CALIBRATION OF A DISCRETE ELEMENT MODEL FOR SILAGE CORN STRAW CONSIDERING THE ENTIRE SHEARING PROCESS BASED ON BAYESIAN OPTIMIZATION

The accuracy of discrete element simulations for silage corn stover is highly dependent on the precise calibration of model parameters. Addressing the relative scarcity of research on identifying DEM parameters for silage corn stover, this study constructs a simplified DEM model based on the Bonding constitutive model for granular materials. Parameter calibration is performed using experimental data on key physical and mechanical properties of the stover. Using the entire shear history stress-strain curve as the calibration benchmark, Gaussian process regression was introduced as a surrogate model. With mean squared error (MSE) as the objective function, a Bayesian optimization algorithm was employed to accurately identify the bonding parameters of the DEM model. The optimal parameter combination yielding the minimum MSE (MSE = 0.0072) was obtained: normal bonding stiffness, tangential bonding stiffness, normal strength, and shear strength were 5.12 × 10⁹ N/m³, 1.28 × 10⁸ N/m³, 1.60 × 10⁷ Pa, and 2.48 × 10⁶ Pa, respectively. To validate this parameter set, three-point bending tests were conducted and compared with simulation results. The bending stress-strain curves from the discrete element model closely matched experimental trends and peak characteristics, confirming the model's accuracy. The proposed Bayesian optimization-based parameter calibration method demonstrates high precision and efficiency. It provides reliable references for discrete element simulations of silage corn stalk processing and the design optimization of key components in harvesting machinery.