XIX Brain Storming Day

Transcription

1 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

2 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

3 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

4 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 Air/PDMS interface Reflectance Trasmittance Air/PDMS Incident Angle [Degree] θ c Fresnel Equations Reflectance R PDMS/Air interface Reflectance Transmittance PDMS/Air TIR Incident Angle [Degree] n _ air = arcsin = n _ pdms

5 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

6 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

7 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 D1=150um -D2=250um -D=300um D2 Out Light 1.2mm Reflectance R Reflectance Transmittance Incident Angle [Degree] TIR

8 250um F=62.5um F F F F N.A. of optic fiber=0.22 Number of rays=1000 λ= 670nm

9 PDMS Air SPLITTER 1X3 microprism Fiber Optic Insertion PDMS-Air Air-PDMS M1 M2 3.4 mm PDMS/Air L1,L3= Focusing Lens L2,L4,L5= Collimating Lens L1 L4 L2 L3 Lenses L5 Reflectance R Reflectance R Reflectance Transmittance Incident Angle [Degree] Reflectance Trasmittance Air/PDMS Incident Angle [Degree] 2.3 mm

10 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

11 Polymeric micro-optic Interface Collect Light Input Fiber Optic Microfluidic Spot Fiber Optic

12 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

13 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

14 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 = (Tetrahedral elements) Superficial Mesh = (Triangular elements)

15 System Modeling on GRID Multi-physic Model and Computational Issues Two Immiscible Fluids Water Air Simulation Time= 8s Time step= s Iterations =20 Inlet Velocity [m/s] Total Iterations 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

16 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)

17 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

18 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 Voltage [V] water 5 Hz - air 12 Hz Time [s]

19 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

20 Data-Driven Identification Input Black Box Output NARX model NARMAX model Parameter Estimation Microfluidic Two-Phase Flow Model Voltage [V] water 5 Hz - air 12 Hz 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( )

21 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

22 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) 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) Luglio 2008, Introduzione al Controllo Non Lineare (Scuola Sidra di Dottorato), Bertinoro (BO)) Settembre 2008, An Introduction to Computational Fluid Dynamics (Scuola Superiore di Catania) Settembre 2009, Parameter Estimation in Physiological Models, Lipari (ME) Conferences Chaos 09 Conference, London, June 2009

23 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, 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 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" Avviso 1575, Catania, Italy, February 10-12, 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.

24 Thanks for the attention