The fluidic treadmill system was designed to observe the sinking velocities of ocean microorganisms like phytoplankton to assess their carbon isolation properties. It used image processing and flow speed feedback control to keep objects in the field of view of a camera. Initial tests showed it could control spheres 0.5-3mm in diameter and determine sinking velocities within 2.7-15.4% error, up to 5.04mm/s. Future improvements could analyze objects as small as 50μm.

1. 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