Using the DEM-CFD method to predict Brownian particle deposition in a constricted tube

Modelling of the agglomeration and deposition on a constricted tube collector of colloidal size particles immersed in a liquid is investigated using the discrete element method （DEM）. The ability of this method to represent surface interactions allows the simulation of agglomeration and deposition at the particle scale. The numerical model adopts a mechanistic approach to represent the forces involved in colloidal suspensions by including near-wall drag retardation, surface interaction and Brownian forces. The model is implemented using the commercially available DEM package EDEM 2.3~, so that results can be repli- cated in a standard and user-friendly framework. The effects of various particle-to-collector size ratios, inlet fluid flow-rates and particle concentrations are examined and it is found that deposition efficiency is strongly dependent on the inter-relation of these parameters. Particle deposition and re-suspension mechanisms have been identified and analyzed thanks to EDEM＇s post processing capability. One-way coupling with computational fluid dynamics （CFD） is considered and results are compared with a two- way coupling between EDEM 2.3 and FLUENT 12.1. It is found that two-way coupling requires circa 500% more time than one-way coupling for similar results.     

Product design based on discrete particle modeling of a fluidized bed granulator

Fluidized bed agglomeration is a process commonly used to construct powdered food or pharmaceutical products to improve their instant properties. This works analyzes the influence of a wide range of operating parameters (i.e., fluidization air flow rate, temperature, and liquid injection rate) on growth rate, process stability, and product particle structure. Different granulator configurations (i.e., top spray, Wurster coater, spouted bed) are compared using identical process parameters. The impacts of both process variables and granulator geometry on the fluidization regime, the particle and collision dynamics, and the resulting product structure and corresponding properties are studied in detailed simulations using a discrete particle model (DPM) and lab-scale agglomeration experiments with amorphous dextrose syrup (DE21). The combination of numerical and experimental results allows to correlate the kinetics of micro-scale particle interactions and the final product properties (i.e., agglomerate structure and strength). In conclusion, detailed DPM simulations are proven as a valuable tool for knowledge-based product design.     

Modeling particle sedimentation in viscous fluids with a coupled immersed boundary method and discrete element method

Numerical techniques have increasingly been used to model fluid–particle two-phase flows. Coupling the immersed boundary method (IBM) and discrete element method (DEM) is one promising approach for modeling particulate flows. In this study, IBM was coupled with DEM to improve the reliability and accuracy of IBM for determining the positions of particles during the sedimentation process within viscous fluids. The required ratio of the particle diameter to the grid size (D/dx) was determined by comparing the simulation results with the analytical solution and experimental data. A dynamic mesh refinement model was utilised in the IBM model to refine the computational fluid dynamics grid near the particles. In addition, an optimum coupling interval between the IBM and DEM models was determined based on the experimental results of a single particle sedimentation within silicon oil at a Reynolds number of 1.5. The experimental results and the analytical solution were then utilised to validate the IBM–DEM model at Reynolds numbers of 4.1, 11.6, and 31.9. Finally, the validated model was utilised to investigate the sedimentation process for more than one particle by modeling the drafting-kissing-tumbling process and the Boycott phenomenon. Benchmark tests showed that the IBM–DEM technique preserves the advantages of DEM for tracking a group of particles, while the IBM provides a reliable and accurate approach for modeling the particle–fluid interaction.     

A new approach for the detection of particle interactions for large-inertia and colloidal particles in a turbulent flow

The purpose of the paper is to present a new principle and a new algorithm for the direct numerical simulation of particle interactions within a turbulent flow. This approach has been developed in order to be able to compute agglomeration kernels with a numerical method which can still be applied at reasonable costs for very small colloidal particles. In this paper, classical algorithms are first tested and analyzed. They are shown to yield correct results but to require the use of time steps that are so small that they become intractable for colloidal particles. Their direct applications using large steps with respect to the relaxation time scale of the smallest particles reveal drastic errors that increase with the time step and with decreasing particle diameters. The new principle introduces the notion of continuous relative trajectories between possible collision partners and evaluates the exact probability for this trajectory to reach the minimum distance where two particles actually collide. Based on this new physical point of view and on the use of a probabilistic approach, a novel algorithm has been devised and numerical outcomes confirm that accurate predictions for the collision kernel are obtained independently of the particle diameter and for very large time steps. It is believed that the present ideas open interesting possibilities for the simulation of particle interactions over a whole range of particle behavior, from a ballistic to a diffusive regime, and can be extended to take into account new phenomena. Although present developments arise in the context of a numerical study, the new ideas that are introduced in this paper rely on the use of continuous stochastic bridges and, in that respect, propose a new approach to address physical issues of two-phase flow modeling.     

Simulation of particle flow in a bell-less type charging system of a blast furnace using the discrete element method

A three-dimensional model was established by the discrete element method （DEM） to analyze the flow and segregation of particles in a charging process in detail. The simulation results of the burden falling trajectory obtained by the model were compared with the industrial charging measurements to validate the applicability of the model. The flow behavior of particles from the weighing hopper to the top layer of a blast furnace and the heaping behavior were analyzed using this model. A radial segregation index （RSI） was used to evaluate the extent of the size segregation in the charging process. In addition, the influence of the chute inclination angle on the size segregation and burden profile during the charging process was investigated.     

Using the discrete element method to assess the mixing of polydisperse solid particles in a rotary drum

Despite the wide applications of powder and solid mixing in industry, knowledge on the mixing of polydisperse solid particles in rotary drum blenders is lacking. This study investigates the mixing of monodisperse, bidisperse, tridisperse, and polydisperse solid particles in a rotary drum using the discrete element method. To validate the model developed in this study, experimental and simulation results were compared. The validated model was then employed to investigate the effects of the drum rotational speed, particle size, and initial loading method on the mixing quality. The degree of mixing of polydisperse particles was smaller than that for monodisperse particles owing to the segregation phenomenon. The mixing index increased from an initial value to a maximum and decreased slightly before reaching a plateau for bidisperse, tridisperse, and polydisperse particles as a direct result of the segregation of particles of different sizes. Final mixing indices were higher for polydisperse particles than for tridisperse and bidisperse particles. Additionally, segregation was weakened by introducing additional particles of intermediate size. The best mixing of bidisperse and tridisperse particles was achieved for top–bottom smaller-to-larger initial loading, while that of polydisperse systems was achieved using top–bottom smaller-to-larger and top–bottom larger-to-smaller initial loading methods.     

A block particle coupled model and its application to landslides

To simulate the progressive failure of slope, a block particle coupled model is introduced. Particle oriented cell mapping(POCM) algorithm is used to enhance the search efficiency, and particlepoint, particle-edge, particle-face contact detecting method is adopted to establish contact pair between particles and blocks precisely. Strain softening Mohr Coulomb model with tensile cutoff is adopted for blocks, and brittle Mohr Coulomb model is used for particles. The particle-block replacement approach is used to describe the fracture and fragmentation process of continuum media. Once the cohesion or tensile strength of one block reaches zero, the block will be deleted,and particles are generated at the same place with all information inherited from the deleted block. Some numerical cases related to landslides demonstrate the precision and rationality of the coupled model.     

Discrete element method study of parameter optimization and particle mixing behaviour in a soil mixer

Soil mixing is an emerging research in the field of construction resource recovery. In this study, the mixing behaviour of soil particles in a mixer is numerically simulated by the discrete element method (DEM). A four-factor, three-level orthogonal experiment is designed to optimize the mixer design by selecting the fly-cutter speed, spindle speed, number of blades and fly-cutter diameter, using Lacey mixing index and power consumption as evaluation indicators. Then, the impact of soil cohesion and type on the mixing behaviour is investigated. The results show that the optimal parameter combination of this experiment is 280 rpm fly-cutter speed, 40 rpm spindle speed, 4 blades and 250 mm fly-cutter diameter. This optimal combination reaches a comparatively uniform state mix in 5.9 s with an average power consumption of 704.11 W. In addition, the wear and tear of the mixer increases as soil cohesion increases, while the mixing quality of materials declines, resulting in a “shaft hugging” phenomenon. The mixing efficiency varies greatly among different soil types, but the radial and tangential velocities have a similar law. This work can provide some guidance for the optimization design of a mixer and study of soil mixing.     

DEM simulation of particle percolation in a packed bed

The phenomenon of spontaneous particle percolation under gravity is investigated by means of the discrete element method. Percolation behaviors such as percolation velocity,residence time distribution and radial dispersion are examined under various conditions. It is shown that the vertical velocity of a percolating particle moving down through a packing of larger particles decreases with increasing the restitution coefficient between particles and diameter ratio of the percolating to packing particles. With the increase of the restitution coefficient,the residence time and radial dispersion of the percolating particles increase. The packing height affects the residence time and radial dispersion. But,the effect can be eliminated in the analysis of the residence time and radial dispersion when they are normalized by the average residence time and the product of the packing height and packing particle diameter,respectively.In addition,the percolation velocity is shown to be related to the vertical acceleration of the percolating particle when an extra constant vertical force is applied. Increasing the feeding rate of percolating particles decreases the dispersion coefficient.     

DEM simulation of particle percolation in a packed bed

The phenomenon of spontaneous particle percolation under gravity is investigated by means of the discrete element method. Percolation behaviors such as percolation velocity, residence time distribution and radial dispersion are examined under various conditions. It is shown that the vertical velocity of a percolating particle moving down through a packing of larger particles decreases with increasing the restitution coefficient between particles and diameter ratio of the percolating to packing particles. With the increase of the restitution coefficient, the residence time and radial dispersion of the percolating particles increase. The packing height affects the residence time and radial dispersion. But, the effect can be eliminated in the analysis of the residence time and radial dispersion when they are normalized by the average residence time and the product of the packing height and packing particle diameter, respectively. In addition, the percolation velocity is shown to be related to the vertical acceleration of the percolating particle when an extra constant vertical force is applied. Increasing the feeding rate of percolating particles decreases the dispersion coefficient.     

Quasi-real-time simulation of rotating drum using discrete element method with parallel GPU computing

Real-time simulation of industrial equipment is a huge challenge nowadays. The high performance and fine-grained parallel computing provided by graphics processing units (GPUs) bring us closer to our goals. In this article, an industrial-scale rotating drum is simulated using simplified discrete element method (DEM) without consideration of the tangential components of contact force and particle rotation. A single GPU is used first to simulate a small model system with about 8000 particles in real-time, and the simulation is then scaled up to industrial scale using more than 200 GPUs in a 1D domain-decomposition parallelization mode. The overall speed is about 1/11 of the real-time. Optimization of the communication part of the parallel GPU codes can speed up the simulation further, indicating that such real-time simulations have not only methodological but also industrial implications in the near future.     

Study of particle rearrangement during powder compaction by the Discrete Element Method

This paper presents simulations of cold isostatic and closed die compaction of powders based on the Discrete Element Method. Due to the particulate nature of powders, densification of the compact proceeds both through the plastic deformation at the particle contact and the mutual rearrangement of particles. The relative weight of each mechanism on the macroscopic deformation process depends on the contact law, the relative density, and the type of stress exerted on the particles (shear or pressure). 3D computer simulations have been carried out to investigate the role of these parameters on the deformation mechanisms of powder compacts. The effect of rearrangement is studied by comparing simulations that use a homogeneous strain field solution for which local rearrangement is omitted and simulations that include local rearrangement. It is shown that local rearrangement has some effect on average quantities such as the average coordination number, the average contact area and the macroscopic stress. The effect on averaged quantities is much stronger for closed die compaction than for isostatic compaction. However the main effect of local rearrangement is to widen the distribution of the parameters that define the contact (contact area in particular). The results of these simulations are compared to available experimental data and to statistical models that use a homogeneous strain field assumption.     

Application of the FEM/DEM and alternately moving road method to the simulation of tire-sand interactions

The three-dimensional finite-discrete element method (FEM/DEM) is applied to the simulation of tire-sand interactions, where the tire is discretized into hexahedron finite elements and sand is modeled by using the discrete element method. The feasibility and effectiveness of the method are proven by comparing the simulation results with the current reported results. Since long test roads are usually required for investigating tire running behaviors, which lead to large-scale simulation models and time consuming problems, the alternately moving road method is proposed to handle this problem. It can simulate tire running behaviors on an arbitrary length sand road with a constant road length value. The numerical model of a lug tire running on a bisectional road with fine and coarse sand is established to verify the feasibility of the method.     

Euler-Lagrange/DEM simulation of wood gasification in a bubbling fluidized bed reactor

We present an Euler–Lagrange method for the simulation of wood gasification in a bubbling fluidized bed. The gas phase is modeled as a continuum using the 2D Navier–Stokes equations and the solid phase is modeled by a Discrete Element Method(DEM)using a soft-sphere approach for the particle collision dynamic. Turbulence is included via a Large-Eddy approach using the Smagorinsky sub-grid model.The model takes into account detailed gas phase chemistry,zero-dimensional modeling of the pyrolysis and gasification of each individual particle,particle shrinkage,and heat and mass transfer between the gas phase and the particulate phase.We investigate the influence of wood feeding rate and compare exhaust gas compositions and temperature results obtained with the model against experimental data of a laboratory scale bubbling fluidized bed reactor.     

Machine Learning for heat radiation modeling of bi- and polydisperse particle systems including walls

We investigated the ability of four popular Machine Learning methods i.e., Deep Neural Networks (DNNs), Random Forest-based regressors (RFRs), Extreme Gradient Boosting-based regressors (XGBs), and stacked ensembles of DNNs, to model the radiative heat transfer based on view factors in bi- and polydisperse particle beds including walls. Before training and analyzing the predictive capability of each method, an adjustment of markers used in monodisperse systems, as well as an evaluation of new markers was performed. On the basis of our dataset that considers a wide range of particle radii ratios, system sizes, particle volume fractions, as well as different particle-species volume fractions, we found that (i) the addition of particle size information allows the transition from monodisperse to bi- and polydisperse beds, and (ii) the addition of particle volume fraction information as the fourth marker leads to very accurate predictions. In terms of the overall performance, DNNs and RFRs should be preferred compared to the other two options. For particle–particle view factors, DNN and RFR are on par, while for particle–wall the RFR is superior. We demonstrate that DNNs and RFRs can be built to meet or even exceed the prediction quality standards achieved in a monodisperse system.     

Amorphous particle deposition and product quality under different conditions in a spray dryer

Deposition of amorphous particles, as a prevalent problem particularly in the spray drying of fruit and vegetable juices, is due to low-molecular weight sugars and is strongly dependent on the condition of the particles upon collision with the dryer wall. This paper investigates the condition of the amorphous particles impacting the wall at different drying conditions with the aim of elucidating the deposition mechanism and physical phenomena in the drying chamber. A model sucrose-maltodextrin solution was used to represent the low-molecular-weight sugar. Particle deposits were collected on sampling plates placed inside the dryer for analyses of moisture content, particle rigidity (using SEM) and size distribution. Moisture content was adopted as a general indicator of stickiness. Product particles collected at the bottom of the experimental dryer were found to have higher moisture than particle deposits on samplers inside the dryer. Moisture content profile in the dryer shows that apart from the atomizer region, where particles are relatively wet, particle deposits at other regions exhibit similar lower moisture content. At the highest temperature adopted in the experiments, particles became rubbery suggesting liquid-bridge formation as the dominant deposition mechanism. Further analysis on particles size distribution reveals a particle segregation mechanism whereby smaller particles follow preferentially to the central air stream while larger particles tend to re-circulate in the chamber, as predicted in past CFD simulation. The findings from this work will form the basis and provide validating data for further modeling of wall deposition of amorphous particles in spray drying using CFD.     

Simulation and calibration of granules using the discrete element method

The discrete element method (DEM), developed by Cundall and Strack (1979) to solve geomechanical problems, is used to simulate the mechanical behavior of granules. According to the DEM, an individual granule can be modeled as a realistic mechanical system consisting of primary particles bonded by interaction forces.Granulometric properties of the model material, zeolite 4A, have been measured to determine their macro properties. To investigate the compression behavior, a compression test was performed using a strength tester on single granules between two pistons. A modeled granule consisting of more than 22,000 primary particles was generated. The micro properties of the modeled granule have been precisely set to allow its macro properties to be equivalent to the macro properties of zeolite 4A granules. To calibrate the mechanical properties, diametrical compression was simulated using two rigid walls stressed at a constant stressing velocity. The force–displacement curve of the modeled granule at compression has been calibrated by the experimental curve of zeolite 4A.     

Effect of particle polydispersity on micromechanical properties and energy dissipation in granular mixtures

A series of numerical tests was conducted to study the micromechanical properties and energy dissipation in polydisperse assemblies of spherical particles subjected to uniaxial compression. In general, distributed particle size assemblies with standard deviations ranging from 0% to 80% of the particle mean diameter were examined. The microscale analyses included the trace of the fabric tensor, magnitude and orientation of the contact forces, trace of stress, number of contacts and degree of mobilization of friction in contacts between particles. In polydisperse samples, the average coordination numbers were lower than in monodisperse assemblies, and the mobilization of friction was higher than in monodisperse assemblies due to the non-uniform spatial rearrangement of spheres in the samples and the smaller displacements of the particles. The effect of particle size heterogeneity on both the energy density and energy dissipation in systems was also investigated.     

Application of DEM modified with enlarged particle model to simulation of bead motion in a bead mill

We applied the discrete element method （DEM） of simulation modified by an enlarged particle model to simulate bead motion in a large bead mill. The stainless-steel bead mill has inner diameter of 102 mm and mill length of 198 mm. The bead diameter and filling ratio were fixed respectively at 0.5 mm and 85%. The agitator rotational speed was changed from 1863 to 3261 rpm. The bead motion was monitored experimentally using a high-speed video camera through a transparent mill body. For the simulation, enlarged particle sizes were set as 3-6 mm in diameter. With the DEM modified by the enlarged particle model, the motion of enlarged particles in a mill was simulated.The velocity data of the simulated enlarged particles were compared with those obtained in the experiment. The simulated velocity of the enlarged particles depends on the virtual frictional coefficient in the DEM model. The optimized value of the virtual frictional coefficient can be determined by considering the accumulated mean value. Results show that the velocity of the enlarged particles simulated increases with an increase in the optimum virtual frictional coefficient, but the simulated velocity agrees well with that determined experimentally by optimizing the virtual frictional coefficient in the simulation. The computing time in the simulation decreases with increased particle size.