Simulation mulation of Pa Particulat ticulate e Solids ids Processin cessing g Usin ing g Discre crete e Eleme ement nt Met ethod od Oleh Baran an
Outli line 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 ormanc nce and scalabil bilit ity Simulat ation ion assist stant ant bene nefits ts Scaled led particle icle approa oach ch Sum umma mary 2
DEM for parti ticle cle flow with th resolv solved ed collision isions 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 3
DEM exam amples ples 4
DEM Governing erning Equatio ations ns Moment mentum m conser nservat ation ion 𝑒𝑤 𝑗 𝑛 𝑗 𝑒𝑢 = 𝐺 𝑗𝑘 + 𝐺 + 𝐺 𝑔𝑚𝑣𝑗𝑒 𝑘 𝑛 𝑗 and 𝑤 𝑗 are mass and velocity of particle 𝑗 , 𝐺 = 𝑛 𝑗 is gravity force, 𝐺 𝑗𝑘 is contact force between particle 𝑗 and element 𝑘 - DEM is a meshless method! - DEM is computationally intensive method! Conse nservati tion on of angul ular r mome mentum ntum 𝑒 𝑒𝑢 𝐽 𝑗 𝜕 𝑗 = 𝑈 𝑗𝑘 𝑘 – 𝑗, 𝐽 𝑗 and 𝜕 𝑗 are the momentum on inertia and rotational velocity of particle 𝑗 . 𝑈 𝑗𝑘 = 𝑠 𝑗𝑘 (𝐺 𝑗𝑘 + 𝐺 𝑠 ) is the torque produced at the point of contact and it is the function of the rolling friction force 𝐺 𝑠 5
DEM in STAR-CC CCM+ + overvie view: : Contact tact Forces es Base e model odel is non-li 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 ompone ponents, nts, 𝑮 𝒐 and nd 𝑮 𝒖 ,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 6
DEM Capab abil ilit ities ies: : Contact tact models els Basic c models odels Classical nonlinear contact force model for Friction, restitution (normal Hertz tz-Mi Mindlin dlin rigid bodies and tangential) Compression and tensile Walt lton on-Br Brawn Linear model for deformable particles stiffnesses • Optional models (for adding to basic model) Force proportional Roll lling ing Set of rolling friction Constant Torque Resi sistan stance parameters Displacement Damping Constant attractive force matching either JKR Work of cohesion, Linear ear Cohesio esion force or DMT model for zero overlap model blending factor Linear and Quadratic Artif tificia icial Visc scosit osity Additional velocity dependent damping model coefficients Max tensile and shear Paral alle lel l Bonds ds For modelling consolidated particles stress, Bond radius Condu duction tion Heat eat For both particle-particle and particle-geometry Ranz-Marshall or user 7 tran ansf sfer er contacts set heat transfer
DEM- Capab abil ilit ities ies: : Pa Parti ticles cles Spherical Partic icle le Type Composite Rigid, unbreakable Clumps Flexible, breakable • Random Injector: on region or Part • Lattice injector: Volumetric injection • Random injector Partic icle le • Part injector: on volume cells Initi tializ alizati ation on Surface or Part Injector: on boundary Surface injection 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 8
DEM Coupling pling to Fluid id Flow Di Felice Schiller-Naumann Drag ag Force Gidaspow (for 2-way coupling only) User defined field function With either Sommerfeld Rotational Drag or user Drag ag Tor orque defined rotational drag coefficient Shear Lift: Choice of Saffman, Sommerfeld, user-set coefficients Lift Force Spin Lift: Choice of Sommerfeld or user-set coefficients Press ssure Grad adient ient Buoyancy force Force Fluid is affected by particles: Momentum source is Two-way coupling ling applied to continuous phase Gravity force, User-Defined Body Force, Particle Other er inter erac actions tions Radiation, Energy model, Passive Scalar 9
DEM Pa Passi ssive e Scalar lar New w in versi sion on 8.06 Arbitr Ar 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 10
Pa Passi ssive e scalar lar for binning ning and mixi ixing ng Source term: Source term: $${ParcelCentroid}[0] * $ParticleDensity * ($${ParcelCentroid}[0]<0) ? 0 : 1) $TimeStep * ($Time > 0 ? 0 : 1) 11
Passi Pa ssive e scalar lar for coati ting g applic lications ations Challe leng nge: e: Impr mprove e inter er-pa partic ticle e coatin ting g uni niform ormity 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: ($$Par arcelCe elCentr troid oid(" ("Cy Cyl")[2] 2] > 0.0 && $$Par arcelCe lCentr troid id(" ("Cy Cyl") ")[2] 2] < 0.22 22 && $$ $$Par arcelCe lCentr troid id(" ("Cyl") ")[0] 0] < 0.05+1.12*$$Par arcelCe lCentr troid id(" ("Cy Cyl")[ )[2] ] ) ? ? 0.1*$Par artic ticle leDens Densit ity : : 0.0 – Coating thickness is accumulated in ‘spray zone’ – Single simulation provides solution for two different spray methods 12
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 𝜚 1 – Eulerian passive scalar 𝜚 2 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 𝜍 1 𝜚 1 − 𝜍 2 𝜚 2 𝐵 𝑞 here 𝑙 = 0.01 is user controlled interaction coefficient, 𝜍 1 and 𝜍 2 are densities of Lagrangian and Eulerian phases, 𝐵 𝑞 is the surface area of the particle 13
Performanc ormance e and Scalability ability 𝜍 DEM timestep Material density of least dense phase 𝑒 𝜍 Diameter of smallest sphere ~ 𝑒 𝐹 𝐹 Young’s Modulus of hardest particle DEM Solver Timescale Number of Total Number of spheres elements Number of CPU Number of faces in mesh Max Physical time Typical al simul ulat ation ion time e of Fluidize uidized d Perform ormanc nce-im improving ving featu tures res: bed d in versi sion on 8.02: 02: – Load Balancing – Per-continuum parallel solver 1 s s Physica cal l time me for 28 h / 118 18 CPU – Maximum Independent Set Algorithm in injectors for 1. 1.3 million ions s of particles cles – Skinning d=2 2 mm, , ρ =2440 kg/m3, E=10 MPa 14
Skin inning ning Conta ntact ct det etecti ection on optimized 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 one neighborhood 15
Simulation mulation Assistant istant Benef efits its 16
Scale led d Pa Particle ticle Approac oach Can we use ‘larger particles’ to reduce particle count without significant change nge in accur uracy acy of the e model? odel? – In particular for fluidized bed application? fine particles of size 𝑒 0 scaled-up particle of size 𝑒 Sug ugges gested d correcti rection on to Gidasp spow drag coef efficient icient 𝑜 𝑒 𝐷 𝑒 ⇒ 𝐷 𝑒 𝑒 0 17
Fluidi idized zed bed set et up 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 - Size of parti ticles les – 0.503 3 mm 18
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