Hierarchical model acquisition and parameter calibration of the corncob based on the discrete element method

文献类型： 外文期刊

第一作者： Han, Dandan

作者： Han, Dandan;Tang, Chao;Li, Wei;Xu, Lijia;Liu, Bo;Wang, Yunxia

作者机构：

关键词： Corncob; Mechanical properties; Hierarchical bonded particle model; Bonding parameters calibration

期刊名称：ADVANCED POWDER TECHNOLOGY （ 影响因子：4.2； 五年影响因子：4.5 ）

ISSN： 0921-8831

年卷期： 2025 年 36 卷 7 期

页码：

收录情况： SCI

摘要： To establish the optimal model and bonding parameters that align with the structural features of corncobs during harvest, a breakable discrete element model was developed, reflecting its two-layer structural composition (core marrow and xylem annulus) through a hierarchical modeling approach. Compression and bending tests were conducted to quantify the biomechanical parameters of each corncob component, which were subsequently utilized for model parameter calibration. The bonding parameters among core marrow-core marrow, xylem annulus-xylem annulus, and core marrow-xylem annulus were calibrated based on the results from compression tests, employing the Plackett-Burman, steepest ascent, and Box-Behnken calibration methodologies. Ultimately, the bending destructive force of the entire corncob and its mechanical bending properties served as evaluation metrics to thoroughly validate the overall characteristic parameters of the corncob. Furthermore, by analyzing the morphological alterations of the corncob during both actual and simulated compression and bending, the findings indicate that the hierarchical DEM model developed in this study, along with the calibrated bonding parameters, demonstrates high accuracy in simulating the crushing behavior of real corncobs. The hierarchical bonded particle model presented herein lays the groundwork for future research aimed at constructing a high-fidelity corn ear model capable of characterizing kernel separation and the breakability of corncobs. (c) 2025 Published by Elsevier B.V. on behalf of The Society of Powder Technology Japan. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序