Discrete element method (DEM) as a numerical technique for modeling granular materials has been developed for predicting the dynamic behavior of bulk particulate systems. Simulation of bulk grain handling in crop harvesting machine using the DEM technique have been challenging owing to the variability of corn conditions (e.g., grain moisture content) and limitation of robust DEM calibration procedures. Although DEM has provided an invaluable qualitative understanding of the particle flow inside or in contact with equipment, quantitatively accurate DEM simulation of particle flow and particle-machine interaction for engineering design and analysis has been generally limited. The objective of this Ph.D. dissertation was to develop a systematic material properties calibration approach to develop a DEM grain model reproducing physics-based bulk behavior of harvested corn kernels.

A five-stage DEM model development framework was introduced for systematic calibration and validation of grain DEM models. This framework was utilized to investigate the effect of corn moisture content on DEM input parameters and bulk behavior of grains in Screw grain auger, hopper discharge application, and clean grain paddle elevator application. Multiple physical experiments were performed on corn samples for characterizing the dynamic behavior of corn. The five-stage DEM model development framework was utilized to develop the DEM model of corn at 11%, 16%, and 26% moisture content levels. DEM simulation predicted the bulk behavior of corn in screw grain auger, hopper discharge flow, and the clean grain paddle elevator application with relative errors of less than 10% compared to physical experiments.