8/13/2019 MSRP PresentationPDF
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An Investigation of Particle Image Velocimetry Techniques
Applied to the Analysisof Wheel-Soil Interaction on Mars Terrain
Simulant
Mobolaji Akinpelu, Dr. Carmine Senatore, Dr. Karl
IagnemmaRobotic Mobility Group, Department of Mechanical
Engineering, Massachusetts Institute of Technology
IntroductionIn 2009, the Mars rover was on Mars and the wheel
got stuck. The
aim of this project is to create or modify software that will
trackMartian soil particleand show how themotionof thewheel affects
the
soil. This project simulates themotionof a wheelof
theMarsRoverona Mars soil simulant. The simulation is used to
understand theforcesthewheel exerts onthe soil andthe movement
andshearing pattern ofthesoil particles. Theoverall goal of
thetasksdescribed in this posteris to investigate available PIV
software for the above purpose andunderstand how to modify the
parameters of the software, based onthe cross-correlation
algorithm, to give the most accurate informationonthe motionof
thesoil.
Problem StatementA sampling of common PIV software shows that
they are made for
particular applications like the study of fluid flow in
biological andgeological applications.
Thisprojectis a preliminaryanalysis of the:o instrumentation
requirements (camera frame rate and
pixel resolution)
o software parameters (interrogation window size, degree
ofoverlapof interrogation windows)
o physical conditions (lighting conditions and test
rigcontainer)
and how to choose these variables so our PIV analysis gives
accurateandusefuldataabout theflowpatterns in thesoil.
References[1] Richard, K., and Ronald, R., 1992, "Theory of
Cross-Correlation
Analysis of PIV Images" Applied Scientific Research., pp.
191-215.[2] Chittiappa, M., 2006, "Particle Image Velocimetr y" pp.
1-63.[3] Ronald, Adrian., and Jerry Westerweel., 2011, Particle
Image
Velocimetry, Cambridge University Press, Cambridge, UK, Chap.
1.
ResultsA sample PIV image was rotated about its center, for one
revolution,in increments of 6 degrees. This process resulted in a
stack of 60images tilted 6 degrees from the previous image .
A PIV software (matpiv) was used to process the 60 images.
The softwares accuracy was determined by comparing the value
ofthe vectors from the software to the theoretical value of the
velocity ofthe simulated circular motion.
After repeating the above process for the 59 vector fields
producedby matpiv, the total percentage error for the x-components
ofvelocities was found to be 0.2277 and the total percentage error
for they-components of velocities was found to be 0.2328.
Based on these results, and the ease of use of matpivwe
arecomfortable with matpiv for our analysis of the motion in the
test-bed.
Methods PIV images areprocessed by sub-dividing twoconsecutive
images ofthe flow into a regular grid of sub-areas that overlap and
finding thevelocityvector for each sub-area by an algorithmlike
cross-correlation.
After a PIV analysis, the overall displacement of particles in
thesub-areasis represented by a peakcorrelation value.
This process produces the most probable displacement vector for
aparticular pattern. When the process is repeated for all sub-areas
ofthe image pair, weget a completevectordiagramof thef
lowstudied.
AcknowledgementsI acknowledge Dr. Karl Iagnemma, Dr. Carmine
Senatore and the MSRPProgram for making this research possible and
successful.
Sample Rotated Image Sample Error Plot
DiscussionsWe have begun to take a look at how the quality of
our input images(image pre-processing) and the filtering tools
available for eachsoftware (vector post-processing) may affect
these accuracy estimates.
We are also investigating the effects of physical
conditions(lighting) and instrument choice ( camera frame rate and
resolution)will have on the accuracy of the vectors from our
proposedexperiments.
The Mars Rover
Experiments Test Bed Sample Picture of Soil Simulant
Particle Image Velocimetry
Cross-Correlation 1 Cross-Correlation 2
Sample Image for Experiment Sample Vector Field
Pixel Values
