TY - JOUR
T1 - What information do Kármán streets offer to flow sensing?
AU - Akanyeti, Otar
AU - Venturelli, Roberto
AU - Visentin, Francesco
AU - Chambers, Lily
AU - Megill, William M.
AU - Fiorini, Paolo
PY - 2011/6/13
Y1 - 2011/6/13
N2 - In this work, we focus on biomimetic lateral line sensing in Krmn vortex streets. After generating a Krmn street in a controlled environment, we examine the hydrodynamic images obtained with digital particle image velocimetry (DPIV). On the grounds that positioning in the flow and interaction with the vortices govern bio-inspired underwater locomotion, we inspect the fluid in the swimming robot frame of reference. We spatially subsample the flow field obtained using DPIV to emulate the local flow around the body. In particular, we look at various sensor configurations in order to reliably identify the vortex shedding frequency, wake wavelength and downstream flow speed. Moreover, we propose methods that differentiate between being in and out of the Krmn street with >70% accuracy, distinguish right from left with respect to Krmn vortex street centreline (>80%) and highlight when the sensor system enters the vortex formation zone (>75%). Finally, we present a method that estimates the relative position of a sensor array with respect to the vortex formation point within 15% error margin.
AB - In this work, we focus on biomimetic lateral line sensing in Krmn vortex streets. After generating a Krmn street in a controlled environment, we examine the hydrodynamic images obtained with digital particle image velocimetry (DPIV). On the grounds that positioning in the flow and interaction with the vortices govern bio-inspired underwater locomotion, we inspect the fluid in the swimming robot frame of reference. We spatially subsample the flow field obtained using DPIV to emulate the local flow around the body. In particular, we look at various sensor configurations in order to reliably identify the vortex shedding frequency, wake wavelength and downstream flow speed. Moreover, we propose methods that differentiate between being in and out of the Krmn street with >70% accuracy, distinguish right from left with respect to Krmn vortex street centreline (>80%) and highlight when the sensor system enters the vortex formation zone (>75%). Finally, we present a method that estimates the relative position of a sensor array with respect to the vortex formation point within 15% error margin.
UR - http://www.scopus.com/inward/record.url?scp=80052529621&partnerID=8YFLogxK
U2 - 10.1088/1748-3182/6/3/036001
DO - 10.1088/1748-3182/6/3/036001
M3 - Article
C2 - 21670492
AN - SCOPUS:80052529621
SN - 1748-3182
VL - 6
JO - Bioinspiration and Biomimetics
JF - Bioinspiration and Biomimetics
IS - 3
M1 - 036001
ER -