extended program with abstracts - luleå university of ...staff.andsan/temp/extended_program... ·...

Extended Program

with Abstracts

March 31 - April 1, 2011

Edinburgh, Scotland

Welcome to EDEM Conference 2011

Greetings!

We are pleased that you are able to join us at this, our third EDEM conference.

We have an excellent program lined up for you with speakers and posters detailing

EDEM successes in a wide variety of industry sectors and engineering disciplines.

Thank you to all who will be sharing their work with us over the next two days.

Despite all that we have achieved since developing the first version of EDEM software

in 2002, we have only just begun. We will continue to focus our software development

efforts on helping engineers to solve the real, everyday challenges faced in designing

bulk material handling, processing, and manufacturing operations and in researching

particle behavior in complex granular systems.

This is an exciting time for both DEM Solutions and our customers. I look forward to the

presentations, to speaking with you over the next few days, and to working with you in

the future.

Best regards,

John Favier

CEO DEM Solutions

March 31st 2011

Contents

Welcome to EDEM Conference 2011 .............................. 2

Technical Sessions ............................................................ 5

Session 1: EDEM Material Model Calibration ................ 6

EDEM Material Model Calibration ............................................ 7

Device for Calibrating DEM Contact Model Parameters ........ 10

Calibration of DEM Models using Bulk Physical Tests ........... 14

The Effect of Electrical Charge and Gravity on Angle of

Repose Formation ................................................................. 20

Session 2: Mining & Mineral Processing .................... 26

EDEM Material Model Calibration Delivering Value for

Transfer Tower Simulation ..................................................... 27

Getting Started with Discrete Element Modelling for Bulk

Solids Transfer ...................................................................... 28

EDEM-Based Design Investigations of Bulk Materials

Handling Equipment .............................................................. 29

Plow System in Underground Mining – A Benchmark

Approach ............................................................................... 31

Discrete Element Modelling for the Simulation of Particle

Distribution to a Sensor-Based Sorter.................................... 32

DEM-PBM Coupling to Predict Breakage in Comminution

Processes ............................................................................. 39

Calibration of Angle of Repose of Glass Beads ..................... 45

Discrete Element Modeling of Fracture Toughness of

Rocks Using Semi-circular Bending Method .......................... 48

Virtual Soil Calibration for Wheel-Soil Interaction

Simulations Using the Discrete Element Method ................... 54

Session 3: Metals Manufacturing ................................. 62

Analysis of the Bell Less Top® Charging System– By the

Discrete Element Method ...................................................... 63

Optimization of Raw Material Transport using a Transfer

Chute .................................................................................... 64

Blast Furnace Charging Simulation using EDEM ................... 66

Coupled DEM-CFD Study of the Blast Furnace Cohesive

Zone ...................................................................................... 68

Session 4: Pharmaceutical Production ....................... 76

Using DEM to Predict Pharmaceutical Tablet Film Coating

Uniformity .............................................................................. 77

A Deeper Understanding of Tablet Coating Processes

Through Discrete Element Method Simulations ..................... 79

DEM Simulation of a Flowability Assessment Method

using Small Sample Quantity ................................................. 85

Session 5: General Industries ....................................... 92

Improving Asphalt Plant Design Using DEM Simulation ........ 93

Simulation of Cutting Process by Hybrid Granular and

Multibody Dynamics Software ............................................... 98

A Numerical Comparison of Mixing Efficiencies of Solids in

a Cylindrical Vessel Subject to a Range of Motions ............. 100

Lunar Dust Mitigation by Travelling Electrostatic Waves ...... 107

Particle Scale Modelling Of Frictional-Adhesive Granular

Materials .............................................................................. 112

Simulation of Pneumatic Conveying Flow Regimes by

Coupled EDEM-FLUENT ..................................................... 115

Bond Models in EDEM ........................................................ 119

Determination of Optimal Process Parameters and

Materials using DEM ........................................................... 121

DEM simulation of parameter effects in the shot peening

process ................................................................................ 129

Failure Modes Observed in Geobag Revetment using

EDEM .................................................................................. 131

A DEM Application to Improve the Design of an Industrial

Prototype ............................................................................. 139

Use of DEM-Simulation in the Basic Research on Screw

Conveyors ........................................................................... 144

Technical Sessions

EDEM Material Model Calibration

Mining & Minerals Processing

Metals Manufacturing

Pharmaceutical Production

General Industries

Session 1: EDEM Material Model Calibration


Richard LaRoche and David Curry

DEM Solutions

Peter Wypych and Andrew Grima

Bulk Materials Handling Australia, University of Wollongong

Richard LaRoche

Vice President of Engineering, General Manager of DEM Solutions (USA) Greater Boston Area, Massachusetts, United Sates of America

DEM Solutions provides the world-leading DEM simulation technology and the

simulation know-how to address the needs of companies who handle and process bulk

materials ranging from coal, ores, and soil to pellets, tablets and powders. Our mission is

to support our customer‘s in-house engineering expertise with our software and

applications know-how to generate substantial return-on-investment through reduced

prototyping and testing costs, lower risk of rework and equipment malfunction, improved

control over final product and process quality, and accelerated product innovation.

Peter Wypych

Founder and General Manager of Bulk Materials Engineering Australia;

Associate Professor, Faculty of Engineering, University of Wollongong

Wollongong, New South Wales, Australia

Bulk Materials Engineering Australia (BMEATM

) is licensed consultancy of the

University of Wollongong and provides optimal designs and solutions for bulk materials

handling plants and processes in all sectors of industry in Australia and around the world

(e.g. mining, minerals processing, export infrastructure, bulk ports and terminals,

shipping, power generation, and so on). Over the past 20 years, we have completed over

1000 projects for industry, and more than 300 companies and organizations have made

use of our bulk materials handling expertise and facilities. Our operation now is a part of

the SMART Infrastructure Facility (focusing on Simulation, Modeling and Analysis for

Research and Teaching)


Richard LaRoche and David Curry, DEM Solutions Ltd.

Peter Wypych and Andrew Grima, Bulk Materials Handling Australia & University of

Wollongong

Abstract

The reliable design and operation of bulk materials handling and processing plants can be

difficult when dealing with complex geometries and difficult-to-handle materials. Often a lack

of detailed analysis of bulk material flow and process boundary interactions can lead to

costly mistakes which can typically be identified easily once in operation. These problems

can occur due to inaccurate characterization during design, miscalculation of particle

trajectories and velocities, and a lack of engineering tools to thoroughly visualize and

analyze material flow through complex designs. This presentation describes the application

of EDEM simulation to bulk material plant design by identifying current issues and presenting

new methods of calibration and length-scale/dynamic validation. Examples and case studies

are presented to answer these key questions:

What is a calibrated EDEM Material Model?

What is the methodology to obtain a calibrated EDEM Material Model?

How much calibration is necessary and how can I access this methodology?

In this presentation, a partnership between Bulk Materials Handling Australia (BMEA) and

DEM Solutions will be introduced which will provide expert guidance and material calibration

services for EDEM customers worldwide.

Device for Calibrating DEM Contact Model Parameters

Johannes Quist

Chalmers University of Technology

Johannes Quist

Project Assistant, Chalmers Rock Processing Research (CRPR) Göteborg, Sweden

Chalmers Rock Processing Research (CRPR) is a research group within the

Department of Product and Production Development focused on scientific and

industrial research on machines and systems for production of rock materials. The

objective is to produce rock material products in a cost-efficient and resource-

economical way, thereby contributing to a sustainable society. The work of the

research group involves developing algorithms, operator interfaces, and simulation

and optimization techniques for optimal operation with an augmented degree of

usage

The Chalmers University of Technology Department of Product & Production

Development focuses on product design, product development and production

systems development and in the interplay between these disciplines. Research at the

Department focuses largely on shortening the lead-time from needs to finished

products while simultaneously achieving added value for the customer. Our largest

customers are in the vehicle and manufacturing industries.

Device for calibrating DEM contact model parameters

Johannes Quist, Research Engineer, Dep. Product & Production Development, Chalmers

University of Technology, Göteborg, Sweden

Introduction

The usage of CAE tools for simulation and modelling of machines and processes is

continuously increasing at engineering departments and in academia. However efforts

towards ensuring trustworthy results by performing calibration tests are often a scarcity. This

may lead to inaccurate or incorrect results, leading to poor decisions. This reliability issue is

vital when it comes to a potential increase in the use of DEM modelling in the industry. When

conducting DEM modelling efforts aimed towards simulating granular media flow behaviour

there are few methods available for calibrating contact model parameters. In this paper a

calibration device is presented as a solution to the problem of choosing correct contact

model parameters that correspond to the flow behaviour of real media.

Approach

The device has been designed with the intension to create three different flow behaviour

situations that can be studied in a sequence. An illustration of the device can be seen in

Figure 1. The flow through the device is filmed as a reference for later reproduction of the

flow in EDEM. The device can be configured in different ways in order to create several

different flow scenarios. By configuring contact model parameters towards different

scenarios the validity of the model will increased compared to a static configuration. Also,

the device is adoptable to different kind of media shape and size.

Figure 1 - CAD model of the calibration device. Red areas represent surfaces important for

the media-geometry interaction behaviour.

Results

In the top section two plates form a chute controlled by a trap-door mechanism. The aperture

length can be varied between four different positions. The angle for the top plates can be

varied continuous between two positions as well as independently of each other. When

releasing the trapdoor mechanism the material will flow through the chute and fall onto the

angled plane in the middle section of the device. The angle of the plane can be between

three discrete steps. Finally the media will reach the bottom of the device and form an

angled bed of material.

Figure 2 - Snapshots from DEM simulation of the flow through the device.

Calibration of DEM Models using Bulk Physical Tests

Mical W. Johnstone

Dunlop Oil and Marine

Jin Y. Ooi

University of Edinburgh

Mical W. Johnstone

Product Support Engineer, Fluid Technology Grimsby, United Kingdom

The Fluid Technology unit focuses on technology for developing, producing and

dealing with hoses and hose assemblies. Products developed in this unit are used to

control media flow in cars, trucks and most industrial applications, including offshore

activities.

Dunlop Oil and Marine, a member of the ContiTech Group, a specialist in rubber

and plastics technology, is a world leader in the design, manufacture and supply of

hoses for the oil, gas, petrochemical and dredging industries, for both offshore and

onshore-based operations.

Calibration of DEM models using bulk physical tests

Mical W. Johnstone, Dunlop Oil and Marine, Grimsby, UK; Jin Y. Ooi, University of

Edinburgh, Edinburgh, UK.

Introduction

Granular materials are present in many industries, ranging from agricultural to

pharmaceutical. The Discrete Element Method (DEM) is becoming an increasingly popular

numerical technique for simulating the behaviour of granular materials for a vast range of

scientific and industrial applications.

A close look into the DEM literature shows the need to validate what DEM can predict and to

develop calibration methodologies for DEM models to produce satisfactory predictions. This

paper describes a summary of a project to develop a methodology to calibrate DEM models

for granular material using bulk responses measured in physical tests. The methodology is a

systematic procedure aiming to optimise the DEM input parameters to predict accurate

numerical results. The procedure consists of four main steps which are described below.

Step 1: Define bulk parameters for calibration

Choosing the appropriate bulk measurements to calibrate the DEM models is of paramount

importance. Suitable measurements should produce sufficiently discriminating values from

variations in material properties so that the optimisation procedure is well posed to infer the

DEM model parameters from these measurements. The measurements should also be

highly repeatable and relatively easy to measure from simple laboratory tests. In this paper,

the number of responses was limited to two to simplify the description, however, the

proposed methodology can accommodate alternative or additional bulk parameters to be

added to the optimisation process to further improve the optimisation.

The two calibration devices and responses that were chosen to illustrate the methodology

Dynamic angle of repose in a rotating drum

Rotating drums have been extensively investigated in the past as they are an integral part to

many industrial processes [2]. This study attempts to use the rotating drum as one of the

bulk calibration devices. A diagram of the experimental setup can be seen in Figure 3 (a).

The drum has an internal diameter of 184mm and a thickness of 20mm. It was made from

acrylic, filled with test solid at 40% and was fixed at rotational speed of 7rpm.

Several flow regimes exist in a rotating drum and are commonly divided into 3 distinct

regimes; intermittent avalanching at low speed, steady inclination at intermediate speed and

S-shape avalanching at relatively high speed. Throughout the various regimes, the dynamic

s this produced a

easier to measure and less susceptible to fluctuation.

A t

was determined manually based on the mid-section of the surface profile. Simple

produced less accurate results as stray particles along the surface would significantly affect

the angle determination.

Confined compression apparatus

A confined compression test is used to investigate the mechanical response of a granular

bulk material subject to vertical compression in a confined cylinder. The confined

compression tester used is a modification of the Masroor et al. (1987) [3] apparatus. The

vertical load applied to the bulk solid is applied by a top platen driven by an INSTRON [4]

machine at a constant rate of 1mm/min. The INSTRON records the applied load and the

vertical displacement. A diagram of the experimental setup is shown in Figure 3 (b). In this

experiment, the bottom edge of the cylinder is free to move, allowing compression from both

the bottom platen as well as the top platen. The sample goes through a loading and

unloading cycle (to 65kPa) to observe both the initial compression and the ‗elastic‘ unloading

response. The speed of loading and unloading is kept constant throughout the experiment.

As the compression stress increases, the sample strain increases and vice versa. The void

ratio of a granular solid e is the ratio of the volume of void to the volume of solid. As the

sample is compressed, the volume of void decreases and therefore e decreases; the sample

is essentially being consolidated. The change in e can be evaluated using the dimensions of

the sample and the platen heights.

Figure 3

logarithmic scale. The advantage of this is that the unloading-reloading curve for a granular

solid is often linear in the semi-logarithmic plot. The unloading trend (triangles in Figure 3c)

ften used in geotechnical

engineering and is a good representation of the bulk unloading response. This means that

equation e = D – depends on the point of unloading and

dependant parameter dependent on the stress history.

(a) Rotating drum and the

dynamic angle of repose

(b) Confined compression

device (c) Bulk unloading stiffness

Figure 3 : Bulk calibration devices and parameters (all dimensions in mm)

Step 2: Create a numerical dataset

184 frSample

Bottom

load cell

Bottom

plate

Strain gauges

Top

plate

FT

FB

Attached to

INSTRON

140

380

250

145

0.570

0.574

0.578

0.582

0.586

10 100

Sam

ple

void

rat

io, e

Average platen compression

stress, sv [kPa]

Loading

Unloading

k

The second step in the methodology is to create a numerical dataset that describes how the

DEM parameters influence the bulk responses when simulating the laboratory devices

numerically. The optimisation procedure described in this paper focuses on determining

DEM interparticle model parameters. The other parameters such as the DEM model

parameters for particle-boundary interactions are largely dependent on specific application

scenarios and should be determined separately. Previous parametric studies have been

carried out by studying the influence of a single parameter on the system‘s response [5,6],

however, this may not adequately capture the combined effects of the model parameters.

Increasing the number of independent model variables rapidly increases the total number of

simulations required to generate the numerical dataset. With 4 DEM parameters and 3

values per variable (e.g. for the shear modulus 1E6, 1E7 and 1E9 are used), running every

possible combination will require 34=81 runs. Design of experiment (DOE) methods were

used to reduce the number of simulations required to create the dataset using partial three

level factorial design [7].

The non-spherical particles in this study were represented using 2 equal overlapping

spheres. More accurate shape representation is possible but will require more spheres and

increase computational time. Some studies have suggested that accurate representation of

particle shape may not be necessary to produce satisfactory predictions, at least for densely

packed granular media under a variety of loading conditions [5,8]. Key DEM implementation

information is given in Table 1. The rotating drum was modelled to the full experimental

apparatus scale. The confined compression cylinder was scaled to 60% of experimental

scale as a previous parametric study revealed that scaling the system down to 60% only

produced a small effect on the unloading stiffness parameter (<10%) that will be factored

into the results.

Table 1 DEM implementation of particles, optimised parameters in bold (PP: Particle to

Particle, PB: Particle to Boundary interactions)

Density of solids [kg/m3] 1000

Poisson's ratio 0.3

Shear modulus [Pa] 1E6/1E7/1E9

Contact model Hertz Mindlin (no

slip)

Coefficient of friction PP 0.1/0.3/0.6

PB 0.3

Coefficient of restitution PP 0.1/0.5/0.9

PB 0.5

Coefficient of rolling friction PP 0.0/0.04/0.1

PB 0.0

Sphere radius [mm] 2.5

Particle aspect ratio 1.20

Step 3: Measure calibration data

Black eyed beans were chosen as the test material in this paper. The main physical

properties were measured including the average particle dimensions: x=9.19, y=6.54 and

z=5.41mm (n=70); the average particle weight 0.2107g (n=70) and the average solid density

5kg/m3. The experimental calibration data was measured using the chosen

compression tester of 1.50E-03 (n=3, COV=2.3%) where the initial void ratio was e0=0.46.

Step 4: Parametric optimisation using calibration data

The final step in the methodology determines optimised parameters by calibrating the

numerical results with the measured data. There are two main parts to the parametric

optimisation. First, a model is created using analysis of variances (ANOVA) based on the

numerical dataset and second, the model is calibrated using the experimental data by

response profiling [9] to determine a set of optimised parameters. The statistical analysis

package chosen to create the model in this paper was Statistica [10]. Using the dataset, a 3

level factorial ANOVA model is created based on the gravitas of the DEM parameters on the

bulk responses. The optimised parameters are determined using the response desirability

profiling algorithm in Statistica which is based on the simplex method of function optimisation

[11].

Methodology verification and validation

A verification and validation was conducted to determine the robustness of the methodology.

The verification was conducted by simulating the two calibration experiments using the

optimised parameters and comparing them with the experimental results. The optimised

parameters produced an accurate dynami

due to the lack of plastic deformation in the contact model used to simulate the experimental

devices. The validation of the optimised parameters in a large scale system was conducted

by predicting the response of a shallow footing penetration on a bed of black eyed beans.

DEM simulations predicted accurate vertical stresses on the footing at penetration depth

between 5 and 25mm and an acceptable degree of accuracy for industrial application up to

30mm where the loading resistance was underestimated by 10%.

References

1. Chung Y.C. and Ooi J.Y. (2008) ―Influence of discrete element model parameters on bulk

behaviour of a granular solid under confined compression,‖ Particulate Science and

Technology, 26, 83-96.

2. A.T. McBride, I. Govender, M. Powell, and T. Cloete (2004), ―Contributions to the

experimental validation of the discrete element method applied to tumbling mills,‖

Engineering Computations, vol. 21, pp. 119-136.

3. S.A. Masroor, L.W. Zachary, and R.A. Lohnes (1987) ―A test apparatus for determining

elastic constants of bulk solids,‖ SEM Spring Conference on Experimental Mechanics,

Houston, TX, USA.

4. Instron (2009) www.instron.com. Norwood, MA, US.

5. Y.C. Chung (2006) ―Discrete element modelling and experimental validation of a granular

solid subject to different loading conditions,‖ University of Edinburgh.

6. J. Härtl (2008) ―A study of granular solids in silos with and without an insert,‖ The

University of Edinburgh.

7. G.E.P. Box and D.W. Behnken (1960) ―Some new three level designs for the study of

quantitative variables,‖ Technometrics, vol. 2, p. 455–475.

8. Härtl J. and Ooi J.Y. (2008) ―Experiments and simulations of direct shear tests: porosity,

contact friction and bulk friction‖ Granular Matter, 10, 263-271.

9. G. Derringer and R. Suich (1980) ―Simultaneous optimization of several response

variables,‖ Journal of quality technology, vol. 12, p. 214–219.

10. StatSoft (2009) ―Statistica‖.

11. R. OʼNeill (1971) ―Function minimization using a simplex procedure,‖ Journal of the

Royal Statistical Society, Series C (Applied Statistics), vol. 20, pp. 338-345.

Acknowledgements

The authors would like to thank DEM Solutions Ltd and the University of Edinburgh for their

support and discussion.

The Effect of Electrical Charge and Gravity on Angle of Repose Formation

Nima Gharib, Robin Briend, Nasim Kaveh-Moghaddam

Peter Radziszewski

McGill University

Nima Gharib

PhD Candidate, Neptec Rover Team (NRT), Delft, The Netherlands

The Neptec Rover Team (NRT), which includes some of the industry‘s leading

technology experts, was brought together to investigate, conceptually design, and

test lunar mobility systems for the Canadian Space Agency. This highly experienced

team has been working together to develop technology for the new Lunar Exploration

Light Rover (LELR). The McGill University team focuses on the definition,

development and validation of a compliant wheel; on the effect of operating one or

more of the recommended mobility systems while in the presence of the fine,

abrasive dust on the lunar surface; and on the identification of strategies to mitigate

dust infiltration and component wear.

The Department of Mechanical Engineering at McGill University has a long

history of excellence in research and teaching. For more than a century, we have

been committed to train the next generation of innovators, industrial leaders and

academics.

The effect of electrical charge and gravity on Angle of Repose formation

Nima Gharib (PhD Candidate), Robin Briend (Graduate MSc Student), Nasim Kaveh-

Moghaddam

(Graduate MSc student), Peter Radziszewski (Associate Professor)

Department of Mechanical Engineering, McGill University, 3480 University St.,

Montreal, Quebec, Canada H3A 2A7

Introduction

Lunar mobility studies require a precise knowledge of the geotechnical properties of the

lunar soil in order to design efficient traction systems. Since the last Apollo missions, the

immense progress of computers on one hand, and the development of the discrete element

method on the other hand, provide ways to test traction system prototypes with simulation.

Before simulating vehicle displacements on the soil strictly speaking and measuring

terramechanics properties of the wheel-soil interactions, one needs to know exactly how to

model a given granular soil and make sure that its mechanical properties, for example its

dynamical response to loads or stresses, are accurately rendered. The angle of repose of a

soil is one of most basic soil property and can be helpful in the calibration of the contact

model and interaction parameters in DEM software. This study focuses on this characteristic

and tries to summarize the effects of different parameters on it, among them particle shape,

electrostatic charge, and gravity.

Simulation Setup

The main parameters used to model the soil are given in Table 1. In order to give accurate

3D extension of [2] results, we used the same values as they did for the simulation

parameters.

Table 1. Definition of the parameters used in the simulations.

Parameter Definition value

R0 Mean spherical particle radius 50 μm

τ Aspect ratio of paired particles

η Size distribution ratio

N Number of particles 3000

ρ Particle density 3000 kg/m3

ν Particle Poisson‘s ratio 0.2

G Particle shear modulus 5×107 Pa

e Particle-particle restitution coefficient

0.5

μs Particle-particle static friction coefficient

μr Particle-particle roling friction coefficient

es 0.5

μss Particle-steel static friction coefficient

0.5

μrs Particle-steel rolling friction coefficient

0.2

To calibrate the model first a simple model was created in EDEM. It consists of a horizontal

steel plate on which a 1.5 mm diameter steel tube rests vertically. 3,000 particles were

generated inside the tube at the beginning of the simulation. The tube is then lifted upward at

the speed of 5 mm/s. After the tube walls lose its contact with the particle pile and the

particles have reached a static state, the simulation was then run for another half a second

in order to make sure that the pile has reached its stationary state. Finally the results were

compared to experimental data obtained using same tube diameter filled with Ottawa sand

(Fig. 1).

After calibrating the software and finding the proper parameters for the soil, the same

approach were carried out to investigate the effect of gravity, particles‘ electric charge, and

particles‘ shape on the formation of Angle of Repose (AOR).

Single spherical particles and the mixture of paired particle created by overlapping two

particles with either same radius or different radius were used in the simulations (Fig. 2).

Each case was studied under moon and earth gravity. Also the effect of electrical charge on

AOR was investigated by having uniform charge distribution for the particle‘s stack.

Results

The angle of repose were calculated at the end of each simulation using a MATLAB code

uses a least-squares fit of the surface particles to define the slopes of the two sides of the

pile at twelve different planes. The angle of repose for the pile is the average angle from the

total 24 slopes. As shown in Fig. 3 the angle of repose of single particles is much smaller

than that of paired particles. Increasing electrostatic charges decreases the angle of repose.

However a weak electrostatic charge has almost no effect on it. While when the electrostatic

charge increases a certain point the angle of repose drops sharply. The gravity in the other

hand doesn‘t have much effect on angle of repose.

It is worth to mention the above-mentioned results are from the cases where friction between

the wall of the tube and the particles and the particles themselves was 0.5. Changing

coefficient of friction might change the results.

Fig. 1 Comparison between experimental and discrete element modeling of Angle of Repose

Fig2. Particle shapes used during simulation

Fig 3. Angle of repose vs. electric charge for different particle shape at earth and moon gravity

Discussion

In order to decrease the simulation time and have consistency between the results in was

decided to import the position of the grains from an ―External Factory‖. That would

dramatically reduce the simulation time specially when the particles were assigned with

electrical charge. The problem that one would face in EDEM it is not possible to import

charged particles. To overcome this problem first the position of whole particles in the stack

were imported without charge except a few particles which were created by a built-in factory

containing the charge of whole stack. The Hertz-Mindlin contact model was modified to

transfer the electrical charge from charged particle to their neighbors which is in contact with.

In This way after a few run times, the whole stack gets charged uniformly and then the

cylinder starts moving up.

To better model the situation on the moon it is recommended to bring in the tribocharging

feature during the simulation. In this way the particles‘ charge will vary when they contact

each other and also the inner surface of the tube. And of course the charge distribution

wouldn‘t be uniform as it was in this study.

Acknowledgement

The authors would like to thank Neptec and CSA as well as NSERC CRD program for the

financial support of this project and also DEM Solutions. Ltd., Edinburgh, Scotland, UK, for

their help and their advices

References

[1] DEM Solutions, Ltd. (2010), ―EDEM 2.3 User Guide,‖ Copyright © 2010, Edinburgh,

Scotland, UK

[2] S. Ji and H. Shen. Two-dimensional simulation of the angle of repose for a particle

system with electrostatic charge under lunar and earth gravity. Journal of Aerospace

Engineering, 22:10_14, 2009.

[3] R.D. Mindlin. Compliance of elastic bodies in contact. Journal of Applied Mechanics,

16:259_268, 1949.

[4] T. Tanaka Y. Tsuji and T. Ishida. Lagrangian numerical simulation of plug flow of

cohesionless particles in a horizontal pipe. Powder Technology, 71:239-250, 1992.

Session 2: Mining & Mineral Processing

EDEM Material Model Calibration Delivering Value for

Transfer Tower Simulation

Carl Stensby Anglo American

Michael Thorenson, ESTEQ Engineering

David Curry, DEM Solutions Carl Stensby

Material Handling Engineer, Anglo Technical Services Johannesburg, South Africa

Anglo American is one of the world‘s largest mining companies, operating in Africa,

Europe, South and North America, Australia and Asia. We mine high-quality assets

such as platinum group metals and diamonds, and other natural resources, including

copper, iron ore, metallurgical coal, nickel and thermal coal.

David Curry

Senior Consulting Engineer

Edinburgh, Scotland


simulation know-how to address the needs of companies who handle and process

bulk materials ranging from coal, ores, and soil to pellets, tablets and powders.

Michael Thorenson

ESTEQ Engineering is dedicated to helping companies develop products more

efficiently by enabling innovation through simulation and testing technology. This is

made possible through two separate and yet highly integrated disciplines, i.e.

Simulation and Testing.

Getting Started with Discrete Element Modelling for Bulk Solids Transfer

ED Birch

DRA Mineral Projects

ED Birch

Manager, Bulk Materials Handling Department Johannesburg, South Africa

In the Bulk Materials Handling Department Birch, who heads the

department, is involved in the design of bulk materials handling plants for coal

and hard rock applications, specialising in the design of conveyors and bulk

solids flow and storage facilities. He is also responsible for management of

the department and development of engineers in the materials handling field

through mentoring and lecturing in the design of conveyors and bulk materials

handling systems.

The DRA Group is a multi-disciplinary, multi-national organization that

specializes in project management in mining, infrastructure and mineral

process plant design and construction. At DRA Mineral Projects oour

business is mining, infrastructure, mineral processing projects and contact

operations. As a total solutions provider, we have the resources to engineer

mining projects to exact client requirements, delivered on time and within

budget anywhere in the world.

EDEM-Based Design Investigations

of Bulk Materials Handling Equipment

Brian Moore

Hatch

Brian Moore

Senior Consultant. Materials Handling Wollongong, NSW, Australia

Moore has more than 30 years experience in bulk materials handling. His expertise

includes system design and life extension of materials handling systems for heavy

industry; mass flow bin design and stockpile reclaim systems and belt conveyor and

transfer chute design.

Hatch is a multi-discipline company that provides consulting, design engineering,

technology, environmental services, operational services and project and

construction management to the mining, metallurgy, energy and infrastructure

sectors from 65 offices around the world. Hatch has more than 8,000 personnel

worldwide engaged in delivering innovative engineering, carefully defined

procurement strategies, comprehensive safety programs, advanced and reliable cost

controls and astute management of construction, commissioning and start-up.

Plow System in Underground Mining – A Benchmark Approach

Martin Geissler and Tatjana Aust

Bucyrus

Dave Curry, DEM Solutions

Martin Geissler

Design Engineer, Engineering Plow Systems, Longwall Product Group Grimsby, United Kingdom

Engineering Plow Systems from Bucyrus, with features offered only by Bucyrus,

the inventor of the plow, our plow systems offer our customers future-oriented

solutions for international hard-coal mining.

Bucyrus is the world leader in the design and manufacture of high productivity

mining equipment for surface & underground mining and the global market leader

and supplier of complete longwall systems. Our equipment and systems are meeting

the demands of underground mining under the most stringent conditions around the

globe. Adapted to the mining challenges faced by our customers today, Bucyrus

customized systems range from hydraulic roof supports, automated plow systems,

shearers, face conveyors and drives to automation and roof support carriers.

David Curry

Senior Consulting Engineer

Edinburgh, Scotland




Discrete Element Modelling for the Simulation of

Particle Distribution to a Sensor-Based Sorter

Dr. Robert Fitzpatrick, Richard D. Pascoe, Hylke J. Glass

Camborne School of Mines, University of Exeter

Robert Fitzpatrick

Experimental Officer for Mineral Processing Cornwall, United Kingdom

As Experimental Officer for Mineral Processing, Fitzpatrick is responsible for

independent research into the field of Minerals Processing, teaching students and

undertaking contract research for external bodies. Current research areas include

investigations into the relationship between throughput and separation efficiency for

Sensor-Based Sorters, investigations into the use of unsupervised clustering and

related techniques as alternate methods of training and monitoring sensor-based

sorters to improve their flexibility and adaptability to minerals applications, and

Investigations into a multi-sensor approach to sensor based sorting, combining

optical, inductive and near infrared (NIR) sensor data to improve separation

efficiency.

Camborne School of Mines (CSM) at the University of Exeter is one of the

world‘s most famous mining schools. Founded in 1888, CSM now has a unique

combination of scientific and engineering expertise in renewable energy, geology,

mining and minerals processing and applies this to world-leading research and

teaching. The research undertaken in the Minerals Processing Department is an

important aspect of this work.

Discrete Element Modelling for the Simulation of Particle Distribution to a

Sensor-Based Sorter

Robert S. Fitzpatrick, Camborne School of Mines, Uni. Of Exeter, Penryn, UK

Richard D. Pascoe, Camborne School of Mines, Uni. Of Exeter, Penryn, UK

Hylke J. Glass, Camborne School of Mines, Uni. Of Exeter, Penryn, UK

Introduction

The hand sorting of objects into groups of similar properties may be considered as one of

the earliest forms of technology. Sensor-based sorting is a technique which seeks to

replicate or improve on the hand sorting process by replacing the human eye and hand with

machine vision and automated ejectors. Specifically, sensor based sorting is an automated

separation technique which exploits measurable differences in the physical properties of

particles, either natural or induced, to produce a distinct response to an applied force

(1Manouchehri, 2003; 2Walsh, 1989). A schematic of a typical sensor-based sorter is shown

in figure 1.

Figure 1: Schematic of CommoDas sensor-based sorter

The sorter can be considered as four interlinked unit operations, shown diagrammatically in

figure 2.

Feed Preparation

Particle Examination

Data Analysis

Ejection System

Output Streams

Input Feed

Figure 2: Unit operations for a sensor-based sorter

The first operation is the preparation of the feed material which can consist of several stages

including, sizing, washing and/or wetting followed by feed rate control, particle alignment,

acceleration and stabilisation (1Manouchehri, 2003). This stage is essential to maximise the

likelihood of collecting sensor data which is representative of the true physical properties of

particles and to ensure that particles are examined singularly. The next operation is the

actual collection of data on individual particles. This is undertaken using one or more

sensors operated in series or parallel. The third operation is that of data analysis, where the

sensor data is utilised by a CPU to classify particles according to pre-determined rules and

thresholds. In the last operation the particles are physically separated into two or more

output streams based on the classification decision. A number of methods have been

employed to this end including air and water jets and mechanical paddles.

The focus of this paper is on the use of sensor-based sorting in the mining and minerals

industry. The primary aim of a mining operation is to completely separate valuable, sellable

minerals from waste rock as cheaply as possible. As such a sensor-based sorting process is

evaluated based on the degree to which it can separate mineral types, i.e. separation

efficiency, and its cost.

For sensor-based sorting the separation efficiency is a function of a machine‘s ability to:

generate sensor data which is representative of physical properties; correctly classify

particles based on this sensor data and then to accurately and reliably actualise the

separation of particles. The cost of sorting is minimised by operating at a high throughput.

Unfortunately, each of the factors which determine separation efficiency is adversely

affected by an increase in throughput due to the increased probability of particles being

examined in close proximity (see figure 3).

Particle Proximity

Particle Flow Direction

Low Throughput High Throughput

Decreasing Average

Figure 3: Relationship between throughput and particle proximity

The decrease in separation efficiency is a result of the increased likelihood of co-deflection

for particles in close proximity during physical separation and the possibility of the

agglomeration of particles and/or the masking of physical characteristics. As a mining

company must have a thorough knowledge of the costs and separation efficiency of a sorter

it must be aware of the relationship between throughput and particle proximity. At present, to

achieve this test work must be physically undertaken to determine the relationship. This is

both time consuming and expensive due to the large mass of material required. For this

reason, it is desirable to be able to predict the relationship for a given sorting process.

Approach

To move towards a solution to this problem, it was decided to investigate the use of discrete

element modelling (DEM) to predict the relationship between throughput and separation

efficiency of a sensor-based sorter by replicating physical experimentation undertaken on a

CommoDas optical sorter and comparing the results. The aim was to compare the proximity

of particles at the end of the feed conveyor for both the simulation and actual tests. These

proximities can be directly related to the expected efficiency of the machine. To test the

models, they would then be used to predict the particle proximities and therefore efficiencies

at other throughputs. These would again be compared to actual test data.

To undertake the modelling, Autodesk® AutoCAD 2008 was used to create a representation

of the feed mechanism for the optical sorter which was then imported into the EDEM® 2.3

particle simulation software, provided by DEM Solutions. Ltd., Edinburgh, Scotland, UK.

Figure 4 on the next page contains an image of the CommoDas optical sorter and of the

CAD representation. It includes:

The feed hopper and vibrating feeder used to control the throughput to the sorter

The chute used to stabilise and disperse the particles

The conveyor used to accelerate and separate the particles

For the DEM simulations, particles were placed in the ‗feed hopper‘ and by the careful

manipulation of sinusoidal translation the throughputs were calibrated to match those used

during physical testing by matching the rate of particles arriving at the end of the conveyor.

The behaviour of particles at these set throughputs was then investigated and their velocity

and positioning tracked. This information was utilised to determine the particle proximity

using a methodology developed by the research team.

Figure 4: Simulation of CommoDas sorter feed mechanism

Particles were created to simulate three size and two shape fractions used during physical

testing. The material models for these and the feed mechanism were calibrated by

comparing the behaviour of the particles with video footage taken of the physical testing,

focussing on the change in the distribution of particles along the width of the conveyor over

time.

Results

The results of the physical separation of particles using the CommoDas optical sorter are

currently incomplete so comparisons with the simulations have not, as yet, been undertaken.

However, initial test work suggests that the DEM simulations provide an accurate

representation of the feed mechanism. For example, table 1 summarises the distribution of

particles along the width of the feed conveyor for both simulated and actual tests.

Feed Hopper

Chute Conveyor

Table 1: Comparison of horizontal distribution of particles

Belt Position Frequency

mm % Actual Simulated

+0-68.75mm 0-12.5 2% 2%

+68.75-137.5mm 12.5-25 5% 4%

+137.5-206.2mm 25-37.5 12% 11%

+206.2-275mm 37.5-50 25% 21%

+275-343.7mm 50-62.5 27% 27%

+343.7-412.5mm 62.5-75 18% 18%

+412.5-481.2mm 75-87.5 8% 10%

+481.2-550mm 87.5-100 4% 7%

The data shows a good correlation between the simulated and actual horizontal distribution

of particles on the feed conveyor. This suggests that the simulations are an accurate

representation of the physical properties and interactions of the feed mechanism and

particles.

Using particle data obtained from the DEM simulations it was possible to create a distribution

of particle proximity. When physical testing is complete this will be compared with data

collected from the optical sensor. An example proximity distribution is shown in figure 5.

0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

1 10 100 1000 10000

Cu

mu

lati

ve F

req

uen

cy

Distance to Nearest Particle (mm)

Cumulative Distribution of Gaps between Particles for Flaky +15-20mm Particles

Figure 5: Particle proximity distribution for DEM simulation

Discussion

The ability to quickly and accurately predict the relationship between throughput and

separation efficiency would drastically reduce the costs involved in implementing a sensor-

based sorter within the mining and minerals industry. The experimentation undertaken is a

step towards this goal; by using DEM to simulate the feed mechanism of a sorter, the affect

of throughput on separation efficiency can be estimated as well as a number of variables

including: particle size and sorter dimensions.

The present work uses particle proximity to estimate sorter efficiency. On completion of this

work, the next stage in research will be to improve on this work by examining in detail the

affect of throughput on the physical ejection of particles. It is hoped that the use EDEM CFD

Coupling for FLUENT® can be used to simulate the sir ejection manifold used by the

CommoDas sorter.

References

1. Manouchehri, H.R., 2003. Sorting in Mining, Mineral Processing and Waste Utilization:

(History, Innovations, Applications, Possibilities, Limitations, and Future) Swedish Mineral

Processing Research Organisation, Stockholm.

2. Walsh, D.E., 1989. What Sort of Ore Sorter?, Alaska Science Forum. Geophysical

Institute, Fairbanks, Alaska.

DEM-PBM Coupling to Predict Breakage in Comminution Processes

Rodrigo Magalhães de Carvalho; Luís Marcelo Tavares,

Universidade Federal do Rio de Janeiro

Rodrigo M. de Carvalho

Research Assistant, Laboratory of Mineral Processing, Department of Metallurgical and Materials Engineering Rio de Janeiro, Brazil

The Laboratory of Mineral Processing is well reputed worldwide for its research in

comminution, ranging from the understanding of fundamentals of breakage to the

development and application of modeling and simulation tools to improving industrial

applications involving size reduction.

Universidade Federal do Rio de Janeiro (UFRJ) is the second largest university in

Brazil, and houses the graduate school of engineering (COPPE). Part of COPPE, the

Department of Metallurgical and Materials Engineering is recognized in Brazil as one

of the leaders in research and development in the various fields from mineral

processing to materials science.

DEM-PBM Coupling to predict breakage in comminution processes

Rodrigo Magalhães de Carvalho; Luís Marcelo Tavares

Department of Metallurgical and Materials Engineering, Universidade Federal do Rio de

Janeiro

Introduction

The discrete element method (DEM) has been used actively for the last 20 years or so to

describe what happens inside grinding mills [1] and, more recently, crushers [2]. Whereas

this technique found almost immediate application in aiding the operator in identifying and

preventing the mill speed and filling that would lead to ultraprojection of grinding media in

semi-autogenous or ball mills for a given mill liner configuration, the application of the

technique as a quantitative tool to predict media and liner wear and size reduction has not

yet reached the maturity required for industrial application [3].

One of the limitations identified in the past has been overcome by the rapid evolution of

computational power, making it possible to simulate, in 3D, the motion of particles – both

grinding media and balls – inside industrial-scale grinding mills and crushers. In the case of

predicting size reduction in comminution machines, the greater challenge remains in

coupling the information that DEM provides, regarding the mechanical environment and on

the flow of solids through the vessel, to material breakage properties that lead to the main

outcome of a comminution process, which is the size distribution of the product. In order to

tackle this problem, three different approaches can be used, which are described as follows.

In the first case, DEM simulates not only the mechanical environment, but also the entire

breakage processes. Particles are built from an aggregate of smaller particles united by a

cohesion force, and breakage occurs when the aggregate receives enough energy to break

the cohesion force among these smaller particles [4,5]. In this case, the outcome from each

breakage event, that is, the breakage of an aggregate, has to be simulated before simulation

starts, which makes it extremely computationally intensive. The second type of models is the

one in which, although particles are also present in the DEM simulations, the outcome from

each breakage event is not calculated by the method, being rather provided by an empirical

model calibrated using particle breakage tests. After each breakage event the broken

particle is replaced by an aggregate of smaller particles, the breakage product. This

approach, called ―fast breakage model‖ has been applied successfully to prediction of cone

crushing [2]. Although more computationally efficient than the first approach, both groups of

models are very demanding of computer power, since new particles are almost continuously

being created in the system.

The third approach, which is the one proposed by the authors, is to use DEM solely to

simulate motion of media and to track the collisions among particles, leaving the population

balance model the task of coupling the collision energy information to the models describing

particle response to each stressing event in order to calculate the overall breakage product.

This approach is particularly well suited to media mills, such as ball, stirred and semi-

autogenous mills. By using this approach the computational power required to run DEM

simulation is significantly smaller than the one required by the first two types of models. This

is illustrated in Figure 1, which demonstrates that the differences in computing power

required by the different approaches become particularly significant when mills of large

diameters are simulated.

Figure 1: Comparison of computational power required in different approaches in

comminution simulation in tumbling mills

Approach

The model initially relies on detailed ore breakage properties which can be obtained by a

number of laboratory tests, describing the ore response to the different breakage

mechanisms (body and surface breakage), the breakage probability and the response to

unsuccessfully breakage events (particle damage) [6].

Once the material (ore) breakage properties are all characterized the mechanistic model

developed by the Authors can be used to simulate many different comminution processes, if

appropriate description of how the energy is transferred to particles are given. This

information can be obtained using DEM simulations.

Figure 2 shows the model framework, having on the left side what model inputs are

necessary to get predictions. As such, PBM requires information related to ore properties,

including breakage properties and feed size distribution. DEM simulations require contact

parameters from ore and the equipment and also equipment design and operational

parameters. Then DEM gives PBM information of the impact energy distribution. The PBM

calculations, which are the core of this model approach, then give predictions of product

particle size distribution, fracture energies (particle strength) and flowrates. This approach

can also be coupled with CFD simulations in order to get information of transport rates and

they can also affect DEM and PBM calculations.

One particular comminution machine of interest is the ball mill. In this device, the mechanical

environment is dominated by the grinding media (typically steel balls), so that nearly all

breakage results from collisions between balls. Particle breakage occurs when ball hits a

particle bed sat upon another steel ball. If particles within this bed have fracture energy

bellow the energy given by ball collision, it may fracture. For this device, no ore particles are

modeled inside DEM and the ore phase is considered as a continuous phase. This requires

an appropriate characterization of DEM contact parameters as the impact between two steel

balls is assumed to involve a virtual bed composed of ore particles. As particle size

distribution has almost no effect on impact energy spectrum given by DEM simulations it is

called one-way coupling, as is also illustrated in Figure 2 where arrows represents the

information flow between model blocks. Details of the model applied to batch ball mills may

be found elsewhere [7].

In contrast to that, two-way coupling must be used whenever particles contained in the ore

also act as grinding media. This is the case of the semi-autogenous mill, in which coarser

ore particles also act as grinding media. These particles have to be considered in order to

get appropriated description of the impact energy spectrum. However, since they change

their size during the process, DEM simulations have to be updated for every significantly

changes in the mill hold-up in an iterative process with PBM simulations. This is called two-

way coupling and is illustrated in Figure 2 by the dashed arrows.

DEM

Breakage

properties

Contact

parameters

Size

distribution

Design

Operational

Model Inputs Model Outputs

Particles

(ore)

Equipment

Fracture

energies

Particle Size

Distribution

Impact energy

distribution

Particle Size

Distribution

CFD

Material flow

PBM

Flow rates

Particle Size

Distribution

Figure 2: Model framework showing the coupling between PBM and DEM and also CFD

Results

In order to validate the model, batch grinding experiments were carried out using a

laboratory ball mill (30x30cm), rotating at 54 rpm. The mill is loaded with 467 steel balls of 25

mm diameter. A certain amount of ore particles were fed to the mill in order to get 100% of

voids between balls filled. DEM simulations of the ball load movement were made using

EDEM® 2.2 particle simulation software provided by DEM Solutions. Ltd., Edinburgh,

Scotland, UK. The mill is shown on Figure 3 as well as its DEM simulation snapshot.

Figure 3: Real lab ball mill (left) and a snapshot of it running on EDEM (right).

Experimental particle size distributions after each grinding time were compared to that

predicted by the mechanistic model, Figure 4 shows the results for two different ores at

different initial conditions in terms of particle size distribution. An estimate of the time

required in the DEM simulations is about 15 minutes, whereas solution of the PBM equations

for the longer grinding times required between 2 and 4 hours of computation using a Xeon

Quadcore X3370 processor.

0.01 0.1 1 10

Particle size (mm)

0.1

1

10

100

Cum

ula

tive p

ass

ing (

%)

Initial

1 min.

4 min.

8 min.

15 min.

Copper Ore -1.68 mm

0.01 0.1 1 10

Particle size (mm)

1

10

100

Cum

ula

tive p

assin

g (

%)

Initial

1 min.

5 min.

10 min.

12 min.

Granulite -4.75 mm

(b)

Figure 4: Experimental (dots) versus simulated (lines) batch grinding results for copper ore

(left) and granulite (right)

Discussion

The simulation results showed very good agreement to the experimental data, which

indicates that the approach proposed is a very powerful tool to predict grinding results. The

PBM calculations can benefit from DEM simulations that can predict how the energy is

transferred to ore particles as a function of design and operational equipment parameters,

without demanding high computational effort as is the case of other approaches that use

DEM.

References

1. Mishra, B.K. and Rajamani, R.K. (1990), ―Motion analysis in tumbling mills‖, KONA

Powder and Particle, No. 8, pp. 92-98.

2. Lichter, J., Lim K., Potapov, A., Kaja, D (2009), ―New developments in cone crusher

performance optimization‖, Minerals Engineering, Vol. 22, pp. 613-617.

3. Powell, M.S. and Morrison, R.D. (2007), ―The future of comminution modelling‖,

International Journal of Mineral Processing, October, Vol. 84, , pp. 228-239.

4. Herbst, J.A. and Potapov, A.V. (2004), ―Making a discrete grain breakage model

practical for comminution equipment performance simulation‖, Powder Technology, Vol. 143-

144, pp. 144-150.

5. Herbst, J.A (2004), ―A microscale look at tumbling mill scale-up using high fidelity

simulation‖, International Journal of Mineral Processing, December, Vol. 74, pp. S299-S306.

6. Tavares, L. M. (2007), ―Breakage of single particles: quasi-static‖, in ―Handbook of

Powder Technology‖, Vol. 12, pp. 3-68.

7. Tavares, L. M. and Carvalho, R.M. (2009), ―Modeling breakage rates of coarse

particles in ball mills‖, Minerals Engineering, Vol. 22, pp. 650-659.

Acknowledgements

The authors thank AMIRA, Vale, CNPq and CAPES for sponsoring the investigation. The

authors would also like to thank DEM Solutions for providing the EDEM software through its

academic program.

Calibration of Angle of Repose of Glass Beads

Stef Lommen, Mahbubur Rahma, Dingena L. Schott,

Gabriël Lodewijks

Delft University of Technology

Dingena L. Schott

Assistant Professor, Section of Transport Engineering and Logistics, Delft, The Netherlands

Within the Section of Transport Engineering and Logistics Schott focuses on the

field of Dry Bulk Transport and Storage, including the logistics and environmental

impact involved. Her team‘s current DEM work is on equipment design and

calibration and validation with the use of EDEM.

The Delft University of Technology Department of Marine and Transport

Technology focuses on the development, design, building, and operation of marine,

dredging and transport systems and their equipment. This requires the further

development of the knowledge of the dynamics and the physical processes involved

in transport, dredging and marine systems, the logistics of the systems and the

interaction between the equipment and control systems.

Calibration of Angle of Repose of Glass Beads

Stef Lommen, Mahbubur Rahman, Dingena L. Schott, Gabriël Lodewijks

Delft University of Technology, Department of Marine and Transport Technology,

Section of Transport Engineering and Logistics, Mekelweg 2, 2628 CD Delft, The

Netherlands

Abstract

This research investigates the possibilities of acquiring reliable parameters through

validation by means of experiments. In addition the focus is on validation and calibration of

simulations. Glass beads were used as test material. The advantage of glass beads is that a

spherical particle model is close to the real shape.

First, analyzing the sensitivity of the parameters used was done. Parameters analyzed are

the shear modulus, coefficient of restitution, static and rolling friction coefficients, Poisson‘s

ratio and the particle density. Numerous simulations have been performed using different

combinations of parameters to study the influence of each parameter. Based on this

sensitivity analysis three sets of parameters are selected and used in validation simulations

using particles of different size.

Physical experiments using glass beads are used to verify the results of the validation

simulations. The angle of repose of glass beads is the bulk behavior characteristic measured

in the experiments and simulations of this study.

Discrete Element Modeling of Fracture Toughness

of Rocks Using Semi-circular Bending Method

Houshin Nejati and Faramarz Hassani

Department of Mining and Materials Engineering

Nima Gharib and Peter Radziszewski

Department of Mechanical Engineering

McGill University

Nima Gharib














academics.

Discrete Element Modeling of Fracture toughness of Rocks Using

Semi-circular Bending Method

Houshin Nejati (PhD candidate) 1, Nima Gharib (PhD candidate)2, Peter Radziszewski

(Associate Professor)2, Faramarz Hassani (Full professor) 1

1 Department of Mining and Materials Engineering, McGill University, 3450 University,

Montreal, QC, Canada H3A 2A7,

2 Department of Mechanical Engineering, McGill University, 3480 University St., Montreal,

Quebec, Canada, H3A 2A7

Introduction

Considering the relation between rigid bodies, Cundall and Strack (1979) proposed the

Discrete Element Method (DEM), for modeling of geomaterials such as rocks and soils,

whose micromechanical behaviour are discontinuous. In this model, soils and rocks are

represented as spherical either rigid or deformable particles. Each particle is modeled by its

trajectory along the system due to the contact forces and external forces acting on each

particle, such as gravity.

DEM models provides better insight of engineering problems in these fields; for instance, the

effect of micro-structural rock characteristic such as existing micro-cracks on the overall

physical behaviour of rock sample (Fu, 2005). Moreover, some emergent properties of

macroscopic system automatically come from discrete model such as transition from brittle

to ductile behaviour, and nonlinear mechanism in deformation (Cundall, 2005). Fracture

toughness is one of the material properties, which defines the resistance of rocks to

deformation and crack propagation when rocks already fractured, hence; fracture toughness

is one of the essential parameters for designing mining structures and tunnelling in cracked

bodies. Moreover, there is an approximate linear relationship between fracture toughness

and other physical properties of rock such as hardness index, Uniaxial Compressive

Strength, and Young‘s modulus in many rocks (Whittaker et al, 1992) and (Nasseri, 2005). In

addition, previous studies show the significant relationship between rock micro-structural

characteristics on fracture toughness, initiation and propagation cracks in rocks. (Nasseri et

al, 2005).

Three basic crack propagation modes in fracture process can be seen: Mode I (tension,

opening), mode II (shear, sliding), and mode III (tearing). As shown in Figure 1, a Semi-

Circular Bending (SCB) specimen is a semi-disc of radius R, placed on two roller support

with 2S span. A prefabricated crack with length a, which makes 90 degree angle with

respect to horizontal, is created in centre of the disc. Herein, EDEM (a DEM based software

developed by DEM Solutions. Ltd.) was employed to model the mode I fracture toughness,

crack propagation and the Crack Tip Opening Displacement (CTOD) of the (SCB) basalt

specimen.

In order to evaluate the accuracy and validity of DEM simulations, its results compared with

expected results from fracture mechanics and continuum modeling using ANSYS. For FEM

based model following assumptions are made: 1-Linear Elastic Fracture Mechanics: LEFM

uses derived elasticity solutions to determine the stress intensity factor KI at a crack tip. 2-

Plane Strain Problem 3- Cracked tip region is meshed using eight- node quadrilateral

element (Barsoum, 1977), also KCALC command is used to calculate KI.

Figure 1: Semi-Circular Bending (SCB) fracture toughness Specimen

Approach

Discrete Element Method is used to simulate a pre-cracked SCB Basalt specimen under a

compression dynamic load in order to study the fracture toughness, fracture propagation and

CTOD. Simulation has been repeated for S/R equal to 0.4, and 0.5. The material property

and main parameters for DEM modeling are given in Table 1. Bonds between the particles

break when the interaction force between particles exceeds the tensile and compression

strength. Broken bonds are representing micro-cracks. ―The location of the crack is assumed

to be the contact point between two discrete elements, and the crack orientation is assumed

to be perpendicular to the line joining the particle centers‖ (Hazzard and Young, 2004). And

macro-cracks will occur when some cracks join together. The fracture toughness mode I for

cubically packed can be calculated using following formula (Doneze et al, 2009):

Ic t

t

K R

Rt

s

s f

Where ts is the tensile strength of the model, R is particle radius and nf is tensile strength of

contact-bond. The initial tensile bonds for this simulation determined by using the calculated

value of Kic from ANSYS and the value of Basalt tensile strength measured in Brazilian

Tensile test from above relationships.

Table 1. Material properties and DEM parameters

SCB Radius 25 mm

Modulus of elasticity 41930 MPa

Poisson‘s ratio 0.16

Density 3000 kg/m3

Maximum Compressive Strength (USC

result)

243.60 MPa

Crack angle with respect to vertical

direction

0 degree

Crack width 0.5 mm

Crack length 15 mm

a/R 0.6

Particle size 0.5 mm

Number of particles 14325

Results

CTOD of EDEM simulations are very similar to the one obtained ANSYS as shown in Figure

2. Approximately a 6.1 % is differential with ANSYS outcome.

In Figure3, the comparison of CTOD for the S/R 0.5 and 0.7 are presented. One of the

advantages of DEM simulations over FEM simulation is that crack propagation (broken

bonds) automatically comes from discrete model as shown Figure 3. It is worth to mention

that the micro-cracks creating by this model match with samples that are broken with tension

experimental tests.

Figure 4 demonstrates the stress intensity distribution of basalt sample with S/R equal to 0.5

modeled in ANSYS which can imply crack propagation and eventually breakage from the tip

of the crack. Ongoing fracture toughness experiments at McGgill University will bring light to

this discrepancy.

Discussion

At first, inbuilt factory was used to generate particles and afterward a plate was defined to

compress the particles, the process was extremely lengthy and the generated bonds

between particles were very poor so that it leads to breaking many bonds even before

applying the compressive load. In order to reduce the pre-processing time, using MATLAB,

the coordinates of the particle centres was generated then imported to EDEM by an external

factory

Authors believe that the using the DEM features such as possibility to simulate roughness of

rock simply with modifying the particle size as well as possibility to model different minerals

with different physical properties will lead to more advance model to study the crack growth

trajectories.

Figure 2. Comparison of the Crack Tip Opening Displacement of DEM and FEM modeling of

Mode I fracture toughness

Fig. 3: First broken bonds S/R values of 0.7, and 0.5

Fig. 4 ANSYS Stress Intensity distribution for S/R 0.5

Acknowledgement

The authors would like to thank DEM Solutions. Ltd., Edinburgh, Scotland, UK, for their help

and their advices

References

CUNDALL, PA., 2002 .Discontinuous future for numerical modeling in soil and rock. In

proceeding of third International Conference on Discrete Element Method- Geotechnical

Special publication, No. 117.

CUNDALL PA, and Strack ODL., 1979 .A discrete numerical model for granular assemblies,

Geotechnique, 29, pp. 47-65.

CUNDALL PA., 2005 .Discontinuous Future for Numerical Modeling in Soil and Rock. In

proceeding of third International Conference on Discrete Element Method- Geotechnical

Special publication, No. 117.

AYATOLLAHI, M.R., ALIHA, MRM., and HASSANI, MM., 2006 .Mixed mode brittle fracture

in PMMA—an experimental study using SCB specimens. Materials Science and Engineering

A 417, pp.348–356.

BARSOUM, R., 1977 .Triangular quarter-point elements as elastic and perfectly-plastic crack

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Virtual Soil Calibration for Wheel-Soil Interaction Simulations Using the Discrete Element Method

Robin Briend, Peter Radziszewski, Damiano Pasini

McGill University

Nima Gharib














academics.

VIRTUAL SOIL CALIBRATION FOR WHEEL-SOIL INTERACTION SIMULATIONS

USING THE DISCRETE ELEMENT METHOD

Robin Briend (Graduate MSc Student),, ), Peter Radziszewski (Associate Professor),

Damiano Pasini (Associate Professor)



Introduction

Lunar mobility studies require a precise knowledge of the geotechnical properties of the

lunar soil when it comes to design adapted and efficient traction systems. Since the Apollo

missions, the remarkable progress of computers allows direct testing of new design

prototypes‘ performances through soil-structure interaction simulations based on the discrete

element method (DEM).

Before simulating traction system displacements on the soil, the virtual soil parameters need

to be calibrated. This study presents a systematic method for the calibration of a granular

soil through four steps: (1) measure of some of the real material properties through a few

experiments, (2) determination of the design variables defining the virtual soil, (3)

construction of surrogate models for the virtual material properties as a function of the

design variables via simulated experiments, and (4) optimization of the design variables

values to fit the virtual soil properties to the real ones.

Two different experiments, the direct shear test and the angle of repose measure, are used

to determine the following material properties: cohesion, internal angle of friction, and angle

of repose. Optimum DEM parameters are computed to characterize two types of soil: silica

sand, based on experimental direct shear test and angle of

repose measures, and lunar regolith, based on data from the

literature.

Approach

To characterize the real soil that needed to be modeled, it was

decided to choose experiments that would give soil properties

relevant to our study – the displacement of a traction system on

a deformable soil – while being simple to model with our DEM

software – to build the virtual soil‘s response surface.

The direct shear test meets these criteria as its experimental setup consists of only three

parts and as it allows both determination of the soil‘s cohesion c and the internal angle of

friction φ, which are crucial in terramechanics. Indeed, the Mohr-Coulomb law shows that the

maximum shear stress τmax of a soil depends on c and φ:

tanmax pc (1)

with p being the pressure in the soil [1]. The maximum shear

stress is important as it determines the maximum thrust of a

wheel or a track on the soil. The setup of the direct shear test is

made of a three-part box filled with the studied granular material (cf. Fig. 1). A constant load

is applied on the top part so that the soil specimen is subjected to a constant pressure p.

The bottom frame of the box remains stationary while an increasing longitudinal force F

applied on the upper frame makes it glide on the bottom one. The longitudinal force F and

the displacement of the upper frame relatively to the bottom one are recorded. As the

contact between the two frames is frictionless, F equals to the shear stress over the box

cross-section area S: F = S·τ. F ultimately reaches a threshold, which corresponds to the

shear failure of the soil on the plane between the two frames: τmax = Fmax/S. This

experiment is run under various pressures p by applying different normal loads on the top

part of the box, and the linear regression of the maximum shear stresses τmax plotted with

respect to p gives the soil cohesion c and internal angle of friction φ according to the Mohr-

Coulomb law (Eq. 1).

The second experiment used for our calibration study is the angle of repose experiment. The

low cohesion and the high deformability of granular soils induce an important phenomenon

on the wheel-soil interaction. Indeed, as a wheel rolling on a granular soil tends to sink, the

resistance to motion of the soil on the wheel depends on how the soil will be moved by the

front of the wheel and recover the side faces. This avalanching process occurring in a

sloping soil can be illustrated by the angle of repose experiment. The angle of repose A of a

granular material is one of its most distinct properties: it imposes the shape of a heap of

gravel or a sand dune, for instance. However, it is not an intrinsic property of the material

and can depend on the experimental conditions. Different experimental setups can be used

to measure it, the most common being the slow lifting of a vertical straw filled with material

and initially laying on a plate, or the lifting of one side of a filled box. In our case, we chose

the straw setup, as it was used by S. Ji and H. Shen in their 2D DEM angle of repose

simulations [2]. We extended their study to 3D simulations to build the surrogate model of

the virtual soil's angle of repose.

To conclude, the three material properties used in this virtual soil calibration process are:

cohesion and internal angle of friction (determined with the direct shear test), and angle of

repose. Table 1 gives the values of these properties for a silica Barco sand (measured by N.

Kaveh-Moghaddam) and lunar regolith (from literature [3]).

Figure 4. The direct shear test experimental setup (image from British Standards Institution BS 1377-1:1990).

The discrete element method software EDEM models granular soils from pre-defined particle

prototypes and particle creation rules, called factories. It computes the interactions between

the granular material and rigid geometries, built from simple polygons through EDEM‘s

graphical user interface or imported from CAD models. A particle prototype consists of a

sphere or a union of spheres, each one characterized by its radius r and center position.

Consequently, a granular material made of non-spherical particles (corn seeds for example)

can be modeled by a particle prototype including several spheres. The particle creation

rules, or factories, define how the particle should be created. The main parameters

characterizing a factory are: number of particles to create, time and place of creation,

particle prototype, size distribution. Each particle prototype or other geometry element used

in a simulation is associated to a material, with density ρ, Poisson's ratio υ and shear

modulus G. The contact model used to compute the interaction forces between two

contacting spheres belonging to two different particles is detailed in EDEM‘s User Guide [4]

and is based on Hertz-Mindlin model [5], [6]. It characterizes the interactions with three

coefficients: restitution e, static friction µs and rolling friction µr. The

coefficient of rolling friction, specific to EDEM, models the effect of

surface roughness on non-spherical shaped particles. Indeed, as the

virtual particles are made of spheres, they can roll on each other

without friction. To avoid this artificial feature, the rolling friction

coefficient introduces an artificial torque in the contact model

opposed to this rolling motion.

In this study, we decided to model the soil from a unique spherical

particle prototype. As explained in the previous section, this particle

prototype is characterized by the following parameters: r, ρ, ν, G, µs,

µr, e. Creating the response surface of our virtual soil as a function of

these 7 parameters would be too long and complex, that is why a

screening experiment will be conducted in order to identify which

ones have a strong impact on the virtual soils properties and which

ones can be neglected. The screening experiment chosen was the angle of repose

experiment. The experimental setup was a 3D extension of S. Ji and H. Shen 2D DEM angle

of repose simulations [2]: a 1.5mm diameter steel tube containing 3,000 particles initially

rests vertically on a plate and is then lifted upward at a speed of 5mm/s (cf. Fig. 2). When

the particle pile reaches a static state, which occurs around t=0.7s, the particle positions are

stored in a table and the angle of repose of the pile is computed with a MATLAB function.

Table 1. Lunar regolith and silica Barco sand

properties: cohesion, internal angle of friction

and angle of repose.

Lunar regolith Silica Barco sand

c 0.1 to 1 kPa 0.024 kPa

φ 30° to 50° 26.6°

A 65° 30°

We first analysed 5 simulations with an initial set of parameters [r=50µm, ρ=3000kg/m³,

ν=0.2, G=5·107Pa, µs=0.3, µr=0.1, e=0.5], which gave an average

angle of repose of 27.47° (standard deviation: 0.52°). Then, we

successively changed one parameter while keeping the other 6 at

their initial values, and computed the average angle (cf. Tab. 2).

Table 2 shows that the angle of repose depends mostly on the static

and rolling friction coefficients, and on the mean particle radius to a lesser extent. As a

consequence, the three parameters (r, µs, µr) are considered as our virtual soil‘s design

variables, and the response surface for the soil properties are built as their function.

Surrogate models describing the

response surface of the virtual soil are

built as a function of its design

variables (r, µs, µr) for each of the

following properties – cohesion,

internal angle of friction and angle of

repose – by simulating the direct shear

test and the angle of repose

experiment for different sets of design

variables.

Table 2. Angle of repose measure after the modification of one parameter in the initial set.

Value of the

modified

parameter

A

(average

of 3 sim.)

Standard

deviation

Value of the

modified

parameter

A

(average

of 3 sim.)

Standard

deviation

r=100µm 26.37° 0.53° µs=0.5 31.48° 0.64°

ρ=4000kg/m³ 27.67° 0.34° µr=0.2 36.57° 0.76°

ν=0.4 27.95° 0.28° e=0.3 27.63° 0.61°

G=7·107Pa 27.06° 0.44° Initial set: 27.47° 0.52°

Figure 5. The angle of repose simulation: the tube is lifted (top) until the virtual soil remains motionless on the plate (bottom).

Figure 3. The direct shear test box before and after translation of the lower frame.

Figure 4. Fx with respect to time under various pressure, for (r=50µm, µs=0.3, µr=0).

The angle of repose simulation has been described in Section 3.2. Let us describe briefly the

direct shear test simulation. To simulate this experiment within an acceptable time, we

modeled only a slice of the box used in the experimental setup (cf. Fig. 1), setting periodic

boundaries on both sides of the slice to simulate an infinitely wide box. The slide width was

set to four times the mean particle radius to make sure that a particle had no chance to

interact with itself because of the periodic boundaries. In our simulations, the upper frame

remained stationary while a translation of constant velocity was imposed to the lower frame

(cf. Fig. 3). A plane macro-particle subject to a constant vertical force applied the desired

pressure on the soil. The horizontal component Fx of the total force of the soil on the lower

frame was recorded. Fig. 4 shows the force Fx plotted with respect to time for different

pressures. Fx reaches a plateau after approximately 0.2s, which allows us to compute the

maximum shear stress τmax = Fmax/S. Cohesion and internal angle of friction were then

computed with the linear regression of τmax with respect to the pressure p (cf. Eq. 1).

Second-order fit

Each surrogate model of the soil‘s properties c, φ and A was then built as a product of two

independent functions: f(r= r0, µs, µr) describing the response surface for r= r0=50µm, and a

dimensionless function of r: g(r). f was computed as the second-order fit of the data points

because this fitting technique is simple and adapted to curved response [7].

Results

For the silica Barco sand, the algorithm gave a satisfactory result. With equal weighting

factors and a convergence criterion of ε = 0.001, it converged after 81 iterations to the

solution x=(r, µs, µr)=(70.85µm, 0.609, 0.0811). The virtual soil estimated properties were

then: c=23.9Pa, φ=29.5°, A=27.8°, whereas the real soil measured values are c=24Pa,

φ=26.6°, A=30° (cf. Tab. 1).

On the other hand, the algorithm did not converge in the regolith case (objective properties:

c=0.3k, φ=40°, A=65°, property of the virtual soil: c=0.621k, φ=31.2°, A=36.7° with the

solution x=(r, µs, µr)=(66.21µm, 1.44, 0.150) ). One reason for that could be the very high

angle of repose of the regolith. As our simulations never showed an angle of repose higher

than 50° (cf. Fig 5), the response surface of the virtual soil never reaches A=65° in the

explored region of the design space. Decreasing the weighting factor of the objective

function ϕ1 (responsible for the angle of repose fit) to one tenth of the other ones gave a

better approximation: c=0.359k, φ=38.7°, A=32.9° with x=(r, µs, µr)=(172.7µm, 1.21, 0.136).

Figure 5. Angle of repose of the virtual soil for various friction coefficients, with r=r0=50µm.

Discussion

This study presents a methodology for the DEM parameter calibration of a virtual soil,

involving geotechnical experiments on the real material and their simulations with a DEM

software. The example given in this paper shows how a virtual granular material can be

calibrated to replicate the behaviour of a silica sand. However, the lunar regolith behaviour

could not be modeled accurately, probably because of a mediocre angle of repose surrogate

model combined with a lacking design space exploration.

This methodology could be greatly improved by a better surrogate model management.

Indeed, an automated exploration of the design space in potentially optimum zones during

the optimization process, which would allow an automated update of the response surfaces,

would ensure high-fidelity surrogate models. Such an automated process was not doable

with our DEM software release, but can definitely be explored in future work.

Acknowledgments

The authors are grateful to Neptec, the Canadian Space Agency, and the NSERC CRD

program for the financial support of this project, and also DEM Solutions. Ltd., Edinburgh,

Scotland, UK, for their help and their advices

References

[1] M.G. Bekker. Theory of Land Locomotion. The University of Michigan Press, 2008.

[2] S. Ji and H. Shen. Two-dimensional simulation of the angle of repose for a particle

system with electrostatic charge under lunar and earth gravity. Journal of Aerospace

Engineering, 22:10-14, 2009.

[3] G. Heiken. Lunar sourcebook : a user's guide to the moon. Cambridge University Press,

1991.

[4] EDEM 2.1.1 User Guide. DEM Solutions, 2009.

[5] R.D. Mindlin. Compliance of elastic bodies in contact. Journal of Applied Mechanics,

16:259 {268, 1949.

[6] T. Tanaka Y. Tsuji and T. Ishida. Lagrangian numerical simulation of plug ow of

cohesionless particles in a horizontal pipe. Powder Technology, 71:239{250, 1992.

[7] Raymond H. Myers and Douglas C. Montgomery. Response Surface Methodology,

second edi-tion. Wiley, 2002.

[8] Jorge Angeles. MECH 577 Optimum Design, Lecture notes. McGill University, 2008.

Session 3: Metals Manufacturing

Analysis of the Bell Less Top® Charging System– By the Discrete Element Method

K. Mutschler, G. Thillen, E. Lonardi, S. Köhler, L. Hausemer,

Paul Wurth

Klaus Mutschler

Design & Welding Engineer, Mechanical Engineering, Components R&D

Luxembourg, Luxembourg

Lionel Hausemer

Project Engineer, Mechanical Engineering, Components R&D

The Paul Wurth Group is one of the world leaders in the design and supply of the

full-range of technological solutions for the iron making industry. The Group also

provides tailor-made equipment and systems for the steel making industry and

affiliated sectors.

Optimization of Raw Material Transport

using a Transfer Chute

Magno Antonio Calil Resende Silveira,

Gustavo Lucas Rocha de Oliveira, Bráulio Viegas da Silva

USIMINAS

Daniel Schiochet Nasato, ESSS

Magno A. Calil Resende Silveira

Development and Research Center

Ipatinga, Minas Gerais, Brazil

Development and Research Center specializes in scientific and technological

knowledge pertaining to the identification, evaluation and exploration of new

technologies in steel production. The focus has included characterization and

evaluation of raw material, inputs, waste and different materials related to the

process of steel production; the improvement and development of processes and

products; cost reduction; characterization and product application engineering; and,

in particular, Calil Resende Silveira works with modeling and simulation-- currently

focusing on the pig iron manufacturing process, using process modeling and

simulation

Andrés Gonzalez Cornejo and Daniel Schiochet Nasato

ESSS gathers the engineering and computer science knowledge necessary to deliver

a complete range of mathematical modeling and numerical simulation solutions to a

wide spectrum of industries. A highly skilled team of engineers and software

developers is always ready to offer the world‘s most comprehensive CAE software

and a full portfolio of services focused on in-house development, consulting,

customization, technical support and training.

Optimization of raw material transport using a transfer chute

Abstract:

Raw material transport using conveyor transfer and chutes may become critical due to

details not covered in the design phase or the low maintenance frequency in the process.

Problems such as material spillage, belt misalignment, breakage and premature wear of

peripherals associated with the system can cause damage to the process. Currently, part of

the raw material is lost in transportation in the Ipatinga ironmaking plant (Usiminas). Aiming

to reduce this loss, a study was done on particular system of pellets transport, which

comprises two conveyor belts and a set of transfer chute, using the EDEM. Experiments

were conducted to obtain some pellet, belt and metal properties needed in the simulation.

Analyzing the simulation results for current conditions it was possible to identify problems

that have occurred in the process. Changes in the model geometry and new simulations

were made and a new geometry that solved the problem of material spillage in the system

was obtained.

Key words: EDEM; Particle simulation; Pellet; Transfer chute.

Author: Magno Antonio Calil Resende Silveira - USIMINAS

Co-Author: Gustavo Lucas Rocha de Oliveira - USIMINAS

Bráulio Viegas da Silva - USIMINAS

Daniel Schiochet Nasato - ESSS

Blast Furnace Charging Simulation using EDEM

Guilherme Defendi; Paulo de Freitas Nogueira;

Anderson Willian de Souza Baltazar

Vale

Andrés Gonzalez Cornejo, ESSS Chile

Daniel Schiochet Nasato, ESSS Brazil

Guilherme Defendi

Ferrous Technology Center

Belo Horizonte, Minas Gerais

The Ferrous Technology Center brings together laboratories and pilot plants along

with equipment devoted to iron ore application studies. Through R&D projects, the

center develops products and technical solutions with an integrated view of the

mining and steelmaking chains.

Vale explores for, produces and sells iron ore and pellets, nickel, copper, coal,

bauxite, alumina, aluminum, potassium, kaolin, manganese, ferro-alloys, cobalt,

platinum-group metals and precious metals. Vale also operates in the logistics,

energy and steelmaking sectors with the mission of transforming mineral resources

into prosperity and sustainable development.

Andrés Gonzalez Cornejo and Daniel Schiochet Nasato

ESSS gathers the engineering and computer science knowledge necessary to deliver

a complete range of mathematical modeling and numerical simulation solutions to a

wide spectrum of industries. A highly skilled team of engineers and software

developers is always ready to offer the world‘s most comprehensive CAE software

and a full portfolio of services focused on in-house development, consulting,

customization, technical support and training.

Blast Furnace Charging Simulation using EDEM

Guilherme Defendi – Vale FTC (Ferrous Technology Center)

Abstract:

For steelmaking, coke and sinter and/or lump and/or pellet must be feed in a blast furnace

so that reduction happens and after melting of metallic charge and pig iron formation. Steel

formation happens in forward stages of refine. Material distribution during blast furnace

charging is very important for a better control on blast furnace operational performance.

Segregation of different materials can happen in a non uniform way in the furnace center

and walls, leading to a higher difficult to reach desired goals. This work present a Discrete

Element Method (DEM) simulation performed in a blast furnace. Material data was firstly

calibrated and the EDEM software was used to analyze material flow during blast furnace

charging to help predict material segregation. Also voidage condition was mapped in

different regions of the furnace, in order to check how segregation will affect voidage and by

consequence gas flow. EDEM was also used to predict compressive forces to understand

how blast furnace charging operation can cause particle breakage in different regions.

Co-Author: Paulo de Freitas Nogueira – Vale FTC (Ferrous Technology Center)

Anderson Willian de Souza Baltazar – Vale FTC (Ferrous Technology Center)

Andrés Gonzalez Cornejo – ESSS Chile

Daniel Schiochet Nasato – ESSS Brazil

Coupled DEM-CFD Study of the Blast Furnace Cohesive Zone

Yuko Enqvist1; Allert Adema1; Vilas Tathavadkar1;

Yongxiang Yang2; Rob Boom1,2

1Materials innovation institute (M2i) 2Delft University of Technology

Yongxiang Yang

Group Leader, Metals Production, Refining and Recycling (MPRR),

Materials Science and Engineering Department (MSE)

Delft, The Netherlands

The Materials Science and Engineering Department (MSE) of TU Delft

focuses on research and education in various aspects of materials science, in

particular for the production of metals, alloys, their processing and structural

control for properties, and recycling. The MPRR group focuses more on the

sustainable development for the metals extraction and recycling technologies,

which is an important component to close the metals cycle.

The Materials innovation institute (M2i), a public-private partnership between the

Dutch government, industry and academia, is the world-class institute for

fundamental and applied research in the fields of structural and functional materials.

The business of materials production focuses on advanced materials coatings and

production technologies. Steel based materials and composite materials are the two

main categories in this sector Working closely together with top-level academic and

industrial partners, we deliver new materials to promote economic growth in our

industrial sector and help bring about a more sustainable society.

Coupled DEM – CFD Study of the Blast Furnace Cohesive Zone

Yuko Enqvist, Materials innovation institute (M2i), Delft, The Netherlands; Allert Adema,

Materials innovation institute (M2i), Delft, The Netherlands; Vilas Tathavadkar, Materials

innovation institute (M2i), Delft, The Netherlands; Yongxiang Yang, Delft University of

Technology, Delft, The Netherlands; Rob Boom, Materials innovation institute (M2i), Delft,

The Netherlands, and Delft University of Technology, Delft, The Netherlands.

Introduction

The cohesive zone in the blast furnace, where iron ores soften and melt, greatly increases

the resistance to the ascending gas flow since the permeability of the ore layers becomes

varied and restricted, accordingly the gas can predominantly flow through intermediate coke

layers. The cohesive zone acts as a gas distributor that has a remarkable impact on the

performance and stability of blast furnace operation. The softening and melting process in

the cohesive zone (Fig. 1) is generally distinguished into three stages, softening, exudation

and melting-down, which depend on ore properties (such as the degree of reduction and

carburisation, the porosity, the slag former composition, and the distribution of the phases)

as well as global furnace operating conditions (temperature, solid packing structure, load,

bed porosity, PCO/PCO2, and so on). Considering the complex operating conditions and a

number of reactions taking place in the blast furnace, the mathematical modeling, coupled

with physical modeling could be a powerful tool for understanding the effect of the

microscopic ore properties and the macroscopic furnace flow conditions on the formation of

the cohesive zone.

The aim of this work is to develop a comprehensive model for predicting the cohesive zone

properties, such as its shape, location, structure, permeability and mineralogical changes, in

conjunction with upper and lower zones to the cohesive zone. The model should allow

predicting the changes of status in the cohesive zone in response to the changes in the

operating conditions.

Modeling Approach

In this work the Discrete Element Method (DEM) – Computational Fluid Dynamics (CFD)

coupling approach is applied to model the solid – gas flow in the blast furnace cohesive

zone. The commercial DEM particle simulation software package EDEM®2.3 (DEM

solutions Ltd., UK) and CFD software package ANSYS FLUENT 12.0 (Ansys Inc., USA) are

used together with EDEM – CFD coupling module for FLUENT® (DEM solutions Ltd., UK).

The lower half of an experimental blast furnace (EBF) [1], excluding the hearth is employed

as the basic geometry of the model (Fig. 2). The hot air is introduced to the furnace at

tuyeres, located close to the bottom of the geometry, flows upward through the descending

packed burden bed, and exits at the top of the furnace. Ore and coke burdens are

alternatively charged on the top of the bed. Ore is charged relatively close to the furnace wall

compared to coke. The burden charging conditions used in this study are listed in Table 1.

As solid burden descends, ore heats up to its melting temperature, and is disappeared, while

coke is removed when entering the raceway. The burden descent velocity is primarily

determined by the coke removal rate at the raceway as well as ore melting. In this study the

burden descent velocity is approximately set to 40 mm/s. It should be noted that the descent

velocity applied is much higher than one in the EBF (0.5 mm/s) in order to generate burden

layer distribution with acceptable CPU time. The motion of the descending solid burden is

determined by the DEM, while the flow of continuum gas is obtained by the CFD over

computational grids. The drag force, and conductive and radiative heat transfer are

calculated by DEM – CFD coupling. The input parameters used in the CFD and DEM

simulations are summarised in Table 2.

The simple softening and melting model is integrated in EDEM in order to predict the

formation of the cohesive zone. The softening is defined when iron ore deforms 50%. In the

model iron ore deforms 50% at 1200C and completely melts (disappears) at 1400C as pre-

set criteria. The deformed particle shape is described by a rubber elasticity model. The

softening and melting model as well as the burden charging and coke removal model are

implemented in EDEM simulation using the EDEM application programming interface (API),

namely Particle Body Force API and Particle Factory API.

As for advanced softening and melting models, the thermodynamic equilibrium of iron ore is

modeled by using the thermochemical software package FactSage© (GTT Technology,

Germany), and the FactSage thermodynamic model is linked to FLUENT by the commercial

thermodynamic programming library ChemApp© (GTT Technology, Germany) in order to

perform the thermodynamic equilibrium computation in each FLUENT cell. Furthermore the

custom EDEM – FLUENT coupling module is implemented in the coupled EDEM – FLUENT

model that can allow the custom burden particle properties to be transferred between EDEM

and FLUENT. The effect of burden charging conditions on burden layer distribution, the

formation of the cohesive zone, and heat transfer are investigated with high burden descent

velocity and artificial burden thermal properties. The present study does not include chemical

reactions and advanced softening and melting model.

Figure 1: Softening and melting process of iron ore pellets in the blast furnace cohesive

zone.

Figure 2: The model geometry used in the DEM and CFD simulations (a) and computational

grid for CFD simulation (b) (28365 cells). For simulation with Case III, the height is

extended by 0.4 m (=8 cells).

Table 1: Burden charging conditions.

Table 2: Input parameters used in the CFD and DEM simulations.

I. Front II. Side

orecokeorecokecoke & ore

1700

1000

0.0315

Case III

close to wall

1700

1000

0.03

Case II

close to wall

1700

2000

0.03

Case I

5730

4000

0.02

very close to wallOre charging

0.02Particle size [m]

5730No. particle per charge

4000Density [kg/m3]

orecokeorecokecoke & ore

1700

1000

0.0315

Case III

close to wall

1700

1000

0.03

Case II

close to wall

1700

2000

0.03

Case I

5730

4000

0.02

very close to wallOre charging

0.02Particle size [m]

5730No. particle per charge

4000Density [kg/m3]

0.25Unit [m]

2.3

Gas inlet

H=0.1

1.2

Raceway

(a) (b)

Simulation Results

The time evolution of solid burden and gas flow fields are investigated at different charging

conditions. The burden heat capacity and thermal conductivity used are 10 J/kg K and 1000

W/m K, respectively, which are artificially adjusted due to the high burden descent velocity.

The bed is initially packed with coke, having a pre-defined temperature distribution. Ore

charging commences when simulation starts. The burden temperature field is generally

stabilised around simulation time of 20 s.

Figure 3 shows the effect of charging conditions on burden layer distribution (A) and on gas

temperature field (B) at simulation time of 30 s. It can be seen that charging conditions

considerably affect the burden layer distribution and gas temperature field. Consequently the

formation of the cohesive zone is varied with charging conditions. When applying more

realistic burden properties, for case II and III, the bed porosity in ore layers is much lower

than in coke layers, thus, gas tends to flow through coke layers, which generates large

temperature variations along the height. It can be also observed that ore charging condition

slightly alters the gas temperature field (Fig. 3 (b) and (c)). The cohesive zone considerably

increases the gas flow resistance, accordingly, coke slit type flow is realized in the

simulations. It might be difficult for the current model to reproduce the temperature field (i.e.

cohesive zone) obtained in the EBF due to the unrealistic burden descent velocity as well as

burden thermal properties used in the simulations.

Gas inlet

45 [m/s]/ 225 [Nm3/h]Blast velocity/ total volumetric flow rate

2.4 [bar]Blast pressure

2200 [°C]Blast and Raceway temperature

Contact model (Hertz Mindlin)

0.05Coefficient of rolling friction

900 [°C]Charging temperature

(2 – 5)104Total number of particles in the domain

110-4 [s] (DEM); 110-3 [s] (CFD) Time step

Particle-particle: 0.5, particle-wall: 0.5Coefficient of static friction

0.25 [-]Poisson’s ratio

Particle-particle: 0.2, particle-wall: 0.5Coefficient of restitution

1107 [Pa]Young’s modulus

Burden

Coke and oreTypes

UniformSize distributions

SphericalShape

Gas inlet

45 [m/s]/ 225 [Nm3/h]Blast velocity/ total volumetric flow rate

2.4 [bar]Blast pressure

2200 [°C]Blast and Raceway temperature

Contact model (Hertz Mindlin)

0.05Coefficient of rolling friction

900 [°C]Charging temperature

(2 – 5)104Total number of particles in the domain

110-4 [s] (DEM); 110-3 [s] (CFD) Time step

Particle-particle: 0.5, particle-wall: 0.5Coefficient of static friction

0.25 [-]Poisson’s ratio

Particle-particle: 0.2, particle-wall: 0.5Coefficient of restitution

1107 [Pa]Young’s modulus

Burden

Coke and oreTypes

UniformSize distributions

SphericalShape

Dark red – ore, grey – coke, and orange – fused ore

Figure 3: The effect of charging conditions on burden layer distribution (A) and on gas

temperature field [K] (B) at simulation time of 30 s.

Discussion

The present results demonstrate that the coupled DEM – CFD approach can provide a good

way to model anisotropic burden descent behaviour and heat transfer between solid burden

and gas. However, it should be pointed out that the obtained results are based on unrealistic

conditions (i.e. high burden descent velocity and artificial burden thermal properties) in order

to reduce computational time, and therefore, is not able to reproduce the key phenomena

observed in the EBF. It is necessary to use actual EBF conditions once implementing

chemical reactions into the coupled EDEM – FLUENT model. Due to the current

computational restriction for EDEM – FLUENT coupling, it is not possible to simulate the

formation of the cohesive zone over a long physical time. The couple EDEM – FLUENT

model can predict the changes of the status in the cohesive zone within a very short physical

(a) Case I (b) Case II

(c) Case III

(A)

(B)

(a) Case I (b) Case II

(c) Case III

time using well-defined initial conditions such as distribution of burden, temperature,

PCO/PCO2, degree of reduction, etc.

References

. J. Sterneland, M.A.T. Andersson, and P.G. Jönsson (2003), ―Iron ore reduction in

experimental blast furnace and laboratory scale simulation,‖ Ironmaking Steelmaking, 30, pp.

313-327.

Acknowledgements

The authors would like to acknowledge Mr. Jan van der Stel and Mr. Mark Hattink from Tata

Steel, Research, Development & Technology, the Netherlands for their continuous support.

We are also grateful to Mr. Christoph Kloss, Johannes Kepler University Lintz, Austria for

development of custom EDEM – FLUENT coupling module. This research was carried out

under project number MC5.06255 in the framework of the Research Program of the

Materials innovation institute M2i (www.m2i.nl), the former Netherlands Institute for Metals

Research.

Session 4: Pharmaceutical Production

Using DEM to Predict Pharmaceutical Tablet Film Coating Uniformity

William R. Ketterhagen

Pfizer

William R. Ketterhagen

Senior Scientist, Process Modeling and Engineering Technology Group,

Pfizer Worldwide Research and Development

Groton, CT, USA

Within the Process Modeling and Engineering Technology Group, Ketterhagen

works to develop models and engineering solutions in support of drug product

development and technology transfer. Modeling efforts range from fit-for-purpose

engineering models to discrete element method (DEM) models for detailed powder

(or tablet) flow predictions. These models are applied in several areas, including

powder characterization, storage, and handling; granulation, and film coating

processes.

At Pfizer, we apply science and our global resources to improve health and well-

being at every stage of life. We strive to set the standard for quality, safety and value

in the discovery, development and manufacturing of medicines for people and

animals. Our diversified global health care portfolio includes human and animal

biologic and small molecule medicines and vaccines, as well as nutritional products

and many of the world's best-known consumer products. For more than 150 years,

Pfizer has worked to make a difference for all who rely on us.

Using DEM to Predict Pharmaceutical Tablet Film Coating Uniformity

William R. Ketterhagen, Pfizer Worldwide Research and Development, Pfizer Inc., Groton,

CT, USA

Film coating of pharmaceutical tablets is an important process that is conducted to help

improve the tablet appearance, the stability of the active drug, taste masking, or, in the case

of controlled release tablets, modify the release profile of the active drug substance.

Regardless of the rationale for applying the coating, the uniformity of the coating is an

important attribute. For immediate release coatings where coatings may be applied primarily

for aesthetic purposes, poor uniformity leads to reduced process efficiency and increases

run times to ensure tablets with the least coating meet quality criteria while other tablets are

over-coated. More serious consequences occur for functional membrane coatings that

control the release of the active drug. In this case, variable coating uniformity may lead to

large variability in the drug release rates. Finally, in instances in which the coating contains

the active drug, any variability in coating can cause dosage form content uniformity issues.

In this work, the discrete element method (DEM) is used to computationally model the

dynamics of tablet motion in a film coating pan. The number and duration of tablet

appearances in a fictitious spray zone are used to predict the coating uniformity. A key

variable explored in this work is the shape of the tablets, where different tablet shapes are

approximated using the ―glued spheres‖ technique. The results show that tablet shape

significantly affects the typical tablet orientation in the spray zone, and thus, the intra-tablet

coating uniformity but had little effect on inter-tablet coating uniformity. The operating

conditions such as pan rotation speed and pan loading are shown to have a significant effect

on inter-tablet coating uniformity, but a relatively small effect on intra-tablet coating

uniformity. These results demonstrate the usefulness of modeling in guiding drug product

development decisions such as shape selection and process operating conditions.

A Deeper Understanding of Tablet Coating Processes Through Discrete Element Method Simulations

Gregor Toschkoff, Daniele Suzzi, Daniel Machold,

Johannes G Khinast,

RCPE, Graz University of Technology

Gregor Toschkoff

Researcher, Research Center Pharmaceutical Engineering (RCPE

GmbH)

Graz, Austria

The Research Center Pharmaceutical Engineering (RCPE GmbH) is an

interdisciplinary research institute in the area of pharmaceutical process and product

development. A central goal of the RCPE is to transform pharmaceutical product

development and process development from empirical approaches to a rational

science-based endeavor, in accordance with ICH‘s Quality-by-Design framework. To

fulfill this mission as an ―innovation company‖ we cooperate with various partners

from science and industry, ranging from small and medium enterprises to global

players from different sectors of the pharmaceutical industry.

Graz University of Technology pursues top teaching and research in the fields of

the engineering sciences and the technical-natural sciences.

A deeper understanding of tablet coating processes through Discrete Element

Method simulations

Gregor Toschkoff, Research Center Pharmaceutical Engineering GmbH, Graz, Austria;

Daniele Suzzi, Research Center Pharmaceutical Engineering GmbH, Graz, Austria; Daniel

Machold, Research Center Pharmaceutical Engineering GmbH, Graz, Austria; Johannes G

Khinast, Institute for Process and Particle Engineering, Graz University of Technology, Graz,

Austria.

Introduction

Drum coating technology is widely used in the pharmaceutical industry to produce tablet

films fulfilling functional and non-functional purposes. In this process, a rotating drum

accounts for the necessary mixing of the tablets, and a coating solution is injected from

above by means of an atomizing nozzle. To enhance the evaporation of the solvent content

of the coating liquid and thus the film formation, an air flow through the system is

established. The applied coating layer(s) fulfill different functions, e.g. taste masking and

coloring, control of the release of the active pharmaceutical ingredient (API) from the core of

a tablet, application of an additional API, or protection of the tablet core from environmental

influences.

For all aspects of the coating mentioned above, the uniformity of coating is of uttermost

importance and represent critical issues in the production of this solid oral dosage forms.

This includes both, inter-tablet uniformity (variation of coating mass from one tablet to the

other) and intra-tablet uniformity (variation of the coating thickness and quality on the surface

of a single tablet) [5]. In fact, inhomogeneity in the coating thickness can lead to significant

variations in APIs delivery rate, as well as compromise the functional attributes of the tablet

film. In many cases, regulations dictate that a single tablet that fails testing will lead to the

rejection of the whole batch.

Although drum coating is a widespread technology in the pharmaceutical industry, numerical

simulation of the process has been scarce so far, and process design is more often than not

based on trial-and-error practices and operator experience. Even if numerical approaches

are more and more supporting the analysis of this complex problem, the uniformity of coating

thickness is nowadays difficult to predict without expensive experimental work [6]. For this

reasons, detailed investigation of the coating process and especially the uniformity of the

coating using DEM simulations is of great interest for the pharmaceutical industry [1].

Figure 1: Geometries of the two tablet coating machines that were used in this work. Left:

Driam Driaconti continuous coater, right: Bohle BFC5 lab-scale coater. The pictures

generated using the EDEM ® 2.3 particle simulation software. For details please refer to the

section ―Approach‖ below.

Approach

The performance of modern coaters strongly depends on the nature of the spray zone where

particles are effectively coated and the transport between the coating zone and the zone

where particles are not seen by the spray. Beside experimental work [2,4], numerical

simulations of particle motion using the Discrete Elements Method (DEM) have become an

extremely important tool in particle technology problems and are frequently used for coater

simulations [8].

The aim of this work is to analyze and understand the effects of parameters like tablet form,

fill volume or pan rotation speed on the intra-tablet coating variability [3] in different coating

devices. To this end, Discrete Element Method (DEM) using the EDEM ® 2.3 particle

simulation software (DEM Solutions. Ltd., Edinburgh, Scotland, UK) is used to numerically

reproduce the tablet motion inside different coating machines, in this case the geometries of

a Driam Driaconti continuous coater(DRIAM Anlagenbau GmbH, Eriskirch, Germany) and a

Bohle BFC5 lab-scale coater (L.B. BOHLE Maschinen + Verfahren GmbH, Ennigerloh,

Germany) are used, see Fig. 1.

The special material attributes of the tablets are known from experiments. For each

geometry different tablet shapes, namely bi-convex, oval and/or round, are modeled by the

―glued spheres‖ approach available in EDEM. Further parameter variations include different

fill volumes or different rotational speeds. For each case, important process attributes (e.g.,

residence time of the tablets under the coating spray, intra-tablet coating variability, tablets

velocities pattern) are investigated

Results and Discussion

For the detailed analysis of the tablets flow inside the bed in terms of mean velocities and

granular temperatures, a MATLAB-based program processing the data exported from EDEM

was used. A main target of the MATLAB post-processing is to evaluate the particle-based

variables on a static grid. In this way, important parameters like particle velocity or rotational

velocity can be averaged over time, and detailed investigation of e.g. local velocity variations

is possible.

DRIAM Driaconti continuous coater

An important quality attribute for tablet coating is the residence time distribution of the tablets

in the spray zone. While this quantity is fastidious to extract by experimentation, it is readily

available from the DEM simulations data. Figure 1 shows the distribution for the DRIAM

continuous coater (see above) for a simulation time of 60 seconds. It can be seen that both

tablet shape and fill level have an influence on the time that a single tablet spends exposed

to the spray. From this information, an expected coating variability and in the end coating

process time can be estimated.

Figure 2: Residence Time Distribution in the spray zone after 60s of simulation, for spherical

tablets (left) and oblong tablets (right)

Every 0.5 seconds, a set of data was exported using the EDEM ® software export dialog.

Based on these data sets, a mean velocity of the tablets was calculated by interpolating

each particle onto a grid. The result is a normalized average velocity on a static grid,

allowing precise comparison of different process parameters. Figure 2 shows a matrix of

results, for different fill levels and different tablet shapes. For example, it showed that round

tablets develop a qualitatively different flow profile, with pronounced disorder in the upper

region.

Figure 3: Normalized time-averaged tablet velocity on the grid for round, oval and bi-convex

tablets at the different coater fill ratios for a vertical slice in the middle of the coating

apparatus.

Bohle BFC5

Another concern that is connected with a spray coating process is the mixing of tablets [7]. In

the simulation setup, the cylindrical coating drum of a BFC5 lab coater was filled with two

sorts of particles, one sort in the front and one in the back region. Figure 4 shows the relative

standard deviation of the binary mixture for round and biconvex shaped particles and two

different rorational speeds. The RSD is calculated by using bins of appropriate size including

all tablets. A high value means high separation, a low value good mixing. On the abscissa,

the number of revolutions is drawn. As can be seen, the mixing per revolution is nearly

constant, but one has to keep in mind that with a higher rotational speed, the same amount

of revolutions and therefore mixing is achieved in shorter time.

Figure 4: Decrease of the Relative Standard Deviation of binary mixture with the number of

revolutions. In both cases, 50s are simulated, therefore the red curve (10 rpm) ends earlier.

As described above for the Driaconti, a time-averaging of particle velocities was done on a

locally stationary grid for the BFC5 as well. The result is shown in Fig. 5. Although the two

coater geometries are quite different, the qualitative difference of the movement pattern

between round and biconvex tablets is the same, with the round tablets showing disorder

near the top of the tablet bed. In the Bohle BFC5, two ―ribbons‖ lead to the good axial mixing

properties of the apparatus along the wall. In Fig. 5, this can be seen as circle-shaped

regions of increased velocity near the coater wall.

Figure 5: Normalized time-averaged tablet velocity on the grid for round and bi-convex

tablets at different coater rotation rates for a vertical slice in the middle of the coating

apparatus.

Conclusion

The DEM simulation has proven to be a valuable tool to gain understanding the dynamical

behavior of the tablets under the spray gun. The gathered information is essential to obtain a

satisfactory intra-tablet coating homogeneity, which in turn is necessary to minimize the

number of tablet batches that have to be rejected. The outcomes of this work aims at

demonstrating the utility of numerical simulation in the development and the design of

pharmaceutical tablet coating processes.

References

Adam, S., Suzzi, D., Radeke, C., Khinast, J.G., 2010. An integrated Quality by Design (QbD)

approach towards design space definition of a blending unit operation by Discrete Element

Method (DEM) simulation. European Journal of Pharmaceutical Sciences, In Press.

Alexander, A., Shinbrot, T., Muzzio, F.J., 2002. Scaling surface velocities in rotating

cylinders as a function of vessel radius, rotation rate, and particle size. Powder Technology

126, 174-190.

Freireich, B., Wassgren, C., 2010. Intra-particle coating variability: Analysis and Monte-Carlo

simulations, Chem. Eng. Sci. 65, 1117–1124.

Ho, L., Müller, R., Römer, M., Gordon, K.C., Heinämäki, J., Kleinebudde, P., Pepper, M.,

Rades, T., Shen, Y.C., Strachan, C.J., Taday, P.F., Zeitler, J.A., 2007. Analysis of sustained-

release tablet film coats using terahertz pulsed imaging. Journal of Controlled Release 119,

253-261.

Kalbag, A., Wassgren, C., Penumetcha, S.S., Perez-Ramos, J.D., 2008. Inter-tablet coating

variability: Residence times in a horizontal pan coater. Chem. Eng. Sci. 63, 2881-2894.

Suzzi, D., Radl, S., Khinast, J.G., 2010. Local analysis of the tablet coating process: Impact

of operation conditions on film quality. Chemical Engineering Science, Volume 65, Issue 21,

Pages 5699-5715.

Tobiska, S., Kleinebudde, P., 2003. Coating uniformity and coating efficiency in a Bohle Lab-

Coater using oval tablets. European Journal of Pharmaceutics and Biopharmaceutics 56, 3-

9.

Ketterhagen, W. R.; am Ende, M. T. & Hancock, B. C., 2009, Process modeling in the

pharmaceutical industry using the discrete element method, Journal of Pharmaceutical

Sciences, , 98, 442-470

DEM Simulation of a Flowability Assessment Method using Small Sample Quantity

Massih Pasha, Colin Hare, Ali Hassanpour, Mojtaba Ghadiri,

University of Leeds

Massih Pasha

PhD Candidate, Institute of Particle Science and Engineering

Leeds, UK

The Institute of Particle Science & Engineering (IPSE) is one of three institutes

within the School of Process Environmental and Materials Engineering (SPEME) of

University of Leeds. The research at IPSE focuses on the engineering science of

advanced particulate systems applied to a range of sectors including, healthcare,

which includes foods and pharmaceuticals; personal and household products, which

includes polymers, biomaterials, and fine chemicals; and minerals and fuels, e.g.

nuclear. In all of these areas IPSE focuses its strength and expertise in

measurement, modeling and manufacture.

The University of Leeds is one of the UK's top research universities, with more than

61% of our research rated as 'world leading' or 'internationally excellent.' Our

academics and their cutting-edge research are in high demand throughout the world,

and we regularly share our expertise with businesses to help them grow.

DEM Simulation of a Flowability Assessment Method using Small Sample

Quantity

Massih Pasha, Institute of Particle Science and Engineering, University of Leeds, Leeds, UK

Colin Hare, Institute of Particle Science and Engineering, University of Leeds, Leeds, UK

Ali Hassanpour, Institute of Particle Science and Engineering, University of Leeds, Leeds,

UK

Mojtaba Ghadiri, Institute of Particle Science and Engineering, University of Leeds, Leeds,

UK

Introduction

Industrial processes involving powder blending, transfer, storage, feeding, compaction and

fluidisation all require reliable powder flow [1]. There are a number of processes which deal

with small amounts of loosely compacted powders. These include filling and dosing of small

quantities of powders in capsules and dispersion for dry powder inhalers and dry particle

sizing. In other cases, the availability of powders for flowability testing is an issue. For

instance in nuclear and pharmaceutical industries, the amount of testing powder is limited

due to ionising radiation for the former and toxicity and cost of drugs for the latter [2].

There exist a number of test methods for evaluation of flow behaviour of powders such as

uniaxial compression test, shear test, raining bed and the Sevilla powder tester [3]. Most of

these test methods require a relatively large amount of powder and measuring the flow

behaviour at relatively high compaction stresses. Hassanpour and Ghadiri [2] introduced a

testing method by ball indentation which can be performed on small amounts of loosely

compacted powders. In the present paper an attempt is made to evaluate the flowability

measurement of cohesive powders using the ball indentation method. The indentation

process and unconfined compression test are simulated using the Distinct Element Method

(DEM) for particles consolidated to different stresses. The correlation between indentation

characteristics and flow behaviour of powders (unconfined yield stress) are investigated by

comparing indentation results with those of unconfined compression.

Approach

In the indentation process, different samples of powders are consolidated into a cylindrical

die at a pressure that forms weak tablets. The die must be made of low friction materials in

order to reduce the effects of wall friction. The weakly formed tablets are then indented using

a spherical indenter and the depth/load cycle is recorded from which the hardness of the

bulk powder can be calculated. During loading, the load is increased at a specified rate until

a desired maximum load is reached. Then the load is decreased to zero at the same rate.

During unloading, the elastic deformation of the sample will recover. Hardness, H, is given

as the ratio of maximum indentation load, Fmax, to projected area of the impression, A,

(Equation 1).

maxFH

A (1)

The projected area can be calculated as follow,

2A dh h (2)

where d is the diameter of the indent, and h is the depth of impression [2].

In indentation test, during formation of the local plasticity zones around the indenter, the

volume of the powder bed present in a yield condition is surrounded by an elastically

deformable region. This leads to an increase in the local yield strength (i.e. hardness) [5]. A

linear relationship between the hardness and yield stress is usually considered:

.H C Y (3)

where H is the hardness, Y is the yield stress and the proportionality factor C is known as

constraint factor. It is important to relate hardness to yield stress, since the flow behaviour is

defined based on the yield strength. Wang et al. [4] has concluded that indentation hardness

and unconfined yield stress have a linear relationship with pre-consolidation pressure for a

number of materials. This corroborates the linear relationship between yield stress and

hardness. The constraint factor for a number of testing powders was also determined, and it

was concluded that it is independent of the pre-consolidation pressure but is material

dependant. For particle assemblies, the constraint factor would depend on single particle

properties which needs to be analysed by the Distinct Element Method (DEM) simulations

[4].

DEM simulations and analysis of the ball indentation technique were conducted using

EDEM® software provided by DEM Solutions. Ltd., Edinburgh, Scotland, UK. The Hertz-

Mindlin elastic contact model alongside a linear cohesion model are used. The cohesion

force between particles is calculated as follow:

F kA (4)

where A is the contact area and k is a cohesion energy density with units Jm-3. The value k

is chosen so that the work of cohesion of the model equates to that of JKR model. The

material properties used in the simulations are summarised in Table 1.

Property Particles Die (Geometry)

Diameter (mm) 1 Ɲ(1,0.14) 39

Density (kg.m-3) 2500 7800

Poisson‘s Ratio 0.25 0.3

Young‘s Modulus (GPa) 55 182

Interface Energy (J.m-2) 0.2-1.0 -

Table 1: Material properties used in the simulations

16,000 particles are generated to form a bed height of ~ 15 mm. The indenter diameter is 13

times greater than that of the particles. Figure 1 shows the indentation simulation inside the

EDEM® environment.

Figure 1: DEM Simulation of ball indentation

In order to investigate the correlation between yield stress and hardness, the unconfined

compression test is also simulated.

The speed at which the piston and indenter are moved is controlled to provide a strain rate

of 1. This shows that the processes are carried out within quasi-static regime, where the

effects of dynamics of the geometries on stresses are minimised [6].

Results

Figure 2 shows the hardness values calculated for a range of maximum indentation load for

three different cohesion levels when the powder bed is consolidated to 10 kPa.

Figure 2: Hardness vs. indentation load for three interface energy values

It can be seen that hardness does not change significantly with indentation load. This shows

that the powder bed is not consolidated during indentation process. If the bed is

consolidated, the hardness value will increase and it will not be representative of the pre-

consolidation of interest.

Figure 3 shows the hardness and unconfined yield stress results obtained from the

simulations for a range of pre-consolidation pressure. The interface energy between the

particles is 1 Jm-2 and the hardness values are obtained with a maximum indentation load of

0.12 N.

0

2

4

6

8

10

12

0.09 0.1 0.11 0.12 0.13 0.14 0.15 0.16

Ha

rdn

ess

(k

Pa

)

Indentation Load (N)

Γ=1.0

Γ=0.5

Γ=0.2

Figure 3: Hardness vs. unconfined yield stress for a range of pre-consolidation pressure

It is clear that there exist a correlation between the hardness and unconfined yield stress.

The constraint factor for the simulated powder with a surface energy of 1 Jm-2 is ~5.

Discussion

The dimensions of the simulated bed and indenter were sufficient to prevent further

consolidation of the bed by indentation in the range tested. These simulations of ball

indentation were coupled with simulations of unconfined compression and showed the

correlation between hardness and unconfined yield stress. For this material the constraint

factor was found to be approximately 5.

The influence of single particle properties such as surface energy, friction, shape and

stiffness on the constraint factor will be analysed in the future simulations. Development of a

relationship between single particle properties and the constraint factor will allow the yield

stress to be inferred from ball indentation experiments alone. Consequently a ball

indentation device could be developed to measure the flowability of powders in-situ, even

when only a small powder quantity is present.

References

1. Prescott J.K., and R.A. Barnum. Pharmaceutical Technology, 2000. 24(10): p. 60.

2. Hassanpour A., and M. Ghadiri, Particle & Particle Systems Characterization, 2007. 24(2):

p. 117.

3. Castellanos A., J.M. Valverde, M.A.S. Quintanilla, Kona, 2004, 22: p. 66.

0

2

4

6

8

10

12

0

2

4

6

8

10

12

0 5 10 15 20U

nc

on

fine

d Y

ield

Stre

ss (K

Pa)

Hard

ness (

kP

a)

Pre-consolidation Stress (kPa)

Hardness

UnconfinedYield Stress

4. Wang C., A. Hassanpour, and M. Ghadiri, Particuology, 2008. 6(4): p. 282.

5. Kozlov G.V., V.D. Serdyuk, and V.A. Beloshenko, Mechanics of Composite Materials,

1995. 30(5): p. 506.

6. Tardos, G. I., S. McNamara, I. Talu, Powder Technology, 2003, 131: p. 23-39.

Session 5: General Industries

Improving Asphalt Plant Design Using DEM Simulation

Andrew Hobbs

Astec

Andrew Hobbs

Research Engineer

Sheffield, UK

As a Research Engineer with Astec Inc., Hobbs performs CFD and DEM analyses

for product development and optimization. Astec has been using EDEM in their

research since the pre-1.0 beta release, and it has become a vital part of their design

process.

Astec Inc., based in Chattanooga, Tennessee, is a global leader in the hot mix

asphalt and road construction industries. Astec was founded in 1972 with the vision

to apply creative thinking and state-of-the-art technology to traditionally low-tech

industries.

Improving Asphalt Plant Design Using DEM Simulation

Andrew Hobbs, Astec, Inc.

Introduction

Astec, Inc. is a global leader in the production of hot mix asphalt equipment. Hot mix asphalt

(HMA) is the most common road surface in the US, comprising approximately 94% of all

roads. HMA is comprised of sand and various sizes of crushed rock, called aggregate,

which is mixed together with liquid asphalt cement binder at temperatures above 180o C.

Designing equipment to produce high quality HMA presents many engineering challenges.

The physics involved HMA production are quite complex and include multiphase heat

transfer, combustion, dense and dilute particle transport, and pollutant formation to name a

few. The harsh environment in many of the internals of the equipment mean direct

observation is very difficult. Simulation methods including Discrete Element Method (DEM)

provide Astec engineers with valuable insight and help them design better, more efficient

asphalt plants. This paper will present several recent case studies.

Approach

Simulations were undertaken using EDEM 2.3 from DEM Solutions Ltd., Edinburgh,

Scotland, UK. After importing CAD geometry, established particle parameters were input

and the simulations were run. In some cases use of the API was made to add in custom

features to expedite runtimes. These custom features include the motion of slat conveyors,

recorded factory inputs, and wall conduction heat transfer. Ensight 9.2 was used for data

visualization in some of the cases.

Results

Case 1: Size segregation

Size segregation is an undesirable phenomena where in particles group according to size. It

can occur anytime aggregate is moved but most commonly occurs at transfer points. It is

difficult to visually identify in the field so simulation is very helpful in minimizing the potential

for segregation. Figures 1 and 2 show the results of segregation studies of the drag

conveyor and silo batchers.

Figure 1. Size segregation in the silo batcher

Figure 2. Size segregation in the drag conveyor

Case 2: Aggregate veiling in the drum

Much previous work has been done to establish the connection between veiling (the

showering of particles inside the drum dryer) and convective heat transfer using the EDEM

CFD Coupling for FLUENT. To reduce the calculation time uncoupled DEM simulations

were run to investigate the veiling performance of a new novel flight design. Simulations of a

single row as well as a full flight layout were run. Bin groups were used to quantify veil

density. Representative results from the full dryer are shown in Figure 3.

Figure 3. Aggregate veiling in the dryer

Case 3: Conductive heat transfer in biomass dryer

DEM was used in the early stages of the design of a biomass dryer used to dry wood chips.

The API was used to write a custom contact model to track the conductive heat transfer from

the heated tubes to the wood particles. The particle thermal properties and conductive heat

transfer coefficients were calibrated using experimental data. These values were then used

with the custom contact model to investigate various designs for the heat exchanger tube

layout.

Figure 4. Wood chip drying in the biomass dryer

Discussion

The use of DEM simulations provides Astec engineers with a tool to both visualize material

behavior inside existing equipment and virtual test new designs before fabrication resulting

in better designs and quicker times to market. In addition the expanded capabilities provided

by the API have permitted simulations that would not have been possible without

customization.

Simulation of Cutting Process by Hybrid Granular and Multibody Dynamics Software

Mahbubur Rahman, Dingena L. Schott, Sape A. Miedema,

Gabriël Lodewijks

Delft University of Technology

Dingena L. Schott

Assistant Professor, Section of Transport Engineering and Logistics, Delft, The Netherlands

Within the Section of Transport Engineering and Logistics Schott focuses on the

field of Dry Bulk Transport and Storage, including the logistics and environmental

impact involved. Her team‘s current DEM work is on equipment design and

calibration and validation with the use of EDEM.

The Delft University of Technology Department of Marine and Transport

Technology focuses on the development, design, building, and operation of marine,

dredging and transport systems and their equipment. This requires the further

development of the knowledge of the dynamics and the physical processes involved

in transport, dredging and marine systems, the logistics of the systems and the

interaction between the equipment and control systems.

Simulation of cutting process by hybrid granular and multibody dynamics

software

Mahbubur Rahman, Dingena L. Schott, Sape A. Miedema, Gabriël Lodewijks

Delft University of Technology, Department of Marine and Transport Technology,

Section of Transport Engineering and Logistics, Mekelweg 2, 2628 CD Delft, The

Netherlands

Abstract

Cutting processes are common for many geotechnical, mining, dredging and bulk materials

handling cases. Understanding the interactive phenomena between granular materials and

cutting tools is very important for designing or evaluating cutting process. Currently few

researchers are conducting research to analyze bulk materials and cutting machine

mechanical interaction.

Simple dry sand cutting is analyzed with computational experiments in this paper to

supplement the knowledge in this field. Granular dynamics software (EDEMTM) and

Multibody dynamics software (MSC.ADAMSTM) are used to simulate sand and cutting tools

operation respectively. This computational experiment represents the complete cutting

process including initial and steady state.

Previously cutting methods were simulated by discrete element methods without proper

loading effects from blade on bulk materials. It has been overcome in this work by using

MBD software and the output is then compared with sand cutting analytical model of

Miedema (2009).

A Numerical Comparison of Mixing Efficiencies of

Solids in a Cylindrical Vessel Subject to a Range of Motions

M. Marigo; D. L. Cairns; M. Davies; E. H. Stitt

Johnson Matthey Catalyst

A. Ingram

Birmingham University

Michele Marigo

Research Scientist, Particle Engineering Birmingham, United Kingdom

As a Research Scientist with Johnson Matthey, Marigo conducts research and

development in the field of particle engineering, including application of DEM and

particle mechanical characterizations.

Johnson Matthey is a speciality chemicals company with core skills in catalysis,

precious metals, fine chemicals and process technology. Principal activities include

the manufacture of autocatalysts, heavy duty diesel catalysts and pollution control

systems; catalysts and components for fuel cells; catalysts and technologies for

chemical processes; fine chemicals; chemical catalysts and active pharmaceutical

ingredients; and the marketing, refining, and fabrication of precious metals. Johnson

Matthey products are sold across the world to a wide range of advanced technology

industries.

A numerical comparison of mixing efficiencies of solids in a cylindrical vessel

subject to a range of motions

M. Marigo, D. L. Cairns, M. Davies and E. H. Stitt, Johnson Matthey Catalyst, Billingham,

UK;

A. Ingram, Birmingham University, Birmingham, UK

Introduction

The mixing of solids is a fundamentally important unit operation in the pharmaceutical, food

and agricultural industries, as well as many others. Controlling the mixing mechanisms is key

to achieving the desired characteristics for a final product; this is difficult to design from first

principles since, in spite of considerable research, fundamental understanding remains

incomplete.

The mixing mechanisms will depend on the mixing action of the mixer (a wide range of

possible designs) and the flow behaviour of the particles. Rotating cylinders for example are

widely used as mixers. In batch mode they usually they consist of a horizontal cylinder

rotating around the central axis [1,2,3,4,5].The motion of the granular bed is predominantly

rotation about the cylinder axis with a cascading free surface: mixing occurs predominantly

in the cross-section with some axial dispersion [6].

1 Y. L. Ding, R. N. Forster, J. P. K. Seville, D. J. Parker, Scaling relationships for rotating

drums, Chemical Engineering Science 56 (2001), pp. 3737-3750

2 Y. L. Ding, R. Forster, J. P. K. Seville, D. J. Parker Granular motion in rotating drums: bed

turnover time and slumping–rolling transition, Powder Technology 124 (2002), pp. 18-27

3 Matthew T. Hardin, Tony Howes, David A. Mitchell Mass transfer correlations for rotating

drum bioreactors, Journal of Biotechnology 97 (2002), pp. 89-101

4 A.C. Santomaso, Y.L. Ding, J.R. Lickiss, D.W. York, Investigation of the Granular Behaviour

in a Rotating Drum Operated over a Wide Range of Rotational Speed, Chemical Engineering

Research and Design 81 (2003), pp. 936-945

5 Abdel-Zaher M. Abouzeid, Douglas W. Fuerstenau, Mixing–demixing of particulate solids in

rotating drums, International Journal of Mineral Processing 95 (2010), pp. 40-46

6 Powder mixing: some practical rules applied to agitated systems M.Poux, J. Bertrand 1990

Slow axial mixing, can be enhanced by incorporating a rocking motion of controlled

amplitude and frequency, this added perturbation accelerates the mixing process [7]. Other

types of motion can therefore be applied to try to enhance mixing in the axial direction: the

hoop mixer and Turbula mixer are typical examples. In both of these examples the material

to be mixed is placed inside a cylindrical mixing vessel which is then subjected to complex,

yet regular, motion. In the hoop mixer the longitudinal axis of the cylindrical container is

inclined at an angle to a horizontal axis of rotation. Under this condition the granular bed is

subjected to radial and axial movement as a result of the gravitational forces which are

acting periodically in the axial direction due to the inclination and the revolving movement of

the cylinder [8]. The movement of the cylindrical container located within the Turbula mixer

chamber comprises two rotations and a horizontal translation. The material within the vessel

is therefore subjected to intensive, periodically pulsating movements as result of the sharp

reversal in direction of translation and the rapid change in orientation of the vessel [9,10].

The purpose of the work reported here is to evaluate the power of DEM to help understand

flow processes and explain mixing mechanisms in different mixing equipments: horizontal

rotating drum, the hoop mixer and the Turbula mixer.

Approach

The commercial three-dimensional DEM code (EDEM® 2.3) has been used in this work.

The three different motions (rotating drum, hoop mixer, Turbula mixer) have been applied to

a cylindrical container, 45 mm in diameter and 80 mm in length, as shown in Fig.1. The

granular system comprises two differently coloured and initially segregated fractions of

otherwise identical monosized spherical particles (3 mm, 9000 particles, 50% fill level) and

7 Carolyn Wightman, Fernando J. Muzzio, Mixing of granular material in a drum mixer

undergoing rotational and rocking motions I. Uniform particles, Powder Technology 98 (1998),

pp. 113-124

8 M. Aoun-Habbache, M. Aoun, H. Berthiaux, V. Mizonov, An experimental method and a

Markov chain model to describe axial and radial mixing in a hoop mixer, Powder Technology

128 (2002), pp. 159-167

9 N. Sommier, P. Porion, P. Evesque, B. Leclerc, P. Tchoreloff, G. Couarraze, Magnetic

resonance imaging investigation of the mixing-segregation process in a pharmaceutical

blender, International Journal of Pharmaceutics 222 (2001), pp. 243-258

10 M. Marigo, D.L. Cairns, M. Davies, M. Cook, A. Ingram, E.H. Stitt, Developing Mechanistic

Understanding of Granular Behaviour in Complex Moving Geometry using the Discrete

two different initial filling conditions have been considered, transverse and axial filling

patterns.

Figure 1: Representation patterns used in the simulation for the three mixers. (a) Transverse

filling (b) Axial filling.

The rate and extent of mixing, quantified using a ―segregation index‖ based on contacts

between two discretely labelled but otherwise identical fractions, was shown to depend on

equipment motion, operating speed and the initial distribution of the fractions.

Results

As shown in Fig.2 the effect of rotational speed is investigated in case of rotating drum filled

for both the filling patterns and the well known characteristics of the horizontal drum

operating in rolling mode were demonstrated: excellent transverse mixing and poor axial

mixing; both improving with speed as the depth of the active layer is shown to increase:

transverse and axial loading. As expected it can be noticed that in case of axial filling the

mixing is very slow as result of only purely dispersive mechanism in axial direction. In cases

of transverse filling the rate of mixing rate is fast since for a rotating drum the radial mixing is

very effective and higher rotation speed leads enhance mixing performance.

Figure 2: Comparison of the segregation index in case of a rotating drum at different speeds.

The hoop mixer incorporates off-axis rotation, causing periodic tilting of the cylinder axis.

The angle of inclination creates a rocking effect, which forces material movement along the

longitudinal axes of the container as highlighted by the black arrows in Fig.3.

Figure 3: Magnitude of velocity in case of hoop mixer.

A comparison between the different types of mixers for the characteristic number of rotation

Nmix (―reciprocal rate of mixing‖) is shown in Fig.4. Interestingly, at low speeds the hoop

mixer and simple rotating drum exhibit similar transverse mixing but increasing speed has

the opposite effect: improving transverse mixing in the drum while worsening it in the hoop.

Axial mixing in the hoop mixer, on the other hand improves with speed. The Turbula displays

a very interesting relationship with speed. At low speeds, its transverse mixing performance

is the same as the horizontal drum and hoop but decreases significantly with increasing

speed, going through a minimum at medium speed before recovering completely at high

speed.

Figure 4: Comparison of rate of mixing for the three mixers.

Conclusions

The present work is an elementary comparison of the effect of axis of rotation and loading

pattern on the mixing performances for a cylindrical vessel moving according different

motions: rotating drum, hoop mixer and Turbula mixer for a drum filled with spherical

particles.

An exponential law was used to describe the mixing behaviour in terms of a characteristic

number of rotations to achieve mixing. It was observed as expected for the rotating drum

operating in rolling mode the axial mixing is purely a dispersive mechanism and the radial

mixing is dominant.

With the hoop mixer it was observed that the rocking motion causes mixing in axial direction

and that the overall mixing efficiency depends on the operating speed. The axial mixing in

case of a hoop mixer improves with the speed whereas the radial mixing slightly degrades

as the speed increases. In the case of the Turbula mixer, we observe a decrease in mixing

efficiency from 23 to 46 rpm and a subsequent increase as speed increases from 46 to 69

rpm for both axial and radial mixing. This appears to be indicative of a transition in the bed

behaviour and mixing mechanism; further experimental investigations are necessary to

properly validate the DEM model and these observations. Further experimental work has

been carried out and it will be reported by comparing DEM simulations and Positron

Emission Particle Tracking mixing experiments on similar conditions for the Turbula mixer.

Acknowledgement: MM would like to acknowledge the EU for financial support through the

Framework 6 Marie Curie Action "NEWGROWTH", contract number MEST-CT-2005-

020724, Johnson Matthey Plc for funding and supporting this research.

References

1 Y. L. Ding, R. N. Forster, J. P. K. Seville, D. J. Parker, Scaling relationships for

rotating drums, Chemical Engineering Science 56 (2001), pp. 3737-3750

1 Y. L. Ding, R. Forster, J. P. K. Seville, D. J. Parker Granular motion in rotating drums:

bed turnover time and slumping–rolling transition, Powder Technology 124 (2002), pp. 18-27

1 Matthew T. Hardin, Tony Howes, David A. Mitchell Mass transfer correlations for

rotating drum bioreactors, Journal of Biotechnology 97 (2002), pp. 89-101

1 A.C. Santomaso, Y.L. Ding, J.R. Lickiss, D.W. York, Investigation of the Granular

Behaviour in a Rotating Drum Operated over a Wide Range of Rotational Speed, Chemical

Engineering Research and Design 81 (2003), pp. 936-945

1 Abdel-Zaher M. Abouzeid, Douglas W. Fuerstenau, Mixing–demixing of particulate

solids in rotating drums, International Journal of Mineral Processing 95 (2010), pp. 40-46

1 Powder mixing: some practical rules applied to agitated systems M.Poux, J. Bertrand

1990

1 Carolyn Wightman, Fernando J. Muzzio, Mixing of granular material in a drum mixer

undergoing rotational and rocking motions I. Uniform particles, Powder Technology 98

(1998), pp. 113-124

1 M. Aoun-Habbache, M. Aoun, H. Berthiaux, V. Mizonov, An experimental method

and a Markov chain model to describe axial and radial mixing in a hoop mixer, Powder

Technology 128 (2002), pp. 159-167

1 N. Sommier, P. Porion, P. Evesque, B. Leclerc, P. Tchoreloff, G. Couarraze,

Magnetic resonance imaging investigation of the mixing-segregation process in a

pharmaceutical blender, International Journal of Pharmaceutics 222 (2001), pp. 243-258

1 M. Marigo, D.L. Cairns, M. Davies, M. Cook, A. Ingram, E.H. Stitt, Developing

Mechanistic Understanding of Granular Behaviour in Complex Moving Geometry using the

Discrete Element Method. Part A: Measurement and Reconstruction of Turbula® Mixer

Motion using Positron Emission Particle Tracking, Computer Modeling in Engineering and

Sciences 1591 (2010), pp.1-22

Lunar Dust Mitigation by Travelling Electrostatic Waves

Nima Gharib; Peter Radziszewski

McGill University

Nima Gharib














academics.

Lunar Dust Mitigation by Travelling Electrostatic Waves

Nima Gharib (PhD Candidate), Peter Radziszewski (Associate Professor)



Introduction

Lunar dust is expected to be electrostatically charged due to solar UV irradiation and its

exposure to the solar wind and cosmic rays. The charged dust particles hover above the

surface of the moon and cover everything that they come into contact with. The dust

particles are so fine and also very abrasive. From mission documents of the six Apollo

missions that landed on the surface of the moon, dust related problems is categorized into

nine main groups; vision obscuration, false instrument reading, lost of foot traction, dust

coating and contamination, seal failures, clogging of mechanisms, abrasion of materials,

thermal control problems, and inhalation and irritation risk. Thereby keeping dust away from

electrical, mechanical and visual devices is a way to increase life expediency of the parts

and be able to have longer mission duration [1].

Lack of atmosphere, high temperature fluctuation and limitation on material quantity that can

be carried to the moon, restrict us to apply terrestrial approaches for sweeping dust away

from surfaces. In this work the possibility of generating traveling electro-magnetic waves by

―electric curtain‖ and using electrostatic and dielectrophoretic forces for dust removal is

investigated. Electric curtain is a device consists of parallel electrodes connected to single or

multi AC power source(s). It generates travelling electromagnetic waves so that particles

within the generated field would move based on their polarity along or against the direction

of the field [2, 4, 6]. The electro-magnetic field can acts as a contactless conveyor which

reduces the potential of damaging delicate surfaces.

Approach

Planar, circular, and tubular configurations have been selected bearing in mind the potential

application they might be used. In the case of tubular configuration both inside and outside

of the tube is studied. Each device is connected to a 3-phase AC power source with the

frequency of 50 Hz. There is a phase lag of Π/3 between each phase which provides

continuous moving waves that will act as an electro-magnetic conveyor.

In the first step the electric fields generated by each configuration need to be determined.

ElecNet software developed by Infolytica Corporation utilize with Finite Element Method to

determine the electric fields around each geometry. The resulting electric field is shown in

Figure 1.

In the second step the calculated electric field is divided into seven time steps and then

imported to EDEM 2.3 particle simulation software with the additional ―field manager‖

module. Material properties used during the simulations are listed in Table 1.

Table 1. Material properties

Poisson‘s ratio 0.25

Shear modulus 1e08 Pa

Density 1000 kg/m3

Work function 0.4 eV

Coefficient of restitution 0.5

Coefficient of static friction 0.5

Coefficient of rolling friction 0.01

The particles created by ―particle factory‖ for all cases are simple spherical particles and

have electric charge of 10-15C. The effect of electric field on uncharged particles with sharp

edges only studied for planar configuration. In this case two types of particles are created

one simple spherical particles and second paired spherical particles made by two

overlapping spheres in order to better model the shape of lunar dust. For the last case

particles do not have initial electric charge instead for the particles and the surface where

they come into contact with work function for is defined. In the other word the particles get

charged by contacting each other and also the surface. That was introduced to the model by

implementing Tribocharging feature of EDEM.

(a) (b)

(c)

Fig. 1. Moving electric field generated by (a) planar (b) tubular (c) configuration while

connected

3 phase AC power source

Results

After the particle created and deposited on the surfaces the modified ―External Force‖ is

added to the model and the simulations were run for another few more time steps. The API

was modified so that it reads seven data series from ―field manager‖ to calculate the forces

on the particles. As shown in the Fig. 2 after the device is turned on, the particles experience

the electric field and move along or against it based on their polarity.

(a) (b)

(c) (d)

Fig. 2 Dust removal by electrostatic forces in (a) planar (b) tubular-outside (c) tubular-inside

(d) circular configurations

Discussion

The efficiency of this method is depends on the particle size, the activation frequency,

voltage profile, distance between electrodes, electrode diameters, and the medium the

device is working in. Therefore to obtain a smooth movement of dust particles above

mentioned variables need to be optimized.

We plan to do some experiments in very low temperature and vacuum condition to simulate

the working condition on the moon and show the potential use of electric curtain for future

space missions.

Acknowledgement

The authors would like to thank Neptec and CSA as well as NSERC CRD program for the

financial support of this project and also DEM Solutions. Ltd., Edinburgh, Scotland, UK, for

their help and their advices

References

[1] J. R. Gaier (2005) NASA, GRC. NASA/TM, Abstract #2005-213610.

[2] A. S. Biris, D. Saini et. al. (2004) IEEE, 2, 1283–1287.

[3] S. Masuda, et al. (1988) IEEE, 24, 217-222.

[4] M. K. Mazumder, R. Sharma, et al, (2007), Particulate Science and Technology, 25, 5-20.

[5] S. Masuda, T. Kamimura (1975) Journal of Electrostaics,1, 351-370.

[6] F. M. Moenser (1995) IEEE, Abstact # 0-7803-2503-6.

[7] DEM Solutions, Ltd. (2010), ―EDEM 2.3 User Guide,‖ Copyright © 2010, Edinburgh,

Scotland, UK.

Particle Scale Modelling Of Frictional-Adhesive Granular Materials

John P. Morrissey; Jin Sun; Jian-Fei Chen; Jin Y. Ooi;

University of Edinburgh

John P. Morrissey

PhD Candidate, Silos and Granular Solids Research Group, Institute for Infrastructure & Environment, School of Engineering Edinburgh, Scotland, United Kingdom

The Silos and Granular Solids Research Group has conducted research and

consultancy in the areas of shell structures, particulate solids mechanics and bulk

handling in support of innovative engineering solutions for over 20 years. The Group

have worked on a wide spectrum of topics including computational modelling of

solids and structures, functional and structural design of silo structures, including

their codification in design standards, material characterization and experimentation,

including solids flow and silo pressures. The focus of recent research is to transform

DEM numerical technique from a largely scientific tool into a quantitative predictive

tool.

The Institute for Infrastructure & Environment (IEE), one of five research

institutes of the School of Engineering at the University of Edinburgh, is one of

Scotland's foremost centres for research in our Built and Natural Environment. The

IEE‘s academic and research staff and postgraduate students together form four

Research Groups covering an extensive range of topics related to the field of Civil &

Environmental Engineering.

Particle Scale Modelling Of Frictional-Adhesive Granular Materials

John P. Morrissey*, Jin Sun*, Jian-Fei Chen*, Jin Y. Ooi*

* Institute for Infrastructure & Environment (IIE), University of Edinburgh, Edinburgh,

Scotland.

E-mail: [email protected]

AbSTRACT

The cohesive strength of a sticky industrial bulk solid is generally recognised to be

dependent on the prior consolidation stress exerted on the bulk solid. As a result of this

characteristic, the previous stress states of a bulk solid leading up to a handling scenario

need to be considered when evaluating the handling behaviour of bulk materials. Many of

the currently implemented DEM contact models that attempt to account for the adhesion that

develops within a granular material, such as the JKR model [1] or capillary force models

[2,3], fail to capture this stress history dependent behaviour and as such may not be

representative of a bulk solid in many handling and processing operations.

This paper describes the development of a new contact model in EDEM that accounts for

this stress history dependent frictional-adhesive behaviour. It is assumed that the adhesive

forces arising within the granular solid from the consolidation stress are responsible for the

handling problems related to bulk materials during production and storage, where high levels

of adhesion developing during material storage can lead to blockages near outlets during

discharge.

In this study a meso-scale approach is adopted here where the aim is to reproduce the

observed stress history dependent bulk behaviour. As a first attempt, a relatively simple bi-

linear spring model giving rise to plastic permanent deformation [4-10] was chosen for the

contact model. A single adhesive force parameter is defined as a function of the maximum

contact overlap for each contact which is tracked continuously throughout the simulation.

The DEM simulations were conducted using EDEM® v2.3 particle simulation software, with

the contact model implemented through the use of the API feature [11,12]. Custom contact

properties were used to record the stress history for the simulation. The initial results show

that the contact model can capture the stress history dependent cohesive behaviour of bulk

materials.

mailto:[email protected]

REFERENCES

[1] K.L. Johnson, K. Kendall, and A. Roberts, ―Surface energy and the contact of elastic

solids,‖ Proceedings of the Royal Society of London. Series A, Mathematical and Physical

Sciences, vol. 324, 1971, p. 301–313.

[2] G. Lian and C. Thornton, ―A theoretical study of the liquid bridge forces between two

rigid spherical bodies,‖ Journal of Colloid and Interface Science, vol. 161, 1993, pp. 138-147.

[3] T. Groger, U. T z n, and D.M. Heyes, ―Modelling and measuring of cohesion in wet

granular materials,‖ Powder Technology, vol. 133, 2003, pp. 203-215.

[4] S. Luding, R. Tykhoniuk, and J. Tomas, ―Anisotropic Material Behavior in Dense,

Cohesive-Frictional Powders,‖ Chemical Engineering & Technology, vol. 26, Dec. 2003, pp.

1229-1232.

[5] S. Luding, ―Anisotropy in cohesive, frictional granular media,‖ Journal of Physics:

Condensed Matter, vol. 17, Jun. 2005, p. S2623-S2640.

[6] S. Luding, ―Shear flow modeling of cohesive and frictional fine powder,‖ Powder

Technology, vol. 158, 2005, pp. 45-50.

[7] S. Luding, K. Manetsberger, and J. Mullers, ―A discrete model for long time sintering,‖

Journal of the Mechanics and Physics of Solids, vol. 53, Feb. 2005, pp. 455-491.

[8] S. Luding, ―Cohesive, frictional powders: contact models for tension,‖ Granular

Matter, vol. 10, 2008, pp. 235-246.

[9] J. Tomas, ―Fundamentals of cohesive powder consolidation and flow,‖ Gran. Matt.,

vol. 6, 2004, pp. 75-86.

[10] J. Tomas, ―Micromechanics of ultrafine particle adhesion–contact models,‖ AIP

Conference Proceedings, American Institute of Physics, 2 Huntington Quadrangle, Suite 1

NO 1, Melville, NY, 11747-4502, USA,, 2009, p. 781.

[11] DEM Solutions Ltd., EDEM 2.3 User Guide, Edinburgh, Scotland, UK. 2010.

[12] DEM Solutions Ltd., EDEM 2.3 Programming Guide, Edinburgh, Scotland, UK. 2010.

Simulation of Pneumatic Conveying Flow Regimes by

Coupled EDEM-FLUENT

Mohammadreza Ebrahimi, Martin Crapper

The University of Edinburgh

Mohammadreza Ebrahimi

PhD Student, PARDEM, Institute of Infrastructure and Environment, School of Engineering Edinburgh, Scotland, United Kingdom

The PARDEM project (PARticle Systems: Training on DEM Simulation

for Industrial and Scientific Applications) brings together industrial and

academic partners to develop the Discrete Element Method (DEM) of modelling and

to predict the behaviour of granular solids such as pellets, grains, sand and biomass

for industrial applications.







Simulation of pneumatic conveying flow regimes by coupled EDEM-FLUENT

Mohammadreza Ebrahimi and Martin Crapper

Institute of Infrastructure and Environment, School of Engineering, The University of

Edinburgh, The King‘s Buildings, Edinburgh, EH9 3JL, UK

To whom correspondence should be addressed: [email protected]

Abstract:

Pneumatic conveying is widely used in various industries for solid handling and

transportation. Generally, depending on particle properties, gas velocity and system

geometry various flow regimes may take place in pneumatic lines. In this study diverse flow

patterns in vertical and horizontal pneumatic conveying are simulated by using coupled

EDEM-FLUENT software. The fluid phase is simulated by using FLUENT to solve time-

averaged Navier-Stokes equation and solid phase is modelled as discrete elements by using

DEM software, EDEM. Two-way coupling through the full momentum exchange between gas

and solid phases is applied in simulation and Eulerian-Lagrangian method is selected to

have better insight to the particle level phenomena.

All operating conditions and particle properties have been extracted from Lim et al.(Lim,

Wang, & Yu, 2006) study to re-simulate their results. For horizontal pneumatic conveying

plug flow, stratified flow, moving dunes and homogeneous flow, and for vertical conveying

dispersed and plug flow are simulated to show the ability of commercial software to re-

simulate DEM-CFD code results. For vertical and horizontal pneumatic conveying effect of

gas velocity on the radial solid concentration profile is also investigated.

0.5 s 2 s 8 s 10 s

Fig1. Dispersed flow regime in vertical pneumatic conveying (gas velocity 24 m/s, 500

particles)

The results illustrate that coupled EDEM-FLUENT software can simulate the gas-solid phase

systems modelled by CFD-DEM code accurately and this software may open a promising

way for further development in two-phase modelling.

Reference:

Lim, E. W. C., Wang, C. H., & Yu, A. B. (2006). Discrete element simulation for pneumatic

conveying of granular material. Aiche Journal, 52(2), 496-509

Bond Models in EDEM

N. Brown; J.F. Chen; J.Y. Ooi;

University of Edinburgh,

S. Cole and M. Cook, DEM Solutions

Nick Brown

Ph.D Candidate, Silos and Granular Solids Research Group, Institute of Infrastructure and Environment, School of Engineering Edinburgh, Scotland, United Kingdom

The Silos and Granular Solids Research Group has conducted research and

consultancy in the areas of shell structures, particulate solids mechanics and bulk

handling in support of innovative engineering solutions for over 20 years. The Group

have worked on a wide spectrum of topics including computational modelling of

solids and structures, functional and structural design of silo structures, including

their codification in design standards, material characterization and experimentation,

including solids flow and silo pressures. The focus of recent research is to transform

DEM numerical technique from a largely scientific tool into a quantitative predictive

tool.







Stephen Cole and Mark Cook

Senior Consulting Engineers




Determination of Optimal Process Parameters and Materials using DEM

Álvaro Guerra Sánchez de la Nieta, Jesús Las Heras Casas,

Andrés García Pascual, Fernando Alba Elías

Universidad de La Rioja

Álvaro Guerra Sánchez de la Nieta

Industrial Engineer; PhD Student, Product Innovation and Industrial

Processes, Department of Mechanical Engineering, School of Industrial

Engineering Logroño, La Rioja, Spain

In Product Innovation and Industrial Processes, engineers focus on the

development of technical capability to design products and processes and efficiently

manage both, from a technological and economic standpoint.

The School of Industrial Engineering at the Universidad del La Rioja offers

degrees in Electrical Engineering, Industrial Electronics Engineering, Mechanical

Engineering, and Industrial Engineering.

Determination of optimal process parameters and materials using DEM

Guerra Sánchez de la Nieta, Álvaro, (Universidad de La Rioja, Logroño, La Rioja, España);

Las Heras Casas, Jesús, (Universidad de La Rioja, Logroño, La Rioja, España); García

Pascual, Andrés, (Universidad de La Rioja, Logroño, La Rioja, España); Alba Elías,

Fernando, (Universidad de La Rioja, Logroño, La Rioja, España).

Introduction

At present, the following methods that are used for dosing additives in preserved foods are

liquid dosing (additives dissolved in the canning liquid) and dry dosing (additives in

powdered or tablet-form). Dry dosing methods are more hygienic and precise than liquid

dosing, imbuing the end foodstuffs with greater quality and safety. However, dry dosing is

used less frequently than liquid dosing because of high labor costs and low productivity.

Thus, an automatic dosing device was designed to provide the use of food additives in the

solid phase, specifically in the form of tablets, avoiding the discharge of wastewater. As a

direct result, the water used in the process is free of any corrosive agent. This extends the

useful life of the canning line machinery. Also, this dosing method improve both productivity

and the safety-quality of the end product, compared with the other dry dosing methods.

This research aims to optimize the device‘s serializer mechanism that we have patented

(Device for supplying / packaged tablets dosing for the food industry; EP1595795). In this

mechanism, a driving force acting on a pair of blades that rotate in the opposite direction,

and determining each unit step to the tablets (Figure 1).

Figure 1: Serialization mechanism: (left) sequence of operation, (right) mechanism operating

in laboratory prototype

Primarily, we tried several geometries of the blades for the tablets (Figure 2).

Figure 2: Some blade designs for the serialization mechanism

Approach

The dosing system is shown below. The purpose is to find the angle and speed of blade

optimal.

Figure 3: (left) Prototype in industrial process, (middle) Catia Dossing prototype model,

(right) Test Dossing prototype

Material Poisson´s

Ratio Shear Modulus Density

Salt 0,2500 1,e+04 2165,0000

Aluminium 0,3500 3,e+10 2700,0000

Polycarbonate 0,3700 8,e+08 1200,0000

Interaction Coef.

Restitution Static Friction Rolling Friction

Salt-Salt 0,5000 0,4500 0,0500

Salt-Aluminium 0,5000 0,3000 0,0100

Salt-Polycarb. 0,5000 0,3300 0,0100

Table 1: Materials characteristics and interactions

Figure 4: (left) Catia tablet model, (center and right) Several particle models created with

different radius and number of surfaces. The tablet model, as particle, is designed to use it

as template to conform with different numbers of surfaces and find a compromise between

simulation time and his approach to real model.

Figure 5: (left) Blade configuration parameters, (right) Geometry model of dossing device in

EDEM

Figure 6: Factory designed to emulate the tablet fallen into the prototype‘s hooper

Figure 7: Simulation parameter configuration

Results

Figure 8: Dossing velocity

Figure 9: (left) Particles compressive, (right) Particles total force

Discussion

This automatic dossing device provides advantages in the food industry: improving

productivity, cost efficiency. According to achieved results in modelling and process

simulation using EDEM, related to real behaviour, this tool is appropriate for this process.

EDEM is an efficient and fast tool to optimize device parameters, like the hooper‘s angle or

blade‘s shape.

In a future work, we hope using EDEM Results data to integrate with data mining

techniques. The objective will be take the best data to feedback it into the model (frictions,

blades velocity or angle) to minimize contacts forces between the tablets, which produce

mass loss in each. Another important task is the calibration of a vibration system to avoid

tablet jams. Moreover, it will let fix a non excessive angle and must respect the tablets mass.

Acknowledgements

This work is made possible thanks to support from the Regional Research Plan of the

Autonomous Community of La Rioja (Spain) through the project FOMENTA 2010/02, and

from the University of La Rioja through the project API10/15.

References

DEM Solutions, Ltd. (2010), ―EDEM 2.2 User Guide,‖ Copyright © 2009, Edinburgh,

Scotland, UK.

Alba, Fernando, Ordieres, Joaquin, Vergara, Eliseo, Martínez de Pisón, Francisco Javier

and Castejón Manuel (2005), European patent, EP 1 595 795 A1, Device for

supplying/dosing packaged tablets for the food industry.

Alba, Fernando, Ordieres, Joaquin, Vergara, Eliseo, Martínez de Pisón, FranciscoJavier,

Pernía, Alpha Verónica, Castejón, Manuel and González, Ana (2005), Utility model, ES 1

059 831, Comprimido de producto aditivo para su dosificación automática a envases en la

industria alimentaria.

Alba, Fernando, Ordieres, Joaquin, Vergara, Eliseo, Martínez de Pisón, Francisco Javier

and Castejón, Manuel (2007), Previous test patent, ES 2 277 503, Mejoras introducidas en

la patente de invención nº P200202907 por: ―Suministrador-dosificador de comprimidos a

envases para la industria alimentaria‖.

DEM simulation of parameter effects

in the shot peening process

Kov Murugaratnam, Department of Engineering Science.

University of Oxford

Kovthaman Murugaratnam

DPhil Student, Discrete Element Research Group, Department of

Engineering Science

Oxford, United Kingdom

Murugaratnam‘s research In the Discrete Element Research Group focuses on the

Shot peening optimization using DEM and is funded by the Engineering and Physical

Sciences Research Council (EPSRC), DEM Solutions, and Rolls Royce.

The Department of Engineering Science at Oxford is the only unified department in

the UK which offers accredited courses in all the major branches of engineering. A

broad view of engineering, based on a scientific approach to the fundamentals, is

part of the tradition that started with our foundation in 1908 - one hundred years of

educating great engineers, and researching at the cutting edge

DEM simulation of Parameter effects in the Shot Peening Process

By Kov Murugaratnam, Department of Engineering Science

University of Oxford, Room 11, Jenkin Building, Parks Road, Oxford, OX1 3PJ

mob: +44 (0) 7837975376

Abstract

Compressive residual stresses are beneficial in enhancing the fatigue life of metal

components. Shot peening (SP) is an industrial cold working process that is applied to

induce a field of compressive residual stresses and modify the mechanical properties of the

metal component. The SP process involves impacting a surface with tiny shots with forces

sufficient to create plastic deformation. The process is governed by a number of important

parameters, such as the shot size, angle of attack, and impact velocity and mass flow rate.

But the relation among the desired peening effect, particularly the residual stress distribution

of the treated surface and the peening parameters is still unknown and need to be

investigated. Modelling the process is very complex as it involves the interaction of a metallic

surface with large number of shots of very small diameter. Shot peening parameters are

customarily chosen on the basis of either empirical laws or past practice.

The objective of this work is to develop a discrete element model that can suitably simulate

the shot peening process so that parameters may be chosen on the basis of mechanical

considerations. A discrete element model with numerous randomly distributed steel shots

bombarding a steel component at various velocities is developed as an example. With this

model, the shot peening shot-shot interaction and shot-target interaction and particularly the

surface coverage, angle of impingement, shot size, impact velocity and the overall shot flow

can be studied in detail and with limited computational effort. A new technique to dynamically

change the coefficient of restitution for repeated impacts of shots in the same spot was

implemented.

Failure Modes Observed in Geobag

Revetment using EDEM

Aysha Akter, Gareth Pender, Grant Wright

School of Built Environment, Heriot Watt University

Martin Crapper, The University of Edinburgh

WaiSam Wong DEM Solutions Ltd

Aysha Aktera

PhD Student , Water Management Research Group, School of Built

Environment,

Edinburgh, Scotland, United Kingdom

Sustainable Water Management Research Group has a strong reputation in

delivering advanced research in all aspects of water management. This group

combines a vast range of expertise following a multi-disciplinary approach against

the new challenges set by the Water Framework Directive, the Intergovernmental

Panel on Climate Change and also the UK Government's Foresight Programme on

Flooding. Research focuses on development and numerical model application for

predicting both flow and transport problems. This research is also supported by

experimental studies in the extensive hydraulics laboratory.

School of the Built Environment at Heriot-Watt is one of the UK‘s leading institutions for

multidisciplinary research and teaching in the built environment and provides a collaborative

research and teaching environment for the core built environment disciplines

Failure modes observed in geobag revetment using EDEM

Aysha Aktera*, Martin Crapperb, Gareth Pendera, Grant Wrighta and WaiSam Wongc

aSchool of Built Environment , Heriot Watt University, Edinburgh, UK.

bSchool of Engineering and Electronics, The University of Edinburgh, The King's Buildings,

Edinburgh, UK.

cDEM Solutions Ltd., Edinburgh, UK.

Introduction

In recent years, sand filled geotextile bags (geobags) have been used as a means of long

term riverbank revetment stabilization. However, despite their deployment in a significant

number of locations, the failure modes of such structures are not well understood. Three

interactions influence the geobag performance, i.e., geobag–geobag, geobag–water flow

and geobag–water flow–river bank. The aim of the research reported here is to develop a

detailed understanding of the failure mechanisms in a geobag revetment using a DEM

model.

Approach

To enhance the fundamental knowledge of the performance of geobags in a revetment, work

has been carried out using 1:10 scale models of geobags, in a laboratory flume. In such

circumstances three interactions influence the geobag performance, i.e., geobag–geobag,

geobag–water flow and geobag–water flow–river bank. In the following EDEM® has been

applied to simulate these interactions and replicate the laboratory observations using the 3D

discrete element method (DEM).

Firstly, for geobag–geobag interaction, the frictional resistance up to the point of geobag

sliding were unknown. To evaluate this, a dry frictional resistance test was carried out on a

wooden test rig consisting of two parts, i.e., a fixed part and the mobile part. The mobile part

of the test rig can move up to 0.10 m downward, and provides a simple representation of

riverbank toe scouring. EDEM ® has been applied to represent this behaviour and thus a

coefficient of friction has been obtained for the geobags.

Secondly, geobag–water flow interaction was studied using 600 model geobags in a

laboratory open channel. Different failure modes were observed at different water levels

through several experiment runs. The active hydrodynamic forces were unknown for these

failure modes. So, to mimic the laboratory observation, a one way coupling of the measured

water velocity field and geobags was run using EDEM®. Thus the coefficient of drag and the

lift force applied by the flowing water to the geobags were obtained from the DEM

simulations.

Finally, the geobag–water flow–river bed interaction was studied through repetition of the

previous experiment on 0.10 m sand bed underneath the model geobag revetment. In

addition to the hydrodynamic forces, toe scouring was added as an additional parameter.

The measurements of bed changes were recorded from the laboratory and used in the

EDEM® model as a moving support boundary. The same one–way coupled model as

previously applied was then used here. The findings provided clarification of geobag

movement due to combined application of the hydrodynamic forces and toe scouring.

-Laboratory revetment setup

of 120 geobags on wooden

test rig showed some

movements with a 0.01 m

downward motion of the

mobile part.

-EDEM geobag revetment

replicates the movement

well.

-EDEM geobag revetment

replicates the observed

failure due to frictional force

in laboratory setup well.

Figure 1: Dry condition: EDEM prediction compared with laboratory observation

(for determination of the coefficient of static friction)

Results

Given variations in bag size used in the experiment, tolerance limits in their initial placement,

ignorance of the bag permeability and its state of wetness, the hypothesis is that the initial

response of any layer geobags in the DEM model would indicate the critical location for bag

instability in the revetment.

Figure 1 represents the geobag–geobag interactions; the static coefficient of friction was

obtained for the desired setup as 0.55. The coefficient of drag and lift forces were acquired

for geobag–water flow interaction, these being 0.5 and 0.8 respectively (Figure 2). The same

Velocity (m/s)

Velocity (m/s)

Mobile Fixed

coefficients could reproduce the laboratory observations for the geobag–water flow

interaction on a mobile sediment bed (Figure 3).

Discussions

In this study a coefficient of friction of 0.55 was found to give the best comparisons; this is

close to the published dry geotextile–sand interaction, which gives a coefficient of friction of

0.57 to 0.70 [1, 2] and to the finding by Recio and Oumeraci [3], for geobag – geobag

interaction under waves, which was 0.53.

A coefficient of drag of 0.5 and coefficient of lift of 0.8 show good agreement with laboratory

observations in higher water level conditions. In the first experiment, the movement of bags

started in the bottom–most layer due to void flow; the DEM model did not predict this,

although it did reproduce the bag movement in the surface level layer and the one below

this.

For more practical interactions (i.e., the geobag–water flow interaction on mobile sediment

bed), the DEM model gave good representation of revetment failure modes in all of the

selected water level conditions, and provides a useful tool for characterizing incipient

revetment failure.

References

1.Garcin P., Faure Y.H., Gourc J.P. and Purwanto E. (1995), ―Behaviour of Geosynthetic

Clay Liner (GCL): Laboratory Tests‖, Proceedings 5th International Symposium on Landfill.

Calgary, 1, pp. 347-358.

2.NAUE GmbH &Co. KG. (2006), ―Advantages of Needle-punched Secutex® and Terrafix®

Nonwoven Geotextiles‖, NAUE GmbH &Co. KG, Germany.

3.Recio, J. & Oumeraci, H. (2009), ―Processes affecting the hydraulic stability of coastal

revetments made of geotextile sand containers‖, Coastal Engineering 56, 260–284.

Acknowledgement

This study is being carried out under the Joint Research Institute (JRI) collaboration in Civil

and Environmental Engineering. Funding for this work from Heriot Watt University through a

James Watt Scholarship and additional support from DEM Solutions Limited and NAUE

GmbH & Co, are gratefully acknowledged.

wa

ter

level u

p to 4

9%

of th

e

revetm

ent h

eig

ht.

A1.Laboratory

Void flow initiated in the

bottom-most layer of bags and

propagated to the next layer

as this became less

supported.

A2.DEM model

The surface water level layer

and the next layer showed

outward movement of the

upstream outer corner. Thus

the bottom-most layers

became exposed to water.

wa

ter

level e

qu

al to

85%

to 1

00

% o

f th

e

revetm

ent h

eig

ht.

B1.Laboratory

Outward movement of

upstream outer corner

adjacent to water surface.

B2.DEM model

Outward movement of

upstream outer corner

adjacent to water surface.

Figure 2(A1 to B2): Visual validation of the DEM simulation against laboratory observations

FLOW

FLOW

FLOW

FLOW

wa

ter

level u

p to 4

9%

of th

e r

evetm

ent h

eig

ht.

Laboratory

A1: Void flow and sliding in the bottom layer;

A2:Detail of failure mode; and

A3: End of the experiment showing ripple bed formation along with a few bags

displaced in the bottom–most layer.

DEM Model

A4: Bottom–most layer showing displacement;

A5: Details of bag displacement showing outward movement of the upstream outer

corner of the bag, similar to A1 and reversed from A2. By comparing with A3, DEM

can be seen to represent failure initiation.

wa

ter

level e

qu

al to

85%

to 1

00

% o

f th

e r

eve

tmen

t h

eig

ht.

Laboratory

B1: Void flow causes uplifting in the bottom-most layer and at the same time sliding

due to overtopping is observed in the next to the surface water level layer.

Velocity (m/s)

Velocity (m/s) Velocity (m/s)

A1 A2 A3

A4 A5

Flow Flow

Flow Flow

B1 B2 B3

B4 B5

Flow Flow

Flow Flow

Velocity (m/s)

B2: Detail of bag movement direction; and

B3: End of the experiment.

DEM Model

B4 and B5: good representation of bag displacement initiation in the bottommost

layer and the next to the surface water level layer. Bag movement directions are

opposite in these two cases. The second category displacement is similar to the

rigid bed overtopping case, and the displacement direction not only satisfies the B2

observation but also confirms that water drag is the significant force. Thus the

reverse direction movement for the first category is due to bed erosion

Figure 3 (A1 to B5): Visual validation of the DEM simulation against laboratory observations

A DEM Application to Improve the Design of an Industrial Prototype

Álvaro Guerra Sánchez de la Nieta, Andrés García Pascual,

Jesús Las Heras Casas, Fernando Alba Elías,

Universidad de La Rioja

Álvaro Guerra Sánchez de la Nieta

Industrial Engineer; PhD Student, Product Innovation and Industrial

Processes, Department of Mechanical Engineering, School of Industrial

Engineering Logroño, La Rioja, Spain

In Product Innovation and Industrial Processes engineers focus on the

development of technical capability to design products and processes and efficiently

manage both, from a technological and economic standpoint.

The School of Industrial Engineering at the Universidad del La Rioja offers

degrees in Electrical Engineering, Industrial Electronics Engineering, Mechanical

Engineering, and Industrial Engineering.

A DEM application to improve the design of an industrial prototype

Guerra Sánchez de la Nieta, Álvaro, (Universidad de La Rioja, Logroño, La Rioja, España);

García Pascual, Andrés, (Universidad de La Rioja, Logroño, La Rioja, España); Las Heras

Casas, Jesús, (Universidad de La Rioja, Logroño, La Rioja, España); Alba Elías, Fernando,

(Universidad de La Rioja, Logroño, La Rioja, España).

Introduction

This work presents how EDEM [1] can be used to improve the design of a new mixer for the

food and pharmaceutical industries. Although the existing mixing industrial prototype (figure

1) already provides adequate times and proportions suitable for further processing

(according to the tests carried out with different granulated materials), it is possible to further

improve its performance by means of numerical simulations. Due to the characteristics of the

materials to be mixed, EDEM is suitable to improve the design of the prototype by modifying,

for instance, the geometry and angle of the blades, the speed of rotation, etc. Thus, a more

efficient design can be obtained in an economical way. At this moment, the optimal

parameters of the simulations in order to achieve the real mixing process results have been

accomplished.

Figure 1. Mixing Industrial Prototype

Approach

The first step was to get a virtual model with EDEM to reproduce the mixing process (figure

2).

Figure 2. Virtual simulation of industrial mixing prototype with EDEM.

Different types of simulations with different particle sizes, densities, friction coefficients, etc.

were analysed in order to find out the optimal parameters which replicate what actually

happens with the industrial mixing prototype (figures 3 and 4).

Thanks to the study conducted by Hassanpour et al [2], it can be considered that larger

particles with a density equivalent to the bulk density of powders are moving with the same

momentum than packets of fine particles. According to this, it was possible to reduce the

computing time by using larger particles, as Table 1 illustrates.

Diameter of particle Simulation time

5 1061 hours (estimated)

10 453 hours (estimated)

15 169 hours (real)

20 72 hours (real)

Table 1. Simulation time of EDEM as a function of the particle size.

Figure 3. Performance testing and sampling at different times and zones.

Results

Figure 4. Different periods of time during the mixing process (experimental and virtual with

EDEM).

Figure 5. Comparison between experimental method (industrial mixing prototype) and virtual

simulation (EDEM): mixing factor against mixing time at different zones in the mixing

chamber.

Discussion

COMPARATIVA DE LOS PROCESOS DE MEZCLA (MÉTODO EXPERIMENTAL Y SIMULACIÓN; 25-75%).

PRUEBAS Nº1, Nº3 Y SIMULACIÓN 1. MUESTRA/TRAMO 1

0%

5%

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25%

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35%

0 60 120 180 240 300 360 420 480 540 600 660 720 780 840 900 960 1020 1080 1140 1200 1260 1320 1380 1440 1500 1560 1620 1680 1740 1800

Tiempo de mezcla (s)Prueba experimental nº 1 Prueba experimental nº 3 Simulación virtual nº 1



0%

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10%

15%

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0 60 120 180 240 300 360 420 480 540 600 660 720 780 840 900 960 1020 1080 1140 1200 1260 1320 1380 1440 1500 1560 1620 1680 1740 1800




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0 60 120 180 240 300 360 420 480 540 600 660 720 780 840 900 960 1020 1080 1140 1200 1260 1320 1380 1440 1500 1560 1620 1680 1740 1800




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0 60 120 180 240 300 360 420 480 540 600 660 720 780 840 900 960 1020 1080 1140 1200 1260 1320 1380 1440 1500 1560 1620 1680 1740 1800


The achieved results (figure 5) show that it is possible to obtain the appropriate simulation

parameters to model the behaviour of the real industrial mixer prototype [3,4]. EDEM is an

efficient and fast tool to optimize device parameters, and future prototypes.

The future research is focused on improve the performance of the industrial prototype based

on this EDEM model. The geometry and angle of the blades, the speed of rotation, the

chamber geometry, etc. will be modified to analyse their effect on the mixing factor and

mixing time.

Acknowledgements

This work is made possible thanks to support from the Regional Research Plan of the

Autonomous Community of La Rioja (Spain) through the project FOMENTA 2010/02, and

from the University of La Rioja through the project API10/15.

References

DEM Solutions, Ltd. (2010), ―EDEM 2.3 User Guide,‖ Copyright © 2010, Edinburgh,

Scotland, UK.

Hassanpour, Ali; Tan, Hongsing; Bayly, Andrew; Gopalkrishnan, Prasad; Ng, Boonho and

Ghadiri, Mojtaba (2010). ―Analysis of particle motion in a paddle mixer using Discrete

Element Method (DEM)‖. Powder Technology, available online 20 August 2010.

García, Andrés (2008). ―Sistema Industrial para el acondicionamiento de aditivo alimentario‖.

Industrial Engineering Final Project. La Rioja University.

García, Andrés (2010). ―Análisis del proceso de mezcla de prototipo industrial. Método

experimental y simulación virtual‖. Diploma of Advanced Studies. PhD courses in Project

Management. La Rioja University.

http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6TH9-50TRX8S-1&_user=10&_coverDate=08%2F20%2F2010&_alid=1444027154&_rdoc=5&_fmt=high&_orig=search&_cdi=5277&_sort=r&_docanchor=&view=c&_ct=6&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=33523a8ae31ea73e308b91f5d15cb8bd

http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6TH9-50TRX8S-1&_user=10&_coverDate=08%2F20%2F2010&_alid=1444027154&_rdoc=5&_fmt=high&_orig=search&_cdi=5277&_sort=r&_docanchor=&view=c&_ct=6&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=33523a8ae31ea73e308b91f5d15cb8bd

Use of DEM-Simulation in the Basic Research

on Screw Conveyors

W. A. Guenthner, S. Rakitsch

Technische Universitat Munchen

Stefan Rakitsch

Research Associate, Institute for Materials Handling Material Flow

Logistics, Mechanical Engineering Munich, Germany

The Institute for Materials Handling Material Flow Logistics of the Technische

Universität München is one of Germanys leading institutes in materials handling with

an experience of over 30 years in scientific research on screw conveyors.

The Technische Universität München is one of the most research-focused

universities in Germany and Europe. The Faculty of Mechanical Engineering offers

degrees in mechanical engineering, Energy and Process Engineering, Product

Development and Design, Automotive and Combustion Engine Technology,

Aerospace Engineering, Mechanical Engineering and Management, Mechatronics

and Information Technology, Medical Technology and Nuclear Technology.

Use of DEM-Simulation in the Basic Research on Screw Conveyors

Stefan Rakitsch, Institute for Materials Handling Material Flow Logistics, Technische

Universität München, Garching b. München, Germany;

W. A. Günthner, Institute for Materials Handling Material Flow Logistics, Technische

Universität München, Garching b. München, Germany;

Introduction

The research project aims at the analysis of inclined screw conveyors. Determining element

of the project is the forecast of the conveying character inside the Screw Conveyor and

therewith the designation of the achievable Volume Flows and the needed Drive Power as

functions of the geometry, operating and bulk material parameters. In order to achieve this

data for numerousness different parameter combinations are gained and statistically

evaluated. The project is funded by the Deutsche Forschungsgemeinschaft (DFG).

The advantages, such as the simple and robust assembly, low equipment and maintenance

costs, low susceptance to failure, and in particular the dust-proof design, often lead to the

use of screw conveyors for example in the field of bulk handling. They are used for the

vertical transport of bulk material from the hold as well as for the inclined transport on the

boom there. Other applications for inclined screw conveyors are found in the silo discharge

in cement plants. But the requirements for reliability, performance and economy but also in

terms of energy efficiency and environmental protection for conveyors for bulk materials

have risen significantly in recent years. The key parameters that need to be determined in

the sizing of screw conveyors are the achievable volume flow respectively the required

geometry and operating conditions to achieve the required flow rate and the necessary

power requirement. These targets must be determinable for the user as simple and practical,

yet safe and reliable, as possible. As there are no calculation rules existing, the project aims

in finding calculation methods for strongly inclined screw conveyors by the use of regression

analyses. A test rig is used to get the required data to develop the calculation methods. But

for e.g. geometry parameters it is not or only with considerable financial effort possible to

vary them. For this reason the decision was made to use DEM-Simulation in the project. This

paper deals with the question how a simulation model for the screw conveyors can be

prepared.

Simulation Model

Of course the screw conveyor is to be of fundamental importance in the simulation. In a first

step of abstraction, therefore, the geometry of the screw conveyor is reduced to the

necessary geometric and functional components [1]. In the case of the investigated screw

conveyor the interaction of the bulk material with the screw and the inner wall of the tube is

primarily of interest. To reduce the number of particles, therefore the function of periodic

boundaries of the simulation program is used. That means that only a short section of the

conveyor is really simulated (in this case 4 pitches). If a particle reaches the end of the

conveyor it is removed and relocated at the beginning of the conveyor with the same

characteristics (position in the cross section, speed, stresses, ...) again. In this way a quasi-

infinitely long conveyor is built.

Figure 1: Tube with Intermediate Bearing and Screw

The dimensions of the conveyor are chosen similar to the existing test rig for the first

simulations and validations. This allows the simulation model to be verified with data from

the test rig. The CAD models of the tube (here with intermediate bearing) and the associated

screw are shown in Figure 1. All geometry models were loaded into the simulation model

over the CAD data interface.

Finally the operating parameters of the screw conveyor, which are simulated, have to be

defined. The rotation speed n, the inclination β, the screw diameter D and the filling level φ

are to be varied. The different rotation speeds can thereby directly be defined as dynamic

properties of the screw. To set the different inclinations of the screw conveyor the vector of

gravity is varied accordingly. This has the advantage that the rest of the simulation model

can be maintained unchanged. To set the respective filling level, first the theoretical filling

level of the conveyor with static screw is calculated. After filling the conveyor with much

more particles as needed and let the particles come to rest, the particles above the

calculated level are cut away. The simulated values of the described operating parameters

are listed in Table 1.

Table 1: Simulated Values of the Parameters of Screw

Values of Parameters

Rotating Speed n

[1/s]

1 3 5 7 9

Inclination β [°] 30 45 60

Filling Level φ [-] 0.2 0.4 0.6

Screw Diameter D

[m] 0,20 0.26 0.40

Simulated Particles

The simulations are performed with PET-Pellets as bulk material. These pellets are also

used in the real test rig of the institute and are thus known in the properties and behaviour.

The particles are cylindrical with an elliptical basic shape and a volume of about 25 mm³.

The CAD-Model and the model of the particle used in the simulation are shown in Figure 2.

The simulation model of the particle consists of the shell of the CAD imported template, on

those properties such as volume, weight, inertia, etc. are based, and nine spheres, which

represent the boundary of the particle in contacts.

Figure 2: CAD-Model and Simulated Model of the Particle

As the mass of the particle is proportional to the numerical time step, the simulation time

would not be practical if using full-scale particles. The second step in the abstraction of the

simulation is therefore to increase the particles so that the realism of the simulation is not

significantly reduced. Therefore the particle properties of the simulation model must however

be adjusted so that the behaviour of particles is still consistent on the real bulk behaviour.

For materials handling problems the inner and outer friction are of particular importance

here [2] and are therefore calibrated together with the bulk density. In preliminary simulations

a particle model with a similar geometry, whose volume is increased by a factor of 20,

carried out to acceptable computing times. To calibrate the particles tests to determine bulk

properties are reproduced in the simulation. The simulated parameters are modified

iteratively as long as the behaviour of the simulation model is equivalent to the real bulk

behaviour with sufficient accuracy. As tests the determination of the bulk density, angle of

repose and wall friction are performed. The experimental setup and the simulation models

for the calibration are based on the recommendations given in FEM 2481 [3]. The results of

these tests of the real PET-Pellets are shown in Table 2. The simulation models to realise

the calibration are shown in Figure 3. In each case, the simulations with the final results are

shown. These are also listed in Table 2.

Table 2: Bulk Materials Parameters of PET-Pellets and in the Simulation

PET-Pellets Simulation

Bulk Density

[kg/m³]

790 793

Angle of Repose

[°] 35,8 35,7

Angle of Slip [°] 21,0 20,9

Figure 3: Simulation Models for Calibration: Bulk Density, Angle of Slip, Angle of

Repose (left to right)

Data Evaluation

As targets, the average axial velocity of the bulk material vax and the torque M measured on

the screw are evaluated. The evaluation is done with the evaluation algorithms of the

simulation program. The axial velocity of the bulk material is exported as the average

velocity of all particles in the section ―Screw Conveyor‖, which represents the complete

conveyor, in axial direction per timestep. From the axial velocity of the bulk material the

coefficient of velocity can be calculated as a function of the pitch S and the rotation

speed n as shown below. The coefficient of velocity is a dimensionless coefficient, which

represents the influence of the geometrical and operational conditions on the achievable

volume flow and is used to compare the performance of different screw conveyors.

nS

vax

(1)

For the torque the axial component of the total torque of the screw per timestep is red-out. It

is the basis for calculating the coefficient of power . This is again a dimensionless

coefficient for the required power of the screw conveyor and is as a function of the filling

level , the screw diameter D, the shaft diameter d, the bulk density , the conveying

length L, the pitch S, the inclination and the coefficient of velocity .

D

S

LgdD

M

sin822

(2)

Validation of Simulation

To validate the whole simulation model the simulated conveyor is compared to results of

tests from the test rig. The screw diameter of the test rig amounts to 0.260 m, shaft diameter

to 0.076 m and the pitch to 0.230 m. For the validation the parameters shown in Table 3 are

chosen. The results of the comparison for the coefficient of velocity and coefficient of power

are also shown there. It can be seen that the simulation represents the conveyance in

inclined screw conveyors sufficiently accurate as the deviation is absolutely always smaller

than 5%.

Table 3: Used Parameter Combinations of the Validation and Results

n Sim Test Deviat

ion Sim Test Deviat

ion

[°] [-] [1/s] [-] [-] [%] [-] [-] [%]

30 0,6 3 1,010 1,015 -0,5 3,791 3,880 -2,3

45 0,2

7 0,703 0,730 -3,8 13,06

1 12,691

2,9

60 0,2

9 0,691 0,713 -3,0 22,41

9 22,836

-1,8

60 0,4 5 0,635 0,617 3,0 6,085 6,256 -2,7

Summary

In the course of the project DEM-Simulation is used to simulate screw conveyors with

parameters, which are not possible to set at a test rig for example different screw diameters.

Therefore it is necessary to abstract and calibrate the real model to get a suitable DEM

model. On the one hand the geometry has to be simplified as much as possible. In this case

only the screw helix and the tube are depicted in the simulation. On the other hand the

simulated particles, which are blown up to shorten the simulation time, have to be adjusted

to get realistic results. This calibration is done by simulating three tests, which are commonly

used to get the bulk properties. In an iterating process the particle parameters in the

simulation are adjusted till the simulated properties are sufficient identical to the real

properties. After having the geometry and the particles the kind of data evaluation must be

defined. Therefore two parameters are selected which are also analysable in the test rig. In

having comparable results the last step in preparing the simulation is now to validate the

simulation model with results of the test rig. This was also done successfully so that a

validated simulation model of the screw conveyor is existing now.

Table of Symbols

Symbol

Unit Name Symbol

Unit Name

D [m] Screw Diameter vax [m/s] Axial velocity of Material

IV [m³/s] Volume Flow β [°] Inclination

L [m] Conveying Length [-] Coefficient of Velocity

M [Nm] Torque λ [-] Coefficient of Power

S [m] Pitch [kg/m³]

Bulk Density

d [m] Shaft diameter φ [-] Filling Level

n [1/s] Rotation Speed of Screw

References

[1] Katterfeld, A.; Krause, F.: Funktionsanalyse eines Rohrkettenförderers mit Hilfe der

Diskrete Elemente Methode (DEM); In: Tagungsband Fachtagung Schüttgutfördertechnik

2004, Technische Universität München, Garching bei München, 2004.

[2] Gröger, T.; Katterfeld, A.: Kalibrierung von DEM-Simulationsmodellen für die

Schüttgutfördertechnik; In: Tagungsband Fachtagung Schüttgutfördertechnik 2005, Otto-

von-Guericke-Universität Magdeburg, Magdeburg, 2005.

[3] FEM 2.481:1997-07: Specific characteristics of bulk products as applicable to

pneumatic conveyors, Fédération Européenne de la Manutention.

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