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