Abstract
The accuracy of particle shape characterization is the key to the study of particle dynamics. This study proposes two modeling methods, super-ellipsoid approach (SE) and multi-sphere approach (MS), to describe the discrete element method (DEM) model of soybean seeds with the goal of improving the accuracy of the discrete element analysis model of soybean seeds. The accumulation process, flow behavior, and mixing characteristics of soybean seeds were studied through experiments and DEM simulation. Three comparative verification tests were carried out to compare the accuracy and potential similarity of the SE particle model and the MS particle model: (1) angle of repose experiment; (2) slope screening experiment; and (3) horizontal rotating cylinder experiment. The simulation and experimental results indicated that when compared with the MS particle model, the soybean seed model established using the super-ellipsoid approach can make the described particle shape more accurate, and also reproduce the accumulation process, flow behavior, and mixing characteristics of soybean seeds more accurately. For the multi-sphere approach, the calculation cost for approximating the DEM model of soybean seeds can be saved when the number of sub-spheres is less, but the accuracy of the particle model will be reduced at the same time. In this paper, the DEM model established using the super-ellipsoid approach can accurately simulate the real movement of soybean seeds. Therefore, the model can provide a new numerical calculation method for the simulation of the particle system in the structure of agricultural machinery.
Highlights
Soybean is an important oil crop in the world
A local coordinate system should be introduced when the particles experiment with the actual experimental results, this paper are in any position in the global coordinate system, in which aims to find out the respective advantages of the two soybean the central axis and principal axis of the particle center seed modeling methods, demonstrate the sufficiency of coincide with the central axis and principal axis of the local realizing super-ellipsoid approach (SE) particle model and multi-sphere approach (MS) particle model, and coordinate system
The study proved the effectiveness of using SE particles to simulate dense particle accumulation by comparing the static angle of repose and porosity between the simulation of particle accumulation and the corresponding experiments
Summary
Soybean is an important oil crop in the world. There are complex interactions among soybean seeds, and between soybean seeds and related mechanical components during the processes of sowing, harvesting, threshing, processing, storage, and transportation. The method is simple was taken as the average value of each triaxial size of the actual soybean seed population, while the ellipsoid model was and efficient because of the contact detection algorithm for irregular shape particles based on spheres It is established using the super-ellipsoid approach and the multisphere approach, respectively. A local coordinate system should be introduced when the particles experiment with the actual experimental results, this paper are in any position in the global coordinate system, in which aims to find out the respective advantages of the two soybean the central axis and principal axis of the particle center seed modeling methods, demonstrate the sufficiency of coincide with the central axis and principal axis of the local realizing SE particle model and MS particle model, and coordinate system This is followed by the use of matrix A provide the basis for the design and optimization of for coordinate transformation, as follows: agricultural machinery structure which has contact with. In DEM simulation, the ratio of searching time between ellipsoidal particles to the whole calculation time will be significantly increased when compared with the simple and efficient contact judgment of spherical particles, and its calculation efficiency is often affected by particle shape, boundary conditions, search algorithm, and other factors
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