experimental and numerical investigations of particle clustering in isotropic turbulence
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Workshop on Stirring and Mixing: The Lagrangian Approach Lorentz Center Leiden, The Netherlands August 21-30, 2006. Experimental and numerical investigations of particle clustering in isotropic turbulence. International Collaboration for Turbulence Research (ICTR). - PowerPoint PPT PresentationTRANSCRIPT
Experimental and numerical investigations of particle clustering in isotropic turbulence
Workshop on Stirring and Mixing: The Lagrangian ApproachLorentz Center
Leiden, The NetherlandsAugust 21-30, 2006
International Collaboration for Turbulence Research (ICTR)
Cornell University SUNY Buffalo Max Planck Institute
Dr. Lance R. Collins Dr. Hui Meng Dr. Eberhard Bodenschatz
Juan Salazar Scott Woodward
Dr. Zellman Warhaft Lujie Cao
S. Ayyalasomayajula Jeremy de Jong
Particle Clustering in Turbulence
Vortices
Strain Region Maxey (1987); Squires & Eaton (1991); Wang & Maxey (1993) Shaw, Reade, Verlinde & Collins (1997) Falkovich, Fouxon & Stepanov (2002); Zaichik & Alipchenkov (2003); Chun, Koch, Rani, Ahluwalia & Collins (2005)
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Turbulence in Clouds
BuoyancyCloud CondensationNuclei (CCN)
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103 m
€
10−3 m
d2 Lawmass
energy
ddt
d(t) =′ K
d(t)
d2(t)∝ td(t)
Current microphysical models predicto too slow “condensational” growtho too narrow cloud droplet distributions
Shaw (2003)
Beard & Ochs (1993)
“… At this rate, we are quite a way off from being able topredict, on firm micro-physical grounds, whether it willrain.” 0.1 m
1 m
10 m
Clouds in Climate Models
Visible Wavelengths Infra Red
High, cold clouds
Low, warm clouds
Distribution of cloud cover profoundly influences global energy balance
Raymond Shaw
Collision Kernel
Particle clustering impacts the RDF
€
Nijc = π dij
2 ni n j gij (dij ) (− wij ) Pij (wij | dij ) dwij− ∞
0
∫
€
dij = (di + d j ) / 2
gij (r) = radial distribution function (RDF)
wij = relative velocity
P(wij | r) = PDF of relative velocity
Sundaram & Collins (1997); Wang, Wexler & Zhou (1998)€
St =1
18
ρ p
ρ
⎛
⎝ ⎜
⎞
⎠ ⎟dη
⎛
⎝ ⎜
⎞
⎠ ⎟2
Monodisperse clustering: drift
€
A ≡St τ η
2
3S2 − R2
[ ]p
€
qrd = − A
rτ η
g(r)
Chun, Koch, Sarma, Ahluwalia & Collins, JFM 2005
€
η
€
r
€
St <<1
Monodisperse clustering: diffusion
Chun, Koch, Sarma, Ahluwalia & Collins, JFM 2005
€
qrD ≡ − B
r2
τ η
∂g∂r
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BL = 0.153
BNL = 0.093
Monodisperse clustering: RDF
St = 0.7
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g(r) =ηr
⎡ ⎣ ⎢
⎤ ⎦ ⎥
A B
0.25
0.20
0.15
0.10
0.05
0.00
A / B
0.200.150.100.050.00
St
Theory 1 Theory 2 DNS Stochastic 1 Stochastic 2
Chun, Koch, Sarma, Ahluwalia & Collins, JFM 2005
Bidisperse clustering
Chun, Koch, Sarma, Ahluwalia & Collins, JFM 2005
€
α€
β€
qrd = − A
rτ η
g(r)
€
A ≡Stβ τ η
2
3S2 − R2
[ ]p
€
η
Bidisperse clustering
Chun, Koch, Sarma, Ahluwalia & Collins, JFM 2005
€
qrD ≡ − B
r2
τ η
∂g∂r
€
BL = 0.153
BNL = 0.093
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qra = − D
∂g∂r
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D = ΔSt( )2 a0 Rλ( )
η 2
τ η2 τ a
Bidisperse clustering: stationary
Chun, Koch, Sarma, Ahluwalia & Collins, JFM 2005
€
g(r) =η 2
r2 + rc2
⎡
⎣ ⎢
⎤
⎦ ⎥
A 2 B
1
2
3
4
5
6
7
8
910
g(r)
0.0012 3 4 5 6 7
0.012 3 4 5 6 7
0.12 3 4 5 6 7
1
r / η
(0.2, 0.2) (0.2, 0.19) (0.2, 0.175)
RDF Measurements
Experiments and Simulations
Direct Numerical Simulations
Turbulence Chamber
38 cm
Fans
Optical Access
Isotropic Turbulence Chamber
Flow Characterization
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Rλ
€
Urms
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Vrms
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ε
€
L
€
η
€
91
€
117
€
130
€
140
€
161
€
173
€
0.286
€
0.447
€
0.564
€
0.651
€
0.777
€
0.906
€
0.283
€
0.451
€
0.577
€
0.651
€
0.790
€
0.942
€
0.817
€
3.16
€
6.63
€
9.72
€
15.9
€
25.5
€
1.26 ×10−2
€
1.31×10−2
€
1.28×10−2
€
1.30 ×10−2
€
1.43×10−2
€
1.39 ×10−2
€
2.54 ×10−4
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1.81×10−4
€
1.50 ×10−4
€
1.37 ×10−4
€
1.21×10−4
€
1.07 ×10−4
Conditions at 6 Fan Speeds (MKS)
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ε =1r
DLL
C2
⎛
⎝ ⎜
⎞
⎠ ⎟
3/2
Metal-Coated Hollow Glass Spheres
Mean = 6 micronsSTD = 3.8 microns1-10 particles/cm3
V = 10-7
Measurements of RDF
Wood, Hwang & Eaton (2005)Saw, Shaw, Ayyalasomayajula, ChuangGylfason, Warhaft (2006)
Turbulence Box Wind Tunnel
Why 3D?
2D Sampling 1D Sampling
1
2
3
456
10
2
3
456
100
2
3
4
g(r)
0.01 0.1 1 10r / η
g3D( )r g2D( )r g1D( )r
€
g2D
rδ
⎛ ⎝ ⎜
⎞ ⎠ ⎟= 2 g3D
rδ
⎛ ⎝ ⎜
⎞ ⎠ ⎟2
+ v2 ⎛
⎝ ⎜ ⎜
⎞
⎠ ⎟ ⎟dv
0
1
∫
€
g1D
rδ
⎛ ⎝ ⎜
⎞ ⎠ ⎟= 4 g3D
rδ
⎛ ⎝ ⎜
⎞ ⎠ ⎟2
+ v2 + w2 ⎛
⎝ ⎜ ⎜
⎞
⎠ ⎟ ⎟dv dw
0
1
∫0
1
∫
Relations
Holtzer & Collins (2002)
€
g3D ri( ) ≡Npi
NpVi V
3D Particle Position Measurement Techniques
1. Particle Tracking Velocimetry (PTV)• Advantages – Lagrangian particle information• Disadvantages – Limited particle number density.
2. Holographic Particle Image Velocimetry (HPIV)• Advantages – Better particle number density than PTV, larger 3D volume
than Stereo PIV• Disadvantages – Cannot resolve time evolution of particles.
40 c
m
1k x 1k CCD
Z
Fan
F
an
F
an
Optical Window
(4 cm)3 Volume
V2
V1V3
X
Y
Z
Numerical ReconstructionIntensity-Based Particle Extraction
Hybrid Digital HPIVNd:YagLaser 532 nm
ReferenceBeam
Beam Expander
Variable BeamAttenuator
Particle Concentration and Phase Averaging
Size Distribution Evolution
0.0
0.1
0.2
0.3
0.4
0.5
0.6
1 2 3 4 5 6 7 8 9
Particle size group
Probability
Phase 01Phase 02Phase 03Phase 04Phase 05
Time Dependence of RDF
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η=150 μm
€
η=120 μm
Direct Numerical Simulations
1283 Grid Points R = 80 1.2 Million Particles (one way coupling) Experimental Particle Size Distribution
Keswani & Collins (2004)
Filtering by camera
Mean = 6 micronsSTD = 3.8 microns
Metal-coated hollow glass spheres
Filtering by camera
Mean = 6 micronsSTD = 3.8 microns
Metal-coated hollow glass spheres
Comparison at R = 130
1
2x100
3
4
g(r)
1086420r / η
St > 0 St > 0.05 St > 0.1 St > 0.15 St > 0.2 St > 0.25 St > 0.3 St > 0.35 St > 0.4 Exp dαtα
Comparison at R = 161
1
2
3
4
5
g(r)
1086420r / η
St > 0 St > 0.05 St > 0.1 St > 0.15 St > 0.2 St > 0.25 St > 0.3 St > 0.35 St > 0.4 Exp dαtα
Summary Clustering results from a competition between inward
drift and outward diffusion Radial Distribution Function (RDF) is the measure for
collision kernel Analysis of RDF involves Lagrangian statistics along
inertial particle trajectories RDF mainly found in direct numerical simulation 3D measurements of RDF using holographic imaging
Reasonable agreement between experiments and DNS Challenges for the measurement
Characterizing flow (dissipation rate, ε) Particle size distribution (will separate particles) Increasing resolution of experiment (smaller separations)
International Collaboration for Turbulence Research (ICTR)