Authors

Abstract

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.

Abstract in Chinese

青贮玉米秸秆离散元仿真的准确性高度依赖于模型参数的精确标定，针对青贮玉米秸秆离散元参数的识别研究相对不足问题，本文基于颗粒粘结本构模型（Bonding）构建了一种青贮玉米秸秆离散元模型，并结合秸秆关键物理与力学特性试验开展了参数标定。以全剪切历程应力-应变曲线为标定基准，引入高斯过程回归作为代理模型，以均方误差（MSE）为目标函数，采用贝叶斯优化算法实现离散元模型粘结参数的准确识别。获得对应最小均方误差（MSE=0.0072）的最优参数组合：法向粘结刚度、切向粘结刚度、法向强度、剪切强度分别为5.12×109 N/m3、1.28×108 N/m3、1.60×107 Pa、2.48×106 Pa。为验证该参数组合的可靠性，进一步开展三点弯曲试验并与仿真结果进行对比。结果表明，离散元模型的弯曲应力-应变曲线与试验结果在变化趋势及峰值特征上吻合度较高，验证了模型的准确性。本研究提出的基于贝叶斯优化的参数标定方法精度和效率较高，可为青贮玉米秸秆加工过程的离散元仿真及收获机关键部件的设计优化提供可靠参考。