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DEM simulation of gas–solid flow behaviors in spout-fluid bed

Chemical Engineering Science61(2006)1571–

1584

https://www.wendangku.net/doc/7817784271.html,/locate/ces

DEM simulation of gas–solid?ow behaviors in spout-?uid bed

Wenqi Zhong?,Yuanquan Xiong,Zhulin Yuan,Mingyao Zhang Education Ministry Key Laboratory on Clean Coal Power Generation and Combustion Technology,Thermoenergy Engineering Research Institute,

Southeast University,Nanjing210096,People’s Republic of China

Received13December2004;received in revised form22August2005;accepted23September2005

Available online8November2005

Abstract

Three-dimensional gas and particle turbulent motions in a rectangular spout-?uid bed were simulated.The particle motion was modeled by discrete element method and the gas motion was modeled by k? two-equation turbulent model.Shear induced Saffman lift force,rotation induced Magnus lift force as well as drag force,contract force and gravitational force acting on individual particles were considered when establishing the mathematics models.A two-way coupling numerical iterative scheme was used to incorporate the effects of gas-particle interactions in volume fraction,momentum and kinetic energy.The gas–solid?ow patterns,forces acting on particles,the particles mean velocities,jet penetration depths,gas turbulent intensities and particle turbulent intensities were discussed.Selected stimulation results were compared to some published experimental and simulation results.

Keywords:Numerical simulation;Discrete element method;Turbulent?ow;Spout-?uid bed;Spouted bed

1.Introduction

Spout-?uid beds have been extensively employed in petro-chemical,chemical and metallurgic industry.In addition to in-jecting the spouting gas through a central nozzle,the?uidizing gas is introduced through a perforated distributor surrounding the central nozzle,which can result in better gas–solid mixing than either spouted beds or?uidized beds(Vukovic et al.,1984; Sutanto et al.,1985;Pianarosa et al.,2000;Xiao et al.,2002, 2005;Zhong and Zhang,2005a–c).In resent years,spout-?uid bed coal gasi?ers have been regarded as alternative gas–solid contactors for a laboratory scale advanced pressurized?uidized bed combustion-combined cycle(APFBC-CC)system and a pressurized partial gasi?cation-combined cycle(PPG-CC)sys-tem in our laboratory(Zhang,1998;Xiao and Zhang,2002; Xiao et al.,2002,2005;Zhong and Zhang,2005a–c).Knowl-edge of gas and particle dynamics in spout-?uid bed is im-portant for evaluation of particle circulation rate and gas–solid contacting ef?ciency especially for rapid reaction in the bed.?Corresponding author.Tel.:+862583795119;fax:+862557714489.

E-mail address:wqzhong@https://www.wendangku.net/doc/7817784271.html,(W.Zhong).

0009-2509/$-see front matter?2005Elsevier Ltd.All rights reserved. doi:10.1016/j.ces.2005.09.015However,as is the case for all dense gas–solid system,it is dif?cult to obtain the measurement of gas and solid dynamics in the whole space of the bed without disturbing the?ow?eld. Numerical simulations have become popular in the?eld of dense gas–solid two-phase?ows in resent years.Numer-ical simulation is a useful tool to get detailed information about the phenomena without disturbing the?ows.The par-ticle motion in gas–solid systems has been calculated by using two kinds calculation models,the trajectory model and the continuum media model.The discrete element method (DEM)is one of the trajectory models(Cundall and Strack, 1979;Tsuji et al.,1993).Since it being successfully em-ployed in dense phase?ows in?uidized bed by Tsuji et al. (1993),signi?cant advances have been accomplished in sim-ulating the gas–solid?ow systems by DEM(e.g.Tsuji et al., 1993;Hoffmann et al.,1993;Tsuji et al.,1997,1998;Tsuji, 2000;Helland et al.,2000;Kawaguchi et al.,2000;Yuan, 2000;Yuu et al.,2001;Xiong,2003;Xiong et al.,2004; Zhou et al.,2004;Takeuchi et al.,2004;Tatemoto et al., 2004;Langston et al.,2004;Bertrand et al.,2005).Due to the development of computer capacities,DEM has become a very useful and versatile tool to study not only the hydrody-namic behaviors of particulate?ows but also of the chemical

1572W.Zhong et al./Chemical Engineering Science61(2006)1571–1584

reactions,and heat and mass transfer at the individual particle scale(Kaneko et al.,1999;Rong and Horio,1999;Zhou et al., 2004).DEM offers a more natural way to simulate a gas–solid ?ow with particles of different size or different density with each individual particle tracked in the simulation.However,the DEM simulation of spout-?uid bed has largely lagged behind, there has been no information concerning such a method to spout-?uid beds so far.

Moreover,almost all researches using DEM neglected the effect of turbulence on the gas-particle motion(Zhou et al., 2004),although both experimental and theoretical results ver-i?ed the strong intensity of turbulence in?uidized bed(e.g. Peirano and Leckner,1988;Zhou et al.,2000,2002).Signi?-cant advances have been accomplished in modeling the turbu-lent?ow so far.There are three main methods to work with the turbulence on the gas-particle motion:(1)Direct numeri-cal simulation(DNS),to simulate the turbulent?ow directly by solving3-D Navier–Stokes equations,this approach needs a very small time step and?ne meshes in order to recognize the turbulence variety in small time and space scale.The method might be restricted by computational memories unless main-frame computers are used(Moin and Mahesh,1998);(2)large eddy simulations(LES),LES can be considered as a spatially ?ltered solution to the Navier–Stokes equations.It also needs a relatively high computational memory but is easier to real-ize in personal computers.This method has widely applied in resent years(Kogaki et al.,1997;Jordan and Ragab,1998; Yang,2000);(3)Reynolds-averaging equations(RAE),appli-cation of RAE to consider the gas turbulence needs to establish a turbulence model(generally called as closure model)in or-der to make the Reynolds stress equations closed(Launder and Spalding,1974),or to establish a turbulent viscosity function representing the turbulence stress.The latter was considered as one of the most promising approach to solve the turbulent?ow (e.g.Simonin and Viollet,1990;Balzer et al.,1995;Ramad-hyani,1997).In this approach,some turbulence model such as zero equation models,one-equation model and k? two equations model can be used according to the determination of turbulent viscosity coef?cient(Singhal and Spalding,1981; Pourahmadi and Humphery,1983;Simonin and Viollet,1990; Balzer et al.,1995;Ramadhyani,1997;Lun,2000).

Zhou et al.(2004)numerically simulated the gas and parti-cle motions in a two-dimensional(2-D)bubbling?uidized bed. The solid phase is modeled with DEM and the gas phase is modeled as two-dimensional Navier–Stokes equations for two-phase?ow with?uid turbulence calculated by LES.For spout-?uid bed,the strong intensity of turbulence of gas–solid mo-tions was detected in bed especially at the distributor region (Zhong and Zhang,2005a,c).Thus,numerical as well as exper-imental approaches aiming at grasping more useful information on the?ow gas–solid turbulent motions in spout-?uid beds are expected.

The objective of the present work is to develop a3-D tur-bulent model accounting for the gas–solid turbulent?ow in a spout-?uid bed.The particle motion is modeled by discrete el-ement method and the gas motion was modeled by k? two-equation turbulent https://www.wendangku.net/doc/7817784271.html,putational models

2.1.Gas phase

The continuity and momentum equations in a3-D geometry come as

j t( g)+

j x i( g u j)=0,(1)

( g u i)+

( g u i u j)

j p

j x i

j x j( ij)

?n p(f D+f LM+f LS)+ g g,(2)

where is the void fraction, g is the gas density,and u i and u j are the gas velocity,i,j=1,2,3,which represent x,y and z directions, ij is the turbulence stress,it can be modeled as

ij=( + t)

j u j

j x i

j u i

j x j

g k ij(3)

in which is the gas dynamic viscosity, t is the turbulent viscosity modeled as t=C g k2/ t,k is turbulence energy, ij is the Kronecker number, t is the turbulence dissipation rate,C is a empirically assigned constant.

In Eq.(2),n p(f D+f LM+f LS)is the interfacial momentum transfer term per unit volume.In which,n p is the number of particles per unit volume and f D is the drag force for single particle.The interphase interactions between the solid and the gas per unit volume due to?uid drag force(Kafui et al.,2002) is given by the following correlation:

n p f D= (u?v p),(4)

where v p is the mean velocity of the particles in the unit volume. Ergun’s equation(Ergun,1952)was used for the dense phase and Wen and Yu’s equation(Wen and Yu,1966)for the dilute phase,and the coef?cient was summarized as follows:

(1? )

d2p

[150(1? )+1.75Re p],( 0.8),

0.75C D (1? )

d2p

?2.7Re p( >0.8).

(5)

C D=

Re p

(1+0.15Re0.687

),(Re p 1000),

0.43(Re p>1000).

(6) Re p=

g |u?v p|d p

(7) in which C D is the drag coef?cient for a single sphere.The terms f LM and f lS in Eq.(2)are the Saffman lift force and Magnus lift force for single particle,which will be described in the next section.

W.Zhong et al./Chemical Engineering Science61(2006)1571–15841573 Table1

The values of empirically assigned constant in k? two equations model

C C1C2C3 k

0.09 1.44 1.92 1.2 1.0 1.33

The gas turbulence kinetic energy equation was sited from

Crowe(2000),which can be expressed as

j t( k)+

j x j( ku j)=

j x j

j k

j x j

+ G k+ k+S k d,(8)

G k= t

j u

j x

j v

j y

j w

j z

j u

j y

j v

j x

2 +

j u

j z

j w

j x

j v

j z

j w

j y

,(9)

S k d= |u?v p|2+ ( v v? u v)(10) the term |u?v p|2in Eq.(10)is the generation term caused by particle resistance,and the term ( v v? u v)is the re-distribution term representing the exchange of kinetic energy between particle and gas.Where, u is the gas?uctuating velocity, v is the particle?uctuating velocity.According to Xiong(2003)and Xiong et al.(2004),the re-distribution term

can be re-scaled as

( v v? u v)=?2 k

l+ d

l+ d,(11)

4d p p

3C D g|u?v p|

,(12)

l=0.35k

,(13) where l is the Lagrangian time scale of gas phase, d is the response time scale of particle phase, g is particle density, is the turbulent Schmidt number,here =0.7.

The turbulence momentum dissipation equation was sited from Bertodano et al.(1994),which can be described as

j t( g )+

j x j( g u j k)

j x j

(C1G k?C2 g )+S d,(14)

S d=C3

S k d.(15)

The values of empirically assigned constant in k? two equations model used in the present work are listed in Table1, they are cited from the previous work(Singhal and Spalding, 1981;Pourahmadi and Humphery,1983;Simonin and Viollet, 1990;Balzer et al.,1995;Ramadhyani,1997).

dash-pot

k t

k n

spring

friction

slide

(a)(b)

Fig.1.Model of particle–particle contact forces.(a)Normal force and(b) tangential force.

The gas?ow in spout-?uid bed can be assumed to satisfy the following function:

p= g RT(16) in which R=287.1N m/(kg K),T=(273.15+25)K.

2.2.Particle phase

The particle motion was calculated three-dimensionally.In-dividual particle motion was traced by using the DEM as in the previous successful work(e.g.Tsuji et al.,1993;Hoffmann et al.,1993;Tsuji et al.,1997,1998;Tsuji,2000;Helland et al.,2000;Kawaguchi et al.,2000;Yuan,2000;Yuu et al.,2001; Xiong,2003;Xiong et al.,2004;Zhou et al.,2004;Takeuchi et al.,2004;Tatemoto et al.,2004;Langston et al.,2004;Bertrand et al.,2005).In the present work,the shear induced Saffman lift force and rotation induced Magnus lift force were considered as well as the drag force,contract force and gravitational force. According to Newton’s equation of motion,the motion of a particle is calculated as

m p

d v p

d t

=f C+f D+f LS+f LM+m p g,(17) d p

d t

=M p

I p

,(18)

where f c is the contact force,f D is the drag force,f LS is the Saffman lift force and f LM is the Magnus lift force.v p is the particle velocity, p is the particle rotational velocity,M p is the particle torque and I p is the particle motion of inertia. 2.2.1.Contact force f C

Cundall and Strack’s(1979)DEM model opened new pos-sibilities for using discrete particle simulation to calculate the dense phase?ows in?uidized bed(Tsuji et al.,1993).Contact forces are described in term of a mechanical model involving a spring,dashpot and friction.The contact force f C is divided into normal(f cnij)and tangential(f ctij)forces,they are mod-eled in Fig.1.These forces can be expressed as the following equations(Tanaka et al.,1991):

f C=f cnij+f ctij,(19)

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W.Zhong et al./Chemical Engineering Science 61(2006)1571–1584

f cnij =(?k n 1.5

nij ? n v rij ·n ij )n ij ,

(20)f ctij =?k t tij ? t v tij ,

(21)

where k n and n are the spring and damping coef?cients in normal direction,respectively.k t and t are the spring and damping coef?cients in tangential direction,respectively. nij and tij are the normal and tangential displacements between particle i and particle j ,respectively.

v tij

|v tij |

.(22)

d a s h -p o t

wall spring

dash-pot s p r i n g

w a l l

friction

slide

(a)

(b)

k t

k n n n

Fig. 2.Model of particle-wall contact forces.(a)Normal force and (b)

tangential force.

Table 2

Calculated correlations for the spring coef?cient and the damping coef?cient Particle–particle Particle–particle Correlations

Number

Correlations

Number

k n =

E p d p 3(1? 2p )

(24)k n =43 1? 2

p E p +

1? 2w

E w ?1 d p 2

(29)

k t =2G p

d p 2? p

0.5n

(25)

k t =8 0.5

2? p G p

2? w

G w

d p 2

(30)

n = 0.25

n (26) n = 0.25

n (31) t = n

(27)

t = n

(32)

G p =

E p

2(1+ p )

(28)

G p =

E p 2(1+ p ),G w =

E w

2(1+ w )

(33)

In general,few particles are in contact with particle i at the same time.Therefore the total force acting on particle i can be obtained by taking the summation of the above forces with respect to j (Tsuji et al.,1993):

f C = j

(f cnij +f ctij ).(23)

The same relations as the above equations are derived for con-tact with the wall when the particle j is replaced by wall,as are modeled in Fig.2.

The spring coef?cients k n and k t are calculated from the fol-lowing equations based on the methods of Hertz’s and Mindlin and Deresezewicz (1953),respectively,and the damping coef-?cients n and t are determined from the method of Tanaka et al.(1991).They are listed in Table 2.

Where p and w are the Poisson ratio of particle and wall respectively.E p and E w are longitude elastic moduli of par-ticle and wall respectively.G p and G w are transverse elastic moduli of particle and wall respectively.When Eqs.(28)and (31)are used,the value of restitution coef?cient e depends only on the value of coef?cient (Tanaka et al.,1991),the relation between them was reference to (Tsuji et al.,1993).The resti-tution coef?cient is constant in present work,it is e =0.9.2.2.2.Drag force f D

The drag force f D for single particle is calculated as

f D =18

g d 2

p C D |u r |u r ,

(34)

where the translational relative velocity,u r is de?ned as u r = (u ?v p ).The drag coef?cient C D is given by Eq.(6).

W.Zhong et al./Chemical Engineering Science61(2006)1571–15841575 2.2.3.Saffman lift force(shear lift force)f LS

Saffman lift force is also called shear lift force.Saffman

(1965)and Saffman(1968)indicated if the particle is relatively

large and the?uid has a relatively large velocity gradient when

it?ows around the particle,a lift force will be generated due

to the velocity difference between the top and bottom of the

particle whether the particle rotates or not.The direction of this

lift force is vertical to the direction of relative velocity between

the particle and?uid.The gas velocity is signi?cant different

between the central dilute region and annular dense region in

a spouted bed(Mathur,1974)and a spout-?uid bed(Pianarosa

et al.,2000).Entrainment of particles from the annular dense

region into the spout region by spouting gas is obvious,which

lead to a continuous and stable spout or jet in the spout-?uid

bed.A shear lift force will generate due to the velocity gradient

when particles entrained into spout region.The force should

not be neglected especially when particles in the boundary of

central dilute region and the annular dense region.

The Saffman lift force is calculated by

f LS=1.615(u?v p)(

g g)0.5d2p C LS

j u

j n

sgn

j u

j n

(n=x,y,z)(35) in which C LS is the Saffman lift coef?cient.C LS in the present work is calculated from the correlation developed by Mei (1992),which is expressed as

C LS=?

(1?0.3314 0.5)exp(?0.1Re p)

+0.3314 0.5,(Re p 40),

0.0524 0.5(Re p)0.5,(Re p>40),

(36)

where,

=0.5d p

|(u?v p)|

j u

j n

.(37)

2.2.4.Magnus lift force f LM

When the gas?ow is not uniform at various locations,the par-ticle will rotate due to the velocity gradient.At a low Reynolds number,the rotation of particle will bring the?uid moving around the particle,which leads to the increasing of?uid ve-locity in the same side as the?ow direction and the decreasing of?uid velocity in the opposite side.A lift force will generate due to the velocity difference between the different sides of the particle.The lift force is called as Magnus lift force.The direc-tion of Magnus lift force is vertical to the direction of relative velocity between particle and?uid.A rotation induced Magnus lift force will generate due to the velocity gradient when parti-cles entrained into the spout region with rotation,which should not be neglected especially when particles in the boundary of central dilute region and the annular dense region.

The Magnus lift force is calculated by the following corre-lation(Lun and Liu,1997;Lun,2000):

f LM=1

g v2r d2p C LM

r×v r

r r

,(38)

where v r is relative velocity between gas and particle,v r=

u?v p. r is the rotation angular relative velocity between

gas and particle, r= g? p and g=0.5?×u.C LM is

Magnus lift coef?cient.C LS in the present work is calculated

from the correlation developed by(Lun and Liu,1997),which

is expressed as

C LM=

| r|

d p,(R

e p 1),

| r|

|v r|d p(0.178+0.822Re

?0.522

),(1

(39)

p was assumed to satisfy the correlation(Rubinow and Keller,

1961)as follows:

m p d2p

d p

d t

p d2p

C t| r| r(40)

in which the coef?cient C t is calculated from the correlation

proposed by Dennis et al.(1980):

C t=

5.32

Re0.5

+37.2

,(Re <20),

6.45

+32.1

,(Re 20)

(41)

where Re is particle rotation Reynolds number,which is de-

?ned as

Re =

p d2p| r|

.(42)

https://www.wendangku.net/doc/7817784271.html,putational conditions

The geometry of the vessel and array of numerical grids is

showed in Fig.3.The spout-?uid bed has a cross section of

300mm×15mm and height of1212mm.The spout nozzle is

20mm×15mm.A V type gas distributor,having a60?incli-

nation angle was at the bottom of the bed.This simulated ves-

sel has a similar geometry as our previous experiments(Zhong

and Zhang,2005a–c),but only half of the size due to restric-

tion of the simulated particles by our computer.The physical

and numerical parameters are summarized in Table3.

In experiments,the minimum spouting velocity,u ms,is de-

termined by visual observation when the spouting fountain just

vanished with decreasing gas velocity(Mathur,1974;Vukovic

et al.,1984;Sutanto et al.,1985).The minimum spouting veloc-

ity at spouting gas nozzle based on our experiment on the spout-

?uid bed vessel(300mm×30mm×2000mm)for the same

operating conditions as the simulations were found to be24m/s

(d p=1.5–2.5mm,mean diameter is about2.0mm)and33m/s

(d p=2.0–3.0mm,mean diameter is about2.5mm).The min-

imum spouting velocity based on simulation was determined

by decreasing super?cial gas velocity gradually(Takeuchi et

al.,2004)or by increasing spouting gas velocity step by step

(Kawaguchi et al.,2000).For the present work,simulations

were performed with decreased spouting gas velocity step by

step from27to20m/s and from36to30m/s for two kinds

of particles,respectively.The simulations at22and30m/s can

1576W.Zhong et al./Chemical Engineering Science61(2006)1571–1584

Fig.3.Geometry of vessel and array of numerical grids.

Table3

Physical and numerical parameters

Properties Value

Bed cross section,A300mm×15mm

Vessel height,H b1212mm

Cell size( x× y× z)10mm×5mm×17.32mm Spouting gas velocity,u s0.39–3.65u ms

Fluidizing?ow rate,Q f0.78Q mf

Gas density, g1.166kg/m3

Gas viscosity, 18.2×10?6Pa/s

Initial bed height,H0500mm

Maximum number of particle,n62000

Particle density, p1020kg/m3

Particle diameter,d p 1.5–2.5mm,2.0–3.0mm,

Gaussian distribution spring coef?cient,k800N/m

Restitution coef?cient,e0.9

Friction coef?cient

Particle–particle, p0.3

Particle-wall, w0.25

Poisson’s ratio

Particle, p0.33

Wall, w0.33

Modulus of longitudinal elasticity

Particle,E p3.0×109N/m2

Wall,E w3.0×109N/m2

Time step of calculation, t1.0×10?6s

just form stable spouting,thus,the velocities were determined as approximate minimum spouting velocities.

The gas phase was solved by non-staggered SIMPLE method (Miler and Schmidt,1988).Time-advancement of pressure and velocity?eld is based on the SMAC method(Amsden and Har-

low,1970).The SMAC method has been successfully applied

to simulation of multiphase turbulence?ow and validated for

studying the?ow structures(Yamamoto,1999).The procedure

was to obtain a velocity prediction?rst,and then correct the

velocity and pressure?eld by a corrector.On the inlet side,a

solid plane wall normal to z-axis was assumed except for the

opening of the nozzle.The no-slip condition was applied at this

wall.For the out?ow boundary Sommerfeld’s non-re?ective

condition and Neuman’s condition j p/j z=0were applied.

This method was also described in detail in previous publica-

tions(Takeuchi et al.,2004).A two-way coupling numerical

iterative scheme(Lun,2000;Xiong,2003;Xiong et al.,2004)

was used to incorporate the effects of gas-particle interactions

in volume fraction,momentum and kinetic energy.

The calculation of particle motions started up with a packed

bed.Particles were loaded into the bed row by row and layer

by layer to form an initial packed bed with height of500mm

and voidage about0.42,the distance between the centers of two

neighboring particle is2r max,r max is the maximum particle

diameter.The time step was determined from the viewpoint of

the stability and computation time expense of calculation.The

method proposed by Tsuji et al.(1993)was used to determinate

the time step.Besides,by comparing of the calculations with

several time steps,i.e.,i×10?7s,i×10?6s and i×10?5s,

where i=1,2,it was found that the time step1×10?6could

make calculations stable under most operating conditions,and

also no much computation time is needed.

4.Results and discussion

4.1.Flow patterns in spout-?uid bed

A total of3s simulation was conducted to observe the gas

and particle?ow patterns.A series of instantaneous snapshots

were generated every0.001s.A full development of a jet in

the spout-?uidized bed at u s=0.76u ms and Q f=0.78Q m f with time are presented in Fig.4.The?ow pattern was known

as“jet in?uidized bed”or“internal jet”according to Vukovic

et al.(1984)and Sutanto et al.(1985).In this case,particles

in the central spout region accelerated by the gas traveling

at relatively high speed to the top of the bed surface,while

particles in the annular region form a packed bed and?ow

steadily downwards,feeding into the spouting(or jet)along its

entire length especially in the V-shape distributor region.These

results agree with the previous experimental investigation on

spout-?uid beds(e.g.Vukovic et al.,1984;Sutanto et al.,1985;

Pianarosa et al.,2000;Zhong and Zhang,2005a–c)and jetting

?uidized bed(e.g.Kimura et al.,1995;Yang,1998).Continuous

circulation of particles can result in a continuous and stable

spout or jet in spout-?uid bed.Images of a jet developing in

the spout-?uidized bed at the same operating conditions except

bed size are showed in Fig.5.These photos were obtained in

our previous investigation(Zhong and Zhang,2005a–c)on a

spout-?uidized bed with its cross section of300mm×30mm

and height of2000mm,the time interval between successive

frames is0.0125s.The simulation results were similar to the

W.Zhong et al./Chemical Engineering Science61(2006)1571–1584

1577

Fig. 4.Development of a jet in the spout-?uid bed(d p=2.0–3.0mm, u s=0.76u ms,Q f=0.78Q mf).(a)t=0s,(b)t=0.002s,(c)t=0.005s, (d)t=0.008s,(e)t=0.011s,(f)t=0.014s,(g)t=0.017s,(h) t=0.20s,(i)t=0.023s,(j)t=0.26s,(k)t=0.29s,.

experiment results except the bubbles on the top of the jet. Much larger bubbles were observed in the experiments than the simulation,as is presented in Figs.4and5.

Fig.6illustrates the instantaneous snapshots of?ow pat-terns in the spout-?uidized bed at various spouting gas veloc-ity for u f=0.78u mf.For u s

not Fig. 5.Photos of development of a jet in the spout-?uid bed (u s=0.76u ms,Q f=0.78Q mf).

able to penetrate the bed,only form a internal jet as shown in Fig.4,Fig.6(a)and(b).When the spouting gas velocity is beyond the minimum spouting velocity(u s>u ms),a spout forms.However,when the spouting gas velocity is even greater (u s>2.5u ms),the bed is in the transport?ow pattern.These results agree with the previous experimental investigation on spout-?uid beds(Vukovic et al.,1984;Sutanto et al.,1985). Previous DEM simulation of?ow patterns in a cylinder spouted bed(Kawaguchi et al.,2000)presented in Fig.7. The minimum spouting velocity based on their simulation is 1.46m/s(super?cial velocity),which was obtained by increas-ing spouting gas velocity step by step.The present simulated ?ow patterns are similar to them,the difference from which is more obvious entrainments of particles from the annular dense region into the spout region,especially in the distributor re-gion,as shown in Fig.6(a)–(d).This can lead to a continuous and stable spout or jet in the spout-?uid bed.The higher the spouting gas velocity,the more particles are entrained into the spout region.These results agree qualitatively with visual ob-servation of physical experiments in spout-?uid beds.

The following section focuses on discussing the?ow behav-iors of gas and particle corresponding to the?ow pattern of “internal jet”or in the?ow regime of“jet in the?uidized bed”, since the spout-?uid bed coal gasi?er should be operated in the ?ow pattern of“internal jet”or in the?ow regime of“jet in the?uidized bed”(Zhong and Zhang,2005b,c).Operation in this?ow regime can prolong the resident time of steam and air in the bed,making it un-easy for much steam and air to pass through the bed and wasted,and enhancing the gas diffusion and particles mixing between the spout region and the annular

1578W.Zhong et al./Chemical Engineering Science61(2006)1571–

1584

Fig.6.Flow patterns in the spout-?uid bed at various spouting gas velocity (d p=1.5–2.5mm;Q f=0.78Q mf).(a)u s=0.39u ms,(b)u s=0.76u ms, (c)u s=1.22u ms,(d)u s=1.75u ms,(e)u s=2.25u ms,(f)u s=2.45u ms, (g)u s=2.65u ms,(h)u s=2.85u ms,(i)u s=3.05u ms,(j)u s=3.25u ms, (k)u s=3.45u ms,(l)u s=3.65u ms.

dense region,obtaining more uniform axial temperature pro-?les in order to improve the gasi?cation

ef?ciency.Fig.7.Flow patterns in a spouted bed at various spouting gas velocity simulated by Kawaguchi et al.(2000).

(

)

u s/u ms

https://www.wendangku.net/doc/7817784271.html,parisons of simulated pressure drop with experimental result at different spouting gas velocities for d p=2.0–3.0mm and Q f/Q mf=0.78.

4.2.Flow behaviors of gas and particles

Fig.8shows the comparisons of simulated pressure drop with experimental result(Zhong and Zhang,2005a–c)at dif-ferent spouting gas velocities.It can be seem that the trend of present simulated results are in well agreement with the ex-perimental results.Though,the simulated vessel is half of the experiment vessel,the result implies the hydrodynamic simili-tude of pressure drop with geometrical similitude at the same dimensionless operating conditions(He et al.,1997;Costa and Taranto,2003).

Fig.9presents the radial concentration of particles at the bed elevation H/D=1.0with different spouting gas velocities for d p=1.5–2.5mm and Q f/Q mf=0.78.The concentration of particles increases along radial direction.Besides,the con-centration of particles in the annular decreases with increas-ing spouting gas velocity due to the spouting gas transfer into

W.Zhong et al./Chemical Engineering Science 61(2006)1571–15841579

0.0

0.10.20.30.4

0.50.60.70.80.9 1.0

0.00.10.20.3

0.40.5

C o n c e n t r a t i o n o f p a r t i c l e s

r/R

Fig.9.Radial concentration of particles with different spouting gas velocities (H/D =1.0,d p =1.5–2.5mm,Q f /Q mf =0.78).

annular,the voidage in the annular dense region increases.For u s /u ms =0.39,the radial concentration of particles increase slightly.It is noted that the concentration of particles in the center of the bed at u s /u ms =0.39is greater than other three curves.Since the sampling location (H/D =1.0)is above the internal jet,which can be seemed in Fig.6(a),the particle con-centration has little grads.For the other three simulation con-ditions,i.e.,u s /u ms =0.76,u s /u ms =1.22and u s /u ms =1.75,the concentrations of particles in the spout region increase slightly with spouting gas velocity,which implies that the en-trainment of particles from the annular dense region into the spout region increases with spouting gas velocity.Experimen-tal results (Kimura et al.,1995;Yang,1998;Pianarosa et al.,2000;Zhong and Zhang,2005b )indicated that entrainments of particles along the spout or jet height especially in the distrib-utor region increase by increasing spouting gas velocity.The higher the spouting gas velocity,the more particles are entrained into the spout region.The ?ow patterns based on simulations (Fig.6a–d)show the same trend.

Variations of percentage of collided particles with time at u s =0.76u ms and Q f =0.78Q mf are presented in Fig.10.In order to obtain the percentage of collided particles,an arith-mometer was used in the program.For DEM simulation,every particle has a unique ID number and is tracked during calcu-lation.In every time step,the program must judge whether a certain particle collides with other particle(s)and whether a cer-tain particle collides with wall.The collision information can be recorded by the arithmometer.The percentage of collided particles represents the particles in collision.It is noted that a strong peak of particle–particle colliding exits at the beginning,which implies an impulsive start-up process when particle be-gin to move.The initial particle–particle collision percentage is over 50%corresponds to the start-up stage with packed bed,and then decreases ?uctuating with time due to the stable jet de-velopment in bed.The existence of a peak for particle–particle colliding in the start-up process was also reported by Zhou et al.(2004).While,the percentage of particle-wall colliding is

0.0

0.1

0.2

0.3

0.40.5

P e r c e n t a g e o f c o l l i d e d p a r t i c l e s

Time (s)

Fig.10.Variations of percentage of collided particles with time (d p =2.0–3.0mm,u s =0.76u ms ,Q f =0.78Q mf ).

10152025303540M e a n p e r c e n t a g e o f c o l l i d e d p a r t i c l e s (%)

u s /u ms

Fig.11.Mean percentage of collided particles with various spouting gas velocity (Q f =0.78Q mf ).

almost zero at the beginning.The mean particle–particle and particle-wall collisions percentage are 28.8%and 4.95%,re-spectively.

Fig.11shows the mean percentage of particles colliding with various spouting gas velocity and particle diameter.The ratio of particle–particle colliding decreases with spouting gas velocity,which agrees with previous investigation (Zhou et al.,2004),while the value increases with particle diameter.It is noted that the mean percentage of particle-wall colliding shows almost no variation with spouting gas velocity and particle diameter in the present work.However,these values are much higher than that in a bubble ?uidized bed by Zhou et al.(2004),which implies more intensive particle interactions in spout-?uid bed.This might result in the intensive motions of gas and particles by adding the spouting gas and ?uidizing gas into the bed synchronously,and the motions are restricted in thin situational vessel.

1580W.Zhong et al./Chemical Engineering Science 61(2006)1571–1584

0102030405060708090100P e r c e n t a g e o f f o r c e a d d i n g t o p a r t i c l e s (%)

r/R

Fig.12.Percentage of forces adding to particles (H/D =1.6,d p =2.0–3.0mm,u s /u ms =0.76and Q f /Q mf =0.

78).

Mean Saffman lift force (f LS )and Magnus lift force (f LM )were considered as well as drag force (f D ),contract force (f C )and gravitational force (f G ).Fig.12illustrates the percentage of force sadding to particles at H/D =1.6mm,every value is a mean absolute value of the particles in a calculated cell.The drag force is the largest in the jet region while the con-tact force is the largest in the annular region especially near the wall.These indicate that the drag force dominates the particle motion in the jet region while the contact force dominates the particle motion in the dense annular region especially near the wall.Zhou et al.(2004)indicated that the particle mean veloc-ity distribution is not correlated to the gas velocity distribution inside the bed,which indicates that the particle motion in the dense zone is dominated by the particle–particle interactions.The percentage of gravitational force decrease at both central and near-wall regions due the increasing of drag force or con-tract force,however the absolute value of gravitational force is invariable.The mean percentage of Saffman lift force and Magnus lift force are almost zero in the jet region and annular dense region,while they reach to 6%of the total forces adding to the particles in the boundary of jet region and annular dense region (about r/R =0.12),which due to the signi?cant dif-ference of gas velocity in the spout jet region and the annular region.Saffman lift force and Magnus lift force contribute to the entrainments of particles from the annular dense region into the spout region,which can lead to a continuous and stable jet in the spout-?uid bed.Fig.13shows the effect of Saffman lift force and Magnus lift force on the simulated pressure drop at different spouting gas velocities.The simulated results without Saffman lift force and Magnus lift force are lower than the ex-perimental results.The larger spouting gas velocity,the larger difference can be seen.While,the simulations with these two forces are in well agreement with the experiments.In another study,Xiong (2003)found that Saffman lift force and Mag-nus lift force take remarkable effect on the simulated results when simulating the particle ?ow behaviors in a high-velocity gas–solid injector.Thus,shear induced Saffman lift force and 0.0

0.5

1.0

1.5

2.0 2.5

0.00.20.40.60.81.01.21.4?P /H 0 (X 104P a /m )

u s /u ms

Fig.13.Effect of Saffman lift force and Magnus lift force on the simulated pressure drop at different spouting gas velocities for d p =2.0–3.0mm and Q f /Q mf =0.78.

rotation induced Magnus lift force is noticeable when simulat-ing the jet,recirculation and boundary ?ows.

The radial distributions of the particle velocity magnitude and particle velocity component in y -direction at various vessel heights are plotted in Fig.14.The particle velocity magnitude decreases with vessel height,they are symmetrical along bed width.The particle velocity component in y -direction is posi-tive in the center and it is negative in the annular region.That is,the particle ?ows at the higher levels move down in the an-nular region.The predicted trends of descent velocity are in good agreement with the measurements at a fully cylindrical Plexiglas column with its diameter of 152mm (Pianarosa et al.,2000).However,the particle velocity predictions at present work is about 1.5–2.0times greater than those measurements.Previous simulations of a spouted bed by Kawaguchi et al.(2000)showed the particle velocity were 2–5times greater than those measured by He et al.(1994).They explained that this discrepancy was due to the continuity problem of solids ?ow to balance the linear expansion of spout diameter with height and the contraction of cross-sectional area of the annular region.The present discrepancy also to be seemed caused by this.Be-cause,the present simulation work on a thin vessel,the front and back walls restrict the expansion of gas in the y -direction,thus the gas jet expands in the x -direction.The jet diameter based on simulation is 25%larger than those measured in fully cylin-drical vessel by Pianarosa et al.(2000),however,it is almost the same as measured in a rectangle rectangular spout-?uid bed (see Figs.5and 6b).It is pity that detailed comparisons of the present simulation to any measurement is not available due to the lack of experimental data obtained in a vessel of a same size and same operating conditions.However,the present work might seem successful according the glancing comparisons to previous valuable simulations and experiments.

The jet is playing an important role in mass transfer,heat transfer,momentum transfer during the chemical process of spout-?uid bed coal gasi?cation.The jet penetration depth can be considered as one of the key parameters to describe the

W.Zhong et al./Chemical Engineering Science 61(2006)1571–15841581

012345678P a r t i c l e v e l o c i t y m a g n i t u d e (m /s )

0.0

0.1

0.2

0.3

0.4

0.50.6

0.7

0.8

0.9

1.0

-2-10123

4P a r t i c l e v e l o c i t y c o m p o n e n t i n z d i r e c t i o n (m /s )

r/R

(a)(b)

Fig.14.Simulation results of the radial distribution of particle velocity at various vessel heights (d p =2.0–3.0mm,u s =0.76u ms ,Q f =0.78Q

mf ).

0100200

300

400

500

J e t p e n e t r a t i o n d e p t h (m m )

u s /u ms

https://www.wendangku.net/doc/7817784271.html,parison of jet penetration depth at various vessel heights by simulation with correlation (d p =2.0–3.0mm,Q f =0.78Q mf ).

jet action in spout-?uid https://www.wendangku.net/doc/7817784271.html,parison of jet penetration depths at various spouting gas velocities by simulation with that of by correlation is showed in Fig.15.The simulation

0.00.1

0.20.30.40.50.60.70.80.9 1.0

0.0

0.10.20.30.40.5

0.6G a s t u r b u l e n t i n t e n s i t y

r/R

0.00

0.05

0.10

0.15

0.20

P a r t i c l e t u r b u l e n t i n t e n s i t y

(a)

(b)

https://www.wendangku.net/doc/7817784271.html,parison of the gas and particle turbulent inten-sity radial distributions in various vessel heights (d p =2.0–3.0mm,u s =0.76u ms ,Q f =0.78Q mf ).

results exhibit that the jet penetration depth is a function of spouting jet velocity.The jet penetration depth increases with increasing spouting jet velocity,which agrees with our previous experiments.The simulation results ?t the calculations by the correlation based on a large amount of experiment data with little variance (Zhong and Zhang,2005b ).

The gas and particle turbulent intensity radial pro?les are pre-sented in Fig.16.The turbulent in a spout-?uid bed is obviously non-isotropic.Unlike those investigations on isotropic turbu-lence (Lun,2000;Zhou et al.,2004),the present gas and particle turbulent intensities are de?ned as 13

( u 2x + u 2y + u 2z )

ˉu and 13

( v 2x + v 2y + v 2z )

ˉv ,respectively.In which,ˉu is the mean gas velocity and ˉv is the mean particle velocity.The gas turbulent intensity is always much larger than particle turbulent intensity.For both phase,the turbulent intensity are higher in the jet region and interface of the jet region and annular dense region than near the wall.The gas/solid turbulence in these re-gions would enhance the gas–solid mixing.It is noted that,un-like the gas turbulent intensity,the particle turbulent intensity in the annular dense region has ?uctuations.This indicates that

1582W.Zhong et al./Chemical Engineering Science61(2006)1571–1584

the particle–particle interactions dominate the particle motion in the dense zone.

5.Conclusions

The gas and particle turbulent motions in a rectangular spout-?uid bed were simulated three-dimensionally.The particle mo-tion was modeled by DEM and the gas motion was modeled by k? two-equation turbulent model.Saffman lift force(Shear lift force),Magnus lift force as well as drag force,contract force and gravitational force acting on individual particles were con-sidered when establishing the mathematics models.A two-way coupling numerical iterative scheme was used to incorporate the effects of gas-particle interactions in volume fraction,mo-mentum and kinetic energy.Selected stimulated results were compared to some published experimental and simulation re-sults.The following conclusions can be drawn based on the simulations:

(1)The greater the spouting gas velocity,the more obvi-

ous entrainments of particles from the annular dense region into the spout region,especially in the distributor region.

(2)The percentage of particle–particle colliding decreases

with spouting gas velocity,while the value increases with particle diameter.The percentage of particle-wall collid-ing shows almost no variation with spouting gas velocity increasing and particle diameter.The drag force dominates the particle motion in the jet region,while the contact force dominates the particle motion in the dense annular region especially near the wall.The mean percentage of Saffman lift force and Magnus lift force are almost zero in the jet region and dense annular region,while they reach6%of the total forces adding to the particles in the boundary of jet region and annular dense region.

(3)The concentration of particles increases along radial di-

rection and decreases with increasing spouting gas.The radial distribution of the particle velocity magnitude de-creases with vessel height,which is positive and symmet-rical along bed width.The particle velocity component in z-direction is positive in the center and it is negative in the annular dense region.

(4)The jet penetration depth increases with increasing of

spouting jet velocity,the simulation results?t the calcula-tions based on the correlation proposed by our previous experiments.

(5)Analyzed by non-isotropic turbulence theory,the results

show that the gas turbulent intensity is always much greater (2–3times)than the particle turbulent intensity.For both phase,the turbulent intensity are higher in the jet region and interface of the jet region and annular dense region than near the wall.

However,there is still a great need for experimental veri?ca-tion of these simulation results.Besides,a set of measurements of the kinematic?ow properties such as particle concentration, particle velocities,turbulence intensities,can be very useful in helping to understand the complex?ow?elds including jet,re-circulation and boundary layer?ows.

Notation

A bed cross section area,mm2

C D drag coef?cient,dimensionless

C LM Magnus lift coef?cient,dimensionless

C LS Saffman lift coef?cient,dimensionless

D column width,mm

d p particl

e diameter,m

e restitution coef?cient,dimensionless

E p Modulus of longitudinal elasticity for particle,

N/m2

E w Modulus of longitudinal elasticity for wall,

N/m2

f C contact force,N

f D dra

g force,N

f G gravitational force,N

f LS Saffman lift force,N

f LM Magnus lift force,N

f cnij normal contact force,N

f ctij tangential contact force,N

g gravitational acceleration vector,m/s?2

G p transverse elastic moduli of particle,N/m2

G w transverse elastic moduli of wall,N/m2

H local position in axis direction,mm

H b vessel height,mm

H o initial bed height,mm

I p particle motion of inertia

k n spring coef?cient in normal direction,N/m

k t spring coef?cient in tangential direction,N/m m p particle mass,kg

M p particle torque,Nm

n p number of particles per unit volume,dimension-less

→

n normal unit vector,dimensionless

n maximum number of particle,dimensionless

p pressure,Pa

Q f?uidizing gas?ow rate,Nm3/h

Q mf minimum?uidizing gas?ow rate,Nm3/h

r local position in radial direction,mm

R half bed width,mm

Re p particle Reynolds number,dimensionless

Re particle rotation Reynolds number,dimension-less

u gas velocity,m/s

u s spouting gas velocity,m/s

u ms minimum spouting gas velocity,m/s

u f?uidizing gas velocity,m/s

u f x x-direction?uidizing gas component velocity, m/s

u f z z-direction?uidizing gas component velocity, m/s

ˉu mean gas velocity,m/s

W.Zhong et al./Chemical Engineering Science61(2006)1571–15841583

u r translational relative velocity,m/s

v p particle velocity,m/s

ˉv mean particle velocity,m/s

v ntij normal relative velocity,m/s

v tij tangential relative velocity,m/s

Greek letters

p particle Poisson’s ratio,dimensionless

w wall Poisson’s ratio,dimensionless

ij Kronecker number,dimensionless

nij normal displacements between particle i and particle j,m

tij tangential displacements between particle i and particle j,m

t time step of calculation,s

x, y, z cell size,mm

u gas?uctuating velocity,m/s

v particle?uctuating velocity,m/s

p pressure drop,Pa

void fraction,dimensionless

n damping coef?cients in normal direction,kg/s t damping coef?cients in tangential direction,kg/s gas dynamic viscosity,Pa s

f slide frictional coef?cient,dimensionless

t gas turbulent viscosity,Pa s

p particle–particle friction coef?cient,dimension-less

w particle-wall friction coef?cient,dimensionless g gas density,kg/Nm3

p particle density,kg/m3

l Lagrangian time scale of gas phase,dimension-less

d respons

e time scale o

f particle phase,dimen-

sionless

ij gas turbulence stress,Pa

turbulent Schmidt number,dimensionless

t turbulence dissipation rate,m2/s3

p particle rotational velocity,s?1 Acknowledgements

Financial supports from the National Key Program of Ba-sic Research in China(G199902210535and2004CB217702), and the Foundation of Graduate Creative Program of JiangSu (XM04-28)are sincerely acknowledged.The authors also ex-press sincere gratitude to the respected professors,Prof.Y.Tsuji and Prof.J.R.Grace,for kindly presenting us some of their valuable papers in year2003,and Prof.M.Horio,E.Anthony and B.Leckner for constructive advices during their visiting periods in our laboratory,which contributed to our research.

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如何使用SolidWorks Flow Simulation分析孔蚀现象

如何使用SolidWorks Flow Simulation分析孔蚀现象 Cavitation in SolidWorks Flow Simulation – 如何使用SolidWorks Flow Simulation分析孔蝕現象 ■實威國際/CAE產品事業部何謂孔蝕現象(Cavitation) 孔蝕現象(Cavitation)也稱之為氣穴現象、空穴。當液體進入管路或閥門時如果壓力低於流體之蒸發壓壓力(Vapor Saturation Pressure)，就會在管路或閥門的流道內產生氣泡。這氣泡不是因為加熱而產生的，而是因為流動造成局部區域流速較快引起局部區域靜壓驟降，氣泡的產生會造成噪音或振動，而且通常是發生在實體表面上，因此會損壞管路或閥門的壁面，進而降低設備的使用壽命。孔蝕現象也常常發生在其他常見的裝置如泵浦、葉輪……等流體機械。若能透過分析軟體在產品設計階段模擬出此現象，則對於產品品質有非常大的保障。 (圖一) 發生孔蝕現象的渦輪葉片(圖片來源:參考資料2)

(圖二) 葉輪模型範例，吸入端至吐出端的壓力曲線，上方曲線是正常的，下方曲線低於蒸發壓力會發生孔蝕現象。孔蝕現象在SolidWorks Flow Simulation 1.SolidWorks Flow Simulation 2006以前版本。SolidWorks Flow Simulation無法直接模擬出孔蝕現象。不過，可以藉由分析結果中負壓的區域指出有孔蝕現象的區域。 2.SolidWorks Flow Simulation 2007之後版本。SolidWorks Flow Simulation有一項新增功能，可以應用來評估是否發生孔蝕現象。

lidWorksFlowSimulation全局旋转与局部旋转的应用

lidWorksFlowSimulation全局旋转与局部旋转的应用发表时间：2014-10-9 作者: 周洲来源: 互联网关键字: SolidWorks Flow Simulation全局旋转局部旋转本文介绍了以离心泵和CPU散热器仿真分析为例，介绍了在运用SolidWorks Flow Simulation进行旋转设置的过程中，设置全局旋转或局部旋转的具体步骤和方法。当我们在SolidWorks Flow Simulation遇到有旋转的情况时，我们会考虑设置全局旋转或局部旋转。设置全局旋转时，所有组件均参与旋转；而设置局部旋转时，只有包括在旋转区域内的组件参与旋转，那这两种情况该如何设置呢？请看下文的实例：离心泵： 1.该离心泵模型由叶轮、盖子以及3个封盖组成，实例是研究空气通过具有旋转叶轮离心泵的流动情况。空气通过进口封盖沿垂直于封盖表面的方向流入离心泵内部，通过旋转的叶轮从出口封盖流出，见图1。图1 离心泵模型 2.通过向导设定分析类型为内部流动，旋转类型为全局旋转，参考轴为Z轴，角速度为 -209.43951rad/s（2000rpm）。见图2：

图2 向导设定分析类型 3.插入进口封盖的边界条件为入口体积流量0.3m3/s，出口封盖的边界条件为环境压力。见图3：

图3 插入进口封盖的边界条件 4.该离心泵只有叶轮转动，而其余组件不参与旋转，因此需要将这些组件视为“定子”的真实壁面。选择插入边界条件，在打开的属性管理器中，选择盖子，在类型下选择“壁面”，设置为“真实壁面”，勾选“定子”。在全局旋转下，不参与旋转的组件必须视为“定子”。如图4所示：

SW里的Flow Simulation散热分析实例教程

SW里的Flow Simulation散热分析实例教程是一个SIMULATION的插件，我用过的版本中只有2011可以模拟。大致方法如下：（现在电脑上的是2010，本本上的是2011，在家里了） 1.建模 2.装配 3.编辑材质 ————————分割线——————进入插件 4.进入Simulation功能模块 5.新算例中选择热力 6.设置对流 1.选择产品与空气接触面（多选，也可选择全部然后去掉没用的面） 2.温度开始时开氏的（K），就是热力学温度，开氏温度=摄氏温度+27 3.15°，你要什么样环境温度可以按照这个公式算一下 3.对流系数，不一样的环境系数不一样，老版本的传热学教材里注明，室内的空气的流通量较小，对流系数在5~8W/(m^2·℃，户外在8~15W/(m^2·℃，可根据使用环境进行设置。 7.热量设置，选择光热器件的面。在这里未必要画出LED，因为那样对于新手很难选到LED底部的，可在几班的模型上拉伸出LED底部面积大小的面，最终模拟出来后去加热阻来算LED结温。一般来讲，LED的功率消耗包括发光和产生热两部分的，正常应该是在30%的光+70%的热，光效不一样的话会有很微妙的影响，可忽略不计的，这里我建议不要这么去考虑，如10W的光源就按照产生10W的热去模拟，而不是7W。（此处30%、70%仅限参考） 8.划分网格，网格化分的越精细，模拟会相对精确，流体分析的模拟软件原理是一样的，有时间可以去了解一下。有一些小结构或者比较碎的结构可能造成网格划分失败，多是因为模型的局部有壁厚过薄或者两零件有干涉的情况，好好检查一下。 9.右键---新算例，上面有选项，稳态和瞬态，此处选择稳态，即达到热平衡后的结果。 10.计算模式哪里有三个选项，选择“D”开头的模式，具体名称忘记了。 11.点击运算 12.等··· 13.等··· 14.配置不好或模型较大的用户请重新启动计算机，双击Solidworks，返回到第一步重新开始。我是这样做的，有高手觉得不妥的话欢迎指导！本打算图文并茂来着，但是电脑在家里，不好意思，就这样将就看吧，要是有什么问题的话，给我留言，但愿对各位有用！ -———————————————————————— 补充：设置的时候有个接触面的设置，那里会具体到两种材料的接触模式所产生的温差。我们也可以把整个系统做的具体一点，如集胶体的厚度或硅胶垫的厚度都把它们拉出来，这样会更好一些。

基于SolidWorks Flow Simulation的比例阀和真空泵的选型与优化

IM 软件世界 · 68 · 在真空泵和罐体之间装一台比例阀，比例阀和真空泵配合可改变抽速，保证罐内恒压。比例阀根据压力变化要求提供维持需要压力，比例阀与真空泵的选型多数靠经验来匹配，往往出现高能耗。通过SolidWorks Flow Simulation 对设备进行分析仿真，通过数据对比最优化的对比例阀与真空泵体的选型。一、问题的提出在真空设备和半导体设备中，常常有这样的工艺要求，某罐体内通入恒定流量的气体，并且保证罐体内恒压。通常采用方案是由一支流量计通入恒定流量的气体，出口连接一台真空泵抽气，在真空泵和罐体之间装一台比例阀，这样比例阀和真空泵配合可改变抽速，保证罐内恒压。图1 如图1所示是一款真空产品真空气路图，工作顺序如下。（1）首先关闭气动挡板阀-Φ100、电磁阀、流量计和电磁充气阀，比例阀开度100%，打开气动挡板阀-Φ16。基于SolidWorks Flow Simulation的比例阀和真空泵的选型与优化撰文/北京七星华创电子股份有限公司工业炉分公司张永军北京盛维安泰系统技术有限公司李跃超（2）然后开启滑阀泵-70L /S 预抽真空，真空度抽至30000Pa 时关闭动挡板阀-Φ16，比例阀开度0%，开启气动挡板阀-Φ100。（3）真空度抽至2000Pa 时，罗茨泵-300L /S 开启。（4）真空度抽至0.5Pa 时，关闭气动挡板阀-Φ100、罗茨泵-300L /S ，开启电磁阀、流量计，流量计保证0.5L /S 流量的氩气。（5）达到0.6atm 时开启气动挡板阀-Φ16，比例阀，比例阀和真空泵组成闭环，由PLC 控制其开度。此设备大部分时间在此状况下工作。在一个实例中，比例阀结构是通径Φ20的蝶阀，阀板在0°～90°转动，以实现0%～100%开启度。在保证0.6atm 恒压时，开启滑阀泵，比例阀开度8%。其8%～100%调节用不到，而且极不灵敏。我们判断比例阀通径选大了。选多大合适呢？结合SolidWorks Flow Simulation 模拟，让我们寻找合适的比例阀通径。 SolidWorks Flow Simulation 是一款比较经典的流体分析软件，它能解决流体流动分析、热分析、共轭传热、瞬态分析，并能作出漂亮视频、图片、图表及报表，且易学易用。除了软件本身向导式的操作流程之外，强大的数据库可以让使用者减少搜集分析所需数据的工作量。更重要的是与CAD 的无缝集成，可以实现分析结果驱动CAD 参数。使用者无需单独创建流体域，网格划分也极大地减少了使用者的工作量。总之无论是软件的工程化界面，全中文的在线帮助文档，都是使工程师不花费过多的精力在

基于SolidWorksFlowSimulation优化球阀结构

基于SolidWorksFlowSimulation优化球阀结构摘要：应用SolidWorksFlowSimulation对一款球阀半载及满载状态下的直口型和圆口型两种球体启闭件进行对比，共设计了四个CFD项目：（1）半载+直口型；（2）半载+直口型；（3）半载+圆口型；（4）满载+圆口型。一、引言球阀因结构简单、密封性好，而且在一定的公称通径范围内体积较小、重量轻、材料耗用少、安装尺寸小且驱动力矩小，操作简便、易实现快速启闭，是近十几年来发展最快的阀门品种之一。其工作原理是：启闭件（球体）由阀杆带动，并绕方工球阀作轴线作旋转运动的阀门，可用于流体的调节与控制，其中硬密封V 型球阀其V型球芯与堆焊硬质合金的金属阀座之间具有很强的剪切力，特别适用于含纤维、微小固体颗料等介质。球阀的主要特点是本身结构紧凑，适用于水、溶剂、酸和天然气等一般工作介质，而且还适用于工作条件恶劣的介质，如氧气、过氧化氢、甲烷和乙烯等，在各行业得到广泛的应用。二、项目描述球阀在使用过程中，通过启闭件的旋转，控制流体的流量。因启闭件长期与流体接触，承受流体的冲压，容易磨损。为提高球阀的使用寿命，有两种方法：（1）选用耐磨性好的材料；（2）优化球阀内部结构，而结构设计是否合理，需要经过物理实验来验证。引入计算流体力学（ComputationalFluidDynamics，CFD）分析后，在做物理实验之前，需要借用流体分析来预测启闭件在使用过程中的与流体间的相互作用，以优化内部结构。为了更好地验证球阀在使用中流量、启闭件阀口状与流体之间的关系，本文以一款球阀为例，设计了四个CFD方案，运用SolidWorksFlowSimulation软件对其阀体进行CFD分析，以对比不同的阀口结构及流量下，各结构内的流体流进球阀内部流体流动状态，以达到优化球阀结构的目的。通过流体分析，可预测不同条件下，流体在球阀内的流动状态，通过对比选择较佳结构设计。此外，球阀的使用者一直有一个误解，认为若流体中夹杂了颗粒，提前过滤流体可有可无，只要增大流体流量，提高流速，就能把杂质冲走。通过粒子示踪等分析，粒子随流体进入球阀后，很难随流体全部带走，因此在球阀使用前，要对流体内的杂质进行过滤，十分必要。球阀在使用过程中，流量可通过外部控制，为方便理解，按1kg/s为满载，0.5kg/s 为半载进行对比。目前市面上，球体启闭件大致也有两种结构，一种是直口型，一种是圆口型。为更好地进行对比，设计了四个CFD方案，如表1所示。

SolidWorks Flow Simulation在气流纺纱机中的应用

SolidWorks Flow Simulation在气流纺纱机中的应用一、引言气流纺纱机又叫转杯纺纱机，气流纺纱有速度大，纱卷大，适应性广，机构简单，不用锭子、钢领、钢丝圈的优点，可成倍地提高细纱的产量。在各种新型纺纱方法与技术中，气流纺纱由于其技术和产品的实用性，得到了大量的推广与应用。气流纺纱的基本工作原理是，将纤维随气流输送到高速回转的转杯内壁，在凝聚槽内形成纱尾，同时被加拈成纱引出，直接绕成筒子。气流纺纱过程中输入的气流不是单一的空气气体，其中含有大量的纺织纤维，如何使用软件进行可视化的CFD模拟分析，是非常具有挑战的问题。本文采用专业的计算流体动力学分析软件SolidWorksFlowSimulation对气流纺纱过程进行数值模拟，分析了内部流体的速度场和压力场分布等，并通过粒子追踪方法，分析了纤维粒子的旋转流动过程。二、模型组成及分析说明气流纺纱机的原始模型含有密封垫、轴承、螺栓等部件，非常复杂。为方便计算分析，本文对原始模型进行了简化处理，其结构基本组成及坐标系如图1。模型由定子、高速转子和外壳三部分组成。其中气流入

口在定子上，直径为1mm，入口流体的质量流率为 0.0002026kg /s，高速转子的转动速度为130000r/min，出口处的压力边界条件为96325Pa。首先，使用FlowSimulation分析不含纤维粒子的气体流动，实际问题中纤维对气体的影响忽略不计;然后在该气体流动迹线分析结果的基础上进行纤维粒子流动分析。三、模型创建 1.初始设置使用FlowSimulation中提供的自动向导创建功能，进行如下设定，国际制(SI)长度单位为mm，旋转速度单位为r/min，质量流率单位为kg/h;分析类型为内部流动，排除内部没有流动条件的空腔;流体类型为空气;默认初始条件;结果求解精度等级设为4，最小间隙设为1mm，其他默认设置。 2.边界条件按图1所示设置入口和出口边界条件，入口质量流率为 0.73kg/h(图2)，出口静压为96325Pa(图3)，指定如图4所示的真实壁面旋转条件，指定旋转速度。 3.初始条件为了加快收敛计算速度，设置壳体内部切向气体初始速度为40m/s。在FlowSimulation中通过设置两个方向的初始条件来实现，此处不再赘述。

(免费版)SolidWorksFlowSimulation的滤清器过滤效果分析

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基于SOLIDWORKSFlowSimulation工业除尘设备导流板设计

基于SOLIDWORKS Flow Simulation I业除尘设备导流板设基于SOLIDWORKS Flow Simulation T业除尘设备导流板设计撰文/陕西美徳资讯有限公司李鹏DS SOLIDWORKS彭军一.问题的提出燃煤锅炉、冶金行业、化工行业等工业设备在工作过程中产生的尾气中含有大量的颗粒污染物（硫化合物如二氧化硫;氮化合物如和N02;碳的氧化物CO和C02;碳氢化合物和卤素的化合物等），这些有害的粉尘及气体如果直接排到大气中就会形成雾霾。所以工业尾气在排放到大气之前就需要进行化学处理，也就是在会产生有害气体及粉尘的工业设备上增加除尘设备（图Do 除尘设备的除尘效果是工业设备需要考虑的重中之中。一般工业除尘主要是减少尾气中的固体颗粒物和有害气体，有害气体通过化学反应减少，固体颗粒物通过电场、水雾等方法排出设备。有害气体能够最大化地进行化学反应直接影响除尘效果，在工业上一般采用使有害气体通过蜂窝状载体催化剂（图2和图3）,在有害气体通过催化剂的瞬间进行化学反应以达到除尘的效果。如何能使有害气体充分地与催化剂发生化学反应将是除尘的核心，如果有害气体与催化剂接触不均匀，除尘效果不好，工业设备排出的烟气一般都是高温、大流量，含有粉尘的气体。如果烟气流畅不均匀将会使催化剂不能完全反应，流速快的地方发生化学反应快，流速慢的地方发生化学反应慢, 烟气中的粉尘也会山于流场不均匀或发生紊流造成对蜂窝催化剂的磨损或粉尘堆积，造成催化剂的浪费及除尘效率低下（图4和图5）。

二.流场优化及导流板设计烟气在进入除尘设备时，山于流速较快、烟道曲直，烟气必然会产生流畅不均匀和紊流等现象（图 4、图5）。为了使烟气均匀地流入除尘设备，就需要在除尘设备进气口加上导流板。导流板的设计乎工计算难度较大，凭经验结果不准确，需要多次样机试制才能完成，设计周期较长，成本增加。如果使用SOLIDWORKS Flow SimulationCFD 软件可以非常容易解决此问题。使用SOLIDffORKS Flow Simulation设计?导流板步骤如下。第一步: 使用SOLIDWORKS建立除尘设备主要结构（图6）。第二步: 使用SOLIDffORKS Flow Simulation建立流场仿真模型。（1）通过没有导流板时的流场分布图（图 4、图5）可以看到流场分布极不均匀，需要设讣导流板;导流板的设讣可在SOLIDWORKS中初步设计（图7）。（2）建立CFD工程算例。通过SOLIDWORKSFlow Simulation向导可以完成工程算例的75%的设置: 定义工程名称、使用的3D模型、工程算例所使用的单位系统、工程算例的类型（内流还是外流）、流体的介质（空气）和默认网格类型（图8，图12）。（3）定义边界条件。 ◎定义入口体积流: 模拟烟气从入口进入，烟气温度350?,速度75m2/so ◎定义出口圧力: 3000Pa负压，模拟风机的作用（图13）。