| Discrete element parameters calibration of Hippophae rhamnoides based on particle scaling theory |
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| DOI：10.7606/j.issn.1000-7601.2024.02.30 |
| Key Words: Hippophae rhamnoides tea dust particle scaling theory DEM parameter calibration |
| Author Name | Affiliation | | WANG Qingze | College of Mechanical and Electrical Engineering, Gansu Agriculture University, Lanzhou, Gansu 730070, China | | YANG Mei | College of Mechanical and Electrical Engineering, Gansu Agriculture University, Lanzhou, Gansu 730070, China | | XIANG Jintian | College of Mechanical and Electrical Engineering, Gansu Agriculture University, Lanzhou, Gansu 730070, China | | ZHANG Qianglin | College of Mechanical and Electrical Engineering, Gansu Agriculture University, Lanzhou, Gansu 730070, China | | ZHANG Wenjie | College of Mechanical and Electrical Engineering, Gansu Agriculture University, Lanzhou, Gansu 730070, China | | LI Xvjie | College of Mechanical and Electrical Engineering, Gansu Agriculture University, Lanzhou, Gansu 730070, China | | HU Jingming | College of Mechanical and Electrical Engineering, Gansu Agriculture University, Lanzhou, Gansu 730070, China |
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| Abstract: |
| To address the lack of accurate contact parameters in the discrete element simulation of sea buckthorn leaf tea hair, a physical and simulation resting angle heap experiment was conducted to calibrate the contact parameters of tea hair. The tea hair particles were simplified as soft spherical particles. By dimensional analysis and particle scaling theory, the aerodynamic equivalent diameter of tea hair was enlarged from 231.37 μm to 1.8 mm. The EDEM software was used with the “Hertz\|Mindlin with JKR” contact model. The repose angle was chosen as the response variable, and a Plackett\|Burman experiment was performed to select the three most significant parameters affecting the resting angle: the tea hair\|tea hair restitution coefficient, the tea hair\|tea hair rolling friction coefficient, and the tea hair\|stainless steel rolling friction coefficient. The optimal parameter ranges were determined using a steepest ascent test. Based on Box\|Behnken experimental design, a second\|order regression equation was established and optimized for the three significant parameters and repose angle. The three significant parameters for the optimal combination were as follow: tea hair\|tea hair restitution coefficient was 0.159, tea hair\|tea hair rolling friction coefficient was 0.290, and tea hair\|stainless steel rolling friction coefficient was 0.239. A comparative analysis between the simulated repose angle values and the real values from physical experiments yielded a relative error was 1.97%, indicating good predictive performance in simulation experiments. |
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