DEM Investigation of the Influence of Particulate Properties and Operating Conditions on the Mixing Process in Rotary Drums: Part 1—Determination of the DEM Parameters and Calibration Process

Abstract

This paper’s goal was to select methods and a calibration procedure which would lead to the determination of relevant parameters of a discrete element method (DEM) and virtual material creation. Seven particulates were selected with respect to their shape (spherical and non-spherical), size and density. The first calibration experiment involved “packing test” to determine the shape accuracy and bulk density of virtual packed particulates. The series of simulations were compared with real experiments, and the size, shape and density of virtual particles were optimized. Using three apparatuses, the input parameter values were experimentally determined for a contact model that defines the behavior of particulates in DEM simulations. The research part of the paper examines the influence of factors such as particle number; pile formation method; and the method of evaluation of the angle of repose on the process of the calibration of virtual material. The most reproducible results were achieved by the “pilling” method and by the rotating drum—both evaluated by the geometric method. However, it is always advisable to make an overall visual comparison of the slope shape between the calibration simulation and the experimental curves. The bowl’s diameter to particle size ratio should be greater than 25, and the calibration experiment should contain approximately 4000 particles to ensure representative results during angle of repose calibration experiment.

Study of Selected Physical-Mechanical Properties of Corn Grains Important from the Point of View of Mechanical Processing Systems Designing

“…Recently, DEM is a method which has been increasingly used in the simulation of machine operation and optimization, devices, and processing of loose material. The precise representation of the particle shape and size affects the accuracy of a DEM model and prediction of grain behavior motion [ 81 , 82 , 83 ].…”

Determination of mechanical properties for wood pellets used in DEM simulations

“…After that instant, Particle A is released and impacts against Particle B, which reaches a final height h B,2 with respect to its initial position, and forms a final angle of inclination with respect to the vertical axis, α B2 . As explained by Hlosta et al (2020a), the particle -particle coefficient of restitution may be obtained by using Eq. (6) or Eq.…”

“…Because of this, sensitivity analyses of some input parameters of individual particles are usually conducted with DEM and combined with the experimental results to develop a numerical model that adequately reproduces the problem considered. Following this scheme, Hlosta et al (2020a) combined the experimental results obtained from several apparatus with numerical simulations in order to validate the values of different interaction coefficients (i.e., the static friction coefficient, the rolling friction coefficient or the coefficient of restitution) required to develop a DEM model. Rozbroj et al (2019) used DEM simulations to conduct a sensitivity analysis of the length accuracy grade of cylindrical pellets with the optimal calibration of time and velocity during the discharge of a cylindrical vessel.…”

Discrete element simulation of the charge in the hopper of a blast furnace, calibrating the parameters through an optimization algorithm

“…• bulk density: equal to the bulk density of real material, which is 508 kg∕m 3 for coke, 1863 kg∕m 3 for sinter and 2088 kg∕m 3 for pellet; • Young modulus: a number of techniques exist for minimizing the computational cost of DEM simulations and one such method is the reduction of particle stiffness, which allows for bigger time steps and therefore fewer iterations in the simulation [21,22,35]. Being this parameter inversely related to the time step, it is set as low as possible: 10 7 Pa , as in [37].…”