Discrete Element Method-Based Stratified Soil Modeling to Improve the Precision of Soil–Machine Interaction Simulations
This study presents an efficient method to determine discrete element model parameters at multiple soil depths and enhance the precision of soil–implement interaction simulations in agricultural machinery by integrating the JKR and Bonding contact models. The soil was divided into three layers based...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/15/6/1421 |
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| Summary: | This study presents an efficient method to determine discrete element model parameters at multiple soil depths and enhance the precision of soil–implement interaction simulations in agricultural machinery by integrating the JKR and Bonding contact models. The soil was divided into three layers based on its characteristics: surface soil layer (0–6 cm), middle soil layer (6–15 cm), and deep soil layer (15–21 cm). The Hertz–Mindlin model with JKR parameters influencing the soil’s angle of repose was determined through repose angle tests, and the Hertz–Mindlin model with Bonding contact parameters was calibrated via direct shear tests. The results of response surface experiments indicate that the optimal parameter combinations for the surface, middle, and deep soil layers are as follows: normal stiffness per unit area of 1.703 × 10<sup>8</sup>, 5.76 × 10<sup>8</sup>, and 7.731 × 10<sup>8</sup> N·m<sup>−3</sup>; shear stiffness per unit area of 4.724 × 10<sup>8</sup>, 5.223 × 10<sup>8</sup>, and 2.403 × 10<sup>8</sup> N·m<sup>−3</sup>; normal strength of 4.893 × 10<sup>7</sup>, 4.373 × 10<sup>7</sup>, and 1.468 × 10<sup>7</sup> Pa; and shear strength of 1.484 × 10<sup>7</sup>, 1.736 × 10<sup>7</sup>, and 6.601 × 10<sup>7</sup> Pa, respectively. The simulation errors in the tests were 1.38%, 2.53%, and 2.84%, respectively. Compared with excavation simulation and field tests, the error was within 10%, confirming that the model can accurately represent soil behavior. The results demonstrate that integrating the JKR and Bonding contact models provides an effective framework for simulating soil–machine interactions and establishes a robust numerical parameter basis for optimizing agricultural machinery design and soil management practices. |
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| ISSN: | 2073-4395 |