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FLUIDIC TREADMILL SYSTEM
_____________________________________________________________________________________________
FOR DETERMINATION OF MICROORGANISM SINKING VELOCITIES
Sponsored by: Dr. Jules S. Jaffe and Dr. Peter Franks
DAMIEN BLAKE - LINGJIE KONG - YULING SHEN - YUE TENG
Department of Mechanical and Aerospace Engineering, MAE 156B
Dr. Jerry Tustaniwskyj
___________________________________________________________________
Click Here for Executive Summary
1. Background
2. Design Overview
3. Performance Summary
4. Flow Profile
5. Error Analysis
Figure 1. Image of Overall Design Solution
1. BACKGROUND
1. BACKGROUND
Background
The Scripps Institution of Oceanography (SIO) is one of the most renowned ocean and earth research centers in the world. Over the past decades they have successfully developed both underwater and laboratory-use instruments to observe microorganisms on the scale of 100 μm or less. One of the most usefully observable of these microorganisms is plankton, which can be as small as only a few micrometers or as large as a hundred micrometers.
Sponsored by SIO, the fluidic treadmill system project aimed to build a laboratory-use instrument that automatically tracked the position and velocity of plankton to keep them in the field of view of a camera and/or microscope. Its primary objective was to create a prototype that used inanimate sinking objects to simulate the downward motion of the plankton. This system consisted of a camera, peristaltic pump, observation chamber, and image processing/control algorithm through serial port PC connection as shown in Figure 1. To accomplish this tracking, compensation of the object’s downward sinking motion was achieved using a vertical laminar flow generated by the pump. In addition, a control algorithm was used to control the pump speed, using the plankton’s displacement with respect to its reference position as a feedback. As a result of this control, the object’s sinking velocity was able to be determined.
2. DESIGN OVERVIEW
2. DESIGN OVERVIEW
Figure 2. CAD of Design Solution. See "Final Design" Page
Design Overview
The final design solution consisted of several main components: a fluidic chamber 8mL in volume, a Logitech camera, a lighting system and mounting apparatus for the chamber, a peristaltic pump and water reservoir, and image processing and control algorithms.
First, the test object was inserted into a fluidic chamber 80 mm tall with a cross section of 10 mm x 10 mm through a shut off valve on top of the chamber. The chamber body was 3D-printed using a proJet 3D printer, and its front and back faces were laser cut from transparent acrylic.Backlighting from an LED light source allowed an image of the object to be captured using a Logitech 2.0 camera with a field of view of 10mm x 7 mm and a resolution of 5μm per pixel. The position of the object was determined on a PC using image processing code and blob analysis, in which the program drew a bounding box around the object and determined its centroid. Then, the relative position between the object centroid and center of the image was calculated. This result of the position offset was sent to a control algorithm in MATLAB, which was connected to a peristaltic pump via serial port connection. The flow rate of a peristaltic pump was adjusted at the command of the controller, which either increased or decreased the vertical flow velocity. As a result, the object reacted to the flow and eventually stabilized.
3. PERFORMANCE RESULTS
3. PERFORMANCE RESULTS
3.1 Proportional (P) and Proportional Integral (PI) Control
3.1 Proportional (P) and Proportional Integral (PI) Control
Proportional (P) and Proportional Integral (PI) Controller is used to realize close loop stability through control feedback. The video below is used to demonstrate the system performance under automatic control.
3.1.1 Test Conditions
Room temperature (~20ᵒ C)
Distilled water and isopropyl alcohol solution mixture
3.174 mm diameter HDPE spheres as test objects
2 MacBook Pro laptops—one with image processing and the other with MATLAB control algorithm, connected via Ethernet cable
The HDPE sphere was inserted through the top nozzle of the chamber and allowed to rest on the surface of the honeycomb diffuser. Then the pump was run at maximum speed to introduce fluid into the chamber until it filled completely. Once this was accomplished, the object was allowed to sink by turning off the pump motor.
3.1.2 Test Video
Video 1. Proportional (P) Controller SIO Fluidic Treadmill
By only using a proportional controller, the ball can be stabilized in the camera field of view. However, it is not close to its zero position, which is the center of the image. Therefore, an integral controller is needed to resolve steady state error.
Video 2. PI Controller with No Steady State Error SIO Fluidic Treadmill
By using both a proportional and integral controller, the steady state error could be eliminated and the ball stabilized in the center of the field of view (zero position).
3.1.3 Test Results
The HDPE sphere was successfully stabilized in the center of the camera field of view which is the zero position through feedback PI control. The object was maintained in the field of view by using an upward vertical flow at around 40 RPM which indicated a flow speed of 1mm/s. This velocity can be equated to the sinking velocity of the HDPE sphere in the isopropyl alcohol solution.
Figure 3. Position vs.Time PI Controller (Enter FOV from Top)
Figure 4. Corresponding Pump RPM vs. Time PI Controller (Enter FOV from Top)
From figure 3 and figure 4, the position error is less than 0.1 mm and the pump RPM error is less than 1RPM. This again proves our theory that PI controller should be sufficient to maintain the sphere in the field of view as well as eliminate its steady state error.
3.2 Pump Calibration
3.2 Pump Calibration
Figure 5. Flow Rate vs. Pump RPM
Figure 6. Centerline Velocity vs. Pump RPM
According to figure 5, the flow rate is calibrated with respect to pump RPM. Then, by using COMSOL 3D simulation software, the centerline velocity vs. pump RPM is calculated, shown in figure 6. This linear relationship enabled the control of the centerline velocity through pump RPM by using linear control theory.
4. FLOW SIMULATION AND EXPERIMENT VERIFICATION
4. FLOW SIMULATION AND EXPERIMENT VERIFICATION
COMSOL 3D simulation is used to figure out the flow profile in figure 7. Then, the real profile is verified in figure 8 by using TiO2 powder tracer.
Figure 7. COMSOL 3D Simulation
Figure 8. Real Profile using TiO2 Power
5. ERROR ANALYSIS
5. ERROR ANALYSIS
5.1 System Error
5.1 System Error
5.1.1 System Error
The pump calibration resulted in an error of 0.43%, as a result of the discrepancy between each data point and the linear fit equation. The Steady State RPM error was calculated from the pump speed at steady state, and was found to be 2.60%. During the automatic control test, its error range was ± 1RPM at a steady state value of 38 RPM. COMSOL 3D simulation of the flow profile estimated the error in the centerline velocity to be 0.65%. The overall measurement error was calculated using root mean square (see Section 8.7.3 in the final report) and found to be 2.71%.
5.1.2 Total System Error
The error velocity gradient in the observation error had the following widths: Less than 5% within 3mm, less than 15% within 4mm, and less than 29% within 5mm. Combined with the system measurement error, using a root mean square approach, the varying overall measurement error was calculated.
Total system error at centerline (root mean square)
2.70%
Total system error at 0 to 1.5 mm from centerline (root mean square)
5.68%
Total system error at 1.5 to 2 mm from centerline (root mean square)
15.24%
Total system error at more than 2mm from centerline (root mean square)
>29.00%
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