Calibration and verification of DEM parameters for dynamic particle flow conditions using a backpropagation neural network

Universal Approach for DEM Parameters Calibration of Bulk Materials

DEM parameters calibration is the most important step in preparing a DEM model. At the same time, the lack of a universal approach to DEM parameters calibration complicates this process. The paper presents the author’s approach to creating a universal calibration approach based on the physical meaning of the friction coefficients and conducting symmetrical experiments at full scale and in a simulation, as well as the implementation of the approach in the form of a physical test rig. Several experiments were carried out to determine the DEM parameters of six material–boundary pairs. The resulting parameters were adjusted using a refinement experiment. The results confirmed the adequacy of the developed approach, as well as its applicability in various conditions. The limitations of both the approach itself and its specific implementation in the form of a test rig were identified.

Understanding flow properties of mannitol powder at a range of temperature and humidity

Inadequate flowability of powders in industries during handling can cause many problems. For example, lack of flow from hoppers, poor tablet weight consistency, and low production rate in tableting. Many factors are known to commonly affect flow properties of powders, such as temperature, humidity and conditioning duration. In this paper, flow properties of a mannitol powder, which was conditioned between 24 and 72 h at various high relative humidities and temperature, were measured using a shear tester. A statistical model was developed to investigate the relative importance of these variables on the mannitol flow properties. The developed model showed all independent variables are significant in estimating bulk cohesion. Two separate approaches were used to evaluate inter-particle forces in the bulk, and how these changed with environmental conditions. First, inter-particle forces were inferred from the measured bulk properties using the Rumpf model approach. Secondly, inter-particle forces were predicted based on a model of moisture present on the particle surface using a combination of Kelvin model with the Laplace-Young (KLY) equation. The second approach also involved a new method to measure surface energy of mannitol powder based on measurements using Finite Dilution Inverse Gas Chromatography (FD-IGC). The surface energies of the mannitol powder were measured at high temperature (35 °C) and at different range of relative humidities. In spite of the fundamentally different approaches to the two ways of inferring inter-particles forces, these forces came out within less than 1.5:1 in magnitude. The Rumpf approach from bulk behaviour data obviously reflected the measured change in behaviour with humidity in particular, but this was not predicted from the KLY approach, however the likely reasons for this are postulated and recommendations for improvement are made.

Dynamic characteristic analysis of vertical screw conveyor in variable screw section condition

Vertical screw conveyors are used widely in industry for elevating bulk materials over relatively short distances, but the problem of insufficient feeding and low conveying efficiency always exist in the vertical conveying process. In this paper, a vertical screw conveyor with variable screw section is presented, and the characteristics of vertical screw conveyor are investigated under the variable screw sections using discrete element method (DEM). The results show that the particle volume fraction in the inlet and the mass flow rate increase in the condition of variable screw section, and the screw rotational speed has a significant influence on mass flow rate. It is evident that the design of variable screw section provides an effective way in improving the particle feeding rate and the conveying efficiency.

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

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.

Calibration and Verification of Dynamic Particle Flow Parameters by the Back-Propagation Neural Network Based on the Genetic Algorithm: Recycled Polyurethane Powder

The discrete element method (DEM) is commonly used to study various powders in motion during transportation, screening, mixing, etc.; this requires several microscopic parameters to characterize the complex mechanical behavior of the particles. Herein, a new discrete element parameter calibration method is proposed to calibrate the ultrafine agglomerated powder (recycled polyurethane powder). Optimal Latin hypercube sampling and virtual simulation experiments were conducted using the commercial DEM software; the microscopic variables included the static friction coefficient between the particles, collision recovery coefficient, Johnson–Kendall–Roberts surface energy, static friction coefficient between the particles and wall, and collision recovery coefficient. A predictive model based on genetic-algorithm-optimized feedforward neural network (back propagation) was developed to calibrate the microscopic DEM simulation parameters. The cycle search algorithm and mean-shift cluster analysis were used to confirm the input parameters’ range by comparing the mean value of the dynamic angle of repose measured via the batch accumulation test. These parameters were verified by the baffle lifting method and the rotating drum method. This calibration method, once successfully developed, will be suitable for use in a variety of fine viscous powder dynamic flow conditions.