cfd modelling of pressure drop and flow distribution in packed bed filters k taylor & ag smith -...
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CFD MODELLING OF PRESSURE DROP AND FLOW DISTRIBUTION
IN PACKED BED FILTERS
K Taylor & AG Smith - S&C Thermofluids Ltd
S Ross & MW Smith - DERA Porton Down
OVERVIEW
• Packed bed filters remove toxic agents from contaminated airstreams
• CFD potential design tool for predicting the flow and pressure drop
• Mathematical model for predicting radial voidage distribution in bed
• Non-uniform voidage distribution included in CFD model • Validated against measurements of pressure drop and
velocity distribution• Potential for CFD modelling of adsorption process also
investigated
GEOMETRY OF FILTER BED
VOIDAGE DISTRIBUTION IN CYLINDRICAL FILTER BEDS
• Radial voidage distribution in ‘snowstorm’ packed filter beds is a function of the ratio: particle size/bed diameter
• Affects the velocity distribution within the filter bed
• Measurements made of voidage distribution for range of particle sizes
• Fitted to modified ‘Mueller’ model
Radial voidage distribution - 4mm beads
0
0.2
0.4
0.6
0.8
1
1.2
0
1.96
3.92
5.88
7.84 9.
8
11.8
13.7
15.7
17.6
19.6
21.6
23.5
26.5
30.4
34.3
38.2
42.1
46.1
distance from the edge of the bed (mm)
Void
age
= b + (1-b)e-brJo(ar*)
GEOMETRY OF FILTER BED
CFD MODEL
• 2-d axi-symmetric BFC model• Grid distribution determined from voidage distribution
to ensure adequate grid resolution near walls• Local voidage distribution coupled to Ergun-Orning
equation for pressure loss through bed:
p/L = 5 So2(1-)2U/3 + 0.29 So(1-
)U2/3 |
| viscous loss
turbulent loss
• Substantial improvement in predictions compared to model using average voidage
PRESSURE DROP - 3mm PARTICLES
predicted and measured pressure drops - comparison of uniform voidage and modified Mueller model
0
1000
2000
3000
4000
5000
6000
7000
0 1 2 3 4 5 6 7 8 9 10
inlet centreline velocity (m/s)
Pre
ss
ure
dro
p (
Pa
)
exp data CFD data - uniform voidage CFD data - Mueller model
PRESSURE DROP VS GRID DENSITY
predicted and measured pressure dropsfor a 20cm cone
0
1000
2000
3000
4000
5000
6000
7000
8000
0 1 2 3 4 5 6 7 8 9 10inlet centreline velocity (m/s)
pre
ssu
re d
rop
(P
a)
exp data - 2mm bead exp data -3mm bead exp data -4mm beadcfd data - 2mm bead cfd dat - 3mm bead cfd data - 4mm beadcfd fine data- 2mm bead cfd fine data- 3mm bead cfd fine data- 4mm bead
VELOCITY DISTRIBUTIONS
predicted and measured velocity profiles - 2-layer modelcentreline velocity 4m/s, 3mm bead and 20cm cone
0.00
0.50
1.00
1.50
2.00
2.50
3.00
0.000 0.005 0.010 0.015 0.020 0.025 0.030 0.035 0.040 0.045 0.050radius (m)
velo
city
(m
/s)
exp data - u/s velocity profile exp data - d/s velocity profilecfd data- u/s velocity profile - k-e model cfd data- d/s velocity profile - k-e modelcfd data- u/s velocity profile- 2-layer model cfd data- d/s velocity profile- 2-layer model
ADSORPTION MODEL
• Transient model to predict ‘breakthrough’• Steady state flowfield used as initial conditions• Adsorption rate source term:
-C/t = 1/ So k (C - Ci)
• Rate of uptake in adsorbent: m/t = /(1-) (-C/t)/z
• Maximum uptake from isotherm equation:mmax = a.b.RH/(1 - RH)
VAPOUR UPTAKE IN FILTER BED
VAPOUR PENETRATION
IMPLEMENTATION WITHIN PHOENICS
• Pre-processor - interprets voidage distribution and basic input parameters - outputs Q1 file
• Additional Q1 commands for adsorption model• GROUND coding for -
porosity from voidage distributioninlet boundary conditions
source terms for pressure loss and
adsorption rate
CONCLUDING REMARKS
• Method for prediction of pressure and flow distribution validated for range of parameters
• Implemented within PHOENICS user routines• Potential for adsorption model demonstrated• Areas for further work:
improvement and validation of asdorption model improved user interface
turbulence modelling within the filter bed