Accurate calibration framework for the discrete element parameters of red kidney bean using flower pollination algorithm

Accurate calibration of discrete element method (DEM) parameters is essential for simulating the mechanical behavior of crop seeds and their interactions with agricultural equipment. However, most DEM parameters for red kidney beans (RKBs) remain unreported, and accuracy of conventional calibration methods still require further improvement. This study developed an accurate calibration approach for determining DEM parameters of RKBs. Physical properties of RKBs, including dimensional characteristics, densities, and frictional parameters, were first measured through laboratory experiments. Significant DEM parameters affecting the repose angle (RPA) were identified using the Plackett-Burman design, and their relationships with RPA were established using the Central Composite design. The optimal combination of parameters was then determined through grey wolf optimizer (GWO) and flower pollination algorithm (FPA). The results showed that moment of 12 s for RPA measurement and lifting velocity of 0.014 m/s were determined on aspects of higher accuracy in measuring repose angles and relatively less computation time and memory of usage in raising cylinder method simulations. An optimal combination of significant DEM parameters of RKBs is coefficient of static friction of 0.1338 and coefficient of rolling friction of 0.0308 optimized using flower pollination algorithm (FPA). The relative error between simulated and measured RPAs is 0.90% and the difference between simulated and measured RPAs did not reach a significant level (p>0.05). These results imply that the proposed calibration method effectively improves model accuracy, providing a reliable reference for DEM parameter calibration of crop seeds and supporting the design of RKB-related mechanized systems.