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Discrete Element Methods in STAR-CCM+
Petr Kodl
CD-adapco
Engineering numerical methods used to simulate motion or large number of interacting discrete objects
Comparable to short range force MD simulations in methodology
Established by P.A. Cundall, O.D.L. Strack: A discrete numerical model for granular assemblies. Geotechnique, 29:47–65, 1979
Classical mechanical method
Mesh free
CPU intensive
– Transient
– Explicit schemes
Provides detail resolution other methods can not achieve
Used to describe wider class of methods but in terms of STAR-CCM+ we focus on granular flows
Bulk state results from particle interactions – no constitutive relation is used
Introduction to Discrete Element Methods (DEM)
Anisotropy
– Stress chains
– Large spatio-temporal fluctuations
Persistent contacts
Shear resistance
Jamming and arching
Reynolds’ dilatancy
Granular materials and their specific properties
Sand
Food particles
Metal particles
Capsules and pills
Slurries
Grains
Soil
Granular materials
When does it make sense?
– Highly loaded particulate flows
– Collisions are important
– Particle shape is important
– Details of collisions are important
– Typical granular flow properties are studied – jamming, shearing
What are the limits for practical problems?
– Fine grain particles (<1e-4)
– Achievable but the CPU time can be prohibitively expensive for industrial
problems
– The collision details are typically not critical outcome
– Very large particles (>1m) where the local deformation is important and the
contact law small deformation assumption is not valid
DEM applications
Implemented within Lagrangian framework
– Reuses known concepts
• Lagrangian phase
• Injectors
• Boundary interactions
• Sub stepping of the solution
Extends concept of Material particle
Additional tracking of
– Orientation
– Angular motion
– Inter-particle collisions
Soft particle model (penalty function based force evaluation)
Not statistical – 1 parcel = 1 particle
DEM in STAR-CCM+
5.06 - 28 Oct, 2010
– Initial DEM release
– Hertz Mindlin contact model
– Spherical and composite particles
– Moving walls via applied velocity
condition
– Stationary mesh and MRF
6.02 - 28 Feb, 2011
– Rigid mesh motion
– Phase specific boundary behavior
– Drag laws suitable for highly
loaded flows
• Ergun equation – Gidaspow
Timeline of DEM in STAR-CCM+
6.04 - 1 July 2011
– Walton-Braun linear hysteretic
contact model
– Parallel bonds
– Flexible / breakable particle
clumps
– Lattice injectors
– Charged particles
6.06 – October 2011
– Cohesive particles
– Improved particle tracking code
– User controlled time steps
– Additional drag coefficients
• Haider Levenspiel
– Two way coupling for charged
particles
Timeline of DEM in STAR-CCM+
7.02
– Randomized position injectors
– Porosity injection limits
– Improved particle-flow interaction
through fast estimate of projected
area and length
– Contact data sources, reports and
visualization
7.04
– Particle trapping walls
– Improved randomization of initial
particle distribution
– Performance optimizations both in
serial and parallel
Timeline of DEM in STAR-CCM+
– Comparison of contact force
models for the simulation of
collisions in DEM based granular
flow codes,
Alberto Di Renzo, Francesco Paolo
Di Maio, 2004, Chemical
Engineering Science
– Aluminum oxide spheres shot
against glass plate with varying
impact angle
– Apparent coefficient or tangential
restitution, rotation rate and
rebound angle compared to
laboratory experiment and
reference implementation
Validation –contact mechanics
– Discrete Particle Simulation of
Solid Flow in Model Blast Furface,
Zongyan Zhou, Haiping Zhu
ISIJ Vol 45, 2005
– Studies solid flow patter in blast
furnace
– STAR-CCM+ compared to
experiment and reference results
Validation – granular flow pattern formation
– STAR-CCM+ solution compared to
Ergun equation
– Tested case – porous bed with
periodic walls
– Analytic solution pressure drop ~
108Pa
Validation – pressure drop
DEM Solutions EDEM
– Mature industry focused code
– STAR-CCM+ will be compared to most frequently in terms of DEM
physic/features
– Founded 2002
– First release of the code in 2005
– First industrial grade release - 1.2 – May 2007
– Second generation solver and internal architecture code released as version
2.0 - 9 May 2008
– Current release EDEM 2.4 - September 16, 2011
Competitive analysis
STAR-CCM+
– Distributed memory (MPI)
• Domain decomposition
• Cluster friendly
– 2d, 3d
– Volumetric representation
• + Allows to solve coupled problems
• - Extra work required for meshing
– Rich, multi physics framework
EDEM
– Shared memory (OpenMP)
• Loop parallelism
• Single workstation
– 3d
– Surface representation
• + Almost no surface preparation
• - Makes coupling difficult
– Single purpose solver code
Competitive analysis – basic characteristics
STAR-CCM+ EDEM
Spherical particles x x
Rigid composites x x
Breakable flexible clumps x Custom coding
Hertz Mindlin x x
Hysteretic model x x
Parallel bonds x x
Cohesion x x
Linear spring Can use hysteretic model x
JKR Can use cohesion model x
Electrostatics 2 way coupled Limited
Particle/flow interaction 2 way coupled No longer supported
Competitive analysis
STAR-CCM+ EDEM
Heat transfer particle-particle, particle-flow,
particle-particle radiation
Particle-particle
Interfaces General Parallel planes
Particle shape editor
x x
Moving geometry Rigid body motion Rigid body motion Easy to setup – no meshing
required Transient post processing Track files Full solution replay
Competitive analysis
Conclusion
– Competitive in terms of implemented features
– Advantage for complex physics
• Reuse of feature implemented for general Lagrangian framework
• Ability to implement more complex physics due to the background FV discretization
– Further improvements
• Simplify the workflow for complex moving geometries
• Transient post processing and solution history
Competitive analysis
Not easy to quantify – depends on characteristics of particular case
– Packing structure
– Distribution of particles in the computational domain
– Amount of physics
– Coupling
– Overall case size
• Overhead of the STAR-CCM+ framework – mostly affecting small cases
• Large cases become memory bound when running on single machine mostly due to
irregular memory access patterns
Performance and scalability
CPU time vs number of particles
– Naively O(N^2)
– Ideally O(N)
• Good collision detector should linearize the
detection time
– Example
• CPU time / solver step vs # of particles
• # of particles up to 150000
• Densely packed
• Credit: Phillip Morris Jones, London Office
Performance and scalability
Solver time vs # of CP
– 3d Hopper
– 100 000 spherical particles
– Well distributed
– Credit: Lucia Sclafani
Performance and scalability
Physics
– Liquid bridges, capillary forces, free surface-particle interaction in VOF
– Mass transfer, drying, coating
– Smooth simulation physics decomposition DEM, FEA, EMP
– Surface only DEM
Performance and scalability
– Improved cache coherency for single workstation runs
– Dynamic particle centric load balancing
GUI and usability
– Transient post processing and solution snapshots
– CAD import and interpolation of particle shape by sphere trees
Future development
Examples
Thank you
Questions?
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