Determination and parameters calibration of the soybean-bonded particle model based on discrete element method

IF 2.8 3区工程技术 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Computational Particle Mechanics Pub Date : 2024-06-27 DOI:10.1007/s40571-024-00792-1

Dan-Dan Han, Qing Wang, Yun-Xia Wang, Wei Li, Chao Tang, Xiao-Rong Lv

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Abstract

To determine the desirable bonding parameters of the soybean-bonded particle model for accidentally simulating the working process of a pneumatic soybean seed-metering device. Taking the compressive destructive force (F_c,p) derived from the uniaxial compression test of soybean seeds as the evaluation index for the compression simulation tests. The Plackett–Burman and the steepest ascent tests were executed to identify the centroids of the influential factors that substantially affect the bonding force of the soybean-bonded particle model. The optimal values of the significance influencing variables were determined based on the Box–Behnken response surface test. The results indicated that the effect of bonded disk radius (R_B,p) between fraction particles on the F_c,p was extremely significant, and the effects of the restitution coefficient (e_p-steel) and static friction coefficient (μ_p-steel) of soybean-steel, normal stiffness per unit area (k_n,p) and critical normal stress (σ_max,p) were found to be statistically significant. The preferred values identified by Box–Behnken response surface test were 0.520 for e_p-steel, 0.274 for μ_p-steel, 4.082 × 10⁷ N/m³ for k_n,p, 3.517 × 10⁵ Pa for σ_max,p, and 0.982 mm for R_B,p, respectively. The compressive destructive force of soybean seeds was 211.32 N at this point, which was 0.2% less than the measured value of 211.74 N. The results of comparing the grain morphologies during the actual and simulated compressions indicated that the compression states had a superior consistency. It was determined that the DEM simulation input parameters for the soybean-bonded particle model calibrated were proven to be effective and dependable. The investigation presented in this paper can be utilized to effectively analyze the working process of the pneumatic soybean seed-metering devices through coupled simulation. It can also serve as a reference for other researchers to construct a particle model for DEM simulation using the BPM approach.

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基于离散元法的大豆结合颗粒模型的确定和参数校准

确定大豆粘合颗粒模型的理想粘合参数，以意外模拟气动式大豆种子计量装置的工作过程。以大豆种子单轴压缩试验得出的压缩破坏力（Fc,p）作为压缩模拟试验的评价指标。通过普拉克特-伯曼试验和最陡坡试验，确定了对大豆结合颗粒模型结合力有重大影响的影响因素的中心点。根据 Box-Behnken 响应面检验确定了重要影响变量的最佳值。结果表明，分数颗粒之间的粘结盘半径（RB,p）对 Fc,p 的影响极其显著，大豆-钢的恢复系数（ep-steel）和静摩擦系数（μp-steel）、单位面积法向刚度（kn,p）和临界法向应力（σmax,p）的影响均具有统计学意义。通过箱-贝肯响应面试验确定的优选值分别为：ep-钢为 0.520，μp-钢为 0.274，kn,p 为 4.082 × 107 N/m3，σmax,p 为 3.517 × 105 Pa，RB,p 为 0.982 mm。此时大豆种子的压缩破坏力为 211.32 N，比测量值 211.74 N 减少了 0.2%。经确定，大豆结合颗粒模型校准的 DEM 模拟输入参数被证明是有效和可靠的。本文介绍的研究可用于通过耦合模拟有效分析气动大豆种子计量装置的工作过程。它还可为其他研究人员利用 BPM 方法构建用于 DEM 仿真的粒子模型提供参考。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computational Particle Mechanics Mathematics-Computational Mathematics

CiteScore

5.70

自引率

9.10%

发文量

期刊介绍： GENERAL OBJECTIVES: Computational Particle Mechanics (CPM) is a quarterly journal with the goal of publishing full-length original articles addressing the modeling and simulation of systems involving particles and particle methods. The goal is to enhance communication among researchers in the applied sciences who use "particles'''' in one form or another in their research. SPECIFIC OBJECTIVES: Particle-based materials and numerical methods have become wide-spread in the natural and applied sciences, engineering, biology. The term "particle methods/mechanics'''' has now come to imply several different things to researchers in the 21st century, including: (a) Particles as a physical unit in granular media, particulate ﬂows, plasmas, swarms, etc., (b) Particles representing material phases in continua at the meso-, micro-and nano-scale and (c) Particles as a discretization unit in continua and discontinua in numerical methods such as Discrete Element Methods (DEM), Particle Finite Element Methods (PFEM), Molecular Dynamics (MD), and Smoothed Particle Hydrodynamics (SPH), to name a few.