Mitigation of Preferential Concentration due to electric charge in the dispersed phase.

27
Mitigation of Preferential Concentration due to electric charge in the dispersed phase

Transcript of Mitigation of Preferential Concentration due to electric charge in the dispersed phase.

Mitigation of Preferential Concentration due to electric charge in the

dispersed phase

2

Overview

• Introduction

• Simplifying assumptions / Numerical method

• Measures of preferential accumulation

– Stokes number dependence

– Dependence on Re ?

• Charged particle simulations

• Conclusions

3

The problem in physical space

• Dispersed phase flows

– continuous phase (fluid)

– dispersed phase (particles)

4

Dispersed phase flows

• Incompressible, homogeneous, isotropic

• Stationarity obtained using artificial forcing

• One-way coupling

– Particles do not influence fluid motion

• Point particles

– Particle wakes are not resolved

– Particle diameter << kolmogorov length scale

• Particle motion is governed only by “drag”

– Gravitational force not modelled

– Particle collisions not modelled (dilute suspension)

– Particle density >> fluid density

• Particles are “stochastic” for the purpose of charged particle simulations

Fluid: Particles:

5

Governing equations

0 u

p

epp

P

p

m

Ftuttxu

dt

du )()),((

1

687.0

2

Re15.01

1

18 pf

p

fp

pe qEF 0. vqE

Fluid: Particles:

*Symbols have usual meanings

Fup

uut

u

2.

Large-scale forcing function added to maintain stationary

turbulence

Modified Stokes drag law (Valid for Rep <= 800)

6

Numerical scheme (Fluid)

jxij

N

Njfjf euxu )(ˆ)(

2

12

)](.[)()(ˆ)(ˆ

22

uuut

uf

0)(ˆ. u

*V. Eswaran and S.B. Pope, Computers and Fluids, Vol. 16(3), pp. 257-278, 1988

Fluid (pseudo-spectral method):

• dealiasing by 2/3rd rule

• temporal discretization using RK3

• stochastic forcing scheme* to sustain kinetic energy

)()(. SEi

SqE v 0. (suppose)

Particle:

7

Numerical Method (summary)

– The turbulence is limited to homogeneous, isotropic case (HIT) in a periodic cube.

– Particles are not resolved.

– Force on particles is due to Stokes drag.

– One way coupling between fluid and particles

8

Simulation parameters

k

pkSt

308 pray rQ

ray

p

Q

QRa

• Mono-sized particles

• number of particles (Np): 100000

• particle stokes numbers

– Stk :0.2 - 20

• Same charge on all particles (Ra = 0.8, γ = 0.05)

• space charge densities (μC/m3): 5, 10, 25, 50, 100

Stokes Number

Rayleigh Number

where,

9

Points to note -

• All simulations for a given Re, are restarted from same fluid realisation.

• Statistics are collected only after fluid has reached stationary state.

• Particle distribution is assumed to reach stationary state when the positions are completely de-correlated from initial position.

Tu

rbu

len

t kin

eti

c en

erg

y

Part

icle

rm

s ve

loci

ty

10

Evidence of preferential concentration

Reλ = 24.24, St = 0.25

*S. Scott, Ph.D. thesis, Imperial College London, 2006

Reλ = 24.24, St = 4.00

11

Clustering at different scales• Clustering occurs broadly at 2 scales –

– Dissipative scales

• particles are centrifuged out of coherent eddies and accumulate in low-vorticity regions.

– Inertial range

• clustering is a multi-scale phenomenon.

• Eddies larger than Kolmogorov length scale play a part in clustering.

12

Measures of Accumulation• Dissipative range measures

– D ( Fessler et al., 1994 )

– Dc ( Wang and Maxey, 1993 )

– Dn

• Inertial (multi-scale) measures

– RDF ( Sundaram and Collins, 1997 )

– D2

• Fluid-particle correlation measures

– <n’e’>, correlation between number density and enstrophy

– ln, number density correlation length scale

13

D2 measure

• Correlation integral, C(r) : number of particles within range r of any given particle

• D2 is slope of curve log( C(r) ) vs log( r )

• D2 is equal to the spatial dimension for uniform distribution (equal to 3 for a 3D distribution)

r

14D2 data for different cases compared to literature

D2 – probability to find 2 particles at a distance less than a given r: P(r) ~ rD2

15

Binning of particles

h

h – scale used for binning particles

n – number density i.e no. of particles / bin volume

<nc> – mean number density i.e total particles / volume of cube

16

D, Dc : deviation from poisson distribution• Dc*, D** : Deviation from

poisson distribution

Pc: probability of finding cells with given number of particles

k : number of particles in a cell

pN

k

uc kPkPD

0

2))()((

nc

D poisson

*L.P. Wang and M.R. Maxey, J. Fluid Mech., Vol. 256, pp. 27-68, 1993

**J.R. Fessler, J.D. Kulick and J.K. Eaton, Phys. Fluids, Vol. 6(11), pp. 3742-3749, 1994

knc

u nck

ekP

!)(

17

‘D’ measure of accumulation

• Bin size used is corresponding to peak value of ‘D’.

Re = 24

18

<n’e’> – correlation between number density and enstrophy

19

Observations

• D and Dc measures clearly depend on bin-size

– Dependence of Re is attributed to less number of ‘smaller particle structures’ at higher Re.

• D2 measure looks at probability of finding particles in shells around a given particle

– Shows nearly no dependence on Re

• <n’e’> and ‘ln’ capture distribution of particle number density

– Show dependence on Re

Destruction using Lorentz forces*

*A. Karnik and J. Shrimpton, ILASS 2008, Sept 8-10, 2008

21

Particle position, fluid velocity

Reλ = 24.24, St(k) = 1.0, Qv=5μC/m3

*S. Scott, Ph.D. thesis, Imperial College London, 2006

Reλ = 24.24, St(k) = 1.6, Qv=100μC/m3

22

Evidence of preferential concentration destruction

Reλ = 24.24, St(k) = 1.0, Qv=5μC/m3

*S. Scott, Ph.D. thesis, Imperial College London, 2006

Reλ = 24.24, St(k) = 1.6, Qv=100μC/m3

23

Evidence of preferential concentration destruction

Reλ = 24.24, St(k) = 1.0, Qv=5μC/m3

*S. Scott, Ph.D. thesis, Imperial College London, 2006

Reλ = 24.24, St(k) = 1.6, Qv=100μC/m3

24

Parametric study of bulk charge density levels

*St = 0.25 for all plots

• Space charge density of 25-50 µC/m3 is sufficient to destroy preferential accumulation

• With increasing Reynolds number, greater charge density is required to significantly destroy accumulation

25

Effect of Stokes Numbers

Reλ = 24.2 Reλ = 81.1

• Charged particle systems continue to exhibit same trends with Reynolds and Stokes numbers as the charge-free case.

26

Schematic of a spray released from a charged injection atomizer

20

22

0

0

tan4tan41

d

z

d

z

QQz

tan20 zd

rz

• The charge level found in this study (50 μC/m3) corresponds to an area about 2 cm from the nozzle tip

• d0= 500 μm, Q0= 0.5 C/m3, θ = 45o

27

Conclusions

• Preferential accumulation is maximum at St ~ 1.0 based on kolmogorov scale, for all the measures used in this study.

• While ‘ln’ shows clear dependence on Re, D2 is insensitive to Re.

• Bulk charge density level of 50 μC/m3 is sufficient to significantly destroy preferential accumulation. This has been consistently observed using different sensors for preferential accumulation.

– The required charge density level mentioned above is attainable within 2 cms from tip of a nozzle in practical charge injection atomizers.