Article(id=1312006435215605967, tenantId=1146029695717560320, journalId=1311385739772133495, issueId=1311999647825101743, articleNumber=null, orderNo=null, doi=10.6041/j.issn.1000-1298.2025.12.069, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1755100800000, receivedDateStr=2025-08-14, revisedDate=1758038400000, revisedDateStr=2025-09-17, acceptedDate=null, acceptedDateStr=null, onlineDate=1790736310056, onlineDateStr=2026-09-30, pubDate=1766592000000, pubDateStr=2025-12-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790736310056, onlineIssueDateStr=2026-09-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790736310056, creator=13701087609, updateTime=1790736310056, updator=13701087609, issue=Issue{id=1311999647825101743, tenantId=1146029695717560320, journalId=1311385739772133495, year='2025', volume='56', issue='12', pageStart='1', pageEnd='818', issueExtLink='null', onlineDate='null', pubDate='1766592000000', pubDateStr='2025-12-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1790734691815, creator='13701087609', updateTime=1790738411040, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1312015247465083866, tenantId=1146029695717560320, journalId=1311385739772133495, issueId=1311999647825101743, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1312015247465083867, tenantId=1146029695717560320, journalId=1311385739772133495, issueId=1311999647825101743, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=750, endPage=760, ext={EN=ArticleExt(id=1312006435416932560, articleId=1312006435215605967, tenantId=1146029695717560320, journalId=1311385739772133495, language=EN, title=Parameter Calibration and Experiment Verification of Shrimp Feed Pellet Based on Discrete Element Method, columnId=1312006333306593742, journalTitle=Transactions of the Chinese Society for Agricultural Machinery, columnName=Agricultural Products Processing, runingTitle=null, highlight=null, articleAbstract=

In order to improve the performance and accuracy of discrete element method simulation optimization in the processes of blanking, conveying, and mechanical spreading of the shrimp feed pellet, the intrinsic parameters of the shrimp feed pellet, the contact parameters between feed, the contact parameters between feed and equipment were systematically calibrated. Taking Tongwei's columnar sinking shrimp feed pellet as research object, the DEM simulation parameters of the shrimp feed pellet were calibrated by combining physical and simulation experiments. Firstly, by using the physical test method, the density, Poisson's ratio, elastic modulus and shear modulus of the shrimp pelleted feed were measured to be 1 177.68 kg/m3, 0.26, 114.77 MPa and 45.58 MPa, respectively through instruments such as high precision digital calipers, electronic balances and pressure testing machines. Through inclined plane method and rebound method, the static friction coefficient, rolling friction coefficient, and restitution coefficient between the shrimp feed pellet and 304 stainless steel were determined to be 0.607, 0.097 and 0.35, respectively. The average stacking angle of the shrimp feed pellet was found to be 26.93°, and the feed's area coverage on a steel plate was 13.32% through stacking tests. Secondly, based on the Plackett-Burman experiment, the factors significantly affecting the physical stacking angle and area proportion were screened out to be the static friction coefficient between feed, the rolling friction coefficient between feed, and the static friction coefficient between feed and 304 stainless steel. Based on this foundation, the steepest ascent experiment was applied to converge the optimization range, and the parameter optimization was carried out by fitting the regression equation between the significant influencing factors and the response values through the Box-Behnken Design experiment. The optimal parameters combination were obtained as follows: the static friction coefficient between feed was 0.207, the rolling friction coefficient between feed was 0.150, and the static friction coefficient between feed and 304 stainless steel was 0.607. Finally, the reliability of the parameters was verified through stacking tests and spiral blanking tests. The findings revealed that the simulated stacking test results were statistically indistinguishable from the physical counterparts, as confirmed by a double-sample t-test. Furthermore, the correlation coefficient between the simulated and physical tests for the discharge amount line under varying speeds was an impressive 0.997, signifying a highly consistent trend with a discrepancy in discharge amount of less than 3% across all speeds. These results underscored the reliability of the calibrated DEM parameters for shrimp pellet feed, thereby establishing a solid data foundation and scientific rationale for investigating the motion mechanisms of shrimp feed pellets and optimizing equipment designs within DEM simulations.

, authors=Leilei CHEN1, Kaiqi SHEN1, Jun LI1, Hongxiang WAN2, Chang CAO2, Qingsong HU1, Zhijian LI1, authorsList=Leilei CHEN, Kaiqi SHEN, Jun LI, Hongxiang WAN, Chang CAO, Qingsong HU, Zhijian LI, authorCompany=null, correspAuthors=Qingsong HU, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1312006443751014665, articleId=1312006435215605967, tenantId=1146029695717560320, journalId=1311385739772133495, language=CN, title=基于离散元仿真的颗粒虾饲料参数标定与试验, columnId=1312006333474365904, journalTitle=农业机械学报, columnName=农产品加工工程, runingTitle=null, highlight=null, articleAbstract=

为系统标定颗粒虾饲料本征参数、颗粒间及颗粒与装备间接触参数,提高颗粒虾饲料下料、输送及机械抛撒等环节离散元仿真优化的性能和精度,以通威柱形沉性颗粒虾饲料为研究对象,通过物理试验和仿真试验相结合的方法进行颗粒虾饲料离散元仿真参数标定。采用高精度数显卡尺、电子秤及压力试验机等仪器,应用物理试验方法测定颗粒虾饲料的密度、泊松比、弹性模量、剪切模量分别为1 177.68 kg/m3、0.26、114.77 MPa、45.58 MPa,通过斜面试验和回弹试验测得颗粒虾饲料与304号不锈钢之间静摩擦因数、滚动摩擦因数、恢复系数分别为0.607、0.097、0.35,进行堆积试验得到颗粒虾饲料堆积角平均值和饲料在钢板上面积占比分别为26.93°和13.32%。基于Plackett-Burman试验筛选出对仿真堆积角和面积占比显著影响因素为颗粒虾饲料-颗粒虾饲料静摩擦因数、颗粒虾饲料-颗粒虾饲料滚动摩擦因数和颗粒虾饲料-304号不锈钢静摩擦因数,在此基础上,应用最陡爬坡试验收敛寻优范围,并通过Box-Behnken Design试验拟合显著影响因素与响应值之间的回归方程进行参数优化,得到最优参数组合:颗粒虾饲料-颗粒虾饲料静摩擦因数0.207,颗粒虾饲料-颗粒虾饲料滚动摩擦因数0.150及颗粒虾饲料-304号不锈钢静摩擦因数0.607。用堆积试验和螺旋下料试验进行参数可靠性验证,结果表明:经过双样本t检验,堆积试验仿真结果与物理堆积结果无显著差异,不同转速下仿真试验和物理试验的下料量折线的相关系数为0.997,趋势一致且各转速下下料量相差小于3%,表明了标定的颗粒虾饲料离散元参数的可靠性与正确性。所标定的离散元参数能为颗粒虾饲料运动机理研究和相关装备设计优化离散元仿真提供数据基础和科学依据。

, authors=陈雷雷1, 沈凯琪1, 李俊1, 宛虹翔2, 曹畅2, 胡庆松1, 李志坚1, authorsList=陈雷雷, 沈凯琪, 李俊, 宛虹翔, 曹畅, 胡庆松, 李志坚, authorCompany=null, correspAuthors=胡庆松, authorNote=

陈雷雷(1981—),副教授,博士,主要从事农业装备与自动化技术研究,E-mail:

, correspAuthorsNote=
胡庆松(1979—),教授,博士,主要从事渔业工程装备技术研究,E-mail:
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陈雷雷(1981—),副教授,博士,主要从事农业装备与自动化技术研究,E-mail:

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陈雷雷(1981—),副教授,博士,主要从事农业装备与自动化技术研究,E-mail:

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1. 压力试验机 2. 压力计 3. 探头 4. 微距摄像头

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1. 数显倾角仪 2. 颗粒虾饲料 3. 手柄 4. 304号不锈钢板 5. 倾角平台

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1. 颗粒虾饲料 2. 落料孔 3. 钢尺 4. 释放与测量平台 5. 304号不锈钢板

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1. 转速控制器 2. 料箱 3. 下料电机 4. 螺旋下料杆 5. 颗粒虾饲料 6. Grid Bin Group

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Parameters codes of Plackett-Burman experiment

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因素编码
-11
颗粒虾饲料-颗粒虾饲料恢复系数X10.100.40
颗粒虾饲料-颗粒虾饲料静摩擦因数X20.1400.260
颗粒虾饲料-颗粒虾饲料滚动摩擦因数X30.0100.300
颗粒虾饲料-304号不锈钢恢复系数X40.200.50
颗粒虾饲料-304号不锈钢静摩擦因数X50.2000.650
颗粒虾饲料-304号不锈钢滚动摩擦因数X60.0100.150
), ArticleFig(id=1312006451506282834, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1312006435215605967, language=CN, label=表1, caption=

Plackett-Burman试验因素编码

, figureFileSmall=null, figureFileBig=null, tableContent=
因素编码
-11
颗粒虾饲料-颗粒虾饲料恢复系数X10.100.40
颗粒虾饲料-颗粒虾饲料静摩擦因数X20.1400.260
颗粒虾饲料-颗粒虾饲料滚动摩擦因数X30.0100.300
颗粒虾饲料-304号不锈钢恢复系数X40.200.50
颗粒虾饲料-304号不锈钢静摩擦因数X50.2000.650
颗粒虾饲料-304号不锈钢滚动摩擦因数X60.0100.150
), ArticleFig(id=1312006451569197395, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1312006435215605967, language=EN, label=Tab. 2, caption=

Design and results of Plackett-Burman experiment

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试验序号试验因素堆积角η′/(°)面积占比S′/%
x1x2x3x4x5x6
111-111123.1014.68
2-111-11128.5311.84
31-111-1116.8719.90
4-11-111-122.8816.10
5-1-11-11122.1414.42
6-1-1-11-1117.4819.46
71-1-1-11-118.7916.29
811-1-1-1119.2818.40
9111-1-1-121.1517.50
10-1111-1-120.8817.73
111-1111-121.4714.93
12-1-1-1-1-1-117.0620.76
), ArticleFig(id=1312006451648889172, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1312006435215605967, language=CN, label=表2, caption=

Plackett-Burman试验方案与结果

, figureFileSmall=null, figureFileBig=null, tableContent=
试验序号试验因素堆积角η′/(°)面积占比S′/%
x1x2x3x4x5x6
111-111123.1014.68
2-111-11128.5311.84
31-111-1116.8719.90
4-11-111-122.8816.10
5-1-11-11122.1414.42
6-1-1-11-1117.4819.46
71-1-1-11-118.7916.29
811-1-1-1119.2818.40
9111-1-1-121.1517.50
10-1111-1-120.8817.73
111-1111-121.4714.93
12-1-1-1-1-1-117.0620.76
), ArticleFig(id=1312006451720192341, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1312006435215605967, language=EN, label=Tab. 3, caption=

Significance analysis of parameters in Plackett-Burman experiment

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响应值因素均方系数估计值FP
η′x15.76-0.694.130.097 9
x240.351.8328.890.003 0**
x312.901.049.240.028 8*
x41.52-0.361.090.344 3
x548.732.0234.880.002 0**
x62.210.431.590.263 5
S′x10.160.120.250.640 9
x27.54-0.7911.520.019 4*
x37.32-0.7811.180.020 5*
x41.070.301.640.256 3
x554.15-2.1282.760.000 3**
x61.77-0.382.710.160 8
), ArticleFig(id=1312006451791495510, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1312006435215605967, language=CN, label=表3, caption=

Plackett-Burman试验参数显著性分析

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响应值因素均方系数估计值FP
η′x15.76-0.694.130.097 9
x240.351.8328.890.003 0**
x312.901.049.240.028 8*
x41.52-0.361.090.344 3
x548.732.0234.880.002 0**
x62.210.431.590.263 5
S′x10.160.120.250.640 9
x27.54-0.7911.520.019 4*
x37.32-0.7811.180.020 5*
x41.070.301.640.256 3
x554.15-2.1282.760.000 3**
x61.77-0.382.710.160 8
), ArticleFig(id=1312006451866992983, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1312006435215605967, language=EN, label=Tab. 4, caption=

Design and results of the steepest ascent experiment

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序号试验因素堆积角η′/(°)η′偏差/%面积占比S′/%S′偏差/%
X2X3X5
10.1400.0100.20017.3935.4319.5847.00
20.1700.0830.31322.2117.5314.8811.71
30.2000.1550.42525.405.6813.823.75
40.2300.2280.53827.562.3412.793.98
50.2600.3000.65030.1311.8811.9210.51
), ArticleFig(id=1312006451946684760, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1312006435215605967, language=CN, label=表4, caption=

最陡爬坡试验方案与结果

, figureFileSmall=null, figureFileBig=null, tableContent=
序号试验因素堆积角η′/(°)η′偏差/%面积占比S′/%S′偏差/%
X2X3X5
10.1400.0100.20017.3935.4319.5847.00
20.1700.0830.31322.2117.5314.8811.71
30.2000.1550.42525.405.6813.823.75
40.2300.2280.53827.562.3412.793.98
50.2600.3000.65030.1311.8811.9210.51
), ArticleFig(id=1312006452080902489, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1312006435215605967, language=EN, label=Tab. 5, caption=

Design and results of Box-Behnken Design experiment

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试验序号试验因素堆积角η′/(°)面积占比S′/%
x2x3x5
1-1-1024.2714.56
21-1029.1213.23
3-11023.1213.98
411028.4612.08
5-10-121.6314.85
610-126.7113.28
7-10124.5013.91
810130.5112.19
90-1-123.8814.18
1001-123.4114.31
110-1127.3113.6
1201126.3212.66
1300026.5913.19
1400026.7613.13
1500026.1913.05
1600025.9513.66
1700027.4412.87
), ArticleFig(id=1312006452164788570, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1312006435215605967, language=CN, label=表5, caption=

Box-Behnken Design试验方案与结果

, figureFileSmall=null, figureFileBig=null, tableContent=
试验序号试验因素堆积角η′/(°)面积占比S′/%
x2x3x5
1-1-1024.2714.56
21-1029.1213.23
3-11023.1213.98
411028.4612.08
5-10-121.6314.85
610-126.7113.28
7-10124.5013.91
810130.5112.19
90-1-123.8814.18
1001-123.4114.31
110-1127.3113.6
1201126.3212.66
1300026.5913.19
1400026.7613.13
1500026.1913.05
1600025.9513.66
1700027.4412.87
), ArticleFig(id=1312006452223508827, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1312006435215605967, language=EN, label=Tab. 6, caption=

Variation analysis of Box-Behnken Design experiment

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响应值来源平方和自由度均方FP
η′模型83.9099.3245.33<0.000 1**
x256.66156.66275.49<0.000 1**
x31.3311.336.470.038 5*
x521.15121.15102.82<0.000 1**
x2x30.0610.060.280.610 5
x2x50.2210.221.070.334 7
x3x50.0710.070.320.588 5
0.0810.080.360.566 2
0.9510.954.630.068 4
3.2613.2615.850.005 3*
残差1.4470.210.130.934 7
失拟项0.1330.04
纯误差1.3140.33
S′模型9.4091.0415.990.000 7**
x25.3115.3181.35<0.000 1**
x30.8110.8112.350.009 8**
x52.2712.2734.730.000 6**
x2x30.0810.081.240.301 6
x2x50.0110.010.090.777 7
x3x50.2910.294.380.074 6
0.0210.020.370.559 8
0.1810.182.740.141 7
0.3810.385.850.046 2*
残差0.4670.070.430.743 8
失拟项0.1130.04
纯误差0.3540.09
), ArticleFig(id=1312006452299006300, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1312006435215605967, language=CN, label=表6, caption=

Box-Behnken Design试验方差分析

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响应值来源平方和自由度均方FP
η′模型83.9099.3245.33<0.000 1**
x256.66156.66275.49<0.000 1**
x31.3311.336.470.038 5*
x521.15121.15102.82<0.000 1**
x2x30.0610.060.280.610 5
x2x50.2210.221.070.334 7
x3x50.0710.070.320.588 5
0.0810.080.360.566 2
0.9510.954.630.068 4
3.2613.2615.850.005 3*
残差1.4470.210.130.934 7
失拟项0.1330.04
纯误差1.3140.33
S′模型9.4091.0415.990.000 7**
x25.3115.3181.35<0.000 1**
x30.8110.8112.350.009 8**
x52.2712.2734.730.000 6**
x2x30.0810.081.240.301 6
x2x50.0110.010.090.777 7
x3x50.2910.294.380.074 6
0.0210.020.370.559 8
0.1810.182.740.141 7
0.3810.385.850.046 2*
残差0.4670.070.430.743 8
失拟项0.1130.04
纯误差0.3540.09
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基于离散元仿真的颗粒虾饲料参数标定与试验
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陈雷雷 1 , 沈凯琪 1 , 李俊 1 , 宛虹翔 2 , 曹畅 2 , 胡庆松 1 , 李志坚 1
农业机械学报 | 农产品加工工程 2025,56(12): 750-760
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农业机械学报 |农产品加工工程 2025 , 56 (12) : 750 -760
基于离散元仿真的颗粒虾饲料参数标定与试验
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陈雷雷1 , 沈凯琪1, 李俊1, 宛虹翔2, 曹畅2, 胡庆松1 , 李志坚1
作者信息
  • 1.上海海洋大学工程学院,上海 201306
  • 2.农业农村部对外经济合作中心,北京 100125
通讯作者:
胡庆松(1979—),教授,博士,主要从事渔业工程装备技术研究,E-mail:
作者简介:

陈雷雷(1981—),副教授,博士,主要从事农业装备与自动化技术研究,E-mail:

Parameter Calibration and Experiment Verification of Shrimp Feed Pellet Based on Discrete Element Method
Leilei CHEN1 , Kaiqi SHEN1, Jun LI1, Hongxiang WAN2, Chang CAO2, Qingsong HU1 , Zhijian LI1
Affiliations
  • 1.College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China
  • 2.Foreign Economic Cooperation Center, Ministry of Agriculture and Rural Affairs, Beijing 100125, China
出版时间: 2025-12-25 doi: 10.6041/j.issn.1000-1298.2025.12.069
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为系统标定颗粒虾饲料本征参数、颗粒间及颗粒与装备间接触参数,提高颗粒虾饲料下料、输送及机械抛撒等环节离散元仿真优化的性能和精度,以通威柱形沉性颗粒虾饲料为研究对象,通过物理试验和仿真试验相结合的方法进行颗粒虾饲料离散元仿真参数标定。采用高精度数显卡尺、电子秤及压力试验机等仪器,应用物理试验方法测定颗粒虾饲料的密度、泊松比、弹性模量、剪切模量分别为1 177.68 kg/m3、0.26、114.77 MPa、45.58 MPa,通过斜面试验和回弹试验测得颗粒虾饲料与304号不锈钢之间静摩擦因数、滚动摩擦因数、恢复系数分别为0.607、0.097、0.35,进行堆积试验得到颗粒虾饲料堆积角平均值和饲料在钢板上面积占比分别为26.93°和13.32%。基于Plackett-Burman试验筛选出对仿真堆积角和面积占比显著影响因素为颗粒虾饲料-颗粒虾饲料静摩擦因数、颗粒虾饲料-颗粒虾饲料滚动摩擦因数和颗粒虾饲料-304号不锈钢静摩擦因数,在此基础上,应用最陡爬坡试验收敛寻优范围,并通过Box-Behnken Design试验拟合显著影响因素与响应值之间的回归方程进行参数优化,得到最优参数组合:颗粒虾饲料-颗粒虾饲料静摩擦因数0.207,颗粒虾饲料-颗粒虾饲料滚动摩擦因数0.150及颗粒虾饲料-304号不锈钢静摩擦因数0.607。用堆积试验和螺旋下料试验进行参数可靠性验证,结果表明:经过双样本t检验,堆积试验仿真结果与物理堆积结果无显著差异,不同转速下仿真试验和物理试验的下料量折线的相关系数为0.997,趋势一致且各转速下下料量相差小于3%,表明了标定的颗粒虾饲料离散元参数的可靠性与正确性。所标定的离散元参数能为颗粒虾饲料运动机理研究和相关装备设计优化离散元仿真提供数据基础和科学依据。

颗粒虾饲料  /  离散元法  /  堆积试验  /  参数标定

In order to improve the performance and accuracy of discrete element method simulation optimization in the processes of blanking, conveying, and mechanical spreading of the shrimp feed pellet, the intrinsic parameters of the shrimp feed pellet, the contact parameters between feed, the contact parameters between feed and equipment were systematically calibrated. Taking Tongwei's columnar sinking shrimp feed pellet as research object, the DEM simulation parameters of the shrimp feed pellet were calibrated by combining physical and simulation experiments. Firstly, by using the physical test method, the density, Poisson's ratio, elastic modulus and shear modulus of the shrimp pelleted feed were measured to be 1 177.68 kg/m3, 0.26, 114.77 MPa and 45.58 MPa, respectively through instruments such as high precision digital calipers, electronic balances and pressure testing machines. Through inclined plane method and rebound method, the static friction coefficient, rolling friction coefficient, and restitution coefficient between the shrimp feed pellet and 304 stainless steel were determined to be 0.607, 0.097 and 0.35, respectively. The average stacking angle of the shrimp feed pellet was found to be 26.93°, and the feed's area coverage on a steel plate was 13.32% through stacking tests. Secondly, based on the Plackett-Burman experiment, the factors significantly affecting the physical stacking angle and area proportion were screened out to be the static friction coefficient between feed, the rolling friction coefficient between feed, and the static friction coefficient between feed and 304 stainless steel. Based on this foundation, the steepest ascent experiment was applied to converge the optimization range, and the parameter optimization was carried out by fitting the regression equation between the significant influencing factors and the response values through the Box-Behnken Design experiment. The optimal parameters combination were obtained as follows: the static friction coefficient between feed was 0.207, the rolling friction coefficient between feed was 0.150, and the static friction coefficient between feed and 304 stainless steel was 0.607. Finally, the reliability of the parameters was verified through stacking tests and spiral blanking tests. The findings revealed that the simulated stacking test results were statistically indistinguishable from the physical counterparts, as confirmed by a double-sample t-test. Furthermore, the correlation coefficient between the simulated and physical tests for the discharge amount line under varying speeds was an impressive 0.997, signifying a highly consistent trend with a discrepancy in discharge amount of less than 3% across all speeds. These results underscored the reliability of the calibrated DEM parameters for shrimp pellet feed, thereby establishing a solid data foundation and scientific rationale for investigating the motion mechanisms of shrimp feed pellets and optimizing equipment designs within DEM simulations.

shrimp feed pellet  /  discrete element method  /  stacking test  /  parameter calibration
陈雷雷, 沈凯琪, 李俊, 宛虹翔, 曹畅, 胡庆松, 李志坚. 基于离散元仿真的颗粒虾饲料参数标定与试验. 农业机械学报, 2025 , 56 (12) : 750 -760 . DOI: 10.6041/j.issn.1000-1298.2025.12.069
Leilei CHEN, Kaiqi SHEN, Jun LI, Hongxiang WAN, Chang CAO, Qingsong HU, Zhijian LI. Parameter Calibration and Experiment Verification of Shrimp Feed Pellet Based on Discrete Element Method[J]. Transactions of the Chinese Society for Agricultural Machinery, 2025 , 56 (12) : 750 -760 . DOI: 10.6041/j.issn.1000-1298.2025.12.069
2022年我国虾类养殖总产量达5.74×106 t,且近年来呈上升趋势[1],随着虾类养殖规模的扩大,虾饲料投喂作业的机械化和自动化愈发重要[2-3]。虾类投喂一般以圆柱形颗粒状沉料为主[4],方便虾类在养殖塘底部摄食,其主要成分包括蛋白质、碳水化合物、脂肪、维生素、矿物质等。对颗粒虾饲料的参数标定是研究饲料与饲料之间及饲料与装备之间运动机理的基础,也是对虾类投饵装备下料精度、抛料均匀度、破碎率等性能参数优化的基础。
离散元法能够分析、模拟散体颗粒在不同结构中的受力情况和运动情况[5-8],从而提高颗粒与装备间互作机理的分析效率和精度[9-10]。为了准确标定颗粒的本征参数、接触参数等离散元仿真参数,以提高离散元仿真分析的可靠性,国内外学者采用了不同的方法对散体颗粒的离散元仿真参数进行标定[11-13]。
学者针对颗粒的不同物理特性设计了对应台架试验标定颗粒的物性参数及颗粒与接触材料之间的接触参数,以颗粒堆积产生的休止角或其他物理试验产生的结果,作为响应值标定颗粒间的接触参数。这种物理试验结合离散元仿真的方法在散体颗粒参数标定中取得了良好的效果。在颗粒饲料的离散元参数研究方面,也有学者将以上方法运用于鱼类颗粒饲料的标定[14-16]。基于相同原理,文献[17]通过拉压试验标定了虾饲料的粘结参数和饲料间接触参数,为改良虾类投饵设备破碎率提供了参考。但在虾类颗粒饲料物性参数及颗粒饲料与装备间接触参数等离散元仿真关键参数的标定尚未有系统性研究报道。此外,文献[13,18]指出目前颗粒的离散元仿真参数标定,主要基于单一休止角作为响应值进行参数标定,可能会产生多组可行的参数组合,标定精度有限,标定结果可靠性差。为更好地研究颗粒虾饲料在装备中的受力情况和运动规律,提高虾饲料下料、输送及机械抛撒等环节离散元仿真优化的性能和精度,本文以柱状颗粒虾饲料作为研究对象,通过单轴平板压缩等台架试验确定颗粒虾饲料的本征参数及颗粒虾饲料与304号不锈钢之间的接触参数;以堆积试验得到的颗粒虾饲料堆积角与面积占比为目标,通过Plackett-Burman、最陡爬坡及Box-Behnken Design试验标定颗粒虾饲料间的接触参数;最终通过堆积试验和螺旋下料试验进行参数可靠性验证。
颗粒虾饲料一般通过造粒、挤压等方法加工成型,以圆柱形颗粒状沉料为主,本文以通威柱形沉性颗粒虾饲料为对象进行研究。采用物理试验方法确定颗粒的密度、泊松比、弹性模量与剪切模量等本征参数。
测定密度时,通过将一定数量颗粒虾饲料的总质量与总体积作商求取密度ρ,计算公式为
式中
      ρ——饲料密度,kg/m3
      m——饲料总质量,kg
      Vi——单颗饲料体积,m3
      di——单颗饲料直径,m
      li——单颗饲料长度,m
      n——虾饲料的总数量
试验时,使用纪铭A6-001型高精度电子秤(精度0.001 g)称取1.000 g虾饲料,使用数显游标卡尺(精度0.01 mm)和数显千分尺(精度0.001 mm)分别测量每颗饲料的长度li和直径di,并对测量结果统计可得颗粒虾饲料长度平均值2.43 mm,标准差0.82 mm,直径平均值1.563 mm,标准差0.033 mm,尺寸分布如图1所示,密度1 177.68 kg/m3。
泊松比是反映材料横向变形的弹性常数,弹性模量和剪切模量是衡量材料抵抗弹性变形的参数,三者均为反映材料物理特性的重要参数,确定这些参数是开展仿真标定试验的前提。本研究采用压力试验机(图2a)对颗粒虾饲料进行单轴平板压缩试验[19],并计算每颗饲料的泊松比、弹性模量和剪切模量,公式[11]为
式中
      μ——泊松比
      ε1——纵向应变
      ε2——横向应变
      Δd——颗粒虾饲料直径变化量,mm
      l0——颗粒虾饲料试验前长度,mm
      d0——颗粒虾饲料试验前直径,mm
      Δl——颗粒虾饲料长度变化量,mm
      E——颗粒虾饲料弹性模量,MPa
      σ——颗粒虾饲料所受应力,MPa
      F0——试验中承受的最大挤压力,N
      A——颗粒虾饲料试验前横截面积,mm2
      K——颗粒虾饲料剪切模量,MPa
试验过程中,需保证颗粒虾饲料处于弹性形变阶段。在该阶段中,颗粒虾饲料纵向应变与轴向应变成正比,而颗粒虾饲料整体长度较小,为更好地观测颗粒的尺寸变化,需挑选尽可能长的颗粒虾饲料进行试验。根据前文饲料长度尺寸分布统计,长度在3.87 mm以上的饲料样本数量较少,故本文选择长度在3.23~3.87 mm范围内的30颗样本进行试验。试验前,先测量和记录颗粒虾饲料的长度和直径,再将其端面向下放置,压缩速度0.02 mm/s,压缩距离0.08 mm,并通过固定机位的微距摄像头(分辨率1 920像素×1 080像素)获取饲料受压前和饲料最大压缩距离下的图像。在两幅图像相同位置截取相同大小的部分后进行二值化,如图2b所示,随后计算截取部分中黑色部分的面积占比,由于压缩试验仅影响截取部分的宽度,故通过比对截取部分面积占比的变化率即可计算颗粒虾饲料在最大压缩距离下的直径变化量。经过试验,颗粒虾饲料受到最大挤压力为4.77~5.66 N,直径变化量为0.008~0.011 mm,泊松比平均值为0.26,弹性模量平均值为114.77 MPa,剪切模量平均值为45.58 MPa。
投饵装置的下料机构与抛料机构主要以304号不锈钢为主要材料,故通过离散元仿真对饲料在投饵装置中的运动情况进行模拟和研究,需要对饲料与304号不锈钢之间的静摩擦因数、滚动摩擦因数和恢复系数进行参数测定。
颗粒虾饲料与304号不锈钢之间的摩擦因数可通过斜面试验进行测定。测定颗粒虾饲料与304号不锈钢之间静摩擦因数时,将304号不锈钢板固定于试验装置上,确保试验平台水平后,随机取1颗颗粒虾饲料按端面朝向滑落方向的方式放置(如图3所示),随后缓慢转动手柄增大装置倾斜角度,并通过高速摄像机记录试验过程,当颗粒虾饲料具有滑动趋势时停止转动手柄,并通过数显倾角仪(精度0.01°)记录该时刻装置的倾斜角度α,计算颗粒虾饲料与304号不锈钢之间静摩擦因数fs,公式[12]为
重复5次试验,得到颗粒虾饲料静摩擦休止角为31.25°,即颗粒虾饲料-304号不锈钢静摩擦因数为0.607。
圆柱形颗粒在斜面上获得滚动趋势时,其滚动摩擦力偶矩等于滚动摩擦因数与斜面对圆柱形颗粒的支持力的乘积[20]。故颗粒虾饲料-304号不锈钢滚动摩擦因数fk计算公式为
式中
      M——滚动摩擦力偶矩,N·m
      Fk——滚动摩擦力,N
      r——饲料半径,mm
测定颗粒虾饲料与304号不锈钢之间滚动摩擦休止角时,使颗粒虾饲料的柱面朝滚动方向放置,其余操作与测定静摩擦因数时相同。重复5次试验,得到颗粒虾饲料滚动摩擦休止角为7.10°,由前文对颗粒虾饲料的尺寸统计可知,颗粒虾饲料直径标准差较小,故将颗粒虾饲料直径平均值1.563 mm的1/2,即0.782 mm代入式(6)进行计算,得到颗粒虾饲料-304号不锈钢滚动摩擦因数为0.097。
恢复系数是用于描述碰撞过程中物体的弹性程度的物理量,可由物体碰撞前后速度之比进行计算[21],公式为
式中
      ex——颗粒虾饲料-304号不锈钢恢复系数
      v1——颗粒虾饲料在接触点处法向瞬时分离速度,m/s
      v0——颗粒虾饲料在接触点处瞬时接触速度,m/s
      g——重力加速度,m/s2
      h——颗粒虾饲料最大回弹高度,mm
      H——颗粒虾饲料初始高度,mm
因此,搭建如图4所示的释放与测量平台进行回弹试验测定颗粒虾饲料的恢复系数。装置顶面中央位置开有直径2 mm的落料孔,且顶面距底部钢板高度300 mm。进行回弹试验时,钢板水平放置,将颗粒虾饲料端面向下于释放平台的小孔处释放,采用高速摄像机记录饲料下端面与钢板接触后的最大回弹高度。为获得较为准确的回弹高度,本研究将高速摄像机安置于30~40 mm的高度范围,使其能够水平读数。为避免颗粒虾饲料经过反复碰撞产生破碎而改变物理性能,从而对最大回弹高度产生影响,每颗颗粒虾饲料进行1次试验,随机取20颗颗粒虾饲料进行试验取平均值。最终,测得颗粒虾饲料回弹平均高度为36.91 mm,恢复系数为0.35。
由前文对1 g颗粒虾饲料的尺寸统计可得,颗粒虾饲料长度标准差相对较大,为了使仿真结果更贴近实际结果,同时尽可能地提升仿真速度,本研究忽略过长、过短的饲料,根据图1a中长度在1.33~3.87 mm的饲料(占总量84%),选取各典型值,即1.65、2.29、2.92、3.55 mm,设定4种不同长度的颗粒虾饲料模型。而颗粒虾饲料直径标准差较小,故采用平均值1.563 mm(取1.560 mm)作为颗粒仿真模型的直径。
柱状颗粒的仿真建模方面,文献[15-16,22]均基于Hertz-Mindlin(no slip)接触模型,采用了多球体均匀排列构成不同长度、直径柱状颗粒仿真模型,且均取得了良好的仿真效果。故本研究参考此方式,基于Hertz-Mindlin(no slip)接触模型[23],通过2、3、4、5个直径1.56 mm的球体分别构建长度为1.65、2.29、2.92、3.55 mm的柱状颗粒虾饲料的仿真模型(图5)。
此外,为验证构建的仿真颗粒能够用于后续的仿真试验,本研究将前文试验所得的颗粒虾饲料本征参数及饲料与304号不锈钢之间接触参数输入构建的仿真颗粒模型,多次进行1.2节中饲料与304号不锈钢之间接触参数标定的仿真试验,并通过EDEM软件后处理模块中的protractor和ruler工具分别测量最大摩擦角与最大回弹高度(图6)。试验可得仿真颗粒最大静摩擦角平均值为31.36°,最大滚动摩擦角平均值为6.95°,最大回弹高度平均值为36.22 mm,与实际台架试验的误差分别为0.35%、2.11%和1.87%,均在3%以下,表明本研究中多球体均匀排布构建的颗粒虾饲料仿真模型具备一定的精度,可用于后续仿真试验。
在投饵装置实际下料和抛料的场景中,颗粒间运动情况较为复杂,将颗粒虾饲料粘于平板上,参照标定饲料-304号不锈钢之间接触参数时使用的物理试验的方法并不能获得较为准确的饲料间接触参数。而通过圆筒提升进行堆积试验能够使颗粒虾饲料在形成散体堆积的过程中产生流动、碰撞以及摩擦等复杂运动,且在堆积试验中产生的堆积角与饲料覆盖面积很大程度上取决于颗粒虾饲料间的恢复系数、静摩擦因数和滚动摩擦因数[23-24]。故为了获得较为准确的颗粒虾饲料间接触参数,本研究通过圆筒提升进行颗粒虾饲料堆积的物理试验获得的颗粒虾饲料实际堆积角与颗粒虾饲料在钢板上实际面积占比作为目标,进行仿真试验,通过调整颗粒虾饲料间接触参数使得到的仿真结果逼近物理试验结果,从而确定颗粒虾饲料间的接触参数组合。
进行物理试验时,依次将边长为300 mm的方形304号不锈钢板和内径60 mm、长度70 mm的304号不锈钢管放置于水平面上,量取120 g颗粒虾饲料倒入其中后,以7 mm/s的速度垂直提升钢管,使颗粒虾饲料在钢板上散落堆积,记录颗粒虾饲料完全静止所用时间并采集如图7所示的颗粒虾饲料堆积图像。为获取较为准确的颗粒虾饲料堆积角与面积占比,减少人工测量误差,本研究将图像导入Matlab中,如图8所示的过程进行处理[25]。重复10次堆积试验,得颗粒虾饲料散落时间约为6.5 s,实际堆积角平均值η=26.93°,实际面积占比平均值S=13.32%。随后,在EDEM中导入堆积试验的仿真模型,并在圆筒内按照图1a中统计的4种长度的颗粒虾饲料的数量比,即17.47∶25.90∶27.11∶13.25,生成4种长度的仿真颗粒共120 g。颗粒虾饲料本征参数按台架试验所得参数设置,圆筒提升速度设置为7 mm/s。仿真试验后从XOY及YOZ两个平面获取仿真堆积角的图像,从XOZ平面获取面积占比图像,并按照图8所示流程处理,获取仿真堆积角及面积占比。
为找到使仿真颗粒最能体现实际情况的颗粒虾饲料间接触参数的最佳组合,本文应用Design-Expert辅助设计试验。首先设计Plackett-Burman试验,以堆积试验获得的堆积角和面积占比为响应值,颗粒虾饲料间及颗粒虾饲料与304号不锈钢之间的接触参数为影响因子[25],通过比较各因子在高水平和低水平下的差异与整体差异来确定各因子的显著性,从而快速筛选出对堆积角与面积占比产生显著影响的因子。随后,根据Plackett-Burman试验筛选出的堆积角与面积占比的显著影响因素设计最陡爬坡试验,以快速收敛仿真结果主要影响因素的取值范围。最后,以显著影响因素的取值范围设计Box-Behnken Design试验,根据试验结果建立堆积角与显著因素之间以及面积占比与显著因素之间的回归方程,结合Design-Expert软件的寻优功能对接触参数的组合进行寻优。
由于堆积试验不仅涉及到颗粒虾饲料间的运动,还存在颗粒虾饲料与304号不锈钢之间的运动,且文献[19,26]提到颗粒间接触参数及颗粒与接触材料之间接触参数对仿真结果具有一定影响,为确定最贴近实际情况的参数组合,本文以颗粒虾饲料间及颗粒虾饲料与304号不锈钢之间的接触参数作为影响因子,颗粒虾饲料仿真堆积角η′及仿真面积占比S′作为响应值,采用Design-Expert软件设计Plackett-Burman试验筛选对仿真堆积结果影响显著的因子。颗粒虾饲料间及颗粒虾饲料与304号不锈钢之间接触参数分别以X1~X6表示,结合文献[14-17,27-28],根据前文试验与仿真预试验,确定各颗粒虾饲料间接触参数及颗粒虾饲料与304号不锈钢之间接触参数的大致取值范围。各参数的高低水平值按表1所示的试验参数列表设计。参数组合方案及试验结果如表2所示,x1~x6分别表示颗粒虾饲料间及颗粒虾饲料与304号不锈钢之间的编码值。
通过Design-Expert软件对试验结果进行方差分析,可得响应值堆积角η′的模型P值为0.006 1(<0.01),决定系数R2=0.941(>0.8),精度为11.777(>4);响应值面积占比S′的模型P值为0.002 9(<0.01),决定系数R2=0.956 5(>0.8),精度为13.241(>4),表明该模型显著,具有可靠性。各参数方差显著性分析如表3所示,颗粒虾饲料-颗粒虾饲料静摩擦因数X2、颗粒虾饲料-304号不锈钢静摩擦因数X5对响应值堆积角η′影响均极显著(P<0.01),颗粒虾饲料-颗粒虾饲料滚动摩擦因数X3对响应值堆积角η′影响显著(0.01<P<0.05);颗粒虾饲料-304号不锈钢静摩擦因数X5对响应值面积占比S′影响极显著(P<0.01),颗粒虾饲料-颗粒虾饲料静摩擦因数X2和颗粒虾饲料-颗粒虾饲料滚动摩擦因数X3对响应值面积占比S′影响均显著(0.01<P<0.05);颗粒虾饲料-颗粒虾饲料恢复系数X1、颗粒虾饲料-304号不锈钢恢复系数X4、颗粒虾饲料-304号不锈钢滚动摩擦因数X6对响应值堆积角η′和响应值面积占比S′的P值均大于0.05,表明其余因素对颗粒虾饲料堆积结果的影响不显著,故后续仅以颗粒虾饲料-颗粒虾饲料静摩擦因数X2、颗粒虾饲料-颗粒虾饲料滚动摩擦因数X3和颗粒虾饲料-304号不锈钢静摩擦因数X5进行最陡爬坡试验的设计。
为进一步确定颗粒虾饲料间接触参数,基于Plackett-Burman试验设计最陡爬坡试验,以快速收敛颗粒虾饲料间接触参数的寻优范围。由于颗粒虾饲料-颗粒虾饲料静摩擦因数X2、颗粒虾饲料-颗粒虾饲料滚动摩擦因数X3和颗粒虾饲料-304号不锈钢静摩擦因数X5对响应值堆积角η′和响应值面积占比S′影响显著,故其取值范围仍分别设置为0.140~0.260、0.010~0.300和0.200~0.650,而非显著影响因素颗粒虾饲料-颗粒虾饲料恢复系数X1取表2中数值的中间水平,颗粒虾饲料-304号不锈钢恢复系数X4、颗粒虾饲料-304号不锈钢滚动摩擦因数X6取物理试验得出的数值。由表3可知,颗粒虾饲料-颗粒虾饲料静摩擦因数X2、颗粒虾饲料-颗粒虾饲料滚动摩擦因数X3和颗粒虾饲料-304号不锈钢静摩擦因数X5对响应值堆积角η′的系数估计值均大于0,对面积占比S′的系数估计值均小于0,即显著因子对堆积角呈正效应,对面积占比呈负效应,故均以固定步长增大的形式设计最陡爬坡试验。最陡爬坡试验设计方案及结果如表4所示。
由表4可得,随着X2、X3、X5逐渐增大,最陡爬坡试验产生的堆积角η′和面积占比S′与物理试验所得结果之间的偏差呈现先减小后增大的趋势,且分别在第4组和第3组试验中得到最小相对误差。故选用第2~5组的参数范围,即颗粒虾饲料-颗粒虾饲料静摩擦因数0.170~0.260、颗粒虾饲料-颗粒虾饲料滚动摩擦因数0.083~0.300、颗粒虾饲料-304号不锈钢静摩擦因数0.313~0.650作为寻优范围进行后续Box-Behnken Design试验。
根据最陡爬坡试验得到的颗粒虾饲料-颗粒虾饲料静摩擦因数、颗粒虾饲料-颗粒虾饲料滚动摩擦因数及颗粒虾饲料-304号不锈钢静摩擦因数的取值区间,设计Box-Behnken Design试验,构建仿真颗粒虾饲料堆积角η′及面积占比S′的回归方程,从而求解最佳的颗粒虾饲料间接触参数组合。Box-Behnken Design试验方案与结果如表5所示。
通过Design-Expert软件对试验结果进行方差分析,可得仿真堆积角与面积占比回归方程分别为
响应值堆积角η′与面积占比S′的回归模型P值均小于0.000 1,表明堆积角与面积占比的回归模型均极显著,此外,堆积角和面积占比回归模型失拟项P值分别为0.934 7和0.743 8(均大于0.05),决定系数R2分别为0.983 1和0.953 6,精度分别为24.651和13.749,变异系数分别为1.74%和1.90%,表明回归模型拟合性较优,精确度高[29],对于仿真堆积角与面积占比具有良好的预测性。另一方面,各参数的方差分析如表6所示,颗粒虾饲料-颗粒虾饲料静摩擦因数X2、颗粒虾饲料-304号不锈钢静摩擦因数X5对响应值堆积角η′及面积占比S′影响均极显著(P<0.01),颗粒虾饲料-颗粒虾饲料滚动摩擦因数X3对响应值堆积角η′显著(0.01<P<0.05),对响应值面积占比S′影响极显著(P<0.01);颗粒虾饲料-304号不锈钢静摩擦因数X5的平方项对响应值堆积角η′及面积占比S′影响均显著(0.01<P<0.05);而剩余的颗粒虾饲料-颗粒虾饲料静摩擦因数X2、颗粒虾饲料-颗粒虾饲料滚动摩擦因数X3和颗粒虾饲料-304号不锈钢静摩擦因数X5之间的交互项及各自的二次项对响应值堆积角η′与面积占比S′不显著(P>0.05)。
运用Design-Expert软件的Optimization Numerical参数优化功能能够以响应值为目标进行参数优化。目标取值范围根据物理堆积试验结果设定颗粒虾饲料堆积角η′和面积占比S′寻优目标范围分别为26.50°~27.70°和13.13%~13.70%。约束条件根据Box-Behnken Design试验中颗粒虾饲料-颗粒虾饲料静摩擦因数X2、颗粒虾饲料-颗粒虾饲料滚动摩擦因数X3和颗粒虾饲料-304号不锈钢静摩擦因数X5高、低水平的取值设定。其中,由于前文颗粒仿真模型确立的试验中,多球颗粒均匀排列建立的仿真颗粒模型在颗粒虾饲料-304号不锈钢静摩擦因数X5=0.607时仿真得到的最大静摩擦角平均值与实际试验结果之间差异较小(<1%),且该值在最陡爬坡试验所得的寻优范围内,故将颗粒虾饲料-304号不锈钢静摩擦因数X5=0.607以定值的形式作为约束条件设定。整体寻优约束条件为
经过寻优所得,当颗粒虾饲料-颗粒虾饲料静摩擦因数取0.207,颗粒虾饲料-颗粒虾饲料滚动摩擦因数取0.150,颗粒虾饲料-304号不锈钢静摩擦因数取0.607时,能够得到与实际堆积情况一致的仿真堆积角(26.93°)和仿真面积占比(13.32%)。
为确保前文各标定试验所得颗粒虾饲料本征参数、颗粒虾饲料间接触参数及颗粒虾饲料与304号不锈钢接触参数能够用于投饵装置机构优化、颗粒虾饲料相关装备的设计等研究,需进行验证试验。本研究将标定所得参数输入颗粒仿真模型,首先进行堆积试验对寻优所得颗粒虾饲料间接触参数进行基本验证,再选取实际投饵场景中的下料过程进行螺旋下料的物理试验和仿真试验,通过比对实际下料情况和仿真下料情况的表现,验证参数能够为对实际情况进行仿真模拟提供参考。
按前文所得参数设置仿真颗粒虾饲料模型,再次进行仿真堆积试验以对标定试验进行初步的验证。得到颗粒虾饲料仿真堆积角分别为27.48°、26.96°、26.63°,平均值为27.02°,与实际堆积角平均值26.93°之间相对误差仅为0.33%。颗粒虾饲料仿真面积占比13.27%、13.32%、13.30%,平均值为13.30%,与实际面积占比平均值13.32%之间相对误差仅为0.15%。使用双样本t检测对仿真堆积结果与实际堆积结果进行分析,得到堆积角P=0.950,面积占比P=0.218,均大于0.05,表明两者与实际结果均无显著差异,表明参数寻优所得的颗粒虾饲料间接触参数具有一定可靠性。试验对比如图9所示。
标定所得的颗粒虾饲料离散元参数将被运用于投饵装置机构优化、颗粒虾饲料相关装备的设计等研究。在实际投饵场景中,颗粒虾饲料从料箱中被取出的过程,即下料过程,存在颗粒虾饲料间及颗粒虾饲料与装置间的碰撞运动和摩擦运动,且颗粒虾饲料本征参数、颗粒虾饲料间接触参数、颗粒虾饲料与接触材料之间接触参数对下料情况有不同程度的影响,具有实际参考价值,可以用于验证参数可靠性。本研究选用自主设计的螺旋下料装置,通过比对不同转速(80~130 r/min)下的螺旋下料实际情况和仿真情况对参数可靠性进行验证。试验装置如图10a所示,主要包括料箱、螺旋下料杆及转速控制器。其中,料箱和螺旋下料杆均为304号不锈钢所制。螺旋下料杆通过连接板与料箱连接,安装于装置中心位置,其底部螺旋叶片处于料箱下料口内,工作时螺旋下料杆旋转,使料箱内的颗粒虾饲料与螺旋叶片产生相对运动从下料口排出。
试验时,将颗粒虾饲料倒入料箱后,打开电源开关,调节转速控制器并通过转速仪测量转速,待转速稳定且出料均匀后,收集从试验装置中排出的颗粒虾饲料,10 s后关闭电源,待颗粒虾饲料不再掉落后对收集到的颗粒虾饲料称量,每个转速下重复试验3次,对下料质量取平均值。
建立简化后的螺旋下料机构三维模型后导入EDEM,如图10b所示,并输入前文所得颗粒虾饲料的本征参数、颗粒虾饲料间接触参数及颗粒虾饲料与304号不锈钢之间的接触参数。为尽量与物理下料试验保持一致,仿真时先设置螺旋下料杆运行一段时间,达到匀速下料状态后,再设置接料盒收集装置所排出的颗粒虾饲料,仿真结束后在EDEM后处理模块中设置Grid Bin Group查看收集到的颗粒虾饲料质量,每个转速下仿真试验重复3次,对下料质量取平均值。
不同转速下的物理试验与仿真试验10 s内下料质量如图11所示。随着螺旋下料杆转速增大,物理下料试验及仿真下料试验中装置在固定时间内下料的质量增长趋势明显,两条折线变化趋势基本一致,经过计算,不同转速下物理下料质量与仿真下料质量相对误差均在3%以内,两折线间相关系数为0.997,接近1,表明两折线变化趋势基本一致,且相关性强。综上所述,该颗粒虾饲料离散元模型和标定所得的颗粒虾饲料本征参数、颗粒虾饲料间接触参数、颗粒虾饲料与304号不锈钢之间接触参数合理,具有一定预测能力,能够对未来通过离散元法优化投饵装置提供一定参考。
(1)通过台架试验结合物理公式测定了颗粒虾饲料密度、泊松比、弹性模量、剪切模量分别为1 177.68 kg/m3、0.26、114.77 MPa和45.58 MPa;搭建颗粒虾饲料-304号不锈钢接触参数测定平台,通过斜面试验与回弹试验测得颗粒虾饲料-304号不锈钢静摩擦因数、颗粒虾饲料-304号不锈钢滚动摩擦因数、颗粒虾饲料-304号不锈钢恢复系数分别为0.607、0.097和0.35;通过堆积试验确定了颗粒虾饲料堆积角平均值26.93°,颗粒虾饲料在钢板上面积占比13.32%。
(2)经过颗粒虾饲料与304号不锈钢之间的仿真接触试验,验证得到基于多球体均匀排列构成的颗粒虾饲料仿真模型具有与实际颗粒虾饲料接近的表现;通过Plackett-Burman试验,以颗粒虾饲料堆积试验获得的颗粒虾饲料堆积角及颗粒虾饲料在钢板上的面积占比作为响应值,筛选出显著影响因素分别为颗粒虾饲料-颗粒虾饲料静摩擦因数、颗粒虾饲料-颗粒虾饲料滚动摩擦因数、颗粒虾饲料-304号不锈钢静摩擦因数;采用最陡爬坡试验收敛接触参数寻优范围,并通过Box-Behnken Design试验得到显著影响因素关于堆积角和面积占比的回归方程,以物理堆积试验得到的堆积角和面积占比为目标进行参数寻优,得到最优参数组合:颗粒虾饲料-颗粒虾饲料静摩擦因数0.207、颗粒虾饲料-颗粒虾饲料滚动摩擦因数0.150和颗粒虾饲料-304号不锈钢静摩擦因数0.607。
(3)使用寻优所得参数组合进行仿真堆积试验,仿真结果与实际结果之间相对误差较小,经过双样本t检测得到P>0.05,表明仿真堆积结果与实际堆积结果无显著差异;利用自主设计的螺旋下料装置进行仿真下料试验和物理下料试验,结果表明不同转速下固定时间内下料量的相对误差均在3%以内,且仿真试验和物理试验的不同转速10 s内下料量折线相关系数为0.997,趋势一致,本文建立的颗粒虾饲料模型和标定的离散元参数可靠且正确。
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2025年第56卷第12期
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doi: 10.6041/j.issn.1000-1298.2025.12.069
  • 接收时间:2025-08-14
  • 首发时间:2026-09-30
  • 出版时间:2025-12-25
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  • 收稿日期:2025-08-14
  • 修回日期:2025-09-17
基金
上海市科技兴农项目产业技术体系建设专项(沪农科产字(2024)第4号)
国家重点研发计划项目(2019YFD0900401)
上海市水产动物良种创制与绿色养殖协同创新中心项目(2021科技02-12)
上海市科技兴农推广项目(沪农科推字(2018)第3-3号)
作者信息
    1.上海海洋大学工程学院,上海 201306
    2.农业农村部对外经济合作中心,北京 100125

通讯作者:

胡庆松(1979—),教授,博士,主要从事渔业工程装备技术研究,E-mail:
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https://castjournals.cast.org.cn/joweb/nyjxxb/CN/10.6041/j.issn.1000-1298.2025.12.069
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2种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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