SLIDE 1
Discre crete e Eleme ement nt Modelling lling in STAR-CCM CM+
Oleh Baran an
SLIDE 2 DEM EM overvie view DEM EM capabi bilit ities ies in STAR AR-CC CCM+ M+
– Particle types and injectors – Contact physics – Coupling to fluid flow – Coupling with passive scalar
Perform
nce and scalabil bilit ity Simulat ation ion ass ssista tant nt bene nefits ts Scaled led particle icle approa
ch Sum umma mary
Outli line
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SLIDE 3
DEM EM is applicable able to solid id flows ws
– When part or whole solid phase is in dense shear flow regime – With particles of different shape and size distribution
DEM for parti ticle cle flow with th resolv solved ed collision isions
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SLIDE 4
DEM exam amples ples
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SLIDE 5 Moment mentum m conser nservat ation ion
𝑛𝑗 𝑒𝑤𝑗 𝑒𝑢 = 𝐺𝑗𝑘 + 𝐺
+ 𝑘
𝐺
𝑔𝑚𝑣𝑗𝑒
𝑛𝑗 and 𝑤𝑗 are mass and velocity of particle 𝑗, 𝐺
= 𝑛𝑗 is gravity force, 𝐺𝑗𝑘 is
contact force between particle 𝑗 and element 𝑘
- DEM is a meshless method!
- DEM is computationally intensive method!
Conse nservati tion
ular r mome mentum ntum 𝑒 𝑒𝑢 𝐽𝑗𝜕𝑗 = 𝑈
𝑗𝑘 𝑘
– 𝑗, 𝐽𝑗 and 𝜕𝑗 are the momentum on inertia and rotational velocity of particle 𝑗. 𝑈𝑗𝑘 = 𝑠𝑗𝑘(𝐺𝑗𝑘 + 𝐺
𝑠) is the torque produced at the point of contact and it is the
function of the rolling friction force 𝐺
𝑠
DEM Governing erning Equatio ations ns
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SLIDE 6 Base e model
linear near Herz-Mi Mindli ndlin mode del
– Details in Di Renzo, A., & Di Maio, F. P. (2004). Comparison of contact-force models for the simulation of collisions in DEM-based granular flow codes. Chemical Engineering Science, 59, 525 –541 – Other models available (next slide)
The e normal rmal and tangen gentia tial l com
ponents, nts, 𝑮𝒐 and nd 𝑮𝒖,of f conta tact ct force ce depen pends ds on overlap, rlap, particle cle proper erti ties es
– Young’s modulus – Density – Size – Poisson ratio, – and interaction properties, for example friction, rolling friction, restitution, etc
DEM in STAR-CC CCM+ + overvie view: : Contact tact Forces es
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SLIDE 7 Basic c models
DEM Capab abil ilit ities ies: : Contact tact models els
Hertz tz-Mi Mindlin dlin Classical nonlinear contact force model for rigid bodies Friction, restitution (normal and tangential) Walt lton
Brawn Linear model for deformable particles Compression and tensile stiffnesses
- Optional models (for adding to basic model)
Roll lling ing Resi sistan stance Force proportional Set of rolling friction parameters Constant Torque Displacement Damping Linear ear Cohesio esion Constant attractive force matching either JKR force or DMT model for zero overlap Work of cohesion, model blending factor Artif tific icia ial Viscosit
- sity Additional velocity dependent damping model
Linear and Quadratic coefficients Paral alle lel l Bonds ds For modelling consolidated particles Max tensile and shear stress, Bond radius Condu duction tion Heat eat tran ansf sfer er For both particle-particle and particle-geometry contacts Ranz-Marshall or user set heat transfer 7
SLIDE 8 Partic icle le Type
Spherical Composite Rigid, unbreakable Clumps Flexible, breakable
Partic icle le Initi tializ alizati ation
Volumetric injection
- Random Injector: on region or Part
- Random injector
- Lattice injector:
- Part injector: on volume cells
Surface injection Surface or Part Injector: on boundary cells, or on planar grid All injectors : - ability to set particle size distributions: constant, normal, log-normal, other
- ability to specify flow rate, initial velocities, orientation, etc
DEM- Capab abil ilit ities ies: : Pa Parti ticles cles
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SLIDE 9 DEM Coupling pling to Fluid id Flow
Drag ag Force Di Felice Schiller-Naumann Gidaspow (for 2-way coupling only) User defined field function Drag ag Tor
With either Sommerfeld Rotational Drag or user defined rotational drag coefficient Lift Force Shear Lift: Choice of Saffman, Sommerfeld, user-set coefficients Spin Lift: Choice of Sommerfeld or user-set coefficients Press ssure Grad adient ient Force Buoyancy force Two-way coupling ling Fluid is affected by particles: Momentum source is applied to continuous phase Other er inter erac actions tions Gravity force, User-Defined Body Force, Particle Radiation, Energy model, Passive Scalar 9
SLIDE 10 New w in versi sion
Ar Arbitr trary y num umbe ber r of new w particle cle proper perti ties es
– Color
- Tracing subset of particles
- Analyzing mixing efficiency
– Particle residence time or displacement
- ‘dead zone’ or ‘risk zone’ analysis of granular flow
– Coating amount
- Residence time in user-defined ‘spray zone’
– Wetness / dryness of particles
- Contribution from several processes
– Amount of chemically active component
Can intera eract ct with h Eu Eulerian ian pass ssive e sc scalar
DEM Pa Passi ssive e Scalar lar
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SLIDE 11
Pa Passi ssive e scalar lar for binning ning and mixi ixing ng
11 Source term: $${ParcelCentroid}[0] * $ParticleDensity * $TimeStep * ($Time > 0 ? 0 : 1) Source term: ($${ParcelCentroid}[0]<0) ? 0 : 1)
SLIDE 12 Challe leng nge: e: Impr mprove e inter er-pa partic ticle e coatin ting g uni niform
ity by using g optima mal l sprayin ying g equipm ipment ent set etting ngs
– Solution: using DEM passive scalar capability
Pa Passive e scalar lar source: ce:
($$Par arcelC elCentr troi
("Cy Cyl")[2] ] > 0.0 && $$Par arcelCe lCentr troid id(" ("Cy Cyl")[ ")[2] < 0.22 && $$ $$Par arcelCe lCentr troid id(" ("Cyl")[ ")[0] < 0.05 05+1 +1.12 12*$ *$$Par arcelCen lCentr troid id(" ("Cy Cyl")[ )[2] 2] ) ? ? 0.1*$ *$Par artic ticle leDen Densit ity : : 0.0
– Coating thickness is accumulated in ‘spray zone’ – Single simulation provides solution for two different spray methods
Pa Passi ssive e scalar lar for coati ting g applic lications ations
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SLIDE 13 Pa Passi ssive e scalar: lar: Lagrangian rangian-Eu Eulerian lerian coupling ling
Example of particles in a pile ‘releasing ‘gas
– Left Inlet air flow 100 m/s, later 10 m/s – Particles initialized with non-zero ‘Particle Gas’ value of passive scalar 𝜚1 – Eulerian passive scalar 𝜚2 has diffusion and convection on, initial value zero in all cells – Volume weighted interaction model for flow rate between passive Lagrangian and Eulerian scalars:
- 𝐾 = 𝑙 𝜍1𝜚1 − 𝜍2𝜚2 𝐵𝑞 here 𝑙 = 0.01 is
user controlled interaction coefficient, 𝜍1 and 𝜍2 are densities of Lagrangian and Eulerian phases, 𝐵𝑞 is the surface area of the particle
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SLIDE 14 Performanc
e and Scalability ability
Perform
nce-im improving ving featu tures res:
– Load Balancing – Per-continuum parallel solver – Maximum Independent Set Algorithm in injectors – Skinning
Typical al simul ulat ation ion time e of Fluidize uidized d bed d in versi sion
02: 1 s Physica sical l time me for 28 h / 118 18 CPU for 1. 1.3 million ions of particles cles d=2 2 mm, , ρ=2440 kg/m3, E=10 MPa
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DEM timestep ~𝑒
𝜍 𝐹
Material density of least dense phase 𝜍 Diameter of smallest sphere 𝑒 Young’s Modulus of hardest particle 𝐹 DEM Solver Timescale Number of elements Total Number of spheres Number of CPU Number of faces in mesh Max Physical time
SLIDE 15 Skin inning ning
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Conta ntact ct det etecti ection
imized
– New skin parameter in DEM solver – the larger the skin distance, the less often neighbor lists need to be re-built,
- but more pairs must be checked for
possible force interactions inside
SLIDE 16
Simulation mulation Assistant istant Benef efits its
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SLIDE 17 Can we use ‘larger particles’ to reduce particle count without significant change nge in accur uracy acy of the e model?
– In particular for fluidized bed application? fine particles of size 𝑒0 scaled-up particle of size 𝑒
Sug ugges gested d correcti rection
spow drag coef efficient icient
𝐷𝑒 ⇒ 𝑒 𝑒0
𝑜
𝐷𝑒
Scale led d Pa Particle ticle Approac
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SLIDE 18 Fluidi idized zed bed set et up
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- J. X. Bouillard, R. W. Lyczkowski and D. Gidaspow, "Porosity Distributions in a Fluidized
Bed with an Immersed Obstacle," AIChE Journal, vol. 35, no. 6, pp. 908-921, 1989
ticles es – 0.503 3 mm
SLIDE 19 Model l parame ameter ers s and studie udied d configu gurati ations
Configuration Particle size (mm) Number of Particles Values of drag scaling exponent n Time to simulate 1 second No coupling DEM+CFD 1 2 1,271,020 1.576 18 h / 106 CPU 28 h / 118 CPU 2 3 374,746 1.858 6.5 h / 46 CPU 14 h / 46 CPU 3-9 4 156,506 1.889, 1.935, 1.977, 2.016, 2.052, 2.085, 2.116 5.1 h / 34 CPU 7.6 h / 34 CPU 10-12 5 79,410 1.997, 2.077, 2.299 3.4 h / 10 CPU 7.9 h / 10 CPU Material E (MPa) Poisson ρ (kg/m3) Particles polyester 10 0.34 2440.0 Geometry aluminum 68000 0.33 2702.0 Contacts Friction Restitution Particle-Particle 0.8 0.01 Particle-Geometry 0.01 19
SLIDE 20 Prep epar aring ing initi tial al configurations igurations
1. 1. Particles ‘poured down’ 2. 2. Bul ulk oscilla illati tion
ed 3. 3. Use ser r Body dy force ce applied ed to remo move e particles cles above e 28 cm Same me proces cess for r each h conside idered red partic icle le size e
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SLIDE 21 Pressure essure drop calculations ulations
Two
couplin pling g activat ated ed
– All three inlets set to have same inlet velocity – Superficial velocity 0.0989 m/s
Simulat ated ed at least st 0.5 5 s in stea eady dy state Pressur ssure e drop
ecorded ded Same me process cess for r all 12 conf nfigurati guration
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SLIDE 22
Pressure essure Drop p (d=4mm) mm)
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SLIDE 23 Pressure essure drop analysis lysis
Com
ared to Er Ergun un estima imate e for 𝑒0 = 0.503 𝑛𝑛 parti ticles cles Ex Exponent
1 corre respond spond to fluidized uidized st state
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𝐷𝑒 ⇒ 𝑒 𝑒0
𝑜
𝐷𝑒
SLIDE 24 Simple le power-la law w correcti rrection
ce was us used d for scaled led particles icles Results sults for pressu ssure re drop
apsed d on si single le cur urve
– Perhaps limited for the particular choice of model parameters
- Low coefficient of restitution
- Further investigations are underway
– More accurate scaling can be set from force balance
- Sakai et. al., Study of a large scale discrete element model for fine particles in a
fluidized bed, Adv. Powder Technology 23 (2012) 673-681
Scaled led particles icles can provi vide de same me press ssure ure drop
s computat putation ion time me
Summa mmary y of fluidi idized zed bed stud udy
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SLIDE 25
Thank k you!
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SLIDE 26
Back-up up slid ides es
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SLIDE 27 Void d fracti tion
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d=3mm Initial d=3mm Final d=4mm Initial d=4mm n=1.9 d=4mm n=2.1 (fluidized)
SLIDE 28 Resu sults lts with th activ ivat ated ed jet et flow (d=5mm mm)
Flui uidize dized d st state e -> > increa reased ed inlet t veloci
ty of jet et boundar ndary y to 5.78 78 m/s
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SLIDE 29
Jet et penetration tration: : experim periment nt and simulation mulation
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t=0 t=0.131 s t=0.210 s
SLIDE 30 Time me and space e average ge solid lid veloc
ity
d=5 mm 17 x 23 bins
– 25.4mm x 25.4mm x 76.2mm
Avera raged ed over 2 s
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