XVIII Brain Storming Day
UNIVERSITA’ DEGLI STUDI DI CATANIADipartimento di Ingegneria Elettrica Elettronica e dei Sistemi DIEES
Florinda Schembri
Tutors: prof. Luigi Fortuna, prof. Maide Bucolo
Microfluidic SystemsMicrofluidic Systems
� In vitro
� In vivo
� in vitro pro in vivo
OptoOpto--Sensing SystemSensing System
Identification & ModelingIdentification & Modeling
� Microscopy-Based
� Opto-Mechanic System
� Polymeric micro-Optic Interface
RealReal--Time Monitoring Time Monitoring
� Point-wise (0D)
� Full-Field (2D)
� Droplet Formation
� Chaotic Advection
� Multiphysics
� CFD on GRID
� 2D numerical
modeling
OutlineCorrelation between
Spatial and Temporal
nonlinear behaviour
Correlation between
Spatial and Temporal
nonlinear behaviour Experimental Modeling of
Two-Phase microfluidic Flow
Experimental Modeling of
Two-Phase microfluidic Flow
Synchronization between a ‘microfluidic air bubble ’ and a
‘bubble robot’ (in collaborazione con l’ ing. Camerano).
Synchronization between a ‘microfluidic air bubble ’ and a
‘bubble robot’ (in collaborazione con l’ ing. Camerano).
Bubbles in microchannels…some applications
Bubble Logic Microreactors
‘Beakers’Mixing
Experimental Modeling
CONTROL
Micro Total Analisys Sistem (µTAS)
Emulsion Science
F. Sapuppo, F.Schembri , M. Bucolo, “Nonlinear Dynamics in Experimental Two-Phase Microfluidics Timeseries”, Chaos 09, June 22-24, 2009, London, UK.
generation and transport of
micrometric bubbles and
droplets in IN VITRO systems
Observing nonlinear Phenomena in Microfluidics
Microscope Opto sensors
Electro – Optical Workbench
frame 3
Processing
- Preprocessing
Nonlinear Time Series Analysis� Reconstructed attractor
� Divergence of trajectories
� Largest Lyapunov Exponent
0.06
0.07
0.08
0.09
0.1
0.11 0.0650.07
0.0750.08
0.0850.09
0.095
0.065
0.07
0.075
0.08
0.085
0.09
0.095
x2x1
x3
Analysis tool: TISEAN 2.1
ThinXXS, Germany
Complex Microfluidic Flow
Two Phase FlowSerpentine Microchannel
Micro pumps:- Frequency- Flow rate [ml/s]
H2O
Water
Analog Signals
relative effect of viscous forces versus surface tension acting
across an interface between a liquid and a gas
The microfluidic process
Droplets Formation
Nonlinear InstabilityFinite time singularity
[Eggers, Rev. Mod. Phys., 1997]
µρ uL=Re
γµu
Ca =
� Mutual interaction between fluids; � Interaction with the channel boundary ;� Flow rates;
Break up driven by:� normal stress� tangential stress
( )( )[ ] ( )NFuupIuut
u T φσκδηρ ++∇+∇+−⋅∇=
∇⋅+∂∂
0=⋅∇ u
Navier Stokes and Mass Conservation Dimensionless Number
1Re
100Re
<<<
Laminar flow
Inertial Force / Viscous Force
)10( 2−≈ OCa
Droplet FormationJunction
Flow FocusingT-junction
Serpentine
One
Phase
Two
Phases
Axi-symmetric flow focusing bubble
generator
The microfluidic process
Nonlinear convection
Two Phase Flow
DynamicsTwo Phase Flow
Dynamics
Nonlinear temporal
dynamics of bubbles
The baker’s trasformation
From Microscopic Process …
… to Macroscopic Behavior
FLOW PATTERN
frame 3
[Song et al., Chem. , Int. Ed. Engl. , 2003]
Reorientation
Centrifugal Forces
Nonlinear Convection
Experimental Setup
R1
R2
S
Serpentine Geometric Specification
Frequency ���� Volumetric Flow Rate [V]
� S: 640 µm;� R1: 280 µm;� R2: 920 µm;
Optical System
Reynolds number <20Capillary number<0.01
ThinXXS, Germany
Pulsatile Pumps
Air
Water (carried fluid)
9.5 9.55 9.6 9.65 9.7 9.75 9.8 9.85 9.9 9.95 100.065
0.07
0.075
0.08
0.085
0.09
0.095water 5 Hz - air 12 Hz
Time [s]
Vol
tage
[V
]
The electro-optic system
Photodiodes (Silonex , SLD-70BG2A)Infrared Rejection Filter -Planar Photodiode
Captur light variation caused by bubbles flow.
CA
D
Design
Projected image
Open Space Design� Multiple Access � Tunable Magnification � Easy Sensing Integration
SIDE VIEW TOP VIEW Magnification=3.1X
Analog Signals
0 10 20 30 40 50 600
5
10
15
20
25
30
35
40
Frequency [Hz]
Am
plitu
de
Pre-processing analysis and filtering
8 8.2 8.4 8.6 8.8 9 9.2 9.4 9.6 9.8 100.065
0.07
0.075
0.08
0.085
0.09
0.095water 5 Hz - air 12 Hz
Time [s]
Vol
tage
[V
]
9.5 9.55 9.6 9.65 9.7 9.75 9.8 9.85 9.9 9.95 100.065
0.07
0.075
0.08
0.085
0.09
0.095water 5 Hz - air 12 Hz
Time [s]
Vol
tage
[V
]
Fast Fourier TransformFast Fourier Transform
Filtering• Low pass (cut off 60 Hz)• Notch (50Hz, pump frequencies)
Filtering• Low pass (cut off 60 Hz)• Notch (50Hz, pump frequencies)
ValidationFiltered vs not filtered
signal
ValidationFiltered vs not filtered
signal
11 11.1 11.2 11.3 11.4 11.5 11.6 11.7 11.8 11.9 120.05
0.06
0.07
0.08
0.09
0.1
0.11
Vol
tage
[V
]
fitered signalfitered signalfitered signalfitered signalnot filtered signalnot filtered signalnot filtered signalnot filtered signal
Experimental validation
Two phase FlowWater 5Hz-Air 7HzTwo phase Flow
Water 5Hz-Air 7Hz
Time [s]
Vol
tage
[V
]
Reference SignalsReference Signals
Water pumped 10Hz Water pumped 10Hz
Air pumped 10Hz Air pumped 10Hz
6.5 6.55 6.6 6.65 6.7 6.75 6.8 6.85 6.9 6.95 70.04
0.042
0.044
0.046
0.048
0.05
0.052
0.054
0.056
0.058
0.06Photodiode Signals
TIme [s]
Vol
tage
[V
]
water 10Hz
water 30Hz air 25Hz
water 30Hz air 5Hzwater 30Hz air 60Hz
water 50Hz air 25Hz
Different input frequencies produce different Bubble Flow
Different input frequencies produce different Bubble Flow
Signals comparison
Time window0.5 sec
Time window0.5 sec
Volumetric Flow rate vs Frequency
Nonlinear time series analysis
� Delay ;
� Embedding Dimension ;
� Divergence of trajectories (dj);
� Asymptotic value of the dj (d∞ )
�Finite size Lyapunov exponents;
False nearest neighbours
[Rosenstein et al., Physica D 65, 117 ,1993].
[E. Aurell et al., J. Phys. A 30, 1, 1997] d∞
Typical trend of the
ddjj between nearby
chaotic trajectories
in semilog scale
(Chua’s system)
( ) ( )i
j
N
i
ijj xx
Nd '
1
1 −⋅= ∑=
∑=
∞→∞ ⋅=N
jj
Nd
Nd
1
1lim
Distance between pairs of j-iteration long
trajectories mediate over N couples
Analysis tool: TISEAN 2.1
Autocorrelation function
Experiment Design
Experimental CampaignExperimental Campaign
Controller (frequency
signal)
H2O
Air
Frequency ���� Volumetric Flow Rate
ThinXXS, Germany
Water freq fixed-Air freq
multiple of the water
freq
Water freq fixed-Air freq
not multiple of the
water freq
Air freq fixed- Water
freq not multiple of the
water freq
Air freq fixed- Water
freq multiple of the
water freq
Air freq fixed- Water
flow rate constant
Nonlinear time series analysis results
0.08
0.09
0.1 0.075 0.08 0.085 0.09 0.095
0.075
0.08
0.085
0.09
0.095
x2x1
x3
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5-3
-2.8
-2.6
-2.4
-2.2
-2
-1.8
-1.6
dj
Time [s]
log(
d j)
10 10.2 10.4 10.6 10.8 11 11.2 11.4 11.6 11.8 12
0.075
0.08
0.085
0.09
0.095segnale filtrato con Notch
Time [s]
Vol
tage
[V
]
Delay Emb. Largest
λ
Dj ris. T
[s]
|D_inf|
10 4 1,10 0,052 1,93
Nonlinear time series analysis results
0.06
0.07
0.08
0.09
0.1
0.11 0.06
0.07
0.08
0.09
0.1
0.11
0.06
0.08
0.1
0.12
x2
3d 355 new
x1
x3
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5-3.6
-3.4
-3.2
-3
-2.8
-2.6
-2.4
-2.2
dj
Time [s]
log(
d j)
10 10.2 10.4 10.6 10.8 11 11.2 11.4 11.6 11.8 120.065
0.07
0.075
0.08
0.085
0.09
0.095
0.1
Time [s]
Vol
tage
[V
]
Delay Emb. Largest
λ
Dj ris. T
[s]
|D_inf|
9 4 0,64 0,25 2,15
Nonlinear time series analysis results
0.060.065
0.070.075
0.080.085
0.09 0.06
0.065
0.07
0.075
0.08
0.085
0.09
0.06
0.07
0.08
0.09
x2
x1
x3
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5-3.8
-3.6
-3.4
-3.2
-3
-2.8
-2.6
-2.4
dj
Time [s]
log(
d j)
10 10.2 10.4 10.6 10.8 11 11.2 11.4 11.6 11.8 120.06
0.065
0.07
0.075
0.08
0.085
0.09
Time [s]
Vol
tage
[V
]
Delay Emb. Largest
λ
Dj ris. T
[s]
|D_inf|
19 4 0,88 0,082 2,73
Nonlinear time series analysis results
3025
0.065
0.07
0.075
0.08
0.085
0.09
0.095
0.1
0.065
0.070.075
0.080.085
0.090.095
0.1
0.065
0.07
0.075
0.08
0.085
0.09
0.095
0.1
x1
3d 205 new
x2
x3
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5-3.3
-3.2
-3.1
-3
-2.9
-2.8
-2.7
-2.6
-2.5
-2.4
dj
Time [s]
log(
d j)
10 10.2 10.4 10.6 10.8 11 11.2 11.4 11.6 11.8 120.065
0.07
0.075
0.08
0.085
0.09
0.095
0.1
Time [s]
Vol
tage
[V
]
Delay Emb. Largest
λ
Dj ris. T
[s]
|D_inf|
12 5 0,58 0,222 2,51
10 Attractors…
0.06
0.08
0.1
0.12 0.06 0.065 0.07 0.075 0.08 0.085 0.09 0.095
0.06
0.07
0.08
0.09
0.1
0.11
x2x1
0.06
0.08
0.1
0.12 0.065 0.07 0.075 0.08 0.085 0.09 0.095
0.065
0.07
0.075
0.08
0.085
0.09
0.095
x2x1
x3
0.06
0.07
0.08
0.09
0.06
0.07
0.08
0.090.06
0.065
0.07
0.075
0.08
0.085
0.09
x1
3d 532 new
x2
x3
0.06
0.08
0.1
0.12
0.065 0.07 0.075 0.08 0.085 0.09 0.095
0.065
0.07
0.075
0.08
0.085
0.09
0.095
x1
3d 305 new
x2
x3
0.06 0.08 0.1 0.120.065 0.07 0.075 0.08 0.085 0.09 0.095 0.1
0.065
0.07
0.075
0.08
0.085
0.09
0.095
0.1
x2
3d 405 new
x1
x3
0.08 0.09 0.1 0.075 0.08 0.085 0.09 0.095
0.075
0.08
0.085
0.09
0.095
x2
3d 155 new
x1
x3
0.08
0.09
0.10.075 0.08 0.085 0.09 0.095
0.075
0.08
0.085
0.09
0.0953d 55 new
x2x1
x3
0.06
0.07
0.08
0.09
0.1
0.11
0.060.07
0.08
0.090.1
0.06
0.08
0.1
0.12
x1
3d 540 new
x2
x3 0.06
0.08
0.1
0.120.065 0.07 0.075 0.08 0.085 0.09 0.095 0.1
0.06
0.07
0.08
0.09
0.1
0.11
x2
3d 205 new
x1x3
0.06
0.07
0.08
0.09
0.10.11 0.065 0.07 0.075 0.08 0.085 0.09 0.095 0.1
0.065
0.07
0.075
0.08
0.085
0.09
0.095
0.1
x2
3d 355 new
x1
x3
Nonlinear time series analysisD-infinite
(aria 5 Hz, acqua 25:2:35 Hz)Aria 5 Hz, acqua 7:5:37 Hz
Noise Rejection
Conclusions
CFD analysis and experimental observation
Computational
Fluid Dynamics
(CFD)Experimentation
Nonlinearity inside droplets
Temporal nonlinear flow
Correlation between spatial and temporal
NONLINEAR BEHAVIOUR
Primary FlowSecondary Flow
Velocity of the bubble relative to
wall
Flow within bubble
F. Sapuppo, F.Schembri , M. Bucolo, “Correlation between Spatial and Temporal Chaotic Behaviour in Two-Phase Microfluidics”, Chaos 09 June 22-24, 2009,
London, UK.
Ch
an
ne
l W
idth
Digital Image25 fps
Digital SlitTime Series
Experimental SetupOptic System Analog Digital
Acquisition
PC based Image
processingPump Control
Discrete Opto-Mechanical System
Two phaseFlow
ThinXXS, Germany
Computational Fluid Dynamics (CFD)
Numerical Model (PDEs)
( )
∇∇−−∇⋅∇=∇⋅+
∂∂
φφφφφεγφ
1ut
0
0
0
2
1
>Φ=Φ
<Φ
phase
countour
phase
( )( )[ ] ( )NFuupIuut
u T φσκδηρ ++∇+∇+−⋅∇=
∇⋅+∂∂
0=⋅∇ u
( ) ( )φρρρρ Hphasephasephase 121 −+=
( ) ( )φηηηη Hphasephasephase 121 −+=
Level Set Equations
Navier Stokes and
mass conservation
Properties of the Two fluids
How we deal with Two Phase Flow?
Mul
tiph
ysic
s A
ppro
ach
Computational Fluid Dynamics (CFD)
y
tyxu
x
tyxu
∂∂−
∂∂ ),,(),,(
2D Vorticity field
amount of “rotation” in a fluid
Spatial nonlinear behaviorSpatial nonlinear behavior
The vorticity is the rotation of thefluid velocity
and as such gives an idea of how the fluid is
moving inside the bubble .
Mixing
2D Analysis
Experiment one: Air 5 Hz Water 10 Hz
-20-10
010
2030
-20
0
20
40-20
-10
0
10
20
30
x1
5 1 3
x2
x3
-20-10
010
20
-20
-10
0
10
20-15
-10
-5
0
5
10
15
x1x2
x3
-15 -10 -5 0 5 10 15-15
-10
-5
0
5
10
15
Aria5-Acqua
20
xy
x1
x2
3D reconstructed attractor and xy view
-40-20
020
40
-40
-20
0
20
40-30
-20
-10
0
10
20
30
x1x2
x3
-30 -20 -10 0 10 20 30-30
-20
-10
0
10
20
305 4 x
x1
x2
Experiment three: Air 5 Hz Water 40 Hz
Volume Fraction of water and Vorticity Field
Experiment two: Air 5 Hz Water 20 Hz
FROM…..Numerical Modeling To….. Experimental Campaigns
IdentificationIdentification …… Chaotic Advection
Synchronization between a ‘microfluidic air bubble ’ and
a ‘bubble robot’CFD Simulation
(Comsol Multiphysics ®)CFD Simulation
(Comsol Multiphysics ®)
Cod
e Im
plem
enta
tion
Cod
e Im
plem
enta
tion File TXT (Velocity Field) File TXT (Velocity Field)
Matlab scriptMatlab script
File TXTVelocity, Time
File TXTVelocity, Time
Bluetooth
Synchronization between a ‘microfluidic air bubble ’ and
a ‘bubble robot’
(in collaborazione con l’ ing. Camerano)(in collaborazione con l’ ing. Camerano)
Water 25 Hz-Air 2HzWater 25 Hz-Air 2Hz
Tw
o-P
hase
Flo
w a
nd C
FD
(C
omso
l Mul
tiphy
sics
®)
T
wo-
Pha
se F
low
and
CF
D
(Com
sol M
ultip
hysi
cs ®
)
Serpentine Channel
Velocity field Vorticity
Volume fraction……..
Velocity field Vorticity
Volume fraction……..
Synchronization between a ‘microfluidic air bubble ’ and
a ‘bubble robot’
(in collaborazione con l’ ing. Camerano)(in collaborazione con l’ ing. Camerano)
0.220.23
0.240.25
0.260.27 0.135
0.14
0.145
0.15
0.1550
0.2
0.4
0.6
0.8
yx
velo
ciy
filed
[m
/s]
v=0.527 m/s
T=0.1 sec
Velocity FieldVelocity Field
Bub
ble
Tra
king
B
ubbl
e T
raki
ng
0.225
0.23
0.235
0.24
0.245
0.25
0.255
0.26 0.136
0.138
0.14
0.142
0.144
0.146
0.148
0.15
0.152
0.154
0.156
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
y
x
velo
city
fie
ld [
m/s
]
v=0.377 m/s
Synchronization between a ‘microfluidic air bubble ’ and
a ‘bubble robot’
(in collaborazione con l’ ing. Camerano)(in collaborazione con l’ ing. Camerano)
T=0.2 sec
Velocity FieldVelocity Field
Bub
ble
Tra
king
B
ubbl
e T
raki
ng
0.225
0.23
0.235
0.24
0.245
0.25
0.255
0.26
0.136 0.138 0.14 0.142 0.144 0.146 0.148 0.15 0.152 0.154 0.156
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
v=0.53 m/s
Synchronization between a ‘microfluidic air bubble ’ and
a ‘bubble robot’
(in collaborazione con l’ ing. Camerano)(in collaborazione con l’ ing. Camerano)
Bub
ble
Tra
king
B
ubbl
e T
raki
ng
T=0.3 sec
Velocity FieldVelocity Field
Synchronization between a ‘microfluidic air bubble ’ and
a ‘bubble robot’
(in collaborazione con l’ ing. Camerano)(in collaborazione con l’ ing. Camerano)
Velocity – Time Micorfluidic Bubble
Velocity – Time Micorfluidic Bubble
Velocity – Time Bubble Robot
Velocity – Time Bubble Robot
Experimental RelationTime-Velocity of the
two systems0.3: 93=0.5275:x
X=Time of the bubble in the new system (Bubble Robot)
[0.1 sec].
Experimental RelationTime-Velocity of the
two systems0.3: 93=0.5275:x
X=Time of the bubble in the new system (Bubble Robot)
[0.1 sec].
Exp
erim
enta
l Ana
lysi
s E
xper
imen
tal A
naly
sis
File TXTVelocity, Time
File TXTVelocity, Time
Future Trend� Several ad hoc experimental campaigns;� New nonlinear analysis methods (peak to peak, Poincaré maps, etc…);� Spectral analysis;� 3D CFD simulations;
Correlation of input parameters to nonlinear indicators (modeling)
Control the nonlinear flow of microbubbles
� Metodi e Modelli Numerici per Campi e Circuiti (Prof. S. Alfonsetti)
� Materiali Polimerici per la Microelettronica (Prof. A. Pollicino)
� Controllo Robusto (Prof. L. Fortuna)
� 6 Febbraio-12 Marzo 2008, International Winter School on Grid
Computing IWSGC’08 (on line course, University of Edimburg)
� 27-29 Novembre 2007, Tutorial su metodi numerici per sistemi di
calcolo parallelo ad alte prestazioni (INFN)
� 14-19 Luglio 2008, Introduzione al Controllo Non Lineare (Scuola
Sidra di Dottorato, Bertinoro (Fo))
� 22-26 Settembre 2008, An Introduction to Computational Fluid
Dynamics (Scuola Superiore di Catania)
Attended courses and Tutorials
Scientific Publications
F. Schembri, F. Sapuppo, E. Leggio, M. Iacono Manno, M. Bucolo, L. Fortuna, “A Grid Computational Approach to a Two Phase Flow in Microfluidics”, Workshop Progetti Grid del PON "Ricerca" 2000-2006 - Avviso 1575, Catania, Italy, February 10-12, 2009.
F. Sapuppo, F.Schembri , M. Bucolo, “Experimental Investigation on Parameters for the Control of Droplets Dynamics ”, Physcon 2009, September 1-4, 2009, Catania, Italy.
F. Sapuppo, F.Schembri , M. Bucolo, “Correlation between Spatial and Temporal Chaotic Behaviour in Two-Phase Microfluidics”, Chaos 09 June 22-24, 2009, London, UK.
F. Sapuppo, F.Schembri , M. Bucolo, “Nonlinear Dynamics in Experimental Two-Phase Microfluidics Timeseries”, Chaos 09, June 22-24, 2009, London, UK.
M. Bucolo, J. Esteve, L. Fortuna, A. Llobera, F. Sapuppo, F. Schembri, ‘A Disposable Micro-lectro-Optical Interface for Flow Monitoring in Bio-Microfluidics’, 12th International Conference on Miniaturized Systems for Chemistry and Life Sciences (µTAS 2008), San Diego, California, October 12-16, 2008.
M. Bucolo, L. Fortuna, A. Llobera, F.Sapuppo, F. Schembri, ‘Integrated Devices for Investigation of Nonlinear Dynamics in Microfluidics’, 10th Experimental Chaos Conference (ECC10), June 3-6, 2008, Catania, Italy
M. Bucolo, L. Fortuna, F. Sapuppo, F. Schembri, ‘Chaotic Dynamics in Microfluidic Experiments’ The 18th Int. Symposium on Mathematical Theory of Networks and Systems (MTNS 2008), Blacksburg, Virginia, USA, 28 July –1 August 2008.
M. Bucolo, L. Fortuna, F. Sapuppo, F. Schembri. (2008). “Experimental Chaos in Microfluidic Devices”. In: The 10th Experimental Chaos Conference”. Catania, Italy, June 3-6, p. 1