final poster3
Post on 14-Jan-2017
40 Views
Preview:
TRANSCRIPT
Prototype Test ○ Test Conditions: ■ 3mm HDPE spheres in isopropyl alcohol solution to
simulate sinking speed of plankton ○ Results: ■ Fully automated control ■ Steady-‐state error less than 0.1 mm
Figure 10: Position vs Time Plot
Figure 11: Processed Images of Controlled Object
FLUIDIC TREADMILL SYSTEM Damien Blake, Lingjie Kong, Yuling Shen, Yue Teng
Department of Mechanical and Aerospace Engineering at University of California, San Diego Sponsored by Dr. Jules S. Jaffe, Dr. Peter Franks, and the Scripps Institution of Oceanography
Overview
● Langer BT100-‐2J Peristaltic Pump ○ Avg. flow velocity ranging from 0-‐2.8 mm/s ○ Reads external signals to control velocity
Figure 4: Peristaltic Pump
● Logitech 2.0 web camera and lighting ○ Resolution of 5 µm/pixel and frame rate of
30 fps ○ Compatible with OpenCV image processing
software
Figure 5: Logitech 2.0 Camera
Figure 1: Fluidic Treadmill System Setup
We would like to thank the following people for their help and exper4se: ● Dr. Jules S. Jaffe and Dr. Peter Franks at the Scripps Ins4tu4on of
Oceanography for sponsoring the project ● Dr. Jerry Tustaniwskyj, Dr. James Babcock, Michael Ix for
guidance and sugges4ons ● Dr. Steve Roberts for technical support on RS-‐485 interface ● Ben Laxton for help with the image processing soPware ● Jessica Garwood for guidance and providing organism samples ● Tom Chalfant, Ian Richardson, Chris Cassidy for components ● J.V. Agnew for purchasing and reimbursment assistance ● Maryam Sarkoush for ACMS machine access
1. Ploug, Helle, and Bo Barker Jorgensen. “A Net-‐jet Flow System for Mass Transfer and Microsensor Studies of Sinking Aggregates.”
2. Batchelor, G.K. (1967). An Introduction to Fluid Dynamics. Cambridge University Press. ISBN 0-‐521-‐66396-‐2 3. Lennart Thomas Bach, Ulf Riebesell, Scarlett Sett, Sarah Febiri, Paul Rzepka, Kai Georg Schulz. “An
approach for particle sinking velocity measurements in the 3–400 μm size range and considerations on the effect of temperature on sinking rates”. Mar Biol. 2012; 159(8): 1853–1864. Published online 2012 May 22
4. Herndl, Gerhard J. "Microbial Control of the Dark End of the Biological Pump." Nature.com. Nature Publishing Group, 29 Aug. 2013. Web. 28 Apr. 2015.
Recommendation Justification Integrate the image processing software and the pump control program into single program
Pump control code can be written in C++ using an open source library
Modify the image processing code to enable tracking of the object
Can control a single object among a cluster of objects
• Simulation of Flow Profile ○ COMSOL 3D Multiphysics ○ Assumptions: ■ Water (density 1 kg / m3) ■ No Slip Boundary Condition ■ Constant Pressure at outlet ■ Incompressible Flow
○ Results: ■ < 5% velocity gradient up to 1.5mm from center
Figure 8: Simulated Flow Profile of Chamber at 40 mm
Figure 9: Actual Flow Profile Using TiO2 Powder Tracer
1
The primary purpose of the fluidic treadmill system was to observe the sinking velocities of ocean microorganisms, such as phytoplankton, in order to assess their carbon isolation properties. This was accomplished using image processing and flow speed feedback control. By keeping them in the field of view of a camera, this iteration of the system was capable of controlling test objects 0.5 mm to 3mm in diameter and determining their respective sinking velocities within an error of 2.7 to 15.4%, up to a maximum speed of 5.04 mm/s . With future improvement, the fluidic treadmill system will be able to analyze objects and organisms to a size of 50 μm.
● Plankton and other microorganisms in the ocean absorb
CO2 that has been absorbed by the water in the ocean. This is productive in counteracting rising CO2 levels and global warming.
● If the plankton are consumed by bacteria or larger organisms, then the CO2 is released. 10% of the carbon level near the ocean surface is exported.
● Plankton that sink to the bottom of the ocean before they are eaten are far more productive in CO2 isolation.
4.
Figure 12: Plankton Role in Carbon Isolation
● Image Processing ○ C++ and OpenCV library ○ Detects the displacement between observed
object and reference position using blob analysis with bounding box
○ Outputs the displacement signal for flow velocity control
Figure 2: Image Processing
● Pump Flow Velocity Control Algorithm ○ MATLAB and RS-‐485 serial communication ○ Based on the displacement signal with a PI
controller
Figure 3: Feedback Control Block Diagram
● Observation chamber ○ Insertion of objects through quick disconnect ○ Allows backside illumination and imaging of
the object
Figure 6: Chamber Setup
● Honeycomb diffuser ○ Laser-‐cut acrylic ○ 9x9 array of 0.75 mm holes with 1mm pitch ○ Provides uniform laminar flow profile
Figure 7: Honeycomb Diffuser
● Water reservoir ○ Water flow and storage ○ Releases air bubbles from the chamber
Entering field of view Pump reacting Staying in field of view
Timeline 1 second 2 seconds Steady State
Object
Water Reservoir
Logitech 2.0 Camera
Fluidic Chamber
LED Lighting
Peristaltic Pump
Flow Profile
Summary of Performance
Future Improvement
Impact on Society
Acknowledgments
References
So$ware Components
Accuracy and Precision
Distance from Centerline Total Maximum Error %
0 mm 2.71 0 to 1.5 mm 5.68 1.5 to 2 mm 15.24 >2.5 mm >29
Hardware Components
Height Adjustment
Quick disconnect shutoff valve
Honeycomb Diffuser at 30mm
Bounding Box
top related