DEM-Based Parameter Calibration of Soils with Varying Moisture Contents in Southern Xinjiang Peanut Cultivation Zones

Wen Zhou, Hui Guo (), Yu Zhang, Xiaoxu Gao, Chuntian Yang and Tianlun Wu
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Wen Zhou: College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Hui Guo: College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Yu Zhang: Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Xiaoxu Gao: College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Chuntian Yang: College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Tianlun Wu: College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China

Agriculture, 2025, vol. 15, issue 17, 1-23

Abstract: To address the insufficient adaptability of imported peanut harvesting equipment’s soil-engaging components to the specific soil conditions in Xinjiang, this study conducted Discrete Element Method (DEM)-based calibration of soil mechanical parameters using field soil samples with 1–20% moisture content from typical peanut cultivation areas in southern Xinjiang. Through the EDEM simulation platform, a comprehensive approach integrating the Hertz–Mindlin with the JKR adhesion model and Hertz–Mindlin with the Bonding model was employed to systematically calibrate nine key parameters: coefficient of restitution, static friction coefficient, rolling friction coefficient, JKR surface energy, normal/tangential stiffness per unit area, critical normal/tangential force, and soil bonding disk radius. Adopting static angle of repose (SAOR) and unconfined compressive force (UCF) as dual-response indicators, a hybrid experimental design strategy combining Central Composite Design (CCD), Plackett–Burman (PB) screening, and Box–Behnken Design (BBD) optimization was implemented. Regression models for SAOR and UCS were established, yielding six sets of soil parameters optimized for different moisture conditions through parameter optimization. Field validation demonstrated the following: ≤3.27% error in SAOR, ≤1.46% error in UCF, and ≤5.05% error in drawbar resistance validation for field digging shanks. Experimental results confirm that the model demonstrates strong prediction accuracy for soils in typical peanut harvesting regions of southern Xinjiang, thereby providing key parameter references for the future self-developed, highly adaptive soil-engaging components with drag reduction optimization in peanut harvesters for the Xinjiang region.

Keywords: soil of south Xinjiang; discrete element method; parameter calibration; static angle of repose; unconfined compressive force (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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