Article(id=1311999772496589438, tenantId=1146029695717560320, journalId=1311385739772133495, issueId=1311999647825101743, articleNumber=null, orderNo=null, doi=10.6041/j.issn.1000-1298.2025.12.024, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1747238400000, receivedDateStr=2025-05-15, revisedDate=1750694400000, revisedDateStr=2025-06-24, acceptedDate=null, acceptedDateStr=null, onlineDate=1790734721539, onlineDateStr=2026-09-30, pubDate=1766592000000, pubDateStr=2025-12-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790734721539, onlineIssueDateStr=2026-09-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790734721539, creator=13701087609, updateTime=1790734721539, 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=267, endPage=278, ext={EN=ArticleExt(id=1311999772706304639, articleId=1311999772496589438, tenantId=1146029695717560320, journalId=1311385739772133495, language=EN, title=Construction and Experiment of Discrete Element Model for Tea Garden Soil-Organic Fertilizer Mixture Based on EDEM, columnId=1311999707161919908, journalTitle=Transactions of the Chinese Society for Agricultural Machinery, columnName=Agricultural Equipment and Mechanization Engineering, runingTitle=null, highlight=null, articleAbstract=

Aiming to improve the simulation accuracy of the discrete element model for tea garden soil-organic fertilizer mixture, focusing on the combination of physical experiments and simulation to calibrate the parameters of tea garden soil and organic fertilizer, by using the slope method, rolling friction test, and coefficient of restitution measurement, the physical and contact parameters of soil and organic fertilizer were obtained, and the mixing angle was measured based on the cylindrical lifting method. The Plackett-Burman experiment was used to screen for significant influencing parameters, and the optimization interval was determined by combining the steepest slope test. A quadratic regression model was established by using the Box-Behnken response surface method to obtain the optimal parameter combination: soil-organic fertilizer recovery coefficient of 0.35, organic fertilizer rolling friction factor of 0.115, and organic fertilizer steel recovery coefficient of 0.606. At the same time, the applicable range of moisture content for the model (5%~10%) was clarified through experimental analysis. The verification test showed that the simulated stacking angle was 24.49°, with a relative error of 1.65% compared with the actual value. A prototype of a tea garden tillage and fertilization machine was built, field rotary tillage and fertilization experiments were conducted, and rotary tillage and fertilization experiments were simulated. The relative errors of soil coverage rate in field experiments and simulation experiments at different rotary tillage speeds (150 r/min, 200 r/min, 250 r/min, and 300 r/min) were 6.21%, 4.52%, 7.13%, and 7.72%, respectively. The soil-rganic fertilizer distribution in simulation experiments was consistent with that in field experiments, verifying the accuracy of the soil-organic fertilizer mixture discrete element model and contact parameters.

, authors=Xiaolong HUAN1, 2, Mengchao FENG1, Chuanyu WU1, 2, Jiangming JIA1, 2, Jianneng CHEN1, 2, authorsList=Xiaolong HUAN, Mengchao FENG, Chuanyu WU, Jiangming JIA, Jianneng CHEN, authorCompany=null, correspAuthors=Jianneng CHEN, 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=1311999778955817663, articleId=1311999772496589438, tenantId=1146029695717560320, journalId=1311385739772133495, language=CN, title=基于EDEM的茶园土壤-有机肥混料离散元模型构建与试验, columnId=1311999707321303462, journalTitle=农业机械学报, columnName=农业装备与机械化工程, runingTitle=null, highlight=null, articleAbstract=

为提高茶园土壤-有机肥混料的离散元模型仿真精度,以茶园土壤与有机肥为研究对象,通过物理试验与仿真模拟相结合的方法进行参数标定。通过斜面法、滚动摩擦试验及碰撞恢复系数测定,获取土壤与有机肥的物理参数及接触参数,并基于圆筒提升法实测混料堆积角。利用Plackett-Burman试验筛选出显著影响参数,结合最陡爬坡试验确定优化区间,通过Box-Behnken响应面法建立二次回归模型,获得最优参数组合:土壤-有机肥恢复系数为0.35、有机肥-有机肥滚动摩擦因数为0.115、有机肥-钢板碰撞恢复系数为0.606。同时通过含水率试验分析明确了模型含水率的适用区间(5%~10%)。验证试验表明,仿真堆积角为24.49°,与实际值相对误差为1.65%。搭建茶园中耕施肥机样机,进行田间和仿真旋耕施肥试验,得到不同旋耕转速(150、200、250、300 r/min)条件下,田间试验和仿真试验覆土率的相对误差分别为6.21%、4.52%、7.13%、7.72%,仿真试验与田间试验的土壤-有机肥分布情况相一致,验证了土壤-有机肥混料离散元模型和接触参数的准确性。

, authors=郇晓龙1, 2, 冯猛超1, 武传宇1, 2, 贾江鸣1, 2, 陈建能1, 2, authorsList=郇晓龙, 冯猛超, 武传宇, 贾江鸣, 陈建能, authorCompany=null, correspAuthors=陈建能, authorNote=

郇晓龙(1995—),男,助理研究员,博士,主要从事现代农业机械设计与试验研究,E-mail:

, correspAuthorsNote=
陈建能(1972—),男,教授,博士,主要从事农业装备及其智能化研究,E-mail:
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Journal of Agricultural Science, 2018, 156(3): 435-448., articleTitle=Screening designs in agricultural experiments, refAbstract=null)], funds=[Fund(id=1311999790955721506, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999772496589438, awardId=32301715, language=CN, fundingSource=国家自然科学基金项目(32301715), fundOrder=null, country=null), Fund(id=1311999791043801891, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999772496589438, awardId=CARS-19, language=CN, fundingSource=财政部和农业农村部:国家茶叶产业技术体系项目(CARS-19), fundOrder=null, country=null), Fund(id=1311999791408706340, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999772496589438, awardId=24242211-Y, language=CN, fundingSource=浙江省农机研发推广一体化项目和浙江理工大学科研业务费专项资金项目(24242211-Y), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1311999779253613248, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999772496589438, xref=1., ext=[AuthorCompanyExt(id=1311999779266196161, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999772496589438, companyId=1311999779253613248, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China), AuthorCompanyExt(id=1311999779274584770, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999772496589438, companyId=1311999779253613248, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.浙江理工大学机械工程学院,杭州 310018)]), AuthorCompany(id=1311999779337499331, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999772496589438, xref=2., ext=[AuthorCompanyExt(id=1311999779345887940, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999772496589438, companyId=1311999779337499331, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.Key Laboratory of Zhejiang Transplanting Equipment Technology, Hangzhou 310018, China), AuthorCompanyExt(id=1311999779354276549, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999772496589438, companyId=1311999779337499331, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.浙江省全省农业智能感知与机器人重点实验室,杭州 310018)])], figs=[ArticleFig(id=1311999783544386285, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999772496589438, language=EN, label=Fig. 1, caption=Sample physical pictures, figureFileSmall=GxiGSuSkMBedDQsotT5gLQ==, figureFileBig=hvXsXXL3eKWwFvRs8oTaBw==, tableContent=null), ArticleFig(id=1311999783624078062, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999772496589438, language=CN, label=图1, caption=样品实物图, figureFileSmall=GxiGSuSkMBedDQsotT5gLQ==, figureFileBig=hvXsXXL3eKWwFvRs8oTaBw==, tableContent=null), ArticleFig(id=1311999783842181871, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999772496589438, language=EN, label=Fig. 2, caption=Shear modulus measurement test device, figureFileSmall=xULikBjJDQGh+EF68dRH/Q==, figureFileBig=5w/a/irrdjPx6p2WGZw2Ig==, tableContent=null), ArticleFig(id=1311999783905096432, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999772496589438, language=CN, label=图2, caption=剪切模量测量试验装置

1. 计算机 2. 试验装置 3. 待测样品

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1. 倾斜钢板 2. 待测样品 3. 角度测量仪

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1. 待测样品 2. 倾斜钢板 3. 角度测量仪 4. 水平钢板 5. 钢尺

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1. 高速摄像机 2. 计算机 3. 直尺 4. 钢板

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1. 圆筒 2. 有机肥 3. 土壤 4. 钢板

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1. 圆筒 2. 土壤 3. 有机肥

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1. 采茶机 2. 肥料箱 3. 螺旋输送器 4. 下料管 5. 旋耕刀

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1. 除草盘 2. 挡草板 3. 刀轴 4. 刀座 5. 弯刀 6. 直刀

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Simulation parameters of soil organic fertilizer mixture stacking angle

, figureFileSmall=null, figureFileBig=null, tableContent=
项目参数数值
土壤-土壤碰撞恢复系数0.21
静摩擦因数0.68
滚动摩擦因数0.27
土壤-钢板碰撞恢复系数0.54
静摩擦因数0.31
滚动摩擦因数0.13
土壤-有机肥碰撞恢复系数X10.2~0.6
静摩擦因数X20.44~0.88
滚动摩擦因数X30.1~0.5
有机肥-有机肥碰撞恢复系数X40.4~0.8
静摩擦因数X50.4~0.9
滚动摩擦因数X60~0.2
有机肥-钢板碰撞恢复系数X70.3~0.9
静摩擦因数X80.5~0.9
滚动摩擦因数X90~0.3
土壤-有机肥JKR表面能X10/(J·m-2)0~0.04
有机肥-有机肥JKR表面能X11/(J·m-2)0~0.03
), ArticleFig(id=1311999787868713745, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999772496589438, language=CN, label=表1, caption=

土壤-有机肥混料堆积角仿真参数

, figureFileSmall=null, figureFileBig=null, tableContent=
项目参数数值
土壤-土壤碰撞恢复系数0.21
静摩擦因数0.68
滚动摩擦因数0.27
土壤-钢板碰撞恢复系数0.54
静摩擦因数0.31
滚动摩擦因数0.13
土壤-有机肥碰撞恢复系数X10.2~0.6
静摩擦因数X20.44~0.88
滚动摩擦因数X30.1~0.5
有机肥-有机肥碰撞恢复系数X40.4~0.8
静摩擦因数X50.4~0.9
滚动摩擦因数X60~0.2
有机肥-钢板碰撞恢复系数X70.3~0.9
静摩擦因数X80.5~0.9
滚动摩擦因数X90~0.3
土壤-有机肥JKR表面能X10/(J·m-2)0~0.04
有机肥-有机肥JKR表面能X11/(J·m-2)0~0.03
), ArticleFig(id=1311999787944211218, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999772496589438, language=EN, label=Tab. 2, caption=

Plackett-Burman test parameters and codes

, figureFileSmall=null, figureFileBig=null, tableContent=
因素编码
-101
X10.20.40.6
X20.440.660.88
X30.10.30.5
X40.40.60.8
X50.400.650.90
X600.10.2
X70.30.60.9
X80.50.70.9
X900.150.30
X10/(J·m-2)00.020.04
X11/(J·m-2)00.0150.030
), ArticleFig(id=1311999788023902995, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999772496589438, language=CN, label=表2, caption=

Plackett-Burman试验因素与编码

, figureFileSmall=null, figureFileBig=null, tableContent=
因素编码
-101
X10.20.40.6
X20.440.660.88
X30.10.30.5
X40.40.60.8
X50.400.650.90
X600.10.2
X70.30.60.9
X80.50.70.9
X900.150.30
X10/(J·m-2)00.020.04
X11/(J·m-2)00.0150.030
), ArticleFig(id=1311999788141343508, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999772496589438, language=EN, label=Tab. 3, caption=

Plackett-Burman test design and results

, figureFileSmall=null, figureFileBig=null, tableContent=
试验序号因素堆积角/(°)
x1x2x3x4x5x6x7x8x9x10x11
11-111-1111-1-1-123.64
211-1111-1-111-124.08
3-1-1-1-1-1-1-1-1-1-1-120.86
4-111-1111-1-1-1123.85
51-1-1-11-11111118.91
6111-1-1-11-111-117.91
7-1-11-111-1111-126.76
81-1111-1-1-1-1-1118.39
9-1-1-11-111-111124.84
10-11-111-111-1-1-118.63
1111-1-1-11-11-1-1122.45
12-1111-1-1-1111123.01
), ArticleFig(id=1311999788208452373, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999772496589438, language=CN, label=表3, caption=

Plackett-Burman试验设计与结果

, figureFileSmall=null, figureFileBig=null, tableContent=
试验序号因素堆积角/(°)
x1x2x3x4x5x6x7x8x9x10x11
11-111-1111-1-1-123.64
211-1111-1-111-124.08
3-1-1-1-1-1-1-1-1-1-1-120.86
4-111-1111-1-1-1123.85
51-1-1-11-11111118.91
6111-1-1-11-111-117.91
7-1-11-111-1111-126.76
81-1111-1-1-1-1-1118.39
9-1-1-11-111-111124.84
10-11-111-111-1-1-118.63
1111-1-1-11-11-1-1122.45
12-1111-1-1-1111123.01
), ArticleFig(id=1311999788288144150, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999772496589438, language=EN, label=Tab. 4, caption=

ANOVA results

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参数效应FP贡献度/%显著性顺序
x1-2.09854.540.021 8*13.962
x2-0.5865.120.078 51.067
x30.6377.690.071 91.276
x40.3018.510.145 40.3010
x5-0.3523.620.129 20.389
x64.654 212.920.009 8**68.831
x7-1.29326.520.035 2*5.333
x80.5865.120.078 51.067
x9-0.89155.960.050 92.545
x101.28319.830.035 6*5.224
x11-0.0712.760.875 10.0111
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方差分析结果

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参数效应FP贡献度/%显著性顺序
x1-2.09854.540.021 8*13.962
x2-0.5865.120.078 51.067
x30.6377.690.071 91.276
x40.3018.510.145 40.3010
x5-0.3523.620.129 20.389
x64.654 212.920.009 8**68.831
x7-1.29326.520.035 2*5.333
x80.5865.120.078 51.067
x9-0.89155.960.050 92.545
x101.28319.830.035 6*5.224
x11-0.0712.760.875 10.0111
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Test design and results of the steepest climb

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序号因素堆积角/(°)相对误差/%
X1X6X7
10.200.3019.4321.97
20.30.050.4521.6413.09
30.40.100.6024.322.33
40.50.150.7526.827.71
50.60.200.9029.0816.79
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最陡爬坡试验设计与结果

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序号因素堆积角/(°)相对误差/%
X1X6X7
10.200.3019.4321.97
20.30.050.4521.6413.09
30.40.100.6024.322.33
40.50.150.7526.827.71
50.60.200.9029.0816.79
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Box-Behnken test factors and coding

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编码因素
X1X6X7
-10.350.070.55
00.400.100.60
10.450.130.65
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Box-Behnken试验因素编码

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编码因素
X1X6X7
-10.350.070.55
00.400.100.60
10.450.130.65
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Box-Behnken test design and results

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试验序号因素堆积角Y/(°)
x1x6x7
100024.68
2-11026.07
30-1123.28
4-10124.27
50-1-122.25
601126.76
701-126.43
810125.04
9-10-124.07
1000023.93
1111026.98
1210-124.87
1300024.22
1400023.64
15-1-1022.30
161-1022.79
1700023.99
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Box-Behnken试验设计及结果

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试验序号因素堆积角Y/(°)
x1x6x7
100024.68
2-11026.07
30-1123.28
4-10124.27
50-1-122.25
601126.76
701-126.43
810125.04
9-10-124.07
1000023.93
1111026.98
1210-124.87
1300024.22
1400023.64
15-1-1022.30
161-1022.79
1700023.99
), ArticleFig(id=1311999788917289758, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999772496589438, language=EN, label=Tab. 8, caption=

Analysis of variance of regression model

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方差来源均方自由度平方和FP
模型33.0793.6734.74<0.000 1**
x11.1011.1010.420.014 5*
x630.50130.50288.32<0.000 1**
x70.374 110.374 13.540.102 1
x1x60.044 110.044 10.416 90.539 1
x1x70.000 210.000 20.002 10.964 5
x6x70.122 510.122 51.160.317 6
0.111 510.111 51.050.338 7
0.330 710.330 73.310.120 4
0.398 810.398 83.770.093 3
残差0.740 570.105 80.303 70.822 5
失拟项0.137 430.045 8
误差0.603 140.150 8
总和33.8116
), ArticleFig(id=1311999788992787231, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999772496589438, language=CN, label=表8, caption=

回归模型方差分析

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方差来源均方自由度平方和FP
模型33.0793.6734.74<0.000 1**
x11.1011.1010.420.014 5*
x630.50130.50288.32<0.000 1**
x70.374 110.374 13.540.102 1
x1x60.044 110.044 10.416 90.539 1
x1x70.000 210.000 20.002 10.964 5
x6x70.122 510.122 51.160.317 6
0.111 510.111 51.050.338 7
0.330 710.330 73.310.120 4
0.398 810.398 83.770.093 3
残差0.740 570.105 80.303 70.822 5
失拟项0.137 430.045 8
误差0.603 140.150 8
总和33.8116
), ArticleFig(id=1311999789080867616, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999772496589438, language=EN, label=Tab. 9, caption=

Scheme and results of verification experiment

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转速/(r·min-1)田间试验覆土率/%仿真试验覆土率/%相对误差/%95%置信区间(田间)/%
15085.65±1.2380.336.21[84.42,86.88]
20091.73±0.8987.584.52[90.84,92.62]
25092.47±1.5685.877.13[90.91,94.03]
30095.56±1.0788.187.72[94.50,96.62]
), ArticleFig(id=1311999790754394913, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999772496589438, language=CN, label=表9, caption=

验证试验方案与结果

, figureFileSmall=null, figureFileBig=null, tableContent=
转速/(r·min-1)田间试验覆土率/%仿真试验覆土率/%相对误差/%95%置信区间(田间)/%
15085.65±1.2380.336.21[84.42,86.88]
20091.73±0.8987.584.52[90.84,92.62]
25092.47±1.5685.877.13[90.91,94.03]
30095.56±1.0788.187.72[94.50,96.62]
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基于EDEM的茶园土壤-有机肥混料离散元模型构建与试验
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郇晓龙 1, 2 , 冯猛超 1 , 武传宇 1, 2 , 贾江鸣 1, 2 , 陈建能 1, 2
农业机械学报 | 农业装备与机械化工程 2025,56(12): 267-278
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农业机械学报 |农业装备与机械化工程 2025 , 56 (12) : 267 -278
基于EDEM的茶园土壤-有机肥混料离散元模型构建与试验
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郇晓龙(1995—),男,助理研究员,博士,主要从事现代农业机械设计与试验研究,E-mail:

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郇晓龙(1995—),男,助理研究员,博士,主要从事现代农业机械设计与试验研究,E-mail:

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郇晓龙1, 2 , 冯猛超1, 武传宇1, 2, 贾江鸣1, 2, 陈建能1, 2
作者信息
  • 1.浙江理工大学机械工程学院,杭州 310018
  • 2.浙江省全省农业智能感知与机器人重点实验室,杭州 310018
通讯作者:
陈建能(1972—),男,教授,博士,主要从事农业装备及其智能化研究,E-mail:
作者简介:

郇晓龙(1995—),男,助理研究员,博士,主要从事现代农业机械设计与试验研究,E-mail:

Construction and Experiment of Discrete Element Model for Tea Garden Soil-Organic Fertilizer Mixture Based on EDEM
Xiaolong HUAN1, 2 , Mengchao FENG1, Chuanyu WU1, 2, Jiangming JIA1, 2, Jianneng CHEN1, 2
Affiliations
  • 1.School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China
  • 2.Key Laboratory of Zhejiang Transplanting Equipment Technology, Hangzhou 310018, China
出版时间: 2025-12-25 doi: 10.6041/j.issn.1000-1298.2025.12.024
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为提高茶园土壤-有机肥混料的离散元模型仿真精度,以茶园土壤与有机肥为研究对象,通过物理试验与仿真模拟相结合的方法进行参数标定。通过斜面法、滚动摩擦试验及碰撞恢复系数测定,获取土壤与有机肥的物理参数及接触参数,并基于圆筒提升法实测混料堆积角。利用Plackett-Burman试验筛选出显著影响参数,结合最陡爬坡试验确定优化区间,通过Box-Behnken响应面法建立二次回归模型,获得最优参数组合:土壤-有机肥恢复系数为0.35、有机肥-有机肥滚动摩擦因数为0.115、有机肥-钢板碰撞恢复系数为0.606。同时通过含水率试验分析明确了模型含水率的适用区间(5%~10%)。验证试验表明,仿真堆积角为24.49°,与实际值相对误差为1.65%。搭建茶园中耕施肥机样机,进行田间和仿真旋耕施肥试验,得到不同旋耕转速(150、200、250、300 r/min)条件下,田间试验和仿真试验覆土率的相对误差分别为6.21%、4.52%、7.13%、7.72%,仿真试验与田间试验的土壤-有机肥分布情况相一致,验证了土壤-有机肥混料离散元模型和接触参数的准确性。

茶园  /  土壤-有机肥混料  /  离散元模型  /  参数标定  /  田间试验

Aiming to improve the simulation accuracy of the discrete element model for tea garden soil-organic fertilizer mixture, focusing on the combination of physical experiments and simulation to calibrate the parameters of tea garden soil and organic fertilizer, by using the slope method, rolling friction test, and coefficient of restitution measurement, the physical and contact parameters of soil and organic fertilizer were obtained, and the mixing angle was measured based on the cylindrical lifting method. The Plackett-Burman experiment was used to screen for significant influencing parameters, and the optimization interval was determined by combining the steepest slope test. A quadratic regression model was established by using the Box-Behnken response surface method to obtain the optimal parameter combination: soil-organic fertilizer recovery coefficient of 0.35, organic fertilizer rolling friction factor of 0.115, and organic fertilizer steel recovery coefficient of 0.606. At the same time, the applicable range of moisture content for the model (5%~10%) was clarified through experimental analysis. The verification test showed that the simulated stacking angle was 24.49°, with a relative error of 1.65% compared with the actual value. A prototype of a tea garden tillage and fertilization machine was built, field rotary tillage and fertilization experiments were conducted, and rotary tillage and fertilization experiments were simulated. The relative errors of soil coverage rate in field experiments and simulation experiments at different rotary tillage speeds (150 r/min, 200 r/min, 250 r/min, and 300 r/min) were 6.21%, 4.52%, 7.13%, and 7.72%, respectively. The soil-rganic fertilizer distribution in simulation experiments was consistent with that in field experiments, verifying the accuracy of the soil-organic fertilizer mixture discrete element model and contact parameters.

tea garden  /  soil-organic fertilizer mixture  /  discrete element model  /  parameter calibration  /  field experiment
郇晓龙, 冯猛超, 武传宇, 贾江鸣, 陈建能. 基于EDEM的茶园土壤-有机肥混料离散元模型构建与试验. 农业机械学报, 2025 , 56 (12) : 267 -278 . DOI: 10.6041/j.issn.1000-1298.2025.12.024
Xiaolong HUAN, Mengchao FENG, Chuanyu WU, Jiangming JIA, Jianneng CHEN. Construction and Experiment of Discrete Element Model for Tea Garden Soil-Organic Fertilizer Mixture Based on EDEM[J]. Transactions of the Chinese Society for Agricultural Machinery, 2025 , 56 (12) : 267 -278 . DOI: 10.6041/j.issn.1000-1298.2025.12.024
茶树作为一种多年生经济作物,其生长和发育需要大量的养分支持。施肥是茶园管理中不可或缺的重要环节,其对提高茶叶产量、改善茶叶品质、提升土壤肥力以及促进茶树健康生长具有重要意义[1]。茶园施肥采用基肥与追肥相结合的方式,通过沟施、撒施等方法,确保茶树在不同生长时期获得充足养分。随着我国农村劳动力人口越来越紧缺,茶园人工施肥,特别是开沟施肥,越来越困难。相比人工施肥,机械施肥的作业质量普遍不高,其中很重要的一个原因是其施肥过程中土壤和有机肥互作关系不清晰。因此,为进一步研究土壤和有机肥的接触参数,有必要建立土壤-有机肥混料的相互作用模型。
在研究土壤与其他物料颗粒之间互作关系的仿真领域中,离散元法得到了广泛应用。在离散元仿真过程中,模型参数的设置对仿真结果的准确性具有重要影响[2-3]。离散元法不仅可以预测颗粒的运动行为[4-5],还可以模拟颗粒的堆积过程,并有效评估不同颗粒参数和颗粒接触模型的适用性[6]。近年来,在农业工程领域,国内外已有众多学者利用离散元仿真法对物质材料颗粒方面进行参数标定的研究。SHAIKH等[7]使用离散元方法模拟不同含水率下单根履带板与黏土壤地形的相互作用,采用Hertz-Mindlin接触模型对各含水率土壤的参数进行标定,并进行了土槽试验以验证仿真的准确性。刘瑞等[8]建立量化描述散体颗粒的运动学方程,分析确定了包衣玉米种子主要接触参数并进行标定。WANG等[9]采用Hertz-Mindlin模型对小麦粉碎过程中接触参数进行标定,随后以小麦籽粒黏结模型的临界载荷为响应值,生成临界载荷的回归方程。将标定参数代入EDEM模型进行计算,计算值与观测值相对误差为1.6%。张胜伟等[10]利用黏结模型并采用自由落体碰撞、斜面滑动法、斜面滚动法对绿豆种子与接触材料间的参数进行了标定,通过排种试验验证表明标定结果可为绿豆精密排种装置的设计与仿真优化提供理论参考。ZENG等[11]基于Hertz Mindlin with Bonding V2模型建立了包衣棉籽与土壤压缩的仿真模型,以峰值压缩力为响应值,采用物理试验与仿真试验相结合的方法,对包衣棉籽与土壤仿真模型的接触参数进行标定。
现有研究主要聚焦于单一均质物料(如土壤、种子、肥料)与作业机具的接触参数标定。然而,茶园中耕施肥作业的核心对象是“土壤-有机肥混料”这一非均质双颗粒体系。该体系具有显著的独特性与复杂性:土壤颗粒与有机肥颗粒在物理和力学性质上存在显著的组分异质性,使得在旋耕等动态机械作用下,2种颗粒间不仅会产生颗粒-颗粒的复杂碰撞、摩擦、挤压行为,还会形成颗粒-机具的强相互作用,而这些接触行为的相互作用共同决定了混料的流动、混合与分布。同时混料中土壤有效覆盖有机肥为理想状态的目标导向性,高度依赖于2种颗粒在机械扰动下的相对运动轨迹和空间配置关系。现有针对单一物料的标定方法难以直接迁移并准确表征这种特定目标下的双组分体系互作机理。
因此,针对单一物料研究的局限性,以“茶园土壤-有机肥混料”这一具有明确农艺目标的非均质双颗粒体系为研究对象,在现有研究方法的基础上,本文分别精细化测定有机肥和土壤的基本物理参数及其相互之间、与钢材之间的接触参数(静摩擦因数、滚动摩擦因数、碰撞恢复系数)。进而,利用离散元模拟软件(Discrete element method,EDEM),以混料堆积角为关键宏观响应指标,采用Plackett-Burman Design筛选显著影响混料行为的接触参数,通过最陡爬坡试验确定优化区间,并运用Box-Behnken响应面法精确标定土壤-有机肥间的最优接触参数组合。最后,通过田间旋耕施肥试验验证所建立模型及标定参数在模拟实际复杂作业过程中的准确性,为应用离散元法深入揭示茶园施肥过程中土壤与有机肥的动态互作机理,并为优化施肥机具设计提供理论基础和参数支撑。
本文研究对象为土壤-有机肥混料。其中有机肥为山东史丹利化肥有限公司的颗粒有机肥。所选颗粒有机肥外观呈规则或不规则颗粒状,其粒径分布范围及物理力学特性代表了当前茶园机械化施肥常用肥料的典型特征(图1a)。土壤采集自浙江省绍兴市御茶村核心茶园区域,该区域是典型的亚热带丘陵茶园,土壤类型为黄壤,是长江中下游茶区广泛分布的代表性土壤类型之一(图1b)。采样时,在选定区域内选取3个具有代表性的地块(分别位于坡顶、坡中、坡底),每个地块按“S”形路线多点(5~7点)采集0~20 cm耕作层土壤(此为茶树根系主要分布和施肥影响层)。采集的土壤样品经自然风干、去除石块和明显植物残体后,混合均匀备用。文献[12]表明,该区域茶园土壤的理化性质在本地区域内具有较好的代表性。虽然有机肥的含水率未被纳入离散元模型的标定参数体系,但该参数对肥料力学特性具有显著影响。具体而言,不同含水率的有机肥在贮存过程中会呈现差异性结块倾向,其施用时的流动特性与土壤接触力学行为也会发生相应改变,直接影响有机肥的贮存稳定性和田间施用效果。
本试验通过烘干法[13]测定土壤和有机肥含水率。将土壤和有机肥样品分别放进培养皿中,并将其放置于恒温干燥箱中,设置干燥温度为105℃,干燥至恒质量。干燥至恒质量后,称量样品并记录。含水率ω的计算公式为
式中
      ω1——烘干前样品与培养皿的质量,g
      ω2——烘干后样品与培养皿的质量,g
      ω0——恒质量后培养皿的质量,g
最后计算得土壤含水率为19.87%,有机肥含水率为6.12%。
采用排水法对颗粒有机肥密度进行测量。用电子天平(精度为0.1 mg)称取20 g有机肥,将其放入盛有水的量筒(分度值1 mL,量程50 mL)中,量筒内排液体积即为加入的有机肥体积。有机肥密度计算式为
式中
      ρ——样品密度,g/cm3
      m——样品质量,g
      V——排液体积,cm3
重复10次试验取平均值,最终得到有机肥密度为1.5 g/cm3。
土壤密度参考文献[14],采用环刀法[15]进行测量。利用环刀(容积200 cm3)采集土壤样品后称量,根据式(2)计算土壤密度,重复10次试验后得到土壤平均密度约为2.6 g/cm3。
由于土壤颗粒尺寸较小,较难测得其剪切模量与泊松比,因此参照文献[16-20],确定土壤泊松比与剪切模量分别为0.3和5×107Pa。有机肥剪切模量是指有机肥抵抗剪切应变的能力。本文在预试验的基础上参照文献[21-22],在剪切模量实验室进行测定,试验装置如图2所示。剪切模量计算式为
式中
      G——剪切模量,Pa
      τ——剪切应力,Pa
      γ——剪切应变
通过测得有机肥剪切应力和应变,计算得到有机肥剪切模量为1.2×106Pa,泊松比为0.25。
通过斜面法[23]测量物料-钢板间的静摩擦因数和物料-物料间的静摩擦因数,通过斜面滚动法[23]测量物料-钢板间的滚动摩擦因数、物料-物料间的滚动摩擦因数,通过自由落体法[24]测得碰撞恢复系数。
静摩擦因数是物料所受最大静摩擦力与法向正压力的比值,能够很好地表达物料与接触固体表面的摩擦性质[25]。静摩擦因数试验装置如图3所示,测量物料与钢板间的静摩擦因数时,将待测样品和角度测量仪放置在钢板(300 mm×300 mm×2 mm)上。由于有机肥颗粒为球体,为防止其发生滚动,降低试验误差,用细针将其穿成一排竖直摆放。开始试验时,钢板水平放置,初始角度为0°,通过拉绳缓慢提升倾斜板一侧,使钢板倾斜角逐渐变大,当待测样品开始有滑动趋势时停止提升,此时记录角度测量仪读数。静摩擦因数计算式为
式中
      μ1——静摩擦因数
      α——斜面与水平面夹角,(°)
为提高试验结果的准确性,重复10次试验取平均值,得到有机肥-钢板的静摩擦因数为0.7。
在测量有机肥与土壤之间的静摩擦因数时,分别制作土壤颗粒板与有机肥颗粒板,即将土壤和有机肥通过胶水固定在钢板上。试验时,先将钢板放于水平位置,将土壤颗粒板放置在钢板上,待测样品放置在土壤颗粒板上,重复上述操作,分别测得土壤-土壤静摩擦因数以及土壤-有机肥静摩擦因数。同理,将有机肥颗粒板放置在钢板上可以测得有机肥-有机肥间静摩擦因数。试验重复5次,测得土壤-土壤静摩擦因数为0.68,土壤-有机肥静摩擦因数为0.66,有机肥-有机肥间静摩擦因数为0.65。
滚动摩擦因数指的是一个物体在另一个表面上滚动时所受到的摩擦阻力与法向力的比值。滚动摩擦因数试验装置由倾斜钢板、水平钢板、钢尺、角度测量仪和支架组成(图4)。在预试验基础上,选取α=30°时测试滚动摩擦因数。测试物料与钢板间滚动摩擦因数时,将待测样品放置于距离倾斜钢板底部200 mm位置上,以初速度为零向下自由滚动至停止,水平滚动距离为d。根据能量守恒定律可得出滚动摩擦因数计算式为
式中
      fk——滚动摩擦因数
      L——物料与倾斜钢板底部距离,mm
重复10次试验取平均值,得到有机肥-钢板的滚动摩擦因数为0.15。
同理,测试物料与物料间滚动摩擦因数时,将颗粒板放置于倾斜钢板与水平钢板上,重复上述操作,可测得土壤-有机肥滚动摩擦因数和有机肥-有机肥滚动摩擦因数。最终测得土壤-有机肥滚动摩擦因数和有机肥-有机肥滚动摩擦因数分别为0.25和0.1。
碰撞恢复系数通过碰撞弹跳试验进行测定[26],试验装置如图5所示。依次选用土壤颗粒板、有机肥颗粒板和钢板作为碰撞板,有机肥颗粒初始释放高度H1分别选取300、350、400 mm,采用高速摄像机记录待测样品接触碰撞板后的反弹高度H2,重复10次试验取均值,可得到有机肥-土壤间碰撞恢复系数、有机肥-有机肥间碰撞恢复系数和有机肥-钢板间碰撞恢复系数,计算公式为
式中
      e——碰撞恢复系数
      vy——物料碰触后径向速度,mm/s
      v——物料碰触后速度,mm/s
      g——重力加速度,m/s2
最后测得有机肥-土壤间碰撞恢复系数为0.41,有机肥-有机肥间碰撞恢复系数为0.59,有机肥-钢板间碰撞恢复系数为0.55。
采用圆筒提升法对土壤与有机肥混合物的实际堆积角进行测定。以所测得的堆积角为基准值,运用离散元模拟软件EDEM对该混料进行仿真模拟。为了确定对土壤-有机肥混合物离散元模拟具有显著影响的因素,利用试验设计软件Design-Expert设计Plackett-Burman试验。在确定显著影响因素后,通过最陡爬坡试验法对这些因素进行最优区间确定。在所确定的最优区间内,选取3个水平点,设计并执行Box-Behnken试验,以建立回归模型并确定最优参数组合。最终,在经过校准的参数设置下,进行土壤-有机肥混合物的仿真堆积角验证试验,以评估所标定参数的精确度。
堆积角是指大量物料自由下落所形成的锥体表面与水平面之间的夹角,堆积角测定是颗粒物料离散元参数标定的一种有效途径[27]。颗粒堆积角是反映散体颗粒群综合宏观特征的量,可以用于确定颗粒物料的流动性及摩擦因数,有助于认识堆积的微观力学机理和评估所采用模型的适用性[28]。
本试验采用圆筒提升法[29]对土壤-有机肥混料堆积角进行测定。试验装置如图6所示,由透明亚克力圆筒(内径50 mm,高250 mm)、钢板、土壤-有机肥混料组成。试验时,为体现土壤与有机肥间的互作形式,将2种物料分层布置,即在圆筒内依次装入土壤、有机肥颗粒,每层50 mm,装满整个圆筒后,将圆筒以速度500 mm/s匀速提升,混料经圆筒底部堆积在底部钢板上。在土壤-有机肥混料静止后,使用电子水平尺测量其堆积角,堆积试验重复5次取平均值,得到土壤-有机肥混料的堆积角为24.9°。
考虑合理简化模型,缩短计算耗时,本研究忽略有机肥不规则形状对滚动摩擦的影响,以基本球体设置肥料颗粒模型,根据有机肥颗粒尺寸建立肥料颗粒离散元模型。由于肥料颗粒大小不均匀,选取长轴尺寸作为肥料颗粒径长,并随机选取100粒肥料,对有机肥粒径分布范围进行统计,测量过程如图7所示。为更准确反映有机肥颗粒实际分布特征,模型中引入多粒径分组策略:将颗粒按粒径分为0~1 mm、1~2 mm、2~3 mm和3 mm以上4组,分别占比3%、25%、67%和5%,各组采用不同的等效球体半径范围。通过EDEM的Particle Size Distribution功能生成符合实测概率分布的颗粒群,较原单一粒径模型更贴近物理实际。设置土壤的球形填充单元半径为3 mm。
添加试验装置钢板的泊松比为0.3,剪切模量为7.9×1010Pa,密度为7 800 kg/m3。应用Solidworks软件按照比例1∶1建立圆筒结构模型并导入EDEM中。参照文献[16]设置土壤其他接触参数和其他离散元模拟参数,如表1所示。
在EDEM软件中,在圆筒内设置5个颗粒工厂,分别生成有机肥颗粒与土壤颗粒。其中距离圆筒底部50、150、250 mm生成土壤颗粒,距离圆筒底部100、200 mm处生成有机肥颗粒。从下往上依次生成颗粒,每个颗粒工厂生成时间为0.5 s。其中每个土壤颗粒工厂生成土壤颗粒的总质量设置为50 g,生成速度为210 g/s;每个有机肥颗粒工厂生成有机肥颗粒的总质量设置为40 g,生成速度为180 g/s。仿真时间设为6.0 s,仿真步长为0.01 s,设定有机肥-土壤颗粒以自由落体运动从圆筒底部流出,重力加速度为9.81 m/s2。为保证有机肥和土壤颗粒能够充分混合,在颗粒生成完毕后,设置圆筒以速度0.5 m/s匀速提升[30]。运用EDEM后处理Clipping模块在颗粒堆垂面处设置Clipping Planes Group截面,利用Tools模块中量角器工具Protractor,分别在颗粒堆截面底面、夹角和斜边上选定3个点,这3个点构成的角度作为堆积角θ,堆积角测量以及仿真几何模型如图8所示。
为明确含水率(ω)对有机肥-土壤混料流动性的影响,设计了含水率梯度5%~15%下的堆积角试验,含水率每2.5%测试一次。土壤含水率按GB/T 50123—2019标准调控,有机肥含水率调控通过喷雾加水并平衡48 h(参照GB/T 8576—2010)实现。每个含水率水平重复5次试验,结果如图9所示。堆积角θ与含水率ω的关系可拟合为
图9表明,当含水率小于等于10%时,含水率每增加1个百分点,堆积角平均增加0.78°;当含水率大于10%时,堆积角增幅加剧(斜率k>1),预示流动性显著下降。基于此,建议本模型在ω∈[5%,10%]范围内直接应用。
本研究建立的土壤-有机肥混料EDEM模型及其标定参数,主要适用于模拟茶园中耕施肥作业中,在特定含水率范围(5%~10%)内,土壤与颗粒有机肥混合物在机械扰动下的宏观流动与混合行为,特别是堆积形态和覆土率等关键宏观指标。同时该混料模型具有一定的局限性,具体体现在以下几点:①颗粒形状简化:模型将有机肥颗粒简化为球体,虽通过参数标定补偿了部分影响,但无法精确模拟不规则形状颗粒特有的滚动、互锁、定向排列等微观力学行为。这限制了模型在高精度模拟颗粒微观运动、局部受力或形状依赖过程(如精确排肥、颗粒破碎)中的应用。②接触模型:采用Hertz-Mindlin(no slip)基础接触模型,未引入更复杂的黏性、塑性或黏结模型。在高含水率(ω>10%)条件下,颗粒间液桥力或土壤粘塑性行为增强,当前模型可能无法准确表征其流动特性。对于土壤黏附、肥料结块或强塑性变形的场景,模型适用性受限。③模型尺度与边界:堆积角仿真采用有限尺寸圆筒,旋耕仿真采用有限尺寸箱体,存在边界效应(如壁面摩擦、侧向约束)。模型难以精确模拟无限大料堆或大规模田间连续作业中的远场流动行为。旋耕仿真中箱体边缘区域的流场和颗粒分布可能与实际无限边界情况存在差异。④参数动态性:标定参数(特别是与含水率、压实状态相关的参数)在动态、高速、高应变的旋耕过程中可能并非完全恒定。模型假设这些参数在仿真过程中保持不变,与实际可能存在动态差异。⑤非均质性表征:虽然引入了多粒径分组,但对土壤颗粒的处理相对简单(单一粒径球体)。实际土壤是包含孔隙、团聚体和水分的复杂多相介质,当前模型对土壤内部结构非均质性及其动态变化(如旋耕过程中的破碎、压实)的表征能力有限。⑥其他影响因素:模型未考虑环境因素(如温度、风化)、有机肥颗粒的长期物理化学变化(如降解、吸湿)或土壤中的生物活动(如根系)对混料力学行为的潜在影响。
为了探究不同影响因素与响应值之间的关联性,本研究采用Design-Expert软件来设计Plackett-Burman试验。该试验通过对比各影响因素在2个水平下的差异,从而评估这些因素的显著性。试验以有机肥-土壤混料堆积角为响应值,试验因素编码如表2所示,试验设计与结果如表3所示。其中x1~x11为因素编码值,方差分析结果见表4。从表4可看出,对有机肥-土壤混料堆积角具有显著性影响的前3个参数分别为x1、x6和x7。这3个参数的P值均小于0.05,贡献度合计88.12%。因此,结合参数显著性及贡献度,选取土壤-有机肥碰撞恢复系数数x1、有机肥-有机肥滚动摩擦因数x6、有机肥-钢板碰撞恢复系数x7进行后续最陡爬坡试验。
Plackett-Burman试验作为筛选设计,其方差分析基于非正交设计的线性回归模型,误差项通过合并非显著因素平方和的方法来估计,这一处理方式可能导致F值与P值的对应关系偏离传统正交试验的统计规律。本研究中显著因素的筛选结果通过后续最陡爬坡试验与Box-Behnken响应面试验进一步验证,确保了参数的可靠性[31-32]。
为确定显著性参数土壤-有机肥恢复系数X1、有机肥-有机肥滚动摩擦因数X6和有机肥-钢板碰撞恢复系数X7的最优值,将其余参数设置为中间水平值,设计最陡爬坡试验如表5所示。根据试验结果可以看出,堆积角随着X1、X6、X7的增加逐渐增大,其相对误差呈先降低后增加的趋势。当X1、X6、X7分别取0.4、0.10、0.60时,仿真堆积角与实际堆积角相对误差最小为2.33%。在后续Box-Behnken试验中,将土壤-有机肥碰撞恢复系数0.4、有机肥-有机肥滚动摩擦因数0.10和有机肥-钢板碰撞恢复系数0.60作为响应面试验设计中心水平,设计三因素三水平正交试验设计。
应用Design-Expert软件进行Box-Behnken试验设计。得到X1、X6、X7与目标值堆积角的二次回归模型。试验因素编码如表6所示,试验设计与结果见表7。
通过对试验结果进行多元回归拟合分析,获得土壤-有机肥混料堆积角与3个显著性参数的二阶回归模型,其回归方程为
试验回归模型方差分析如表8所示。根据Box-Behnken响应面试验结果,回归模型极显著(P<0.000 1);决定系数R2=0.978 1,趋近于1,表示回归方程拟合程度较好;土壤-有机肥碰撞恢复系数X1、有机肥-有机肥滚动摩擦因数X6对堆积角影响显著(P<0.05);变异系数(CV)为1.33%,表示该试验具有较高可靠性;精确度为24.45,说明该试验精确度较高。
由图10a可知,因素X1、X6在所选水平范围内,堆积角随X1、X6的增大而增大。其中,随着X6的增加,堆积角迅速增大,斜度较大;随着X1的增加,堆积角缓慢增大,斜度较小。当X1、X6均处于较高水平时,堆积角的变化最为明显。由图10b可知,因素X1、X7交互作用对堆积角的影响并不明显,其响应面几乎为一个水平面。由图10c可知,因素X6、X7在所选范围内,堆积角随X6的增大而增大,堆积角变化较大;随X7的增大先下降后上升,堆积角变化较小。总体来说,X1、X6、X7的交互作用对堆积角的影响并不显著。
利用Design-Expert软件的优化模块,以堆积角相对误差的最小值为目标,对回归方程进行求解,分析响应曲面,对回归模型进行优化。目标及约束条件方程为
经数据处理,得到回归模型的最优参数组合为:土壤-有机肥碰撞恢复系数为0.35,有机肥-有机肥滚动摩擦因数为0.115,有机肥-钢板碰撞恢复系数为0.606。将标定后的最优接触参数代入EDEM软件中,进行5次堆积角仿真试验,测得堆积角平均值为24.49°,如图11所示。相对于物理试验得到的平均值(24.9°)误差为1.65%,说明所得最优仿真接触参数组合与实际值基本一致,可用于后续的EDEM仿真试验。
为进一步验证土壤-有机肥离散元模型和接触参数的可靠性,通过样机进行田间旋耕施肥试验,并利用建立的土壤-有机肥离散元模型和标定的接触参数进行EDEM仿真试验模拟旋耕施肥试验,在旋耕刀不同转速条件下(150、200、250、300 r/min)研究对比田间试验和仿真试验的旋耕施肥效果,分析覆土率s的实测值和仿真值并计算相对误差。覆土率s可以衡量旋耕施肥作业质量,较高的覆土率意味着肥料更好地被土壤覆盖,施肥效果较好,其计算公式为
式中
      a1——表面颗粒中土壤颗粒所占面积,m2
      a0——表面颗粒总面积,m2
田间试验所用茶园中耕施肥机如图12所示,采茶机后方装有肥料箱和旋耕装置。开始施肥作业时,肥料箱底部螺旋输送器开始运作,将肥料箱内有机肥向外输送,有机肥通过下料管散落在旋耕装置前方,同时采茶机向前运动,旋耕刀开始旋转,将有机肥旋耕进土壤中。开始试验时,为了与仿真过程相对应,先将有机肥撒施在垄间,再进行旋耕翻土。旋耕结束后,在旋耕路线的起点、中间及终点处随机选取3个500 mm×500 mm地块,并用相机拍摄其表面颗粒图像。每次试验在茶园不同地块(坡顶、坡中、坡底)重复进行3次,最终结果取3次的平均值。施肥效果如图13a所示,旋耕效果如图13b所示,其中大部分有机肥被旋耕入土,少量有机肥裸露在土壤表面,未被土壤覆盖的有机肥在图13b中用圆圈突出展示。
根据茶园中耕施肥机的设计尺寸,利用Solidworks软件建立三维模型,为简化分析,仅保留旋耕装置,将旋耕刀和轴合并成一体,将模型导入EDEM软件中进行处理。所用旋耕刀如图14所示,该旋耕刀为组合旋耕刀,即最内侧一排为直刀,外侧两排为弯刀的直弯组合刀。为了防止缠草现象,在内侧直刀和电机之间加一除草盘,并在内外两刀座间加一挡草板,阻止杂草等缠入。刀具安装时通过销连接固定在电机轴上,安装完成后总宽度约50 cm,耕深可达15 cm。为模拟茶垄间的旋耕环境,设置500 mm×2 000 mm×200 mm的箱体,箱体内添加颗粒工厂,采用前文标定好的接触参数和离散元模型设置土壤和有机肥颗粒。先在底部生成约150 mm高的土壤颗粒,然后在土壤颗粒上方生成约350 g的有机肥颗粒,生成位置随机。生成的颗粒分布如图15所示,其中红色为有机肥颗粒,黄色为土壤颗粒。土壤和有机肥颗粒生成完成后,旋耕刀开始运动,设置旋耕刀耕深为120 mm,前进速度为1 m/s,转速分别设置为150、200、250、300 r/min,旋耕仿真时间为2 s,即旋耕完整段箱体颗粒。仿真结束后在后处理中获得旋耕结束后的土壤、有机肥颗粒图像。旋耕施肥仿真试验如图16所示。
田间试验和仿真试验的覆土颗粒如图17所示,利用Python的Pillow库和Numpy库对其进行处理,通过阈值分割与形态学运算识别土壤与有机肥颗粒,计算覆土率的平均值与标准差。数据处理时剔除异常值,最终结果取3次重复的平均值。最后得到4种旋耕速度(150、200、250、300 r/min)下施肥旋耕的覆土率实测值和仿真值如表9所示,覆土率s的相对误差分别为6.21%、4.52%、7.13%和7.72%。其中误差来源可能有以下几点:①土壤空间差异:茶园黄壤的黏粒含量在不同坡位差异达10%~15%,导致土壤-有机肥混料流动性波动。②测量方法误差:图像识别时有机肥与土壤颗粒的颜色阈值分割存在±2%的识别误差。③设备操作误差:旋耕机前进速度波动((1.0±0.1)m/s)影响耕深一致性,影响最终试验结果。
对试验数据进行可靠性分析,采用t分布计算95%置信区间。表9中的计算结果表明各转速下覆土率的置信区间宽度为1.23%~1.56%,均小于实测值的5%,说明试验数据具有较高可靠性。相对误差(4.52%~7.72%)均在工程可接受范围内(<10%),且置信区间未包含零值,验证了仿真与田间结果的显著性差异。
田间试验的3次重复表明,覆土率的变异系数(CV)为1.02%~1.68%,均小于5%,满足农业试验精度要求。尽管存在土壤空间变异与测量误差,但仿真与田间结果的相对误差均小于8%,且95%置信区间重叠度低。以上结果表明,在转速范围150~300 r/min内该土壤-有机肥混料离散元模型和接触参数可用于离散元仿真试验,为后续仿真模拟提供理论支撑。
(1)通过搭建物理试验平台测定了颗粒有机肥物理参数:直径平均值约为2.5 mm,密度为1.5 g/cm3,平均含水率为6.12%。利用EDEM仿真软件,采用默认的Hertz-Mindlin无滑动接触模型,基于圆筒提升法形成的堆积角对土壤-有机肥混料进行离散元仿真,标定了土壤-有机肥之间的相关参数。
(2)通过Placket-Burman Design试验得到影响土壤-有机肥颗粒堆积角的显著参数:土壤-有机肥颗粒间碰撞恢复系数、有机肥颗粒间滚动摩擦因数、有机肥颗粒-钢板碰撞恢复系数。经过最陡爬坡试验优化显著性参数范围,后续通过Box-Behnken Design试验建立二次回归模型进行分析,由方差分析可知:土壤-有机肥颗粒间碰撞恢复系数与有机肥颗粒间滚动摩擦因数影响显著。
(3)以物理试验得到的堆积角为目标值,对二次回归方程优化,得到显著性参数最优值:土壤-有机肥颗粒碰撞恢复系数为0.35、有机肥-有机肥颗粒间滚动摩擦因数为0.115、有机肥颗粒-钢板碰撞恢复系数为0.606。在最优参数组合下进行仿真堆积角试验,与实际物理堆积角相对误差为1.65%。
(4)搭建茶园中耕施肥机样机,进行茶园旋耕施肥田间试验和参数标定后的仿真旋耕施肥试验,得到旋耕刀不同转速(150、200、250、300 r/min)条件下,田间试验和仿真试验覆土率的相对误差分别为6.21%、4.52%、7.13%、7.72%。通过误差分析和模型可靠性分析,得到仿真与田间结果的相对误差均小于8%,且95%置信区间重叠度低,表明模型在转速范围150~300 r/min内具有可靠的预测能力。仿真试验的土壤-有机肥颗粒分布情况与田间试验的分布情况相一致,验证了土壤-有机肥混料离散元模型和接触参数可用于离散元仿真试验。
  • 国家自然科学基金项目(32301715)
  • 财政部和农业农村部:国家茶叶产业技术体系项目(CARS-19)
  • 浙江省农机研发推广一体化项目和浙江理工大学科研业务费专项资金项目(24242211-Y)
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2025年第56卷第12期
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doi: 10.6041/j.issn.1000-1298.2025.12.024
  • 接收时间:2025-05-15
  • 首发时间:2026-09-30
  • 出版时间:2025-12-25
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  • 收稿日期:2025-05-15
  • 修回日期:2025-06-24
基金
国家自然科学基金项目(32301715)
财政部和农业农村部:国家茶叶产业技术体系项目(CARS-19)
浙江省农机研发推广一体化项目和浙江理工大学科研业务费专项资金项目(24242211-Y)
作者信息
    1.浙江理工大学机械工程学院,杭州 310018
    2.浙江省全省农业智能感知与机器人重点实验室,杭州 310018

通讯作者:

陈建能(1972—),男,教授,博士,主要从事农业装备及其智能化研究,E-mail:
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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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