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

doi:10.3969/j.issn.1004-1524.20250458

Acta Agriculturae Zhejiangensis ›› 2026, Vol. 38 ›› Issue (8): 1683-1694.DOI: 10.3969/j.issn.1004-1524.20250458

• Biosystems Engineering • Previous Articles Next Articles

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 Online:2026-08-25 Published:2026-09-14

Abstract

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

CLC Number:

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.

Figures/Tables 11

References 26

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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

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

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Figures/Tables 11

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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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