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3-D and Quasi-2-D Discrete Element Modeling of Grain Commingling in a Bucket Elevator Boot System
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.orgCitation: Transactions of the ASABE. 55(2): 659-672. (doi: 10.13031/2013.41367) @2012
Authors: J. M. Boac, M. E. Casada, R. G. Maghirang, J. P. Harner III
Keywords: Bucket elevator boot, Discrete element method, Grain commingling, Soybeans, Three-dimensional and quasi-two-dimensional simulations
Unwanted grain commingling impedes new quality-based grain handling systems and has proven to be an expensive and time-consuming issue to study experimentally. Experimentally validated models may reduce the time and expense of studying grain commingling while providing additional insight into details of the particle flow. In this study, grain commingling in a pilot-scale bucket elevator boot was first modeled in three-dimensional (3-D) discrete element method (DEM) simulations. Experiments on the pilot-scale boot were performed using red-dyed and clear (uncolored) soybeans to validate the 3-D DEM model. Predicted results from the 3-D boot model generally followed the experimental data but tended to underpredict commingling early in the process. To reduce computational time, quasi-two-dimensional (quasi-2-D) DEM simulations were also evaluated. Comparison of predicted average commingling of five quasi-2-D boot models with reduced control volumes (i.e., with boot widths from four to seven times the mean particle diameter) led to the selection of the quasi-2-D model with a boot width of 5.6 times the mean particle diameter (i.e., 5.6d) to reduce computational time. In addition, the 3-D and quasi-2-D (5.6d) models were refined by accounting for the initial surge of particles at the beginning of each test and correcting for the effective dynamic gap between the bucket cups and the boot wall. The quasi-2-D (5.6d) models reduced simulation run time by approximately 70% compared to the 3-D model of the pilot-scale boot. Results of this study can be used to accurately predict commingling levels and improve grain handling, which can help farmers and grain handlers reduce costs and maintain grain purity during transport and export of grain.(Download PDF) (Export to EndNotes)