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2.5 DEM Modeling:

The dynamics of a particulate phase in gas-solid fluidized bed system is very

complicated due the presence of complex interaction forces between particle-particle,

particle-wall and particle-fluid. To understand this complicated dynamics of particulate

system, it is necessary that the underlying mechanism express in terms of these interactions.

To carry out this process it is necessary that the calculation should be performed at particle

scale level, progressing in this direction Cundall and Strack (1979) develop a discrete element

method (DEM). In DEM simulation, the Newtonian equations of motion are solved for each

particle in a system of gas-solid fluidized bed. Therefore from DEM simulation the dynamic

information such as individual particle path trajectory and transient force acting on individual

particle can obtained which is very difficult if not impossible practically. DEM modeling

simulation has been used mainly for dilute system but computer of large memory and high

speed make it possible to simulate large particle system such as gas-sold fluidized beds at

laboratory scale.

DEM simulation can be roughly divided into two groups:

Soft Particle Approach

Hard Particle Approach

In Soft particle approach, originally developed by the Cundall and Strack (1979), particles

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and the particles far away through the propagation of disturbance waves (Zhu et. al.,2007).

In DEM approach such type of complexity solved by assuming the numerical time step lessthan a critical value so that during a single time step the disturbance cannot propagate from

the particle and fluid farther than its immediate neighboring particles and vicinal fluid

(Cundall and Strack, 1979). Thus, at all times the resultant forces on a particle can bedetermined from its interaction with the contacting particles. In soft particle approach, it is

also assumed that the contact time during the collision is comparable to free flight time andtherefore during this times the colliding particles, numerically allowed to suffer minute

deformation from which the contact forces are evaluated.

2.5.1 Governing Equation:

Therefore the governing equations for soft sphere approach can be written as:

For particulate phase,

The translational force on ith

particle is

Net force = Force due to Collision + Cohesive Force(van der Waal Force) + Pressure Force +

Viscous Drag Force + Force due to Gravity.

(2.5.1)

Th t ti l f ith

ti l i

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() (2.5.5)

Where Vis the cell volume,Np are the number of particles,Vp is the particle volume and the

-function is defined as: { (2.5.6)Therefore the integral is over the domain so that successively all the surface of all particles

are sampled and each such sampled local drag force is calculated.

2.5.2 Coordinate System:

According to the soft particle approach two particle i and j are said to be in contact if ,

| | ( ) (2.5.7)Where and are position vector of two particle i and j at the time of collision and Ri and Rjare the actual radius of the solid particle.

The normal unit vector is defined as:

|| (2.5.8)The velocity of the particle i with respect to the velocity of the particle j is given as:

( ) ( ) (2.5.9)The normal and tangential components of the relative are given as: ( ) and ( ) (2.5.10)

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2.5.3 Contact Force:

As the contact between two particles in soft sphere approach is not at a single point but ona finite area due to deformation of the particle. Therefore, the contact traction distribution

over this area can be divided into two components, one along the contact plane i.e. the

tangential plane and other along the normal to the plane. However, it is very difficult to

calculate in general mathematical way the contact traction distribution over this area and then

the force and torque acting upon the particle. This is because it involves many physicalfactors such as shape factor, material properties and movement position of the particles. In

alternate way DEM usually adopt simple model or equations to calculate the force and the

torque resulting from the contact between the particles.

There are various model proposed in open literature among them most intuitive and

appealing are the linear model. The most common linear model in open literature is linear

spring-dashpot model proposed by the Cundall and Strack (1979). In spring-dashpot model,

the spring takes care of elastic deformation while the dashpot takes care of viscous

dissipation. There are other models, which are theoretically more sound but very complex,

like simplified Hertz-Mindlinand Deresiewicz model,Walton and Brauns model etc.In our work, the simple linear spring-dashpot model would be used to modeling the contact

force between the particles. This model consists of a spring, a dashpot and friction slider as

shown below in Figure 2.

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Force = (spring constant)(displacement); (2.5.11)

= -k, where is the deformation in the spring.For dashpot element, Newtons law of viscosity holds:

Viscous stress strain rate (i.e. rate of change of displacement);Viscous stress = (damping coefficient) (strain rate)

= - (2.5.12)Therefore normal component of contact force between two colliding particle is

= -knn n (2.5.13)and the tangential component is

= -ktt t (2.5.14)Where n, the normal component of deformation can be calculated asn = ( ) | | (2.5.15)

and the tangential component of the displacement can be calculated as the integration of the

tangential component of relative velocity between the time t0 to t(where the t0 is the time

when the deformation start)

t(t) =

(2.5.16)

Tangential collision could be sticking or sliding depending upon magnitude of tangential

f i if h i l f i h h f i i l f h i ill b lidi

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( ) (2.5.20)

2.5.4 Particlefluid interaction forces:

The particle-fluid interaction force particularly drag force is the driving force for fluidization,

therefore particle-fluid interaction force should be properly considered. Till date there are

many forces have been implemented in DEM simulation like particle-fluid drag force,

pressure gradient force,virtual mass force, Basset force, and lift forces.For an isolated particle in a fluid the drag force expression is well established. As there are

three regions Stokes region, transition region and the Newtons region and the drag force for

these three regions are well established by proper drag coefficient. But the drag force on aparticle in fluidized bed is very difficult to calculate as the presence of other particle reduce

the space for fluid to flow and hence increase the fluid velocity as a result of this shear stress

on the surface of the particle increases. The particle-fluid drag force in a particulate flow is

determined by empirically and numerically. The empirical correlation are based either on bed

pressure drop (Ergun, 1952; Wen and Yu, 1966) or bed expansion experiment (Richardson,1971).The effect of the presence of other particles is considered in terms of local porosity,involving the exponent (see Table 5) and related to the flow regimes or particle Reynolds

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( ), Rep= | |

Di Felice (1994) | | Where is voidage function (

) ( )

( ), Rep= | | = 3.7 -6.5exp[-(1.5-logRep)2/2]

Fluidized

Particle

Beetstra et al.( 2007)

()

( )

| |

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Table 6: DPM modeling in batch fluidization

S.No. Reference Concluding Remark Other Remark

1. Y. Tsuji, T. Kawaguchi and T. Tanaka,Discrete particle simulation of two-

dimensional fluidized bed. PowderTechnology 77 (1993) 79-87

2-D DEM was attempted to simulate motion of

individual particle using Cundall and Stracks DEM

model, and taking into account the Gidaspows dragclosure and the simulated result was compared to the

experiment and it is found that simulated pressure

fluctuation are of higher amplitude than the

experimental one.

Gas-solid bubbling fluidized bed with

2400 spherical glass beads with spring

constant 800 N/m, friction coefficients0.3and coefficient of restitution 0.9

Gas(air) Solid(Glass)

g=1.26kg/m3 s=2700kg/m

3

g=1.810-5

Pa.s ut=18 m/s

dp=4 mm

2. B.P.B.Hoomans, J.A.M. Kuipers, W.J.Briels and W.P.M. van Swaaij, Discreteparticle simulation of bubble and slug

formation in a two-dimensional gas-

fluidized bed: a hard-sphere approach.Chemical Engineering Science, Vol. 51, No.

1, pp. 99-118, 1996

Hard sphere approach with Wen & Yus dragcorrelation. In ideal collision condition (e = 1, = 0) no

bubble formation was found under bubbling condition.

Therefore these parameters should be of realistic value.

Simulation with realistic value of e and showed highly

realistic flow behavior of the group D-powder. Result

obtained were in agreement with Tsuji et al.,(1993)


2400 spherical aluminum particle

Gas(air) Solid(Aluminum)

g=1.26kg/m3 s=2700kg/m

3

g=1.810-5 Pa.s ut=18.1 m/s

dp=4 mm

3. B. H. Xu and A. B. Yu, Numericalsimulation of the gas-solid flow in a

fluidized bed by combining discrete

particle method with computational

fluid dynamics. Chemical EngineeringScience, Vol. 52, No. 16, pp. 2785-2809,

1997

2-D DEM simulation of particle in fluidized bed using

Cundall and Stracks DEM model, and Di Felices dragclosure. Hysteric feature of bed pressure drop vs. gas

superficial velocity is predicted. Typical solid flow

pattern with bubble formation is observed.The predicted

minimum fluidization velocity in good agreement with

experiment.


2400 spherical aluminum particle, k =

50,000 N/m, = 0.3, = 0.15Gas (air) Solid (Aluminum)

g=1.26kg/m3 s=2700kg/m

3

g=1.810-5

Pa.s ut=18.1 m/s

dp=4 mm

4. T. Kawaguchi, T. Tanaka, Y. Tsuji,Numerical simulation of two-dimensionalfluidized beds using the discrete element

method (comparison between the two- andthree-dimensional models). PowderTechnology 96 (1998 ) 129-138

2-D & 3-D DEM simulation of particles using Cundall

and Stracks DEM model, and Gidaspows drag closure .Once the particles are fluidized, flow patterns including

the period of bubble formation agree for all conditionsin both cases except for the motion of particles near the

comers. In 2-D model, particles near the corners also

move, while they do not move in the experiments. In 3-

D model, particles near the comers do not move. The

effect of the coefficient of friction on the fluidization is

more obvious when partition walls are inserted in the

bed.


2400 spherical aluminum particle,

spring constant 800 N/m, friction

coefficients 0.1,0.2,0.3.Gas (air) Solid (Aluminum)

g=1.26kg/m3 s=2700kg/m

3

g=1.810-5

Pa.s ut=18.1 m/s

dp=4 mm

5. Yasunobu Kaneko, Takeo Shiojima,Masayuki Horio, DEM simulation offluidized beds for gas-phase olefin


Cundall and Stracks DEM model, and Gidaspows dragclosure. Distributor design has an effect on the


14000 & 28000 spherical particle in

polymerization of ethylene and

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polymerization. Chemical EngineeringScience 54 (1999) 5809-5821

temperature profile and hot spot form on distributor near

the wall. Degree of mixing could be used as an effective

index to identify and prevent hot spouting.

propylene, spring constant 800 N/m,

friction coefficients 0.3.Gas(ethylene,

propylene)

Solid(Polyethylene

, Polypropylene)

g=20.44,74.84

kg/m

3

s=717,667kg/m3

g=110-5 Pa.s

(each)

Umf=0.112,0.066

m/s

6. Shinichi Yuu, Toshihiko Umekage, YuukiJohno, Numerical simulation of air andparticle motions in bubbling fluidized

bed of small particles. Powder Technology110 (2000) 158168.


Hertzs contact theory and Schiller-Naumans dragclosure incorporated Lift force. The simulation well

describe the bubble formation, bubble disruption, bubble

coalescence and slugging in fluidized bed. Simulated

minimum fluidization velocity is in good agreement

with experimental result. Fluctuation of gas flow

enhanced by the presence of solid particle.


100,000 spherical with spring constant

800 N/m, friction coefficients 0.3.Gas (air) Solid (glass)

g=1.26kg/m3 s=2500kg/m

3

g=1.810-5

Pa.s ut=2.6 m/s

dp=0.31 mm

7. M. J. Rhodes, X. S. Wang, M. Nguyen, P.

Stewart, K. Liffman, Study of mixing ingas-fluidized beds using a DEM model.Chemical Engineering Science 56 (2001b)

2859-2866

2-D DEM simulation of individual particle using

Cundall and Stracks DEM model, and taking intoaccount the Gidaspows drag closure. Lacys (1954)index method was proposed for mixing of solids. Effect

of various parameters like solid density, size of

particles, particle diameter and superficial gas velocity

was studied. It was revealed that rate of mixing

increases with the gas velocity while the degree of

mixing achievable is unaffected. Simulated mixing

index was comparable to that of experimental one.

Gas-solid mixing in fluidized bed with

14,000 spherical with spring constant800 N/m, friction coefficients 0.3.Gas (air) Solid (glass)

g=1.26kg/m3 s=2650kg/m

3

g=1.810-5

Pa.s Umf=0.8 m/s

Gas vel.=1, 1.2,

1.4, 1.6 m/s

dp=1 mm

8. M. J. Rhodes, X. S. Wang, M. Nguyen, P.Stewart, K. Liffman, Use of discreteelement method simulation in studying

fluidization characteristics: influence ofinterparticle force. Chemical EngineeringScience 56 (2001a) 69-76

2-D DEM simulation used to study the influence of

cohesive force on the fluidization behavior. With the

DEM simulations, Umf and Umb velocities were

estimated, it was suggested that a very small range of anon-bubbling region exist even for the particle with non

cohesive interparticle force. Umfis insensitive to change

of the interparticle forces whereas Umb increase with

increase of the interparticle forces for Geldart group A

and B. It was also concluded that large forces (e.g.,

liquid bridge, magnetic) acting between large particles

produce the same effects as small forces (e.g., van der

Waals) acting between small particles.

Gas-solid mixing in fluidized bed with

4000 spherical with spring constant 800

N/m, friction coefficients 0.3.

Gas (air) Solid (glass)g=1.26kg/m

3 s=1590-2650kg/m

3

g=1.810-5 Pa.s Umf=0.8 m/s

dp=1 mm

9. B.G.M. van Wachem, J. van der Schaaf,J.C. Schouten, R. Krishna, C.M. van den

Validation of 2-D DEM simulation using hard sphere

approach and Wen & Yus drag closure with theGas-solid bubbling fluidized bed with

3110 polystyrene spherical particles

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Bleek, Experimental validation ofLagrangianEulerian simulations offluidized beds. Powder Technology 1162001. 155165

experiment. There is a one difficulty in 2-D LagrangianEulerian model is to convert the 2-D void fraction of the

particle to 3-D void fraction is most important factor

and it require more accurate expression. A 3-D DEM

simulation can capture the behavior of the physics of the

fluidized bed more precisely.

with coefficient of restitution is 0.9 and

the friction coefficients is 0.3.

Gas (air) Solid(Polystyrene)

g=1.26 kg/m3 s=1150 kg/m

3

g=1.710-5

Pa.s Umf= 0.74 m/sdp=1.545 mm

10. K. D. Kafuia, C. Thornton, M. J. Adams,Discrete particle-continuum fluid modelingof gassolid fluidized beds. ChemicalEngineering Science 57 (2002) 23952410

2-D DEM simulation using Hertzs contact theory, andthe Di Felices drag closure.Coupling of particulate phase with continuous phase

was done using pressure gradient force(PGF) and it is

found that result are more consistent with PGF model

than the other model using a buoyancy force based on

the fluid density (FDB model). Umf calculated from

simulation is in good agreement with the experimental

value.


2400 spherical aluminum particle

Gas(air) Solid(Aluminum)

g=1.26kg/m3 s=2700kg/m

3

g=1.810-5 Pa.s ut=18.1 m/s

dp=4 mm

11. Sunun Limtrakul, AtivuthChalermwattanatai, Kosol Unggurawirote,Yutaka Tsuji, Toshihiro Kawaguchi, Wiwut

Tanthapanichakoon, Discrete particlesimulation of solids motion in a gassolidfluidized bed. Chemical EngineeringScience 58 (2003) 915921

2-D DEM simulation using Cundall and Stracks DEM

model, and taking into account the Gidaspows dragclosure. The simulation applied to predict the flowpattern, mixing and segregation of solids particles in a

cylindrical fluidized bed. It is found that sold ascending

at the centre and descending near the wall. This finding

is agreed with the experimental result by Moslemian

(1987).


different number of particles differentsize and different particle. The springconstant 800 N/m, friction coefficients

0.3and coefficient of restitution is 0.9.

Gas used was air.

12. X. S. Wang, M. J. Rhodes, Determinationof particle residence time at the walls of gas

fluidized beds by discrete element method

simulation. Chemical Engineering Science58 (2003) 387395

3-D DEM simulation using Cundall and Stracks DEMmodel, and taking into account the Gidaspows dragclosure. Particle residence time distribution was

evaluated near the wall of the fluidized bed reactor. It

was found that the mean residence time for the smaller

particle is lower, back mixing in the system occur.Pressure drop in 3-D bed was found much better as

compared to 2-D


600,000 & 500,000 number of particle

of size 0.5 mm and 1 mm respectively

spherical glass particle with spring

constant 800 N/m, friction coefficients

0.3and coefficient of restitution 0.9Gas(air) Solid(glass)

g = 1.26 kg/m3 s=2650kg/m

3

g = 1.810-5

Pa.s ut= 4.4 & 6.6 m/s

dp= 0.5 and 1 mm

13. Y. Q. Feng, B. H. Xu, S. J. Zhang, and A.B. Yu, Discrete Particle Simulation of GasFluidization of Particle Mixtures.AIChE(2004) Vol. 50, No. 8

2-D DEM simulation using Cundall and Stracks DEMmodel and Di Felices drag closure. Segregation andmixing of binary mixtures of particles in agas-fluidized

bed was studied and it was found that the degree of


22,223 & 2777 number of particle of

size 1 mm and 2 mm respectively,

Youngs Modulus 800 N/m2, Poisson

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mixing/segregation is affected by the gas velocity.Effect of interaction force between particles and

between particle and fluid on mixing/segregation was

studied and it is found that these forces varies

temporarily as well as spatially.

ratio 0.3, friction coefficients 0.3and

damping coefficient 0.2

Gas(air) Solid(glass)

g = 1.206 kg/m3 s=2500kg/m

3

g = 1.810-5 Pa.s ut= 8.5 & 12 m/s

dp= 1 and 2 mm

14. Sunun Limtrakul, Asada Boonsrirat,Terdthai Vatanatham, DEM modeling andsimulation of a catalytic gassolid fluidizedbed reactor: a spouted bed as a case study.Chemical Engineering Science 59 (2004)

52255231

2-D DEM simulation using Cundall and Stracks DEMmodel and Erguns drag closure. A model was developthat combined DEM and mass transfer throughout the

catalytic gas-solid fluidized bed. Decomposition of

ozone on oxide catalyst was studied. DEM- mass

transfer model provides the information regarding the

particle velocity and distribution, gas velocity, void

fraction, and conversion profiles in the bed. Results are

in good agreement with experiment.

Spouted bed with 40,000 number of

particles. The spring stiffness

coefficient is 800N/m, friction

coefficient 0.3 and coefficient of

restitution 0.9

Gas(air) Solid

g = 1.206 kg/m3 s=2200kg/m

3

g = 1.810-5 Pa.s ut= 17 m/s

dp= 4.4 mm

15.Haosheng Zhou, Gilles Flamant, Daniel

Gauthier, DEM-LES of coal combustion ina bubbling fluidized bed. Part I: gas-particle

turbulent flow structure. ChemicalEngineering Science 59 (2004) 41934203

2-D DEM simulation using Cundall and Stracks DEMmodel and Wen & Yus drag closure. LES used tosimulate the gas phase turbulence. It was studied that an

intensive particle turbulent region exists near the wall,

and the gas stress is always much higher than the

particle stress. The lower the inlet gas velocity, the

higher the ratio of particle collision. Reaction rate for

coal combustion is included. Gas phase turbulence

intensity is much higher than that of particulate phase .

The temperature of coal particles is much higher than

the bed temperature for different conditions.

Coal combustion gas-solid bubbling

fluidized bed with 1460 & 20 number

of particles, Youngs Moduli 15GPa &3GPa, Poisson ratio 0.3 & 0.37,of sand

and coal respectively. Friction

coefficients 0.3and restitution

coefficient 0.2

Gas(air) Solid

g = 1.206 kg/m3 p,sand=2600 kg/m

3

p,coal=1100 kg/m3

g = 1.810-5 Pa.s ut,sand= 8.7 m/s

ut,coal= 7.9-8.2 m/s

dp,sand= 1mmdp,coal= 0.8-2mm

16. M. Ye, M.A. van der Hoef, J.A.M. Kuipers,A numerical study of fluidization behaviorof Geldart A particles using a discrete

particle model. Powder Technology 139(2004) 129139.

2-D DEM simulation using Cundall and Stracks DEMmodel and Ergun drag closure(when 0.8) was used. Interparticlevan der Waal force was included for the study of Geldart

A particles. It was observed that velocity fluctuation are

anisotropic in homogeneous and bubbling regime and

drag force have dominant role in bubbling regime.

Gas-solid bubbling fluidized bed. The

friction coefficients 0.3, normal and

tangential coefficient of restitution are

0.9 each, normal and tangential spring

stiffness are 7104 N/m, 2104 N/m

respectively,

Gas(air) Solid

g = 1.206 kg/m3 s=900kg/m

3

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g = 1.810-5

Pa.s Umf= 0.004 m/s

dp= 0.1 mm

17. Huilin Lu, ShuyanWang,Yunhua Zhao, LiuYang, Dimitri Gidaspow, Jiamin Ding,

Prediction of particle motion in a two-dimensional bubbling fluidized bed using

discrete hard-sphere model. ChemicalEngineering Science 60 (2005) 32173231

2-D DEM simulation using hard sphere approach with

Wen & Yus drag closure. To simulate the gas phaseturbulence, sub grid scale gas turbulent model is used. It

was found that bubble formation, growth and eruption

were predicated in the bubbling fluidized bed with a jet.

The velocity distribution was found to be close to

Gaussian distribution. An Anisotropy occur in the

velocity fluctuation of particulate phase. Paticle pressure

calculate from the normal stress is of the same

magnitude as the value calculated by using KTGF.


900 & 1200 number of particles. The

friction coefficients 0.1, coefficient of

restitution 0.9 &1.0, Coefficient of

tangential restitution 0.3

Gas(air) Solid

g = 1.206 kg/m3 s=2700kg/m

3

g = 1.810-5 Pa.s ut= 8.8 m/s

dp= 4 mm

18. M. Ye, M.A. van der Hoef, J.A.M. Kuipers,From discrete particle model to acontinuous model of Geldart A particles.Chemical Engineering Research and

Design, 2005, 83(A7): 833843

2-D DEM simulation using Cundall and Stracks DEMmodel and Gidaspow drag closure. In this work

basically excess compressibility was introduce to

modify the KTGF

Gas-solid bubbling fluidized bed. The

no particles was 500. The friction

coefficients 0.3, normal and tangential

coefficient of restitution are 0.9 each,

normal and tangential spring stiffness

are 7104 N/m,

Gas(air) Solid

g = 1.206 kg/m3 s=900kg/m

3

g = 1.810-5

Pa.s Umf= 0.004 m/s

dp= 0.1 mm

19. Jai Kant Pandit, X.S. Wang, M.J. Rhodes,On Geldart Group A behavior in fluidizedbeds with and without cohesive interparticle

forces: A DEM study. Powder Technology164 (2006) 130138

2-D DEM simulation using Cundall and Stracks DEMmodel and Gidaspow drag closure. In this work it was

observed that imposing the cohesive interparticle force

on Geldart group B, characteristics of Geldart group A

were observed. Imposed cohesive forced Geldart groupB follow Richardson-Zaki equation. Bed expansion is

unaffected at given air velocity for different magnitude

of interparticle force


900 & 1200 number of particles. The

friction coefficients 0.1, coefficient of

restitution 0.9 &1.0, Coefficient of

tangential restitution 0.3

Gas(air) Solid

g = 1.206 kg/m3

s=400-1000 kg/m3

g =1.810-5

Pa.s ut= 0.7-1.2 m/s

dp= 0.15-0.5 mm

20. Lu Huilin, Shen Zhiheng, Jianmin Ding, LiXiang, Liu Huanpeng, Numericalsimulation of bubble and particles motions

in a bubbling fluidized bed using direct

simulation Monte-Carlo method. PowderTechnology 169 (2006) 159171

2-D DSMC for particle-particle interaction incorporated

the Di Felices drag closure. Collision probability wasmodified by the radial distribution function and

restitution coefficient is modified by the impact angle. It

was observed that wavelet transform and wavelet multi-

resolution analysis can be used as a tool to reveal the

non-linear dynamic characteristic of the sold as well as


25000 number of particles. The friction

coefficients 0.3, spring stiffness is 800

N/m.

Gas(air) Solid

g = 1.206 kg/m3 s=2500 kg/m

3

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gas bubble of a bubbling fluidized bed. Some of theKTGF quantities such as granular temperature, solid

pressure were calculated.Mean as well as fluctuated

component of velocities are compared with experimental

data.

g =1.810-5

Pa.s ut= 2.6 m/s

dp= 0.31 mm

21. Wenqi Zhong, Yuanquan Xiong, ZhulinYuan, Mingyao Zhang, DEMsimulation ofgassolid flowbehaviors in spout-fluid bed.Chemical Engineering Science 61 (2006)

1571 1584

3-D DEM simulation of spouted bed with cylindricalgeometry including k- turbulence model for continuumphase and incorporating Ergun drag closure(when 0.8). Saffmanand Magnus lift force was incorporated and they

conclude that drag force is much higher at the centre and

the contact force near the wall is much higher than the

drag force. Gas phase turbulence is much higher than the

particulate phase turbulence. Saffman and Magnus lift

force are almost zero in jet region and annulus region

while it is about 6% of total force in the boundary of jet

and annulus region.

Gas-solid spouted bed with 62000number of particles. The friction

coefficients (particle-particle 0.3,

particle-wall 0.25), spring stiffness is

800 N/m, Restitution coefficient, 0.9

Gas(air) Solid

g = 1.166 kg/m3 s=1020 kg/m

3

g =1.8210-5

Pa.s ut= 7.9, 8.8 m/s

dp= 2, 3 mm

22. Hideya Nakamura, Satoru Watano,Numerical modeling of particlefluidization behavior in a rotating fluidized

bed. Powder Technology 171 (2007) 106117

2-D DEM simulation of a rotating fluidized bed Cundalland Strack contact model with Di Felice drag closure. It

was found that predicted Umfwas comparable with that

of calculated one. Calculated fluidization behavior like

regime transition, periodic bubbling fluidization is in

good agreement with that of experimental one obtained

by the high speed camera.

Gas-solid rotating fluidized bed with16000 number of particles. The friction

coefficients (particle-particle 0.32,

particle-wall 0.34), spring stiffness is

800 N/m, Restitution coefficient, 0.9

Gas(air) Solid

g = 1.206 kg/m3 s=918 kg/m

3

g =1.8210-5 Pa.s ut= 2.1 m/s

dp= 0.5 mm

23. Alberto Di Renzo, Francesco Paolo DiMaio, Rossella Girimonte, Brunello

Formisani, DEM simulation of the mixing

equilibrium in fluidized beds of two solidsdiffering in density. Powder Technology184 (2008) 214223

2-D DEM simulation using Cundall and Strack contact

model with Di Felice drag closure. In this work the

mixing index of two different solid with same size at

different gas velocity was studied and simulated datawas in good agreement to that of experiment.

Gas-solid bubbling fluidized bed of

two kind of solid with equal volume

fraction (50% each) with total 15000

number of particles. The frictioncoefficients 0.3, Restitution coefficient,

0.9, Youngs modulus 0.1GPa,Poissons Ratio 0.25Gas(air) Solid

g = 1.206 kg/m3 s= 2480, 7600

kg/m3

g =1.8210-5 Pa.s ut= 2.1 m/s

dp= 0.433 mm

24. C.R. Mller, D.J. Holland, A.J. Sederman, 2-D DEM simulation to calculate the velocity and Gas-solid rotating fluidized bed with

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S.A. Scott, J.S. Dennis, L.F. Gladden,Granular temperature: Comparison ofMagnetic Resonance measurements with

Discrete Element Model simulations.Powder Technology 184 (2008) 241253

granular temperature, using Hertzian contact model innormal direction and Cundall and Strack contact model

in tangential direction, three different drag correlation

were used (a)Ergun and Wen and Yu, (b)Di Felice (c)

Beetstra drag closure and it was shown that change in

the drag correlation have some minor change. It was

find out that Beetstra drag closure is in best agreement

with the experiment. There is no effect of restitution

coefficient on particle velocity although bit larger effect

on granular temperature.

9240 number of particles. The friction

coefficients 0.1, elastic modulus

0.12MPa, Poissons Ratio 0.33,Restitution coefficient, 0.97

Gas(air) Solid

g = 1.206 kg/m3

s= 1000 kg/m3

g =1.8210

-5 Pa.s ut= 5.5 m/s

dp= 1.2 mm

25. X.-L. Zhao, S.-Q. Li, G.-Q. Liu, Q. Song,Q. Yao, Flow patterns of solids in a two-dimensional spouted bed with draft plates:

PIV measurement and DEM simulations.Powder Technology 183 (2008) 7987

2-D Spouted bed with draft plate,was investigated with

DEM simulation using Cundall and Strack contact

model and Ergun and Wen and Yu drag closure model,

and PIV measurement. It was found that the particle

move individually not as particle cluster as found in

2DSB. DEM simulation provide good prediction on

particle vertical velocity along the bed vertical line.

Spouted bed with draft pate . The

friction coefficients 0.3, spring

stiffness is 800 N/m, Damping

coefficient 0.0042 kg/s, Restitution

coefficient, 0.9.

Gas(air) Solid

g = 1.206 kg/m3 s= 2380 kg/m

3

g =1.8210

-5

Pa.s ut= 12 m/sdp= 2 mm

26. Takuya Tsuji , Keizo Yabumoto,Toshitsugu Tanaka, Spontaneousstructures in three-dimensional bubbling

gas-fluidized bed by parallel DEMCFDcoupling simulation. Powder Technology184 (2008) 132140

3-D DEM simulation with Cundall and Strack contact

model and Gidaspows drag closure model. 4.5 millionparticles was tracked using 16 CPUs, one of the largest

system in this type of simulation. In ideal condition

multiple bubbles was formed in larger cross section

while slug formation in small cross section was

observed. An approximate bubble size was estimated

from the simulation. The bubble size is not expected to

change significantly if we extend the cross-section size

further. Circulation of the particulate solid observed.3-D

visualization of bubble shape was also conducted.

Gas-solid bubbling fluidized bed with4.5 million particles. The friction

coefficients 0.3, Restitution coefficient,

0.9, Normal spring coefficient 800N/m,

tangential spring coefficient is 200 N/m

Gas(air) Solid

g = 1.206 kg/m3 s= 2700 kg/m

3

g =1.8210-5 Pa.s ut= 11.8 m/s

dp= 4 mm

27. Jin Sun, Francine Battaglia, ShankarSubramaniam, Hybrid Two-Fluid DEMSimulation of Gas-Solid Fluidized Beds.Journal of Fluids Engineering (2007), Vol.

129 / 1395.

2-D DEM simulation using linear spring-dashpot model,

Hertzian model for contact force and Gidaspows dragclosure. The result were compared with the experimental

data and with two fluid model, it was found that DEM

simulation is capable of predicting general fluidized

bed dynamics, i.e., pressure drop across the bed and bed

expansion, which are in agreement with experimental

measurements and TF model predictions.DEM model

have the capability to capture more structural

information of the fluidized beds than the TF model.


2400 and 4000 particles. The friction

coefficients 0.3, Restitution coefficient,

0.9, spring coefficient 800N/m,

Gas(air) Solid

g = 1.206 kg/m3 s= 2700, 2526

kg/m3

g =1.8210-5 Pa.s ut= 17, 14 m/s

dp= 4, 2.5 mm

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28. Yurong He, Tianyu Wang, Niels Deen,Martin van Sint Annaland, Hans Kuipers,

Dongsheng Wen, Discrete particlemodeling of granular temperature

distribution in a bubbling

fluidized bed. Particuology 10 (2012) 428437

2-D DEM hard sphere model simulation with various

closure models was used to study the distribution of

particle and bubble granular temperature and it was

found that the gas superficial velocity is most effective

parameter on the granular temperature of particle and

bubble whereas the effect of drag closure is more on

large scale variable such as bubble. Simulated results

were compared with experimental results by Mller et

al. (2008) showing reasonable agreement.


9240 particles. The friction coefficients

0.1,normal restitution coefficient, 0.97,

tangential restitution coefficient, 0.33.

Gas(air) Solid

g = 1.206 kg/m3 s= 1000 kg/m3g =1.8210

-5Pa.s ut= 5.15 m/s

dp= 1.2 mm

DEM2

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