    5 7 0.45 0.20 0.030 817.00
    6 7 0.45 0.45 0.055 415.24
    7 7 0.70 0.45 0.080 266.53
    8 12 0.45 0.45 0.080 230.83
    9 7 0.20 0.45 0.080 241.72
    10 2 0.45 0.45 0.030 877.41
    11 7 0.45 0.70 0.080 257.49
    12 12 0.45 0.70 0.055 391.03
    13 7 0.45 0.45 0.055 421.78
    14 7 0.45 0.45 0.055 429.95
    15 7 0.45 0.45 0.055 427.71
    16 7 0.45 0.20 0.080 256.03
    17 7 0.70 0.20 0.055 415.11
    18 12 0.45 0.45 0.030 793.51
    19 12 0.70 0.45 0.055 354.93
    20 2 0.20 0.45 0.055 456.22
    21 7 0.45 0.70 0.030 843.15
    22 2 0.45 0.20 0.055 488.20
    23 2 0.45 0.45 0.080 295.52
    24 7 0.20 0.20 0.055 396.58
    25 7 0.20 0.70 0.055 396.66
    26 2 0.70 0.45 0.055 455.17
    27 12 0.20 0.45 0.055 342.45
    28 7 0.45 0.45 0.055 421.21
    29 12 0.45 0.20 0.055 350.32
     1)${x_5}$:土壤−65Mn板间JKR表面能,${x_6}$:土壤−65Mn板间恢复系数,${x_7}$:土壤−65Mn板间静摩擦系数,${x_8}$:土壤−65Mn板间滚动摩擦系数
     1)${x_5}$: JKR surface energy between soil-65Mn plate, ${x_6}$: Restitution coefficient between soil-65Mn plate, ${x_7}$: Static friction coefficient between soil-65Mn plate, ${x_8}$: Rolling friction coefficient between soil-65Mn plate
    下载: 导出CSV

    表  6   土壤滚动距离回归模型方差分析1)

    Table  6   Variance analysis of regression model of soil rolling distance

    方差源
    Variance
    source
    平方和
    Sum of
    squares
    自由度
    Degree of
    freedom
    均方
    Mean
    square
    F P
    模型
    Model
    1.20 × 106 24 50 020.25 1 479.47 <0.000 1**
    ${x_5}$ 5 519.75 1 5 519.75 163.26 0.000 2**
    ${x_6}$ 2.74 1 2.74 0.08 0.790 1
    ${x_7}$ 190.58 1 190.58 5.64 0.076 5
    ${x_8}$ 3.29×105 1 3.29×105 9 721.81 <0.000 1**
    ${x_5}{x_6}$ 45.77 1 45.77 1.35 0.309 3
    ${x_5}{x_7}$ 1 079.12 1 1 079.12 31.92 0.004 8**
    ${x_5}{x_8}$ 92.26 1 92.26 2.73 0.173 9
    ${x_6}{x_7}$ 39.88 1 39.88 1.18 0.338 5
    ${x_6}{x_8}$ 700.40 1 700.40 20.72 0.010 4*
    ${x_7}{x_8}$ 152.40 1 152.40 4.51 0.101 0
    $x_5^2$ 360.96 1 360.96 10.68 0.030 9*
    $x_6^2$ 6.96 1 6.96 0.21 0.673 7
    $x_7^2$ 1 546.55 1 1 546.55 45.74 0.002 5**
    $x_8^2$ 81 826.04 1 81 826.04 2 420.20 <0.000 1**
    $x_5^2{x_6}$ 27.16 1 27.16 0.80 0.420 8
    $x_5^2{x_7}$ 17.67 1 17.67 0.52 0.509 7
    $x_5^2{x_8}$ 0.530 4 1 0.53 0.02 0.906 4
    ${x_5}x_6^2$ 534.97 1 534.97 15.82 0.016 4*
    ${x_5}x_7^2$ 472.32 1 472.32 13.97 0.020 2*
    $x_6^2{x_7}$ 200.80 1 200.80 5.94 0.071 4
    $x_6^2{x_8}$ 841.73 1 841.73 24.90 0.007 5**
    ${x_6}x_7^2$ 96.19 1 96.19 2.85 0.166 9
    $x_5^2x_6^2$ 41.41 1 41.41 1.22 0.330 5
    $x_5^2x_7^2$ 1 052.52 1 1 052.52 31.13 0.005 1**
    纯误差
    Pure error
    135.24 4 33.81
    总和
    Sum
    1.20×106 28
     1)${x_5}$:土壤−65Mn板间JKR表面能,${x_6}$:土壤−65Mn板间恢复系数,${x_7}$:土壤−65Mn板间静摩擦系数,${x_8}$:土壤−65Mn板间滚动摩擦系数;“*”“**”分别表示在P<0.05和P<0.01水平影响显著(P值检验法)
     1)${x_5}$: JKR surface energy between soil-65Mn plate, ${x_6}$: Restitution coefficient between soil-65Mn plate, ${x_7}$: Static friction coefficient between soil-65Mn plate, ${x_8}$: Rolling friction coefficient between soil-65Mn plate; “*” and “**” indicate signifcant effect at P < 0.05 and P < 0.01 levels respectively (P-value test method)
    下载: 导出CSV
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    1. 张运春,欧春予,张桥英. 氮添加对不同密度入侵植物喜旱莲子草生长的影响. 生态环境学报. 2020(09): 1745-1751 . 百度学术

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出版历程
  • 收稿日期:  2023-09-25
  • 网络出版日期:  2024-05-19
  • 发布日期:  2024-05-29
  • 刊出日期:  2024-07-09

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