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dc.contributor.author
Verma, Siddhartha
dc.contributor.author
Papadimitriou, Costas
dc.contributor.author
Lüthen, Nora
dc.contributor.author
Arampatzis, Georgios
dc.contributor.author
Koumoutsakos, Petros
dc.date.accessioned
2019-12-11T13:20:03Z
dc.date.available
2019-12-11T13:05:48Z
dc.date.available
2019-12-11T13:20:03Z
dc.date.issued
2020-02
dc.identifier.issn
0022-1120
dc.identifier.issn
1469-7645
dc.identifier.other
10.1017/jfm.2019.940
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/384771
dc.description.abstract
Natural swimmers rely for their survival on sensors that gather information from the environment and guide their actions. The spatial organization of these sensors, such as the visual fish system and lateral line, suggests evolutionary selection, but their optimality remains an open question. Here, we identify sensor configurations that enable swimmers to maximize the information gathered from their surrounding flow field. We examine two-dimensional, self-propelled and stationary swimmers that are exposed to disturbances generated by oscillating, rotating and D-shaped cylinders. We combine simulations of the Navier–Stokes equations with Bayesian experimental design to determine the optimal arrangements of shear and pressure sensors that best identify the locations of the disturbance-generating sources. We find a marked tendency for shear stress sensors to be located in the head and the tail of the swimmer, while they are absent from the midsection. In turn, we find a high density of pressure sensors in the head along with a uniform distribution along the entire body. The resulting optimal sensor arrangements resemble neuromast distributions observed in fish and provide evidence for optimality in sensor distribution for natural swimmers.
en_US
dc.language.iso
en
en_US
dc.publisher
Cambridge University Press
dc.subject
Swimming
en_US
dc.subject
Flying
en_US
dc.title
Optimal sensor placement for artificial swimmers
en_US
dc.type
Journal Article
dc.date.published
2019-12-10
ethz.journal.title
Journal of Fluid Mechanics
ethz.journal.volume
884
en_US
ethz.journal.abbreviated
J. Fluid Mech.
ethz.pages.start
A24
en_US
ethz.size
29 p.
en_US
ethz.grant
Fluid Mechanics in Collective Behaviour: Multiscale Modelling and Applications
en_US
ethz.identifier.wos
ethz.identifier.nebis
ethz.publication.place
Cambridge
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.::03499 - Koumoutsakos, Petros (ehemalig) / Koumoutsakos, Petros (former)
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00003 - Schulleitung und Dienste::00022 - Bereich VP Forschung / Domain VP Research::02803 - Collegium Helveticum / Collegium Helveticum
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00003 - Schulleitung und Dienste::00022 - Bereich VP Forschung / Domain VP Research::02803 - Collegium Helveticum / Collegium Helveticum
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.::03499 - Koumoutsakos, Petros (ehemalig) / Koumoutsakos, Petros (former)
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00003 - Schulleitung und Dienste::00022 - Bereich VP Forschung / Domain VP Research::02803 - Collegium Helveticum / Collegium Helveticum
ethz.grant.agreementno
341117
ethz.grant.fundername
EC
ethz.grant.funderDoi
10.13039/501100000780
ethz.grant.program
FP7
ethz.date.deposited
2019-12-11T13:05:57Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2019-12-11T13:20:16Z
ethz.rosetta.lastUpdated
2023-02-06T17:57:27Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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