UNIVERSITA DEGLI STUDI DI CATANIA Dipartimento di Ingegneria Elettrica Elettronica e dei Sistemi DIEES XIX Brain Storming Day Florinda Schembri Tutors:prof. Maide Bucolo, eng. Francesca Sapuppo Coordinator: prof. Luigi Fortuna
Data-Driven Driven Identification Time Series Analysis Models selection Parameter Identification System Modeling Multi-physic models Numerical methods Computational issue Control System Input-Output variables Internal parameters Microfluidic Systems In vitro In vivo in vitro pro in vivo Real-Time Monitoring Opto-Sensing System Point-wise (0D) Distributed Map Full-Field (2D) Microscopy-Based Opto-Mechanic System Polymeric micro-optic Interface
Model Identification A Grid Computational Approach to Two-Phase Flow in Microfluidics Data-Driven Identification Time Series Analysis Models selection Parameter Identification System Modeling Multi-physic models Numerical methods Computational issue Control System Input-Output variables Internal parameters Microfluidic Systems In vitro In vivo in vitro pro in vivo Real-Time Monitoring Opto-Sensing System Point-wise (0D) Distributed Map Full-Field (2D) Microscopy-Based Opto-Mechanic System Polymeric micro-optic Interface A Polymeric Device for Distributed Detection and Control in Microfluidics
Polymeric micro-optic Interface n_air=1 n_pdm s=1.41 Incident angle Surfaces Geometry Prism Lenses Mirror Waveguide Splitter Ad hoc optics. Design-CAD Raytracing Simulations Snell Law Advantages: Hight optical trasparency above a wavelength of 230 nm ; Low self-fluorescence; Low interfacial free energy; Stable against umidity and temperature; Can be cured by UV light ; Eleastometric properties ; Mecanically durable; Low-cost; Low-Toxicity; Chemical inertness; CMOS compatible; Integration of microlenses-mirrors made of PDMS in the device; Disadvantages: Volume changes; Elastic deformation; PDMS Reflectance R 1.2 1 0.8 0.6 0.4 0.2 0 Air/PDMS interface Reflectance Trasmittance Air/PDMS -0.2 0 10 20 30 40 50 60 70 80 90 100 Incident Angle [Degree] θ c Fresnel Equations Reflectance R 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 PDMS/Air interface Reflectance Transmittance PDMS/Air TIR 0 0 5 10 15 20 25 30 35 40 45 50 Incident Angle [Degree] n _ air = arcsin = 45. 17 n _ pdms
From Local to Distributed Actions Source GICSERV-2008: POLYMERIC MICRO-OPTICAL INTERFACE Design Simulation Realization Detection Light Splitters GICSERV-2009: Polymeric Device for Distributed Detection and Control in Microfluidics Source Design and Simulation Source 1x4 Prism Based Microfluidic System Microfluidic System 1x3 MMI Y Detection Detection
From Local to Distributed Actions A Polymeric Device for Distributed Detection and Control in Microfluidics Distributed Optical Sensing Flow Fluid/Particles Properties Morphological Properties. Microfluidic System -Portability; -Transparency; -No expencive laboratory facilities; Optical Actuation Optical Tweezers Optical Switches Opto-Thermal Effects
PDMS Air SPLITTER 1X4 Fiber Optic Insertion Collimating Lens 250um F=62.5um Pillars Second Splitting 0.7mm Mirrors (TIR condition) First Splitting F F F F 1 N.A. of optic fiber=0.22 Number of rays=1000 λ= 670nm PDMS/Air 0.9 0.8 0.7 -D1=150um -D2=250um -D=300um D2 Out Light 1.2mm Reflectance R 0.6 0.5 0.4 0.3 0.2 0.1 Reflectance Transmittance 0 0 5 10 15 20 25 30 35 40 45 50 Incident Angle [Degree] TIR
250um F=62.5um F F F F N.A. of optic fiber=0.22 Number of rays=1000 λ= 670nm
PDMS Air SPLITTER 1X3 microprism Fiber Optic Insertion PDMS-Air Air-PDMS M1 M2 3.4 mm 1 0.9 0.8 0.7 PDMS/Air L1,L3= Focusing Lens L2,L4,L5= Collimating Lens L1 L4 L2 L3 Lenses L5 Reflectance R Reflectance R 0.6 0.5 0.4 0.3 0.2 0.1 Reflectance Transmittance 0 0 5 10 15 20 25 30 35 40 45 50 Incident Angle [Degree] 1.2 1 0.8 0.6 0.4 0.2 0 Reflectance Trasmittance Air/PDMS -0.2 0 10 20 30 40 50 60 70 80 90 100 Incident Angle [Degree] 2.3 mm
Real-case:fiber optic with 0.22 numerical aperture N.A. of optic fiber=0.22 Number of rays=1000 λ= 670nm Ideal-case: Parallel Incoming Rays F=100um F F F
Polymeric micro-optic Interface Collect Light Input Fiber Optic Microfluidic Spot Fiber Optic
Polymeric micro-optic Interface Future Trend Data-Driven Identification System Modeling Time Series Analysis Models selection Parameter Identification Multi-physic models Numerical methods Computational issue Control System Input-Output variables Internal parameters Microfluidic Systems In vitro In vivo in vitro pro in vivo PC-Based Processing Real-Time Monitoring Point-wise (0D) Distributed Map Full-Field (2D) Opto-Sensing System Microscopy-Based Opto-Mechanic System Polymeric micro-optic Interface Control System Actuator Input Fiber Optic Acquisition Board Fiber Optic
Model Identification A Grid Computational Approach to Two-Phase Flow in Microfluidics Data-Driven Identification Time Series Analysis Models selection Parameter Identification System Modeling Multi-physic models Numerical methods Computational issue Control System Input-Output variables Internal parameters Microfluidic Systems In vitro In vivo in vitro pro in vivo Real-Time Monitoring Opto-Sensing System Point-wise (0D) Distributed Map Full-Field (2D) Microscopy-Based Opto-Mechanic System Polymeric micro-optic Interface A Polymeric Device for Distributed Detection and Control in Microfluidics
System Modeling on GRID Numerical Method: Final Element Method (FEM) Two inlet Serpentine Micromixer Length [between confluence(c) and outlet(o)]=121mm Squared section (WxW)= 640x640 µm2 Internal radius of curvature= 320 µm(ri) External radius of curvatures=960µm(re) Tetrahedral Mesh and Boundary Condition Outlet Wetted Wall Water Inlet Not Wetted Wall Air Inlet Volumetric Mesh = 2.724.041 (Tetrahedral elements) Superficial Mesh = 302.820 (Triangular elements)
System Modeling on GRID Multi-physic Model and Computational Issues Two Immiscible Fluids Water Air Simulation Time= 8s Time step=0.0002 s Iterations =20 Inlet Velocity [m/s] Total Iterations 800.000 Numerical Modeling Navier Stokes PDEs Volume of Fluid model (Two-Phases) surface-tracking technique Parallel Fluent 6.23 MPI process Mesh Partitioning (Metis ) 48 CORE
System Modeling on GRID Bubbles Dynamics 1 Time step 8 sec on GRID Infrastructure (0.22 s) on a Core2 Quad 2.4 GHz (5 min) on GRID Infrastructure (6 days) on a Core2 Quad 2.4 GHz (137 days)
Model Identification A Grid Computational Approach to Two-Phase Flow in Microfluidics Data-Driven Identification Time Series Analysis Models selection Parameter Identification System Modeling Multi-physic models Numerical methods Computational issue Control System Input-Output variables Internal parameters Microfluidic Systems In vitro In vivo in vitro pro in vivo Real-Time Monitoring Opto-Sensing System Point-wise (0D) Distributed Map Full-Field (2D) Microscopy-Based Opto-Mechanic System Polymeric micro-optic Interface A Polymeric Device for Distributed Detection and Control in Microfluidics
Data-Driven Identification Knowledge about the System (a priori) - Physics law - Laminar flow - Range Dimensionless numbers - External inputs Experimental Informations (a posteriori) Time Series Analysis -Velocity - Two phase flow patterns -Nonlinear Indicators u ρ t + u u = u = 0 Navier Stokes equation and mass conservation equation T [ pi + η( u + ( u ) )] + F Air af Capillary Number = Ca = V air µu γ Air Fraction V air + V water Ca 2 O(10 ) Reynold Number Re = Re < ρ ul µ 100 Re << 1 Water (carried fluid) 0.08 Pulsatile Pumps 0.075 0.07 Voltage [V] 0.095 0.09 0.085 water 5 Hz - air 12 Hz 0.065 9.5 9.55 9.6 9.65 9.7 9.75 9.8 9.85 9.9 9.95 10 Time [s]
Data-Driven Identification Experimental and Simulation Informations: Sinusoidal inputs and flow rate; Inlets flow rate; Two-phase flow patterns; Nonlinear Indicators; Model Selection: Linear; Nonlinear; Structure; Parameter Estimation Model Validation No Yes
Data-Driven Identification Input Black Box Output NARX model NARMAX model Parameter Estimation Microfluidic Two-Phase Flow Model Voltage [V] 0.095 0.09 0.085 0.08 0.075 0.07 water 5 Hz - air 12 Hz 0.065 9.5 9.55 9.6 9.65 9.7 9.75 9.8 9.85 9.9 9.95 10 Time [s] Parameter Estimation Experimental Information + a priori Information Duffing and van der Pol equations with periodic forcing term m x+ 2b x+ k ( 2 1+ cx ) sin vt x+ µ 2πυτ = ( ) 2 x 1 x+ x = a sin( )
Data Driven Identification Future Trend Data-Driven Identification Time Series Analysis Models selection Parameter Identification System Modeling Multi-physic models Numerical methods Computational issue Control System Input-Output variables Internal parameters Microfluidic Systems In vitro In vivo in vitro pro in vivo PC-Based Processing Real-Time Monitoring Point-wise (0D) Distributed Map Full-Field (2D) Opto-Sensing System Microscopy-Based Opto-Mechanic System Polymeric micro-optic Interface Microfluidic Two-Phase Flow Model Control System Actuator Input Fiber Optic Acquisition Board Fiber Optic
Attended courses and Tutorials Metodi e Modelli Numerici per Campi e Circuiti (Prof. S. Alfonsetti) Materiali Polimerici per la Microelettronica (Prof. A. Pollicino) Controllo Robusto (Prof. L. Fortuna) 27-29 Novembre 2007, Tutorial su metodi numerici per sistemi di calcolo parallelo ad alte prestazioni (INFN) 6 Febbraio-12 Marzo 2008, International Winter School on Grid Computing IWSGC 08 (on line course, University of Edimburg) 14-19 Luglio 2008, Introduzione al Controllo Non Lineare (Scuola Sidra di Dottorato), Bertinoro (BO)) 22-26 Settembre 2008, An Introduction to Computational Fluid Dynamics (Scuola Superiore di Catania) 13-26 Settembre 2009, Parameter Estimation in Physiological Models, Lipari (ME) Conferences Chaos 09 Conference, London, 22-24 June 2009
Conferences Scientific Publications M. Bucolo, J. Esteve, L. Fortuna, A. Llobera, F. Sapuppo and 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 and 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 and F. Schembri. (2008). Experimental Chaos in Microfluidic Devices. In: The 10th Experimental Chaos Conference. Catania, Italy, June 3-6, p. 1 M. Bucolo, L. Fortuna, F. Sapuppo and F. Schembri, Chaotic Dynamics in Microfluidic Experiments The 18 th Int. Symposium on Mathematical Theory of Networks and Systems (MTNS 2008), Blacksburg, Virginia, USA, 28 July 1 August 2008. F. Schembri, F. Sapuppo, E. Leggio, M. Iacono Manno, M. Bucolo and 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 and 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 and M. Bucolo, Nonlinear Dynamics in Experimental Two-Phase Microfluidics Timeseries, Chaos 09, June 22-24, 2009, London, UK. F. Sapuppo, F.Schembri and M. Bucolo, Experimental Investigation on Parameters for the Control of Droplets Dynamics, Physcon 2009, September 1-4, 2009, Catania, Italy. Journal F. Sapuppo, F. Schembri, L. Fortuna and M. Bucolo. (2009). Microfluidic Circuits and Systems. IEEE CIRCUITS AND SYSTEMS MAGAZINE, THIRD QUARTER 2009.
Thanks for the attention