基于离散元法的新疆地区红花田土壤参数标定

doi:10.3969/j.issn.1004-1524.20250458

浙江农业学报 ›› 2026, Vol. 38 ›› Issue (8): 1683-1694.DOI: 10.3969/j.issn.1004-1524.20250458

基于离散元法的新疆地区红花田土壤参数标定

汪浩远¹(), 许燕¹^,²^,^*(), 周建平¹^,², 李先康¹, 左佳伟¹, 徐翔¹

¹ 新疆大学机械工程学院, 新疆乌鲁木齐 830017
² 新疆维吾尔自治区农牧机器人及智能装备工程研究中心, 新疆乌鲁木齐 830017

收稿日期:2025-06-24 出版日期:2026-08-25 发布日期:2026-09-14
作者简介:汪浩远,研究方向为农业机器人与智能装备。E-mail:382456442@qq.com
通讯作者: *许燕,E-mail:lilixiu_z@163.com
基金资助:
新疆维吾尔自治区“天山英才”培养计划科技创新领军人才项目(2022TSYCLJ0044);新疆大学优秀研究生创新项目(XJDX2025YJS202)

Calibration of soil parameters for safflower fields in Xinjiang, China, based on discrete element method

WANG Haoyuan¹(), XU Yan¹^,²^,^*(), ZHOU Jianping¹^,², LI Xiankang¹, ZUO Jiawei¹, XU Xiang¹

¹ School of Mechanical Engineering, Xinjiang University, Urumqi 830017, China
² Agriculture and Animal Husbandry Robot and Intelligent Equipment Engineering Research Center of Xinjiang Uygur Autonomous Region, Urumqi 830017, China

Received:2025-06-24 Published:2026-08-25 Online:2026-09-14

摘要/Abstract

摘要：

为获得准确的新疆地区红花田土壤离散元仿真参数,构建适用于不同含水率红花田土壤的仿真模型,采用物理试验与仿真试验相结合的方法,分别配置含水率为10%、15%、20%、25%、30%的土壤样本,以堆积角为响应值,对土壤参数进行标定研究。首先,利用漏斗法建立含水率-堆积角模型,获取堆积角与含水率的数学关系。然后,基于Hertz-Mindlin with JKR离散元接触模型,设计Plackett-Burman试验,对9个初始参数进行筛选,发现土壤剪切模量、土壤-土壤静摩擦系数、土壤-土壤滚动摩擦系数和JKR表面能对堆积角影响显著(p<0.05)。接着,利用最陡爬坡试验缩小关键参数的取值范围,基于Box-Behnken试验建立堆积角-离散元参数回归模型,并对回归模型进行参数寻优。选取7种不同含水率的土壤进行参数寻优,然后对比仿真模型与物理试验的结果,发现其相对误差小于5%,可用于后续试验。对比承压物理试验与离散元仿真的入土阻力数据,结果显示,二者变化规律一致:在0~50 mm深度范围内,相对误差低于10%;当深度超过50 mm时,误差增大。上述结果表明,本研究构建的模型在浅层土壤力学行为模拟中具备有效替代真实土壤的可行性。研究结果可为后续农业装备优化触土部件、提升田间通过性提供数据基础和技术支持。

关键词: 红花田土壤, JKR模型, 离散元法, 堆积角, 参数标定

Abstract:

To obtain accurate discrete element simulation parameters of soil in safflower fields in Xinjiang and develop simulation models applicable to safflower field soil with different moisture contents, physical experiments combined with simulation tests were adopted. Soil samples with moisture contents of 10%, 15%, 20%, 25% and 30% were prepared separately, and the repose angle was taken as the response value to conduct calibration of soil parameters. Firstly, the funnel method was used to establish a moisture content-repose angle model to acquire the mathematical relationship between repose angle and moisture content. Subsequently, based on the Hertz-Mindlin with JKR contact model, the Plackett-Burman design was carried out to screen nine initial parameters. It was found that soil shear modulus, soil-soil static friction coefficient, soil-soil rolling friction coefficient and JKR surface energy exerted significant (p<0.05) effects on the repose angle. Afterwards, the steepest ascent test was adopted to narrow the value ranges of key parameters. A regression model between repose angle and discrete element parameters was established based on the Box-Behnken design, and parameter optimization was performed on the regression model. Seven soil samples with different moisture contents were selected for parameter optimization. The comparison between simulation and physical test results showed that the relative error was less than 5%, which verified the applicability of the model for subsequent tests. By comparing the penetration resistance data obtained from confined compression physical tests and discrete element simulations, consistent variation trends of penetration resistance were observed. The relative error was lower than 10% within the penetration depth of 0-50 mm, while the error increased when the depth exceeded 50 mm. These results indicate that the model established in this study can effectively replace real soil for simulating mechanical behaviors of topsoil. The findings can provide data support and technical reference for the optimization of soil-engaging components of agricultural equipment and the improvement of field trafficability in further research.

Key words: soil of safflower field, JKR model, discrete element method, repose angle, parameter calibration

中图分类号:

汪浩远, 许燕, 周建平, 李先康, 左佳伟, 徐翔. 基于离散元法的新疆地区红花田土壤参数标定[J]. 浙江农业学报, 2026, 38(8): 1683-1694.

WANG Haoyuan, XU Yan, ZHOU Jianping, LI Xiankang, ZUO Jiawei, XU Xiang. Calibration of soil parameters for safflower fields in Xinjiang, China, based on discrete element method[J]. Acta Agriculturae Zhejiangensis, 2026, 38(8): 1683-1694.

图/表 11

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含水率/% Moisture content/%	泊松比 Poisson’s ratio	弹性模量/MPa Elastic modulus/MPa
10	0.35	5.31
15	0.35	4.21
20	0.36	3.88
25	0.38	3.54
30	0.40	3.22

含水率/% Moisture content/%	泊松比 Poisson’s ratio	弹性模量/MPa Elastic modulus/MPa
10	0.35	5.31
15	0.35	4.21
20	0.36	3.88
25	0.38	3.54
30	0.40	3.22

编号 No.	各因素设定值Assigned values of each factor				α/(°)	相对误差/% Relative error/%
编号 No.	B/Mpa	C	D	I/(J·m^-2)	α/(°)	相对误差/% Relative error/%
1	0.800	0.200	0.04	0.100	28.21	21.42
2	1.003	0.318	0.17	0.223	32.44	9.64
3	1.206	0.436	0.30	0.346	35.02	2.45
4	1.409	0.554	0.43	0.469	38.28	6.63
5	1.612	0.672	0.56	0.592	42.22	17.60
6	1.815	0.790	0.69	0.715	44.98	25.29

编号 No.	各因素设定值Assigned values of each factor				α/(°)	相对误差/% Relative error/%
编号 No.	B/Mpa	C	D	I/(J·m^-2)	α/(°)	相对误差/% Relative error/%
1	0.800	0.200	0.04	0.100	28.21	21.42
2	1.003	0.318	0.17	0.223	32.44	9.64
3	1.206	0.436	0.30	0.346	35.02	2.45
4	1.409	0.554	0.43	0.469	38.28	6.63
5	1.612	0.672	0.56	0.592	42.22	17.60
6	1.815	0.790	0.69	0.715	44.98	25.29

含水率/% Moisture content/%	B/MPa	C	D	I/(J·m^-2)	实测值/(°) Measured results/(°)	仿真值/(°) Simulation results/(°)	相对误差/% Relative error/%
10	1.150	0.415	0.219	0.281	32.20	33.03	2.58
15	1.126	0.458	0.227	0.236	33.40	34.65	3.74
20	1.327	0.463	0.197	0.242	35.90	36.42	1.45
25	1.371	0.542	0.266	0.351	37.18	38.01	2.24
30	1.151	0.545	0.340	0.461	40.10	41.41	3.27

基于离散元法的新疆地区红花田土壤参数标定

Calibration of soil parameters for safflower fields in Xinjiang, China, based on discrete element method

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参考文献 26

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序号 No.	各因素试验水平Experimental levels of each factor									α/(°)
序号 No.	A	B	C	D	E	F	G	H	I	α/(°)
1	1	1	-1	1	1	1	-1	-1	-1	35.43
2	-1	1	1	-1	1	1	1	-1	-1	29.57
3	1	-1	1	1	-1	1	1	1	-1	38.59
4	-1	1	-1	1	1	-1	1	1	1	48.02
5	-1	-1	1	-1	1	1	-1	1	1	38.86
6	-1	-1	-1	1	-1	1	1	-1	1	44.01
7	1	-1	-1	-1	1	-1	1	1	-1	21.55
8	1	1	-1	-1	-1	1	-1	1	1	35.43
9	1	1	1	-1	-1	-1	1	-1	1	40.59
10	-1	1	1	1	-1	-1	-1	1	-1	47.21
11	1	-1	1	1	1	-1	-1	-1	1	46.03
12	-1	-1	-1	-1	-1	-1	-1	-1	-1	23.57

序号 No.	各因素的设定水平Setting level for each factor				α/(°)
序号 No.	B	C	D	I	α/(°)
1	-1	-1	0	0	36.43
2	1	-1	0	0	38.74
3	-1	1	0	0	38.63
4	1	1	0	0	40.32
5	0	0	-1	-1	32.32
6	0	0	1	-1	41.45
7	0	0	-1	1	37.72
8	0	0	1	1	41.78
9	-1	0	-1	-1	36.79
10	1	0	0	-1	38.78
11	-1	0	0	1	38.25
12	1	0	0	1	40.02
13	0	-1	-1	0	36.48
14	0	1	-1	0	37.36
15	0	-1	1	0	41.98
16	0	1	1	0	42.13
17	-1	0	-1	0	37.42
18	1	0	-1	0	39.28
19	-1	0	1	0	40.84
20	1	0	1	0	42.39
21	0	-1	0	-1	37.81
22	0	1	0	-1	38.68
23	0	-1	0	1	39.08
24	0	1	0	1	40.43
25	0	0	0	0	34.86
26	0	0	0	0	35.52

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