MODELS AND SYSTEMS FOR THE CONTROL OF TWO-PHASE

PROCESSES IN MICROFLUIDICSFabiana Cairone

Tutor: Prof. ssa Maide Bucolo

Dottorato di Ricerca in Ingegneria dei Sistemi, Energetica, Informatica e delle Telecomunicazioni (XXXI Ciclo)

TWO-PHASE FLOWS IN MICROFLUIDICS

JOURNAL PUBBLICATIONS

•F. Cairone, S. Gagliano, M. Bucolo, ‘Experimental study on the slug flow in a serpentine microchannel’, International Journal Experimental Thermal and Fluid Science, 76: 34-44 (2016).

•F. Cairone, S. Gagliano, D. C. Carbone, G. Recca, M. Bucolo, ‘3D Printed Embedded PDMS Micro-Optofluidic Switch’, Microfluidics and Nanofluidics, 20: 61-71 (2016).

•F. Cairone, D. Ortiz, P.J. Cabrales, M. Intaglietta, M. Bucolo, ‘Emergent behaviors in RBCs flows in micro-channels using digital particle image velocimetry’, Microvascular Research, 116:77-86 (2017).

•F. Cairone, P. Anandan, M. Bucolo, ‘Nonlinear systems synchronization for modeling two-phase microfluidics flows’, Nonlinear Dynamics, 92, 1: 75–84 (2017).

•F. Cairone, D. Mirabella, P. J. Cabrales, M. Intaglietta, M. Bucolo, “Quantitative Analysis of Spatial Irregularities in RBCs Flows”, Chaos, Solitons and Fractals, 2018, 1-7.

CONFERENCE PUBBLICATIONS

•F. Cairone, M. Bucolo, ‘Data-Driven Identification of Two-Phase Microfluidic Flows’, 24th Mediterranean on Control and Automation (MED16), Athens, Greece, June 21-24, 2016.

•F. Cairone, M. Bucolo, ‘Design of Control Systems for Two-Phase Microfluidic Processes’, 24th Mediterranean Conference on Control and Automation (MED16), Athens, Greece, June 21-24, 2016.

•F. Cairone, P. Anandan, M. Bucolo, ‘Modelling Two-Phase Microfluidic Dynamics’, Complex Engineering (Compeng), Catania, Italy, July 04-05, 2016.

•F. Cairone, D. Sanalitro, D. Ortiz, P.J. Cabrales, M. Intaglietta, M. Bucolo, ‘DPIV analysis of RBCs flows in serpentine micro-channel’, European Conference on Circuit Theory and Design (ECCTD 17), Catania,

Italy, September 4-6, 2017.

•F. Cairone, A. Amenta, M. Bucolo, ‘Micro-Opto-Fluidic Systems for Real Time Control of Two-phase Processes’, Convegno automatica.it (SIDRA 17), Milan, Italy, September 11-13, 2017.

•F. Cairone, A. Amenta, M. Bucolo, ‘Platform for real-time open loop control of slug flows’, 5th European Conference on Microfluidics (µFlu18), Strasbourg, France, February 28-March 2, 2018.

•F. Cairone, M. Bucolo, “Complex Spatio-Temporal Patterns in Red Blood Cells Flows”, CNNA, Budapest, Hungary, August 27-30 2018.

3D PRINTING MICRO-OPTOFLUIDIC DEVICE FOR TWO-PHASE FLOWS DETECTION

Slug Flows Characterization

Red Blood Cells (RBCs) Flows Investigation

Slug Flows Modeling

50 55 60 65 70 75 80 850

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Frequency (Hz)

AF = 0.182

y

yi-NW

yi-NN

20 25 30 35 40 45 500

1

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Frequency (Hz)

AF = 0.433

y

yi-WN

yi-NN

50 55 60 65 70 75 800

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Frequency (Hz)

AF = 0.733

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yi-NN

Slug Flows Real-Time Control

0 50 100 150 200 250 300 350 400 450 500-2.5

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Time [s]

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Complete Filtered signal

A B C

Freq_des 1 Hz Freq_des 5 Hz Freq_des 10 Hz

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0.9998 249.2 0.02984 7.886

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Peak # Position Height Width Area

A

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Peak # Position Height Width Area

1 4.951 39.73 0.2921 12.35

Frequency (freq) [Hz]

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120 Peak # Position Height Width Area

1 10.33 63.08 0.1251 8.399

Frequency (freq) [Hz]

C

CLOSED LOOP CONTROL SYSTEM

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Soft sensor

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law

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INPUT AIR

DOMINANCE

INPUT WATER

DOMINANCE

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TER

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IR D

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NC

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--- SLOW (V 1 ml/min)

A

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water

air

w

AT THE INLETS

• fluid dominance

• constant flow rates

RBCs in PBS

PULSATILE FLOW

SINGLE CELLHIGH

CONCENTRATION LOW

CONCENTRATION

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Frequency [Hz]0 1 2 3 4 5

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Frequency [Hz]

0 10 20 30 40 50 60 70 80 90 100 110-1

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Frequency [Hz]

Power 1 mW Power 5 mW Power 10 mW

MATERIAL TECHNOLOGY

• RAPID FABBIRCATION • EASY TO USE• LOW COST

• GOOD OPTICAL PROPERTY• BIO-COMPATIBILITY• CMOS COMPATIBILITY

MICRO-OPTICS DESIGN

PDMS 3D PRINTING

MECHANICS

MICRO-MECHANICS

ELECTRONICS

MICRO-ELECTRONICS

OPTICS

MICRO-OPTICS

FLUIDCS

MICRO-FLUIDCS

INTEGRATION

MICRO-OPTOFLUIDIC DEVICE

Air

Input optical fiberPDMS

Output optical fiber

Water

Waveguide

Multi-phase flows identification and control at micrometric

scales is one of the main open issue in the construction of

highly complex microsystems, where fluids and micro-

particles can circulate in a controlled manner performing a

large number of tasks in a maze of micro-channels. Firstly,

it is necessary to characterize the flows considering the

signals acquired by the optical system; by defining certain

parameters it is possible in the identification process and

its control, defining appropriate control laws. From a

technological point of view, by using the low cost 3D

Printing, it is possible to create channels quickly and

inexpensively with unprecedented complexity, as well as

integrating micro-optics components.

TWO IMMISCIBLE FLUIDS WATER - AIR

PARTICLES SUSPENSION IN A FLUID RED BLOOD CELLS - PLASMA

fw=fa=1.5 [ml/min] fw=1.5 fa=5 [ml/min] fw=6 fa=1.5 [ml/min]

A=0.1 [a.u.] A=10 [a.u.] A=100 [a.u.]

INPUT-1

INPUT-2

OUTPUT

TWO-PHASE PROCESS

ACTUATIONCONTROL

ACTUATIONCONTROL From MACRO To MICRO

From MACRO To MICRO

DETECTIONFrom MICRO To

MACROSIGNAL ANALYSIS

PROCESSES MONITORING BY

SIGNALS IMAGES

PROCESSES CONTROL BY

PRESSURE LASER LIGHT

Fast and slow slugs passage in a test section of a micro-channel

were monitored optically. Their dynamics were characterized by

the establishment of optical signal analysis procedures.

Slug tracking was realized by data-driven approaches using

Neural Network and Wavenet. Nonlinear modelling was obtained

by systems synchronization.

Open and closed loop control strategies based on the slugs

frequency were defined and implemented in Labview platform.

The RBCs collective behaviors in a

micro-channel was studied

implementing a 2D image processing

procedure based on the digital

particle image velocimetry (DPIV).

Starting from the behavioral

classification based on the three flows

patterns identified as (Weak Activity,

Vorticity, Alignment), an analysis to

detect the spatial irregularities in the

flows distribution was carried out. The

velocity gradients and four nonlinear

parameters (shear rate, strain rate,

vorticity, divergence) were computed

from the time-varying velocity maps

and used to provide a quantitative

characterization of the flows features.

A PDMS 3D printing technique was used for producing a two-phase flows detector. In micro-optofluidic devices, it is important to deliver, as closer as

possible, the input light to the fluidic process and then, to collect the output signal. A protocol was established to realize the micro-optofluidic two-

phase flows detector, that integrate the optic components and the fluidic part. The polymer selected to realize the device was the PDMS.

Considering fluids with different indexes flowing in a micro-channel and an incident laser beam that interferes with them, it is possible to obtain

different light transmission based on fluid properties. The two-phase flows detector was tested considering slug flows and particles flows. The slug

flows were also studied dynamically.

Data-Driven Approaches Nonlinear System Synchronization

0

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AIR WATER PBS OIL RBCs-C1 RBCs-C2

Power (in V)