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ARS Home » Plains Area » Manhattan, Kansas » Center for Grain and Animal Health Research » Stored Product Insect and Engineering Research » Research » Publications at this Location » Publication #387287

Research Project: Advancing Technologies for Grain Trait Measurement and Storage Preservation

Location: Stored Product Insect and Engineering Research

Title: Discrete element method simulation of wheat bulk density as affected by grain drop height and size distribution

item PETINGCO, MARVIN - Kansas State University
item Casada, Mark
item MAGHIRANG, RONALDO - University Of Illinois
item THOMPSON, SIDNEY - University Of Georgia
item MCNEILL, SAMUEL - University Of Kentucky
item MONBTROSS, MICHAEL - University Of Kentucky
item TURNER, AARON - Clemson University

Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/21/2022
Publication Date: 3/21/2022
Citation: Petingco, M.C., Casada, M.E., Maghirang, R.G., Thompson, S.A., McNeill, S.G., Monbtross, M.D., Turner, A.P. 2022. Discrete element method simulation of wheat bulk density as affected by grain drop height and size distribution. Transactions of the ASABE. 65(3):555-566.

Interpretive Summary: Accurate predictions of grain bulk density in storage are needed in the grain industry for design, inventory, and auditing purposes. However, experimentally-based predictions of compacted bulk density are notoriously inaccurate because of the wide range of grain properties and storage and handling conditions included, making accurate theoretical prediction methods, such as the discrete element method (DEM) of modeling, desirable. DEM evaluates the movement and interactions of each particle, making it effective for studying how handling processes and material properties affect the bulk density. We evaluated two DEM particle models (single-sphere and five-sphere) for simulating the effect of grain drop height on the bulk densities of two cultivars of hard red winter wheat. The two cultivars have different particle size distributions, which produced differences in measured bulk density. The DEM model simulated the two measured densities using either particle model, predicting lower simulated bulk density for the correct cultivar based on the particle size distributions. The simulations also correctly predicted bulk density increasing with higher grain drop heights. Simulation results for the simpler, faster single-sphere particle model were comparable with the five-sphere particle model when calibrated correctly with separate contact parameters for each size fraction of wheat. The five-sphere particle model was better for simulating the heap profile of wheat observed in the experiments, but has a disadvantage of requiring greater computational effort. This study provides a better understanding of the influence of particle shape, drop height, and size distribution on simulating wheat bulk density with the discrete element method.

Technical Abstract: Grain bulk density varies widely depending on kernel properties and handling practices. The discrete element method (DEM) can model such behavior at the particle level including wide-ranging interactions with equipment. The objective of this study was to develop a DEM model to predict wheat bulk density as affected by grain drop height and size distribution. Bulk density of two wheat samples was measured experimentally for a range of drop heights with a modified test weight per bushel apparatus and was simulated in EDEM® v2018.1 using single-sphere and five-sphere particle models that accounted for kernel size fractions. For both particle models, simulations matched the observed behavior, showing a bulk density increase with increasing grain drop height and bulk density differences between samples due to different kernel size fractions. The single-sphere particle model predicted the bulk density with higher accuracy than the five-sphere particle model, whereas the five-sphere model (which more accurately represented the shape of wheat kernels) allowed better simulations of the heap profile at a cost of longer computation times. These particle models can be used to simulate bulk density of wheat under compaction, and to improve prediction models of grain pack factor for wheat.