中国农业科技导报 ›› 2024, Vol. 26 ›› Issue (2): 127-136.DOI: 10.13304/j.nykjdb.2022.0835
• 智慧农业 农机装备 • 上一篇
陈林1(), 余南辉1, 王立宗1, 范吉军2(
), 雷港1, 刘晓鹏3, 周龙1, 周劲2
收稿日期:
2022-10-02
接受日期:
2023-04-22
出版日期:
2024-02-15
发布日期:
2024-02-04
通讯作者:
范吉军
作者简介:
陈林 E-mail:1575810724@qq.com;
基金资助:
Lin CHEN1(), Nanhui YU1, Lizong WANG1, Jijun FAN2(
), Gang LEI1, Xiaopeng LIU3, Long ZHOU1, Jin ZHOU2
Received:
2022-10-02
Accepted:
2023-04-22
Online:
2024-02-15
Published:
2024-02-04
Contact:
Jijun FAN
摘要:
为确定米糠和碎米分离装备离散元仿真所需的接触参数,利用台架试验与仿真试验对米糠、碎米颗粒接触参数进行测量和标定。分别采用自由落体碰撞法、碰撞扩散法、斜面滑动法和斜面滚动法标定了碎米与亚克力间的碰撞恢复系数为0.45、静摩擦系数为0.38、滚动摩擦系数为0.17,米糠与亚克力间的碰撞恢复系数为0.15,静摩擦系数为0.76。基于休止角测量试验,结合MATLAB软件图像处理技术获取了颗粒堆休止角;通过最陡爬坡试验和二次正交旋转组合试验,建立了影响因素与休止角之间的二次回归模型,并以休止角的相对误差最小为约束条件,对回归方程寻优求解,得到了最佳参数组合,其中,碎米间静摩擦系数0.60、滚动摩擦系数0.40、碰撞恢复系数0.61,米糠间静摩擦系数0.81、滚动摩擦系数0.18、碰撞恢复系数0.15。研究结果为米糠和碎米分离装备离散元仿真参数设置提供了参考。
中图分类号:
陈林, 余南辉, 王立宗, 范吉军, 雷港, 刘晓鹏, 周龙, 周劲. 米糠和碎米的接触参数测量与离散元仿真标定[J]. 中国农业科技导报, 2024, 26(2): 127-136.
Lin CHEN, Nanhui YU, Lizong WANG, Jijun FAN, Gang LEI, Xiaopeng LIU, Long ZHOU, Jin ZHOU. Measurement of Contact Parameters and Discrete Element Simulation Calibration of Rice Bran and Broken Rice[J]. Journal of Agricultural Science and Technology, 2024, 26(2): 127-136.
材料 Material | 密度Density/(kg·m-3) | 泊松比 Poisson’s ratio | 剪切模量Shear modulus/Pa |
---|---|---|---|
碎米Broken rice | 1 500 | 0.4 | 1.1e+7 |
米糠Rice bran | 1 600 | 0.3 | 1.1e+7 |
亚克力Acrylic | 1 200 | 0.4 | 6.2e+7 |
表1 材料本征参数
Table 1 Material parameter
材料 Material | 密度Density/(kg·m-3) | 泊松比 Poisson’s ratio | 剪切模量Shear modulus/Pa |
---|---|---|---|
碎米Broken rice | 1 500 | 0.4 | 1.1e+7 |
米糠Rice bran | 1 600 | 0.3 | 1.1e+7 |
亚克力Acrylic | 1 200 | 0.4 | 6.2e+7 |
序号Number | 试验因素Test factor | 试验结果Test result | |||
---|---|---|---|---|---|
x5 | x6 | x7 | θy ´/(°) | ||
1 | 0.35 | 0.10 | 0.25 | 20.5 | 41.3 |
2 | 0.45 | 0.20 | 0.35 | 25.2 | 27.8 |
3 | 0.55 | 0.30 | 0.45 | 27.8 | 20.2 |
4 | 0.65 | 0.40 | 0.55 | 33.1 | 5.2 |
5 | 0.75 | 0.50 | 0.65 | 41.3 | 18.3 |
6 | 0.85 | 0.60 | 0.75 | 43.3 | 24.1 |
表2 最陡爬坡试验设计与结果
Table 2 Steepest climb test design and results
序号Number | 试验因素Test factor | 试验结果Test result | |||
---|---|---|---|---|---|
x5 | x6 | x7 | θy ´/(°) | ||
1 | 0.35 | 0.10 | 0.25 | 20.5 | 41.3 |
2 | 0.45 | 0.20 | 0.35 | 25.2 | 27.8 |
3 | 0.55 | 0.30 | 0.45 | 27.8 | 20.2 |
4 | 0.65 | 0.40 | 0.55 | 33.1 | 5.2 |
5 | 0.75 | 0.50 | 0.65 | 41.3 | 18.3 |
6 | 0.85 | 0.60 | 0.75 | 43.3 | 24.1 |
编码Code | 试验因素Test factor | ||
---|---|---|---|
x5 | x6 | x7 | |
-1.682 | 0.48 | 0.23 | 0.38 |
-1 | 0.55 | 0.30 | 0.45 |
0 | 0.65 | 0.40 | 0.55 |
1 | 0.75 | 0.50 | 0.65 |
1.682 | 0.82 | 0.57 | 0.72 |
表3 仿真试验因素编码
Table 3 Simulation test factor coding
编码Code | 试验因素Test factor | ||
---|---|---|---|
x5 | x6 | x7 | |
-1.682 | 0.48 | 0.23 | 0.38 |
-1 | 0.55 | 0.30 | 0.45 |
0 | 0.65 | 0.40 | 0.55 |
1 | 0.75 | 0.50 | 0.65 |
1.682 | 0.82 | 0.57 | 0.72 |
序号Number | 试验因素Test factor | Y/% | ||
---|---|---|---|---|
A | B | C | ||
1 | -1 | -1 | -1 | 20.2 |
2 | 1 | -1 | -1 | 11.2 |
3 | -1 | 1 | -1 | 8.5 |
4 | 1 | 1 | -1 | 20.6 |
5 | -1 | -1 | 1 | 15.2 |
6 | 1 | -1 | 1 | 13.7 |
7 | -1 | 1 | 1 | 5.5 |
8 | 1 | 1 | 1 | 18.3 |
9 | -1.682 | 0 | 0 | 9.9 |
10 | 1.682 | 0 | 0 | 15.3 |
11 | 0 | -1.682 | 0 | 26.0 |
12 | 0 | 1.682 | 0 | 24.3 |
13 | 0 | 0 | -1.682 | 3.6 |
14 | 0 | 0 | 1.682 | 0.6 |
15 | 0 | 0 | 0 | 2.6 |
16 | 0 | 0 | 0 | 3.0 |
17 | 0 | 0 | 0 | 2.5 |
18 | 0 | 0 | 0 | 3.6 |
19 | 0 | 0 | 0 | 2.5 |
表4 二次正交旋转组合试验设计与结果
Table 4 Experimental design and results of quadratic orthogonal rotation combination
序号Number | 试验因素Test factor | Y/% | ||
---|---|---|---|---|
A | B | C | ||
1 | -1 | -1 | -1 | 20.2 |
2 | 1 | -1 | -1 | 11.2 |
3 | -1 | 1 | -1 | 8.5 |
4 | 1 | 1 | -1 | 20.6 |
5 | -1 | -1 | 1 | 15.2 |
6 | 1 | -1 | 1 | 13.7 |
7 | -1 | 1 | 1 | 5.5 |
8 | 1 | 1 | 1 | 18.3 |
9 | -1.682 | 0 | 0 | 9.9 |
10 | 1.682 | 0 | 0 | 15.3 |
11 | 0 | -1.682 | 0 | 26.0 |
12 | 0 | 1.682 | 0 | 24.3 |
13 | 0 | 0 | -1.682 | 3.6 |
14 | 0 | 0 | 1.682 | 0.6 |
15 | 0 | 0 | 0 | 2.6 |
16 | 0 | 0 | 0 | 3.0 |
17 | 0 | 0 | 0 | 2.5 |
18 | 0 | 0 | 0 | 3.6 |
19 | 0 | 0 | 0 | 2.5 |
方差来源 Soruce of variation | 平方和 Mean square | 自由度 Freedom | F值 F value | P值 P value |
---|---|---|---|---|
R2 =0.995 3 | RAdj2=0.990 7 | Predicted R2 =0.964 3 | CV=7.28% | Adep precision=38.901 0 |
模型Model | 1226.53 | 9 | 213.39 | <0.000 1** |
A | 45.33 | 1 | 70.98 | <0.000 1** |
B | 5.75 | 1 | 9.00 | 0.015 0* |
C | 9.59 | 1 | 15.02 | 0.003 8* |
AB | 169.28 | 1 | 265.07 | <0.000 1** |
AC | 11.52 | 1 | 18.04 | 0.002 2** |
BC | 0.245 0 | 1 | 0.383 6 | 0.551 0 |
A2 | 170.84 | 1 | 267.51 | <0.000 1** |
B2 | 868.12 | 1 | 1 359.34 | <0.000 1** |
C2 | 0.4161 | 1 | 0.651 6 | 0.440 4 |
残差Residual | 5.75 | 9 | ||
失拟项Lack of fit | 4.86 | 5 | 4.35 | 0.089 5 |
纯误差Pure error | 0.892 0 | 4 | ||
总和Sum | 1 232.28 | 18 |
表5 二次回归模型方差分析
Table 5 ANOVA of quadratic regression model
方差来源 Soruce of variation | 平方和 Mean square | 自由度 Freedom | F值 F value | P值 P value |
---|---|---|---|---|
R2 =0.995 3 | RAdj2=0.990 7 | Predicted R2 =0.964 3 | CV=7.28% | Adep precision=38.901 0 |
模型Model | 1226.53 | 9 | 213.39 | <0.000 1** |
A | 45.33 | 1 | 70.98 | <0.000 1** |
B | 5.75 | 1 | 9.00 | 0.015 0* |
C | 9.59 | 1 | 15.02 | 0.003 8* |
AB | 169.28 | 1 | 265.07 | <0.000 1** |
AC | 11.52 | 1 | 18.04 | 0.002 2** |
BC | 0.245 0 | 1 | 0.383 6 | 0.551 0 |
A2 | 170.84 | 1 | 267.51 | <0.000 1** |
B2 | 868.12 | 1 | 1 359.34 | <0.000 1** |
C2 | 0.4161 | 1 | 0.651 6 | 0.440 4 |
残差Residual | 5.75 | 9 | ||
失拟项Lack of fit | 4.86 | 5 | 4.35 | 0.089 5 |
纯误差Pure error | 0.892 0 | 4 | ||
总和Sum | 1 232.28 | 18 |
方差来源 Soruce of variation | 平方和 Mean square | 自由度 Freedom | F值 F value | P值 P value |
---|---|---|---|---|
R2 =0.998 8 | RAdj2=0.997 6 | Predicted R2 =0.985 3 | CV=8.38% | Adep precision =42.844 5 |
模型Model | 1 200.53 | 7 | 205.72 | <0.000 1** |
A | 40.37 | 1 | 48.43 | <0.000 1** |
B | 7.71 | 1 | 9.24 | 0.011 2* |
C | 12.08 | 1 | 14.49 | 0.002 9** |
AB | 156.65 | 1 | 187.90 | <0.000 1** |
AC | 8.40 | 1 | 10.08 | 0.008 8** |
A2 | 173.99 | 1 | 208.71 | <0.000 1** |
B2 | 885.88 | 1 | 1 062.64 | <0.000 1** |
残差Residual | 9.17 | 11 | ||
失拟项Lack of fit | 8.28 | 7 | 5.30 | 0.063 1 |
纯误差Pure error | 0.892 0 | 4 | ||
总和Sum | 1 209.70 | 18 |
表6 二次优化后回归模型方差分析
Table 6 ANOVA of regression model after second optimization
方差来源 Soruce of variation | 平方和 Mean square | 自由度 Freedom | F值 F value | P值 P value |
---|---|---|---|---|
R2 =0.998 8 | RAdj2=0.997 6 | Predicted R2 =0.985 3 | CV=8.38% | Adep precision =42.844 5 |
模型Model | 1 200.53 | 7 | 205.72 | <0.000 1** |
A | 40.37 | 1 | 48.43 | <0.000 1** |
B | 7.71 | 1 | 9.24 | 0.011 2* |
C | 12.08 | 1 | 14.49 | 0.002 9** |
AB | 156.65 | 1 | 187.90 | <0.000 1** |
AC | 8.40 | 1 | 10.08 | 0.008 8** |
A2 | 173.99 | 1 | 208.71 | <0.000 1** |
B2 | 885.88 | 1 | 1 062.64 | <0.000 1** |
残差Residual | 9.17 | 11 | ||
失拟项Lack of fit | 8.28 | 7 | 5.30 | 0.063 1 |
纯误差Pure error | 0.892 0 | 4 | ||
总和Sum | 1 209.70 | 18 |
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