Biomimetic Survival Hydrodynamics and Flow Sensing · onlineandin print.) Changes may still occur before final publication online and in print Annu. Rev. Fluid Mech. 2016.48. Downloaded

FL48CH01-Triantafyllou ARI 1 July 2015 13:56

RE V I E W

S

IN

AD V A

NC

E

Biomimetic SurvivalHydrodynamics and FlowSensingMichael S. Triantafyllou,1 Gabriel D. Weymouth,2

and Jianmin Miao3

1Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge,Massachusetts 02139; email: [email protected] Marine and Maritime Institute, University of Southampton, SO16 7QFSouthampton, United Kingdom3School of Mechanical and Aerospace Engineering, Nanyang Technological University,639798 Singapore

Annu. Rev. Fluid Mech. 2016. 48:1–24

The Annual Review of Fluid Mechanics is online atfluid.annualreviews.org

This article’s doi:10.1146/annurev-fluid-122414-034329

Copyright c© 2016 by Annual Reviews.All rights reserved

Keywords

biomimetics, maneuvering, flow control, sensors, robotics

Abstract

The fluid mechanics employed by aquatic animals in their escape or attackmaneuvers, what we call survival hydrodynamics, are fascinating becausethe recorded performance in animals is truly impressive. Such performanceforces us to pose some basic questions on the underlying flow mechanismsthat are not yet in use in engineered vehicles. A closely related issue is theability of animals to sense the flow velocity and pressure field around themin order to detect and discriminate threats in environments where vision orother sensing is of limited or no use. We review work on animal flow sensingand actuation as a source of inspiration and as a way to formulate a numberof basic problems and investigate the flow mechanisms that enable animalsto perform these remarkable maneuvers. We also describe some intriguingmechanisms of actuation and sensing.

1

Review in Advance first posted online on July 16, 2015. (Changes may still occur before final publication online and in print.)

Changes may still occur before final publication online and in print

Ann

u. R

ev. F

luid

Mec

h. 2

016.

48. D

ownl

oade

d fr

om w

ww

.ann

ualr

evie

ws.

org

Acc

ess

prov

ided

by

Mas

sach

uset

ts I

nstit

ute

of T

echn

olog

y (M

IT)

on 0

7/21

/15.

For

per

sona

l use

onl

y.


1. INTRODUCTION

The survival of marine animals depends on their capability of performing extremely rapid ma-neuvers, whether they escape predators, attack prey in or out of the water, or jump upstream inwaterfalls. Measured performance in live animals confirms the expectation that their function hasbeen perfected through evolution. One can think of few instances in which animal performance ismore striking, as fish 30 cm long can reach accelerations exceeding 10 times gravitational accel-eration in a fraction of a second, and small squids can reach burst speeds of 25 body lengths persecond. Such performance is based on underlying flow mechanisms that are not currently used inengineered vehicles. Another critical element for survival is the ability of animals to sense the ve-locity and pressure fields around them in order to detect and discriminate threats in environmentswhere other sensing is of limited use.

The intrinsic unsteadiness in the actuation of fish locomotion becomes a major asset whenlarge forces and moments must be generated rapidly in escape maneuvers, reinforced by theflexibility and compliance of the body, fins, and wings of the animals. The interplay betweenvorticity generation and imparting momentum, and hence kinetic energy, to the fluid to powerthe animals’ survival hydrodynamics is truly fascinating, as evolution has perfected its function. Inaddition to rapidly maneuvering fish, we consider the cephalopods, such as the octopus and squid,which make up a particularly intriguing class of animals because they incur large deformationsand volume changes, forming shapes that apparently violate engineering rules of streamlining, yetachieve escape speeds matching those of the fastest animals, despite their lack of a skeleton. Thecephalopods serve as a prime example of bodies that undergo rapid shape and volume changes,so that fluid energy can be recovered and boundary layer vorticity can be shed simultaneouslyfrom large sections of their surface, in what is termed global vorticity shedding, to power theirremarkable fast-starting maneuvers.

The ability of marine animals to detect their environment through pressure and velocity sensingis best exemplified by the blind cave fish, which darts through cluttered cave environments inabsolute darkness, and the harbor seal, which detects the minute fluctuations in the wake of preyup to 30 s after it has passed. Survival has dictated the development of such hydrodynamic sensing,which is totally absent from engineered structures. Again, we review work that poses some basicquestions: How detectable is a flow from a localized distributed array of sensors? How can wedetect flows down to 1 mm/s while moving at high speed? Overall, we selectively review workon animal flow sensing and actuation to investigate those unsteady flow mechanisms that areemployed by animals in survival hydrodynamics.

2. ESCAPE HYDRODYNAMICS

When fish or cephalopods attack or escape, they employ very large body and shape deformations,making the related flow-structure interaction problem radically different from that of rapidly ma-neuvering rigid-hull vehicles. The measured performance in marine animals in terms of achievinghigh acceleration very rapidly is impressive, particularly because it is achieved within a heavy liq-uid. The mechanics to develop the needed thrust require that the animal rapidly move a largemass of its surrounding water without incurring large drag and unrecoverable added mass forces.This simple remark helps us derive unifying principles of actuation.

2.1. Rapid Maneuvering Through Large Body Flexing

Fast-starting fish employ large-curvature flexing of their bodies to achieve impressive acceleration.Webb (1976) explored the fascinating fast-starting performance of the trout, establishing the

2 Triantafyllou ·Weymouth · Miao


Ann

u. R

ev. F

luid

Mec

h. 2

016.

48. D

ownl

oade

d fr

om w

ww

.ann

ualr

evie

ws.

org

Acc

ess

prov

ided

by

Mas

sach

uset

ts I

nstit

ute

of T

echn

olog

y (M

IT)

on 0

7/21

/15.

For

per

sona

l use

onl

y.


kinematics that lead to such accelerations (see review in Domenici 2011). There is a preparatoryphase in which the fish rapidly bends its body into a highly curved shape resembling either aletter C (C-start) or a letter S (S-start). This is followed by a propulsive phase in which the bodygradually straightens out, in a traveling wave-like fashion, starting with the head and ending withthe tail (Webb 1976), ultimately returning to its regular swimming mode. Harper & Blake (1991)and Domenici & Blake (1997) provided data for other species, especially the pike, an aggressivefish whose maximum acceleration is even more impressive than the trout’s, exceeding 10 timesgravitational acceleration. Fish that turn very rapidly while moving forward employ maneuversthat resemble fast starts in many aspects, except that the initial phase, employed by a fish at restto commence motion, is not needed (Epps & Techet 2007, Wu et al. 2007).

Simple dynamic considerations show that a fast-starting fish must rapidly accelerate as large amass of water as possible and move it in the proper direction. For a given required fluid impulse(i.e., a given product of fluid mass and velocity), the energy that will be conveyed to the fluid isproportional to the product of mass and velocity squared. Hence, it is advantageous to involvea larger fluid mass so as to reduce the imparted velocity. The acceleration of fluid also impliesthe generation of vorticity, to be conveyed to the fluid as required by the kinematics of theaccelerated flow. This vorticity must be generated at the body and fins of the fish, requiring acarefully choreographed motion driven by its muscles and fins working at full capacity and in aprecisely timed manner. Hence, the available muscular power defines the limits of performance(Wakeling & Johnston 1998), but the use of suitable hydrodynamic mechanisms is critical to allowthis maneuver to materialize. Figure 1 provides a sequence of particle image velocimetry (PIV)images of the vorticity in the flow around a turning danio. Initially, vorticity is confined to theboundary layer. As the fish moves, the vorticity entrains the kinetic energy imparted to the fluidthrough its large body flexing to form two vortex rings, a smaller one that powers the initiationof the turn and the rotation of the body and then a stronger ring that provides the required forceneeded for the rapid acceleration. Borazjani et al. (2012) computationally studied a much fasterC-start, obtaining qualitatively similar results, but identified further vortical structures.

When reviewing the literature on live fish, we must account for several complicating factorsgiven that an escape maneuver involves a stimulus from a predator and is part of a predator-preyinteraction problem (Weihs & Webb 1984). Hence, depending on the specific circumstances, thespecific body shape employed (C-shaped versus S-shaped) provides different kinematics, and theuse of fins can also vary, affecting performance (Weihs 1973, Tytell & Lauder 2008). However,some basic fluid mechanisms emerge irrespective of the specifics of actuation.

To begin, it is important to raise the question of whether hydrodynamic considerations arethe dominant factor in fast starts. To test this hypothesis, Gazzola et al. (2012) optimized throughsimulation a single type of maneuver (C-shaped) of a simply shaped fish (zebrafish larvae) thatinvolves only its body and caudal fin. The criterion was to maximize the motion of the fish’s centerof mass within a given fixed time for both the preparatory and propulsive phases. Because the timeis fixed, this criterion is also equivalent to maximizing speed or acceleration. The optimizedkinematics closely resembled the observed live fish kinematics provided in U.K. Muller et al.(2008), hence confirming the hypothesis that hydrodynamics dominate the kinematics of C-starts.

Simulation shed further light on the actuation mechanics. Gazzola et al. (2012) used tracerparticles to connect the flow features observed during the first phase of the maneuver, when theflow is largely irrotational, except within the boundary layer of the fish, with the vortical flowfeatures obtained in the wake of the fish at the latter half of the maneuver. The vorticity shed inthe wake flow was traced back to the surface of the body and caudal fin. More importantly, theauthors established that the mass of fluid, which is accelerated and captured by the body of the fishin the preparatory phase of the C-start maneuver, either is contained in or surrounds the vortical

www.annualreviews.org • Biomimetic Survival Hydrodynamics and Flow Sensing 3


Ann

u. R

ev. F

luid

Mec

h. 2

016.

48. D

ownl

oade

d fr

om w

ww

.ann

ualr

evie

ws.

org

Acc

ess

prov

ided

by

Mas

sach

uset

ts I

nstit

ute

of T

echn

olog

y (M

IT)

on 0

7/21

/15.

For

per

sona

l use

onl

y.


Vorticity (1/s)

–60 –40 –20 0 20 40 60

1 cm0 s 0.030 s 0.060 s

0.090 s 0.120 s 0.150 s

0.180 s 0.210 s 0.240 s

0.270 s 0.300 s 0.330 s

Γ2A

Γ1A

Γ2B

Γ1B



Ann

u. R

ev. F

luid

Mec

h. 2

016.

48. D

ownl

oade

d fr

om w

ww

.ann

ualr

evie

ws.

org

Acc

ess

prov

ided

by

Mas

sach

uset

ts I

nstit

ute

of T

echn

olog

y (M

IT)

on 0

7/21

/15.

For

per

sona

l use

onl

y.


a b

Vorticity fieldz component of ω

Particle evolution seeded att/Tp = 0.5

t /Tp = 0.13

1.04

0.43

1.61

0.71 t /Tp = 0.5 0.75

1.0 1.252.15

Figure 2(a) Vorticity field (z component of ω) in six snapshots of the C-start maneuver of a larval fish, showing the formation of the actuatingvortex rings. (b) Evolution of passive tracer particles seeded at t/Tp = 0.5. Three sets of particles (shades of gray) are initialized atdifferent regions and are overlaid on the vorticity field. Figure adapted with permission from Gazzola et al. (2012).

patterns of the wake flow (Figure 2). The irrotational-like flow at the preparatory phase, consistingof dipole-like structures at any particular planar cut that is perpendicular to the mid-line curve(as shown in Zhu et al. 2002), transitions eventually to form two vortex rings. Hence, a principalconclusion of Gazzola et al. (2012) is that the strikingly large initial curvature of the C-startmaneuver serves to engage as much fluid mass as possible: The body motion conveys significantkinetic (added mass–related) energy, which is then shed in the fluid together with body-generatedvorticity to form two vortex rings powering the C-start. Hence, the initial potential flow-like addedmass energy is used ultimately as a propellant in this C-start maneuver (Triantafyllou 2012), amechanism that is also employed under different conditions by cephalopod fast starts, as shownin the next section.

Animals may employ their fins in an effort not only to better control the C-start but alsoto increase the kinetic energy imparted to the flow during body bending. Figure 3 provides thekinematics of a bluegill sunfish, which employs its fins during a fast start. The two phases, prepara-tory and propulsive, produce peaks in the acceleration curve, which remains positive throughoutthe maneuver (Chadwell et al. 2012). Conte et al. (2010) tested a robotic device shaped like apike by starting from a highly curved, C-like configuration, storing sufficient potential energy todrive a fast escape when released. With the preparatory phase omitted that would have impartedsignificant added mass energy, the acceleration fluctuates initially, even reaching negative values

←−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−Figure 1Sequence of vorticity field snapshots obtained through particle image velocimetry at the mid-height planarcuts in the C-start of a danio fish. Vortices marked �1A and �1B are cuts of the first vortex ring, and �2A and�2B are cuts of the second vortex ring. Figure adapted with permission from Epps & Techet (2007).



Ann

u. R

ev. F

luid

Mec

h. 2

016.

48. D

ownl

oade

d fr

om w

ww

.ann

ualr

evie

ws.

org

Acc

ess

prov

ided

by

Mas

sach

uset

ts I

nstit

ute

of T

echn

olog

y (M

IT)

on 0

7/21

/15.

For

per

sona

l use

onl

y.


a60

40

20

Dis

pla

cem

en

t (m

m)

0

b1.5

1.0

0.5

Ve

loci

ty (

m/s

)

0

c60

40

20

–20–10 0 20 3010 60

Acc

ele

rati

on

(m

/s2)

Time (ms)

S1preparatory

S2propulsive

DS1

DS2

vmax

amax2amax1

T0

0

40 50

Figure 3(a) Displacement, (b) velocity, and (c) acceleration as a function of time in the two phases (S1, preparatory;S2, propulsive) of a C-start for a bluegill sunfish, which also employs its fins. Figure adapted with permissionfrom Chadwell et al. (2012).

(Figure 4), unlike the acceleration of a fast-starting fish. Still, the robot achieved a peak accel-eration of 40 m/s2, converting 10% of its initial energy to final kinetic energy, and the resultingkinematics are similar to those of fish.

2.2. Fast Starts Through Volume Change

Another group of animals that undergo large-scale deformations during escape maneuvers com-prise the cephalopods: the octopus, cuttlefish, and squid. The octopus first hyperinflates its mantlecavity, filling it with water, which it then rapidly expels in the form of a propelling jet throughthe mantle orifice (Packard 1969, Wells 1990, Huffard 2006). As in the fast starts of fish discussedabove, there is a major advantage to accelerating as large a mass of fluid as possible. Hence, the bodyinflation at the onset of the maneuver must be correspondingly large, increasing the lateral dimen-sions substantially. The normally streamlined mantle becomes quite bluff; flow around a similarlyshaped rigid-body geometry would incur large energy penalties in the form of flow separation dragand increased added mass force. However, the flexible, rapidly deflating mantle completely altersthe dynamics of the flow, inducing mechanisms of separation elimination and added mass energyrecovery, as investigated by Weymouth & Triantafyllou (2013) and demonstrated experimentallyby Weymouth et al. (2015).

The core issue in determining the propulsive performance of the shrinking body is the evo-lution of the boundary layer vorticity at the external surface of the body as it undergoes largedeformations. If this vorticity is shed into the flow, a drag wake will be established surrounding



Ann

u. R

ev. F

luid

Mec

h. 2

016.

48. D

ownl

oade

d fr

om w

ww

.ann

ualr

evie

ws.

org

Acc

ess

prov

ided

by

Mas

sach

uset

ts I

nstit

ute

of T

echn

olog

y (M

IT)

on 0

7/21

/15.

For

per

sona

l use

onl

y.


0 10 20 30 40 50 60 70 80− 0.2

0

0.2

0.4

0.6

0.8

1.0

1.2

Time (ms)

Robot

Fish

Dim

en

sio

nle

ss a

cce

lera

tio

n

Figure 4Experimentally measured acceleration in a fast-starting robot (solid red line) versus acceleration measured inlive fish (dashed blue line; Harper & Blake 1991). Accelerations have been nondimensionalized by theirmaximum values. Figure adapted with permission from Conte et al. (2010).

the propulsive jet emanating from the orifice, hence severely reducing the escape speed of theanimal.

We first turn to the example of a shape change leading to vortex formation in Taylor’s (1953)classic study of a vanishing disk. The solid disk rapidly melts into fluid, and the bound vorticityis assumed to roll up into a single vortex ring. Weymouth & Triantafyllou (2012) used viscoussimulations to demonstrate that this assumption is mostly borne out in the case of a meltingcylinder, whose diameter is reduced by 20%, and the resulting pair of vortices induces large dragforces. They also found that the flow can be completely different depending on the form of theprescribed surface kinematics. If the body, instead of melting, collapses by undergoing a rapidprescribed shape change, as is more relevant to the octopus, then the vortex layer does not shedand roll up. Instead, it remains close to the body, while its total circulation decreases, as the bodyshrinks, through vorticity annihilation (Figure 5). As a result, instead of a purely resistive addedmass force and separation drag, rapid shrinking causes an additional added mass–related term inthe form of positive thrust, through added mass kinetic energy recovery, and the near eliminationof viscous drag.

Both the circulation reduction and thrust force result from the pressure gradient on the body,generated by rapidly reducing its volume. Let us consider the pressure distribution on the surfaceof a sphere of instantaneous radius R, translating at speed U in a potential flow:

p |r=R = 32ρ

(RU + UR

)cos θ + 9

16ρU2 cos 2θ + C(t), (1)

where C(t) is a constant over the surface and does not contribute to the force or generation ofvorticity (Weymouth et al. 2015). For a rapidly shrinking body, the first term implies very lowpressure at the front of the sphere (θ = 0) and very high pressure at the back (θ = π ). The two-dimensional form of this estimate was shown to be remarkably close to the viscous simulationsof Weymouth & Triantafyllou (2012). This gradient generates a new sheet of vorticity on thesurface of the sphere that lies directly under and has opposite sign than the boundary layer vorticity



Ann

u. R

ev. F

luid

Mec

h. 2

016.

48. D

ownl

oade

d fr

om w

ww

.ann

ualr

evie

ws.

org

Acc

ess

prov

ided

by

Mas

sach

uset

ts I

nstit

ute

of T

echn

olog

y (M

IT)

on 0

7/21

/15.

For

per

sona

l use

onl

y.


1 0.75 0.5 0.25 0–0.6 –0.4 –0.2 0 0.2

–6.0 –0.6 0.06

Vorticity (ωU/D)

–0.06 0.6 6.0

0.4 0.6− 40

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

− 2

0

2

4

6

8

10

θ/π(x – xc)/D

y/D

Pre

ssu

re (p

– p

π)ρ

U2

0.30

Time(tU/D)

0.29

0.310.28

0.32

0.27

0.33

0.260.340.250.35

b

a

Figure 5(a) Vorticity field from viscous simulations of a circular cylinder whose diameter shrinks rapidly by 20% as it moves from right to left.(b) The plot of the pressure relative to the stagnation point value on the cylinder surface (leading edge at left, trailing edge at right) atseveral snapshots in time, showing a large, thrust-producing pressure gradient. The pressure gradient generates a sheet of opposite-signvorticity (red in back face of cylinder in panel a) under the previously formed boundary layer (blue in panel a), resulting in vorticityannihilation. The pressure gradient generates thrust that is an order of magnitude larger than the steady drag on a rigid body. Figureadapted with permission from Weymouth & Triantafyllou (2012).

formed previously during the onset of translation. The induced normal velocity, pointing towardthe surface, forces these two layers against each other, partially annihilating the boundary layercirculation. Recent experiments on flat plates recorded reduced circulation when rapidly changingthe apparent cross-stream width of the body (Kriegseis et al. 2013), which may arise from the samemechanism.

The pressure gradient also induces a thrust force on the body that transfers energy betweenthe fluid and body, of the form

Fa = − ddt

(maU ) = −maU − maU ,

where ma is the instantaneous added mass of the object. Solid bodies accelerating in a fluid ex-perience an opposing force −maU, but a shrinking body recovers fluid energy to develop thrustequal to −maU, a term that dominates the overall force at the end of the maneuver. Consequently,larger initial added mass inertia is actually beneficial in the case of fast starts, as long as that inertiais recovered and released during the maneuver. The added mass is fixed for a rigid vessel, makingit an additional payload to be dragged along during the escape. In contrast, when the added massenergy is recovered and released, as done in fish by bending and in cephalopods by shrinking, thebody effectively turns the original added mass into a propellant. Spagnolie & Shelley (2009, p. 12)also showed this when a moving sphere undergoes a shape change to become a prolate ellipsoid,resulting in a sudden increase, or burst of velocity, as the added mass momentum turns into bodymomentum: “While a reduced virtual (added) mass gives a reduced acceleration reaction, a reduc-ing virtual mass can generate a boost in velocity.” For a self-propelled deflating body, the recoveryof fluid kinetic energy in inviscid flow can accelerate it to speeds 82% greater than a rigid rocket,and even 20% greater than an ideal rocket in vacuum (Weymouth et al. 2015).



Ann

u. R

ev. F

luid

Mec

h. 2

016.

48. D

ownl

oade

d fr

om w

ww

.ann

ualr

evie

ws.

org

Acc

ess

prov

ided

by

Mas

sach

uset

ts I

nstit

ute

of T

echn

olog

y (M

IT)

on 0

7/21

/15.

For

per

sona

l use

onl

y.


As implied by the pressure equation, the relative strength of this effect and the successful controlof the viscous boundary layer are dependent on a nondimensional shape-change rate to overcomethe rate of diffusion and separation in the boundary layer. As shown by Weymouth et al. (2015), itis insightful to compare a shrinking deformable body to the application of suction on a rigid-bodyboundary layer, as both processes induce a normal velocity to the body surface, although shrinkinginduces it without actual mass flow through the membrane. The authors defined a deflation scalingparameter, σ ∗, in analogy to the suction scaling parameter of a cylinder:

σ ∗ = VAU

√Re, (2)

where V is the rate of change of the volume of the body, the Reynolds number is defined asRe = UL/ν, and A = πD2

/4 is the frontal area. For a sphere, an estimated threshold value ofσ ∗ is 2.41π . Hence, the required deflation rate decreases with the Reynolds number, which wasconfirmed by viscous simulations and experimental testing of a robot (Weymouth & Triantafyllou2013, Weymouth et al. 2015). As shown in Figure 6, the robot experiences an instantaneous thrustforce that is 30% greater than the thrust provided by the propulsive jet (and avoids a 50% reductionin the net force owing to drag forces, as in a rigid body), resulting in a fast-start maneuver with53% hydrodynamic efficiency. Other soft and deforming animals and mechanisms may employsimilar mechanisms to help with their efficient periodic propulsion.

0 0.5 1.0 1.5Time (s)

Rigidrobot

Thrust >jet thrust aloneDeflating

robot

t = 0 s

L = 0.27 m 7.7L

0.52 s 0.72 s 0.86 s 0.98 s 1.14 s 1.31 s

0 0.5 1.0 1.5Time (s)

a

b c d

0

0.2

0.4

0.6

0 0

0.5

1.0

1.5

0 0.5 1.0 1.5Time (s)

ΣFFj

KEPΔtU (L/s)

10.0

7.5

5.0

2.5

12.5

Figure 6Self-propelled tests of an octopus-inspired robot of length L = 0.27 m, consisting of a three-dimensional printed rigid hull with asynthetic rubber membrane stretched over it that is inflated to power a fast start as it subsequently deflates, generating a propulsive jet.(a) Sequential snapshots of the fast start of the deflating robot, marked with time. (b) The robot achieves speeds of 10L/s in 1 s (the blueline is the average of 10 runs). (c) Fluid force as a function of time, divided by the force developed by the jet. The robot experiencesmore thrust than that provided by the jet (force ratio greater than 1, shown by the horizontal dotted line) due to added mass energyrecovery. The red line is the fluid force on a rigid, streamlined robot under similar conditions. (d ) Integrated power as a function oftime, showing that over 50% of the initial energy is recovered. Figure modified with permission from Weymouth et al. (2015).



Ann

u. R

ev. F

luid

Mec

h. 2

016.

48. D

ownl

oade

d fr

om w

ww

.ann

ualr

evie

ws.

org

Acc

ess

prov

ided

by

Mas

sach

uset

ts I

nstit

ute

of T

echn

olog

y (M

IT)

on 0

7/21

/15.

For

per

sona

l use

onl

y.


2.3. Unifying Principles in Fish and Cephalopod Fast Starts

Fish employing C-starts and cephalopods escaping through the rapid deflation of their initiallyhyperinflated mantle are found to use the same principles. Fish engage in large-curvature maneu-vers to impart large kinetic (added mass) energy to the fluid, which is entrained and released inthe vortex rings that power their maneuvers. Similarly, escaping cephalopods are initially bluff,imparting large kinetic (added mass) energy to the fluid, which they recover as they deflate rapidlyto produce thrust in the later stages of their maneuvers, when it is most useful, because their massis substantially less as they have expelled fluid from their mantles.

However, this performance is conditional on avoiding uncontrolled flow separation. In thecase of fish, a highly choreographed motion ensures that they do not create large angles of attackwith respect to the flow. In the case of cephalopods, a more intricate mechanism of separationelimination is employed as deflation must be rapid enough to create opposite-sign vorticity topartially eliminate the boundary layer vorticity and impose a normal velocity component to thebody surface that prevents separation. Both components are equally important so that the fluidkinetic energy is recovered by the body rather than shed into the fluid together with the excessboundary layer vorticity.

3. FLOW SENSING IN AQUATIC ANIMALS AND MARINE VEHICLES

For fish survival, timely and accurate sensing is as important as agile actuation, whether it is toavoid predators or to detect prey. For example, C-starts can generate strong and long-lasting flowsignatures (Niesterok & Hanke 2012), and swimming fish can be tracked via their wake long afterthey have passed. Although vision, hearing, and smell are also employed, many aquatic animalspossess the unique ability to detect prey or predators and even form a three-dimensional map ofthe surroundings by sensing the velocity and pressure fields through multiple sensors distributedalong their bodies. We first selectively review the literature on the physiology (structure, size,location, and density of the sensors) and reported function of the animal sensors to assess thefeasibility and limitations of flow sensing and then review the efforts to replicate this function withengineered sensors.

3.1. Flow Sensing: The Lateral Line of Fish

Detailed, excellent recent publications provide rich information on the function of animal sensing(Bleckmann et al. 2014, Coombs et al. 2014). Fish utilize cues from their lateral line system,so named because its most visible part is a line of scales running down the side of the fish, tolocate predators or prey and underwater objects and form schools (Pitcher et al. 1976, Chagnaud& Coombs 2014, Webb 2014b). The capabilities of this organ are best exhibited by the blindcave fish, Astyanax mexicanus fasciatus, because of its lack of functional eyes. Cave fish, typicallyup to 10 cm in length, live in dark caves and have atrophied eyes but are capable of forming athree-dimensional map of their surroundings by sensing flow velocity and pressure variations (vonCampenhausen et al. 1981, Montgomery et al. 2001).

The sensory units of the lateral line, the neuromasts, are spread across large portions of thesurface of the fish’s body. Each neuromast sensor consists of hair cells that are embedded ina cupula containing a soft gelatinous material. Each hair cell consists of a long kinocilium andan attached bundle of stereocilia, shorter and graded in height. The hair cells are connected tothe afferent fibers at the base and form the principal sensing element. There are two types ofneuromasts: superficial neuromasts (SNs), which are freestanding on the surface of the skin and



Ann

u. R

ev. F

luid

Mec

h. 2

016.

48. D

ownl

oade

d fr

om w

ww

.ann

ualr

evie

ws.

org

Acc

ess

prov

ided

by

Mas

sach

uset

ts I

nstit

ute

of T

echn

olog

y (M

IT)

on 0

7/21

/15.

For

per

sona

l use

onl

y.


100 μm

Cupularfibrils

EXTERNALFLOW

a b

Hair cells

Figure 7(a) Electron micrograph of juvenile cichlid fish with a body length of ≈11 mm, showing lateral line canals with large pores, presumptivecanal neuromasts (short red arrows) under the eye, which will be enclosed in canals when the fish is older, and superficial neuromasts(double yellow arrows). The opening between the black arrow heads on the snout is the nostril. Panel a taken with permission from Webb(2014b). (b) Microscopic image of an array of cupulae of superficial neuromasts on the body of a fish, dyed with methylene blue. Panel btaken with permission from McConney et al. (2009b). (c) Schematic illustration of a superficial neuromast and cupula subject to flow.Panel c adapted with permission from Kottapalli et al. (2013).

exposed to the flow (Figure 7b), and canal neuromasts (CNs), which are embedded inside lateralline canals (Abdel-Latif et al. 1990, Montgomery et al. 1997, Montgomery et al. 2001, Coombset al. 2014, Webb 2014a).

There are differences between SNs and CNs stemming from their distinct morphology, such asthe difference in the shape of the cupula. For example, a CN has a hemispherical cupula hundredsof micrometers in diameter, whereas the SN cupula is oval shaped, 10–60 μm wide, and 50–400 μmhigh. Additionally, SNs typically contain roughly 10 hair cells, whereas CNs contain hundreds,or even sometimes thousands, of hair cells (van Netten 2006). The cupula of an CN is found toslide on its epithelium like a rigid body resisted by spring-like hair bundles, which is unlike theelongated cupula of an SN, which bends in the flow like a beam. Both SNs and CNs contain twogroups of oppositely oriented hair cells that are spatially intermingled.

SNs have high sensitivity for flow sensing and can even sense the flow created by planktonicprey in water. At a frequency of approximately 100 Hz, a displacement of 1 nm is sufficient to causea neural response, whereas displacements greater than 100 nm cause the saturation of individualhair cells (van Netten 2006).

CNs are embedded in subdermal canals and are exposed to the flow through pores (Figure 7a),whose walls are formed by rigid structures, such as bones or scales. The canal lateral line systemcomprises a trunk canal and cephalic canals. The trunk canal typically consists of one or multiplelinear canals that run laterally down each side of the body, whereas the cephalic canals are complexand form a three-dimensional pattern around the head (Webb 2014a). A single neuromast isembedded between two pore openings, actuated only when there is a pressure difference betweenthe consecutive pores. The motion of water inside the canal is impeded by the inertia of waterand the friction offered by the canal walls. At low frequencies, the friction generated by the wallsof the canal is dominant (Montgomery et al. 1995), whereas at high frequencies the mass offluid in the canal acts as a low-pass filter. As outlined by van Netten (2006), the mass of the CNcupula and the stiffness induced by the large number of hair cells result in a resonance frequencybelow 100 Hz, whose ultimate effect is to render the sensor sensitivity flat up to a frequency ofroughly 100 Hz. The CN acts effectively as a band pass filter of oscillatory pressure gradients.



Ann

u. R

ev. F

luid

Mec

h. 2

016.

48. D

ownl

oade

d fr

om w

ww

.ann

ualr

evie

ws.

org

Acc

ess

prov

ided

by

Mas

sach

uset

ts I

nstit

ute

of T

echn

olog

y (M

IT)

on 0

7/21

/15.

For

per

sona

l use

onl

y.


That neuromasts are concentrated at locations where changes in pressure are greatest duringmotion through water, while their morphology changes as fish develop through the embryonic,larval, and juvenile stages, points to the continuous optimization of the location of these sensorsfor best flow sensing (Webb 2014a, Ristroph et al. 2015).

The lateral dimension of the SN is of the order of 100 μm, so for flow speeds up to 1 m/s, theReynolds number is below 100 in open flow. Hence, accounting for the effect of the boundarylayer thickness, which is typically larger than the SN height, the relevant Reynolds number iseven smaller (McHenry & Liao 2014). As a result, the force on the SN is largely viscous, and it isunlikely for the neuromast to develop a Karman street, whose unsteady fluctuations would causeconsiderable measurement noise. Similarly, the internal flow in the canal due to external pressuregradients is likely to be far smaller than 1 m/s; hence a CN is also not expected to develop anunsteady wake, despite its larger size. It is estimated that, as a lower threshold, CNs can respondto internal velocities down to 1–10 μm/s or accelerations of 0.1–1 mm/s2 and SNs to internalvelocities down to 25–60 μm/s (van Netten 2006, Rapo et al. 2009).

3.2. Forming Three-Dimensional Images Through Near-Body Flow Sensing

The lateral line is known to help fish perform complex sensing tasks, such as detecting threats tospark C-starts, as well as assisting in Karman gaiting through vortex detection (Liao 2006, 2007).The central question is on the possibility of forming a three-dimensional map of the flow, at leastin the vicinity of the fish, through velocity and pressure measurements obtained using multiplesensors spread over a specific area of a moving body, such as the SNs and CNs of the lateral lineof fish. Montgomery et al. (2001) determined that while SNs in cave fish detect flow patterns overthe surface of the body, the CNs’ most important role is in active hydrodynamic imaging, creatinga cognitive three-dimensional map of their environment through continuous swimming aroundobstacles so as to generate strong pressure gradients.

3.2.1. Flow reconstruction: inviscid theory. For two-dimensional inviscid flow, it can be shownthat a body moving in prescribed motion around another body can use distributed measurementsobtained over some time period to create a complete map of the flow, provided the two bodiesare in relatively close proximity, of the order of one body length or less (Sichert et al. 2009,Bouffanais et al. 2010); the presence of an external flow makes identification easier. Bouffanaiset al. (2010) analyzed the mapping of an environment via pressure signals, first to determine thepressure signal of a given obstacle shape and then to find the location and shape of a body given itspressure signal. They found that insufficient information is provided about the shape by a pressuresignal at a single location. The information regarding the size, orientation, and position decaysproportionally to 1/d2, 1/d2, and 1/d3, respectively, where d is the distance between the sensor andthe body. An unscented Kalman filter was used with noisy pressure signals, obtained as the sensormoved around an object, to detect the position and shape of the pressure-generating body. Theauthors found that the fish had to be within a distance of 6D, where D is the size of the object, todetect size and within 3D to detect position. Obtaining any further details of the shape requiresan even shorter distance.

Windsor et al. (2010) studied the effects of a fish gliding parallel to a wall in close proximityand found that detection is possible at distances less than 10% of the length of the fish. Gao &Triantafyllou (2012) used pressure sensor arrays in a maneuvering underwater vehicle to detectthe flow direction using unscented Kalman filtering and used the flow measurements in a feedbackloop to control its orientation. A critical element in the controller during vehicle linear or angular



Ann

u. R

ev. F

luid

Mec

h. 2

016.

48. D

ownl

oade

d fr

om w

ww

.ann

ualr

evie

ws.

org

Acc

ess

prov

ided

by

Mas

sach

uset

ts I

nstit

ute

of T

echn

olog

y (M

IT)

on 0

7/21

/15.

For

per

sona

l use

onl

y.


acceleration was to subtract the pressure caused by the vehicle’s own motion (added mass–relatedpressure).

3.2.2. Flow reconstruction: viscous effects. In viscous flows, potential flow methods becomeinvalid once flow separation occurs, and there are no direct methods for object identification. Afterfirst identifying the principal features of the vortical wake of several bluff cylinders by performing aprincipal component analysis on a set of training data, Fernandez et al. (2011b) used a streamlinedbody instrumented with an array of seven commercially available pressure sensors to estimate theshape, position, and velocity of any one of the bluff cylinders translating in close proximity. A cleardifferentiation could be made between a circular cylinder and a square cylinder, for example.

Bleckmann & Zelick (1993) towed objects in linear or slightly curved paths very close (1 cm) toelectric fish. Responses increased with object speed, eventually showing saturation. As the distanceincreased, the response decayed as a power function of distance. A rectangular object on a circularorbit was towed at various speeds near a catfish (H.M. Muller et al. 1996) and close to a goldfish(Mogdans & Bleckmann 1998), eliciting responses that increase with the speed of towing. Theperipheral lateral line units of the goldfish responded with predictable patterns. Plachta et al.(2003) towed a sphere 8 mm in diameter at constant speed close to a goldfish and found threetypes of response. First, some lateral line units (likely to be SNs) responded maximally when watervelocities across the surface of the fish increased owing to sphere movement. Second, other unitsresponded only when the sphere passed a certain location on the fish’s head or body, but did notpositively correlate with the water motions at that location (likely to be CNs). Third, other unitsresponded only after the sphere had passed the fish.

Fish receive stimuli not only from external disturbances, but also from flow disturbances causedby their own motion. This requires two strategies. First, fish must switch off sensor input in theabsence of external stimuli, and second, they must be capable of either storing self-made flownoise patterns, so as to subtract them from the total sensory input, in the same way they learnto discriminate between objects (von Campenhausen et al. 1981), or engaging their brain whenactively swimming to subtract flow noise using the motor excitation as a reference. As expected,an actively flapping fish generates more noise; hence, fish have a sensory advantage when gliding.

Fish are known to wander about obstacles to detect their shape (von Campenhausen et al. 1981).Fernandez et al. (2011a) employed potential flow methods and unscented Kalman filters to exper-imentally detect the position and shape of a cylinder or ellipse using noisy pressure measurementsobtained over a period of time at three locations on a streamlined foil gliding at constant speed inclose proximity. As shown in Figure 8a,b, a first pass by the foil allowed a good first approximationof the location and size of the cylinder, and then a second pass allowed a very accurate estimateof both. However, once the foil went past the cylinder, strong vortical structures emanated fromthe foil owing to hydrodynamic interaction with the cylinder, causing large pressure oscillationsin the measurements (Figure 8c).

For simple objects, pressure measurements made up to the time at which the flow separatesmay or may not be sufficient for detection, and this is generally a serious limitation for invis-cid methods. However, Maertens & Triantafyllou (2014) showed that the dominant frequencyand wavelength of these unsteady vortical structures, whose effect on the pressure can be seenin Figure 8c beyond the vertical dotted line, develop as a result of an instability of the av-erage shape of the boundary layer profile of the foil and hence can be predicted using insta-bility theory. This instability amplifies the potential flow-like disturbances caused in the frontpart of the foil. Because the form of these disturbances is specific to the cylinder shape thatgenerated them, they can be further used to identify its shape. The findings support the in-triguing possibility that the significant unsteady viscous effects caused by nearby bodies on the



Ann

u. R

ev. F

luid

Mec

h. 2

016.

48. D

ownl

oade

d fr

om w

ww

.ann

ualr

evie

ws.

org

Acc

ess

prov

ided

by

Mas

sach

uset

ts I

nstit

ute

of T

echn

olog

y (M

IT)

on 0

7/21

/15.

For

per

sona

l use

onl

y.


1

2

3

123

123

123123

123

123

123

123

–0.05

0

0.05

0.10

Po

siti

on

(m

)

Cp'

Time (tU/c)

x/c > 0.20; inviscid predictions fail

0

0.05

0.10

0.15

0.20

0

0.05

0.10

0.15

0.20

0.2

0.1

–0.20

–0.40

–0.020

0.020.040.060.080.100.12

–0.15–0.15 –0.10–0.10 –0.05–0.05 00 0.05–0.02

00.020.040.060.080.100.12

–0.020

0.020.040.060.080.100.12

–0.15 –0.10 –0.05 0–0.10–0.10–0.10

SENSORS

SensorPrediction

True

Estimated

Data

SensorSensor

–0.20 0.10 000–0.02

00.020.040.060.080.100.12

–0.20–0.20

0

0.05

0.10a iii b

c

iii iv

i ii

iii iv

Position (m)

–0.15 –0.10–0.10 0.10

–0.5 0 0.5

–0.20 –0.05 00

Figure 8(a) Sequential snapshots of experimental shape identification of an elliptical cylinder using three pressure sensors placed on a glidingfoil ( gray streamlined shape) of chord c = 10 cm, moving at velocity U. The dots on the foil denote sensors: Sensor 1 (orange) is near thenose, followed by sensors 2 (dark yellow) and 3 (red ). The dotted blue lines denote the true position and shape, and the solid green linesrepresent the estimated position and shape. (b) Same experiment repeated using the results from the experiment shown in panel a as afirst guess. Panels a and b adapted with permission from Fernandez et al. (2011b). (c) Pressure measurements from the three sensors as afunction of the relative position between the foil leading edge and cylinder right edge, x/c. Note the large oscillations beyond x/c =0.20 (vertical dotted line), when the cylinder is above the middle of the foil. Panel c adapted with permission from Maertens &Triantafyllou (2014).

fish boundary layer, far from preventing detection, could be used by animals to better identifyobjects.

3.2.3. Tracking swimming fish: the pulsating dipole analogy. At relatively small distancesaway from a vibrating, constant-volume, flexible body, the leading hydrodynamic term is thatof an oscillating-strength dipole. Hence, the pulsating dipole flow generated by a harmonicallyvibrating rigid sphere has become the canonical model for studying the detection of swimmingfish or zooplankton when their wakes are not involved. Mottled sculpin approximately 10 cm inlength, for example, will strike at a vibrating sphere in the dark when placed within a distanceroughly equal to their length (Coombs & Conley 1997).



Ann

u. R

ev. F

luid

Mec

h. 2

016.

48. D

ownl

oade

d fr

om w

ww

.ann

ualr

evie

ws.

org

Acc

ess

prov

ided

by

Mas

sach

uset

ts I

nstit

ute

of T

echn

olog

y (M

IT)

on 0

7/21

/15.

For

per

sona

l use

onl

y.


Montgomery et al. (2001) found that SNs in cave fish detect flow patterns over the surface ofthe body and can help in orienting the fish within a current (rheotaxis) and possibly in localizingsmall vibrating sources, although CNs play the principal role in detecting small prey. Hassan(1993) showed that the flow generated by a vibrating sphere near the cast model of a blind fishhas characteristic patterns that can be detected by distributed SNs, provided that the (potentiallystrong) effects of the boundary layer are accounted for, and by distributed CNs if the effect of theorientation of the canal relative to the position of the sphere has been accounted for. Curcic-Blake& van Netten (2006) used a Teflon sphere with 5-mm diameter vibrating in a direction roughlyparallel to a ruffe lateral line canal, 10 cm in length, at a frequency of approximately 70 Hz, placedat distances of the order of 10 mm. The hydrodynamic signals detected by the lateral line havesharply characteristic shapes as a function of length, allowing the precise detection of the sphere’slocation, and the amplitude and frequency of oscillation. There is a lower detection threshold,estimated at 2Ds, where Ds is the distance between two adjacent pores, and an upper distancethreshold estimated at L/

√2, where L is the length of the lateral line canal. Consistent with the

idea of detecting the pulsating sphere through its spatial signature, striking fish employ strategiesthat keep the sphere lateralized (Coombs & Conley 1997).

Rapo et al. (2009) studied the flow effects on a fish-like body caused by an oscillating sphere.The unsteady boundary layer on the fish has a typical thickness of 80 μm at 80 Hz and alters themagnitude and phase of the velocity within this distance. They found that the potential flow canbe used to calculate relatively accurately the pressure gradient patterns to the lateral line system,but not the velocity field, because of the oscillatory boundary layer on the skin of the fish.

3.2.4. Tracking vortical structures with neuromasts. Fish generate strong vortices and formvortical wakes and jets for their propulsion and maneuvering. Hence, tracking other fish or formingschools requires the detection of vortical structures. The characteristic low-pressure signature ofa vortex makes it easily detectable through pressure sensing. Thus, it is anticipated that this isa function primarily for the CNs, although SN arrays can also assist. Additionally, for a fish tomove or position itself stably within vortical flows, it must sense the oncoming vortices. Troutplaced behind a bluff cylinder are capable of extracting energy from the oncoming wake (Liao et al.2003) by slaloming through the Karman street vortices; hence, they must be capable of sensingvortices. Beal et al. (2006) showed that with pressure sensors, it is possible to extract net energyfor self-propulsion from a Karman street using a flapping foil. Liao (2006) studied the effect ofvortical flows on the lateral line of trout, finding that the lateral line plays a greater role than visionin holding station in a Karman street.

Chagnaud et al. (2006) stimulated the lateral line of a goldfish with a vortex ring that movedlaterally and at a close distance to the fish. They concluded that fish might be able to extractsensory information from complex stimuli such as vortices by comparing the activity of a wholearray of neuromasts.

Several authors have established the feasibility of tracking vortices and vortical wakes: Yang et al.(2006) for vortical wakes and Ren & Mohseni (2012) for regular or reverse Karman vortex streetsusing CN-like sensors distributed over the surface of a body, which provide sufficient informationto extract the circulation of individual vortices, the wavelength of the Karman street, the distancebetween the body and the fish h, and the speed of propagation of the street. Similarly, using an arrayof pressure sensors, Akanyeti et al. (2011), Venturelli et al. (2012), Salumae & Kruusmaa (2013),and Chambers et al. (2014) demonstrated the feasibility of extracting features from the flow, suchas the vortex shedding frequency, traveling speed, wake wavelength, and turbulence intensity.

Fernandez et al. (2011a) determined the strength and position of a free vortex pair translatingnear a streamlined body using sparse pressure sensor measurements and a potential flow model



Ann

u. R

ev. F

luid

Mec

h. 2

016.

48. D

ownl

oade

d fr

om w

ww

.ann

ualr

evie

ws.

org

Acc

ess

prov

ided

by

Mas

sach

uset

ts I

nstit

ute

of T

echn

olog

y (M

IT)

on 0

7/21

/15.

For

per

sona

l use

onl

y.


2

4

1

3

4 3 2 1

Po

siti

on

(m

m)

Vorticity(1/s)

Position (mm)

LEV

Vorticity from

particle image velocimetry

150 200 250 300 350

−300

−250

−200

−150

−100

−50

−40

−20

0

20

40

60

80

100

120

11 12 13 14 15 16

−250

−200

−150

−100

−50

0

50

100

150

Pre

ssu

re (

Pa

)Time (s)

Filtered pressure sensor output

SENSORS

a b

Figure 9Vortex detection on a stalling foil at an angle of attack of 35◦, translating at 0.3 m/s, equipped with four pressure sensors (colored dots onthe foil). (a) Vorticity field obtained via particle image velocimetry. A shed leading edge vortex (LEV) is clearly visible adjacent to thesecond sensor. (b) Time trace of the pressure at the sensors, starting from the leading edge: sensor 1 (orange), 2 (dark yellow), 3 (red ), and4 ( purple). The gray vertical dotted line corresponds to the time at which the image in panel a was taken. Figure adapted withpermission from Fernandez et al. (2011a).

with an extended Kalman filter, comparing it to simultaneous PIV measurements. Additionally,an array of pressure sensors mounted inside a model sailboat hull was used to identify helicalvortex shedding from the keel line during maneuvering motions. Finally, they demonstrated thatthe characteristic pressure signature of a vortex is detectable by even a small number of sensors(Figure 9).

3.3. Biologically Inspired and Biomimetic MEMS Sensors

MEMS and NEMS (micro/nanoelectromechanical systems) technology offers unique advan-tages for developing inexpensive, low-power arrays of microflow and pressure sensors emulat-ing the function of the lateral line. Inspired by the neuromasts on various species, Yang et al.(2006, 2011), Chen et al. (2007), Dagamseh et al. (2012, 2013), Tao & Yu (2012), McConneyet al. (2009a), and Klein & Bleckmann (2011) developed MEMS-based sensors using piezore-sisitive and piezoelectric materials, resistive polymer, and capacitive as well as optical meth-ods to convert the flow-induced deflection of pillars, rods, bars, or membranes to electricalsignals.

Figure 10 shows an artificial SN-like velocity sensor (Liu 2007). The hair cells (less than1 mm in height) did not extend beyond the boundary layer generated by the flow (Chen et al.2007; Dagamseh et al. 2012, 2013). McConney et al. (2009b) and Kottapalli et al. (2013) developedbiomimetic SN-like structures with an artificial cupula, proving that sensitivity can be enhancedby at least an order of magnitude. Kottapalli et al. (2013) reported a biomimetic SN polymerMEMS flow sensor using an LCP membrane, a high aspect ratio, and a 3-mm-high Si60 polymer



Ann

u. R

ev. F

luid

Mec

h. 2

016.

48. D

ownl

oade

d fr

om w

ww

.ann

ualr

evie

ws.

org

Acc

ess

prov

ided

by

Mas

sach

uset

ts I

nstit

ute

of T

echn

olog

y (M

IT)

on 0

7/21

/15.

For

per

sona

l use

onl

y.


a

100 μm Contact pad

Contact pad Goldpiezoresistor

2 mm

Location toposition

Si60 pillar

b c

Figure 10Biomimetic MEMS sensors. (a) Sensors featuring SU-8 hair cells, 700 μm tall, located at the distal end of a microcantilever withembedded piezoresistors at the hinge, demonstrating ultrahigh sensitivity in sensing flow velocity and direction. Panel a taken withpermission from Liu (2007). (b) Image of a liquid crystal polymer membrane (2-mm diameter) pressure sensor. Panel b modifiedwith permission from Kottapalli et al. (2012b). (c) Piezoresistive sensor with cupula and encapsulated nanofibers. Panel c taken withpermission from Kottapalli et al. (2013).

hair cell, fabricated stereolithographically (Figure 10). Through proper material selection anddesign, they enhanced the sensitivity of the device without compromising the robustness.

Targeting real-time flow sensing on unmanned underwater vehicles, Kottapalli et al. (2011,2012a,b), Asadnia et al. (2013a,b), Fernandez et al. (2011b), and Dusek et al. (2013) adapted abioinspired approach, eliminating the fragile hair cell element of the sensor, and developed lateralline–inspired flexible arrays of polymer MEMS sensors and self-powered piezoelectric sensors.Polymer sensors were developed using liquid crystal polymer (LCP) as a sensing membrane mate-rial because it is far more robust for underwater applications than is silicon (Kottapalli et al. 2011,2012a,b; Miao et al. 2013).

Most research has focused on developing single flow sensors based on the SN structure,whereas the CN structure has received less attention (Yang et al. 2011, Ren & Mohseni 2012).Klein & Bleckmann (2011) developed artificial lateral line canal sensors that use optical sensingby determining the flow-mediated light transfer efficiency through tiny silicon bars (which actas hair cells) embedded in canals. CN-inspired sensors placed in a canal were developed byKottapalli et al. (2014b). The distance between two artificial canal pores is 6 mm, and the canalpore diameter is 1 mm.

Dusek et al. (2013) conducted field experiments using an autonomous surface vehicle equippedwith three experimental pressure sensor arrays complemented by commercially available sensors.Two flexible arrays were developed using a silicon piezoresistive sensor die mounted on LCP andflexible printed circuit board substrates, respectively. A third sensor array was developed usingcarbon black polydimethylsiloxane (PDMS) composite patterned in a strain-gauge arrangementon a flexible PDMS substrate. The authors demonstrated that the vehicle steady and transientmotions could be detected accurately based on the pressure measurements of any of the threesensor designs.

3.4. Tracking Wakes with Pinniped Whiskers

Observations on pinnipeds, such as sea lions and harbor seals, revealed an impressive ability forprey tracking. Dehnhardt et al. (1998) studied the response of a harbor seal to dipole stimuli,showing their ability to detect minute water movements, as low as 0.3 mm/s. In a subsequent study,Dehnhardt (2001) showed that a blindfolded seal can follow the trail of a small model submarine,even after 30 s had elapsed, when the wake defect velocity was approximately 30 mm/s.Schulte-Pelkum et al. (2007) reported that seals can accurately track hydrodynamic trails created



Ann

u. R

ev. F

luid

Mec

h. 2

016.

48. D

ownl

oade

d fr

om w

ww

.ann

ualr

evie

ws.

org

Acc

ess

prov

ided

by

Mas

sach

uset

ts I

nstit

ute

of T

echn

olog

y (M

IT)

on 0

7/21

/15.

For

per

sona

l use

onl

y.


a b c

Artificialwhisker

Harbor sealwhisker

Whiskercross

section

22

2

2

2

1 1

2

1

2

1

2

1

1

1

1

Figure 11(a) Comparison of a sensor reproducing the harbor seal whisker shape and a whisker from a harbor seal (maximum width 1.15 mm).(b) Sequence of snapshots of a whisker vibrating inside the wake of an upstream cylinder; steady flow is from left to right. The redellipses depict the instantaneous position of the cross section of the whisker at the same plane as the flow visualization of oncomingvortices using upstream released dye. Vortices (shown as outlined patches) move from left to right. (c) Same image as in panel b, keepingthe vortices stationary to show the slaloming path of the whisker, driving its high sensitivity of response and frequency locking. Figureadapted from Beem & Triantafyllou (2015).

by another seal, irrespective of their starting orientations, and even after a delay of 15–20 s.Wieskotten et al. (2010) proposed that seals use both the structure and spatial arrangement of thevortices in hydrodynamic trails for detection. Spedding (2014) provided basic scaling argumentsfor how the seals may be able to track a wake of a certain width. Wieskotten et al. (2011) showedthat seals can distinguish certain towed shapes (e.g., triangle, square) based on the wake they leave.

Miersch et al. (2011) found a reduced sensitivity for sea lions compared with that of harborseals, attributing the difference to the special, undulatory whisker shape of the latter. This uniquegeometry of the harbor seal vibrissae, detailed by Hanke et al. (2010) and Ginter et al. (2010, 2012),consists of a spanwise undulatory shape with a span-varying elliptical cross section (Figure 11).Hanke et al. (2010) showed that these undulations disrupted the coherence of the shed vortexstreets and minimized the induced lift forces behind stationary whiskers. Beem & Triantafyllou(2015) conducted forced and free vibration tests on scaled-up models of harbor seal whiskers,and Hans et al. (2013) conducted three-dimensional simulations, confirming that the whiskershave a significantly reduced amplitude of vortex-induced vibrations, to less than one-tenth of thatexperienced by a circular cylinder. The reduction occurs when the whiskers are aligned to the flowalong their long axis, whereas when exposed at an angle of attack, significant vibrations occur.

As shown by Beem & Triantafyllou (2015), the strong directional sensitivity of the harborseal whisker explains its ability to sense minute vortical flows. When towed along its streamlinedaxis, it vibrates with amplitudes below 5% of its diameter. However, when placed within a wake,it is also subject to a transverse flow velocity component, which causes a dramatic change, as



Ann

u. R

ev. F

luid

Mec

h. 2

016.

48. D

ownl

oade

d fr

om w

ww

.ann

ualr

evie

ws.

org

Acc

ess

prov

ided

by

Mas

sach

uset

ts I

nstit

ute

of T

echn

olog

y (M

IT)

on 0

7/21

/15.

For

per

sona

l use

onl

y.


the vibratory response increases by an order of magnitude. Equally important is the locking inof the frequency of the whisker response to the dominant frequency of the wake. The authorsidentified an energy extraction mechanism driving these wake-induced oscillations of a whiskerplaced within an oncoming Karman wake, which is associated with the slaloming motion of thevibrating vibrissa shown in Figure 11. The motion of the vibrissa has similarities with the motionof an energy-extracting foil (Streitlien et al. 1996, Beal et al. 2006) and a trout swimming in thewake of a cylinder (Liao et al. 2003).

Engineering applications have emerged. For example, Solomon & Hartmann (2006) used steelwhiskers fitted with strain gauges; Stocking et al. (2010) and Eberhardt et al. (2011) proposedwhisker-like sensors mounted on a cone-within-a-cone capacitor base to measure capacitancechanges as the sensor moved due to fluid motion. Valdivia et al. (2012) developed flow sensorsbased specifically on the undulatory shape of a harbor seal whisker. Valdivia & Bhat (2014) studiedthe effects of the follicle material properties on their sensitivity and range (Valdivia et al. 2013)and developed a tunable whisker sensor.

3.5. Assessment of Bioinspired Flow Sensors

Emulating the sensing capabilities of fish will be unquestionably of great value for the flow con-trol, navigation, and maneuvering of engineered vehicles and structures and experimental fluidmechanics. However, the complexity of the structure and function of the animal organ and theprocessing of its information by the central nervous system are still not well known and understood.It will take a coordinated effort in animal studies and sensor development using novel technologiesto generate a sensing capability to provide in real time a three-dimensional map of the near-fieldunderwater environment.

For sensors mounted on the surface of a moving body, pressure sensors have an advantage overvelocity sensors, as the boundary layer poses no problems for the former, as long as it remainsattached, because the pressure remains constant throughout the thickness of the boundary layer.Pressure sensors using a thin diaphragm have been successfully tested (Figure 10), but theysuffer from the need to equilibrate the ambient pressure, particularly for deep-water applications,where hydrostatic pressure is the dominant component. Biomimetically designed CN-like pressuresensors provide a fine alternative solution.

Velocity sensors similar to SNs are significantly influenced by the boundary layer; such effectsvary with Reynolds number and hence with the relative speed of the body and the surrounding flow,requiring adjustment of their calibration. In addition, the sensors have to be very small; otherwise,they will generate a Karman street of their own, adding significant noise to the measurements.

To emulate the sensing abilities of fish, the artificial sensors must also match the sensitivity ofthe lateral line, while preventing self-generating flow noise. The use of a biomimetically designedcupula-like structure (Figure 10) increased the sensitivity significantly. Inspired by the fact thateach hair cell consists of bundles of kinocilia, which move in near unison and hence allow themagnification of the produced signal (Corey et al. 2004), Asadnia et al. (2013b) developed asensor consisting of graded rows of bundled PDMS polymer pillars connected at the top withpolyvinylidene fluoride polymer fiber links. The links stretch and generate electric charges whenthe pillars, which are graded in height, bend in response to flow (Figure 12), thus amplifying thetotal signal significantly.

Inspired by the properties of pinniped whiskers, Asadnia et al. (2015) developed a neuromastmicrofabricated by stereolithography with an undulating shape and placed it on a self-poweredpiezoelectric diaphragm sensor (Figure 12c). The sensor demonstrated a high signal-to-noiseratio and high directional flow-sensing capability. In addition, this sensor can be scaled up in size



Ann

u. R

ev. F

luid

Mec

h. 2

016.

48. D

ownl

oade

d fr

om w

ww

.ann

ualr

evie

ws.

org

Acc

ess

prov

ided

by

Mas

sach

uset

ts I

nstit

ute

of T

echn

olog

y (M

IT)

on 0

7/21

/15.

For

per

sona

l use

onl

y.


a b c

Figure 12MEMS sensors emulating advanced biomimetic features. (a) A schematic illustration of a hair cell–inspired NEMS sensor, showing sixrows of PDMS polymer pillars of graded height (maximum height of 425 μm). The links connecting the distal tips of the pillars areformed by polyvinylidene fluoride polymer, which generates electric charges as the pillars bend, causing stretching in the fibers,amplifying the signal. (b) Photograph of the implemented NEMS sensor. Panels a and b taken with permission from Asadnia et al.(2015). (c) Stereolithography fabricated whisker-like sensor on a piezoelectric diaphragm. The whisker height is 3 mm. Panel c takenwith permission from Kottapalli et al. (2014a).

to measure much higher velocities, given the Karman street–canceling properties of its undulatoryshape.

The study of animal sensors reveals that the complexity of the form and structure of theirorgans plays an important role in their outstanding sensitivity and function. Moreover, as moreinformation is becoming available on how the multiple-sensor input is transmitted and processedby the central nervous system, it is anticipated that significant progress will be made in the nearfuture toward the goal of generating a distributed touch-at-a-distance flow-sensing system.

DISCLOSURE STATEMENT

The authors are not aware of any biases that might be perceived as affecting the objectivity of thisreview.

ACKNOWLEDGMENTS

The authors acknowledge support from the Singapore-MIT Alliance for Research and Technol-ogy, ONR, and the MIT Sea Grant Program. M.S.T. would like to dedicate the paper to thememory of Dr. Mark Grosenbaugh, friend and fellow traveler in biomimetics from the early days.

LITERATURE CITED

Abdel-Latif H, Hassan E, von Campenhausen C. 1990. Sensory performance of blind Mexican cave fish afterdestruction of the canal neuromasts. Naturwissenschaften 77:237–39

Akanyeti O, Venturelli R, Visentin F, Chambers L, Megill WM, Fiorini P. 2011. What information do Karmanstreets offer to flow sensing? Bioinspir. Biomim. 6:036001

Asadnia M, Kottapalli AGP, Miao J, Triantafyllou MS. 2015. Artificial ciliary bundles with nanofiber tip links.arXiv:1505.02340 [physics.flu-dyn]

Asadnia M, Kottapalli AGP, Shen Z, Miao J, Barbastathis G, Triantafyllou MS. 2013a. Flexible, zero powered,piezoelectric MEMS pressure sensor arrays for fish-like passive underwater sensing in marine vehicles.In 2013 IEEE 26th Int. Conf. Micro Electro Mech. Syst. (MEMS), pp. 126–29. New York: IEEE

Asadnia M, Kottapalli AGP, Shen Z, Miao J, Triantafyllou MS. 2013b. Flexible and surface-mountable piezo-electric sensor arrays for underwater sensing in marine vehicles. J. IEEE Sens. 13:3918–25



Ann

u. R

ev. F

luid

Mec

h. 2

016.

48. D

ownl

oade

d fr

om w

ww

.ann

ualr

evie

ws.

org

Acc

ess

prov

ided

by

Mas

sach

uset

ts I

nstit

ute

of T

echn

olog

y (M

IT)

on 0

7/21

/15.

For

per

sona

l use

onl

y.


Beal D, Hover F, Triantafyllou MS, Liao J, Lauder G. 2006. Passive propulsion in vortex wakes. J. Fluid Mech.549:385–402

Beem HR, Triantafyllou MS. 2015. Exquisitely sensitive seal whisker-like sensors detect wakes at large dis-tances. arXiv:1501.04582 [physics.flu-dyn]

Bleckmann H, Mogdans J, Coombs SL, eds. 2014. Flow Sensing in Air and Water: Behavioral, Neural andEngineering Principles of Operation. New York: Springer

Bleckmann H, Zelick R. 1993. The responses of peripheral and central mechanosensory lateral line units ofweakly electric fish to moving objects. J. Comp. Physiol. A 172:115–28

Borazjani I, Sotiropoulos F, Tytell ED, Lauder GV. 2012. Hydrodynamics of the bluegill sunfish C-startescape response: three-dimensional simulations and comparison with experimental data. J. Exp. Biol.215:671–84

Bouffanais R, Weymouth GD, Yue DKP. 2010. Hydrodynamic object recognition using pressure sensing.Proc. R. Soc. A 467:19–38

Chadwell BA, Standen EM, Lauder GV, Ashley-Ross MA. 2012. Median fin function during the escaperesponse of bluegill sunfish (Lepomis macrochirus). I: Fin-ray orientation and movement. J. Exp. Biol.215:2869–80

Chagnaud BP, Bleckmann H, Engelmann J. 2006. Neural responses of goldfish lateral line afferents to vortexmotions. J. Exp. Biol. 209:327–42

Chagnaud BP, Coombs S. 2014. Information encoding and processing by the peripheral lateral line system.See Coombs et al. 2014, pp. 151–94

Chambers L, Akanyeti O, Venturelli R, Jezov J, Brown J, et al. 2014. A fish perspective: detecting flow featureswhile moving using an artificial lateral line in steady and unsteady flow. J. R. Soc. Interface 11:20140467

Chen N, Tucker C, Engel JM, Yang Y, Pandya S, Liu C. 2007. Design and characterization of artificialhaircell sensor for flow sensing with ultrahigh velocity and angular sensitivity. J. Microelectromech. Syst.16:999–1014

Conte J, Modarres-Sadeghi Y, Watts M, Hover F, Triantafyllou MS. 2010. A fast-starting mechanical fishthat accelerates at 40 m s−2. Bioinspir. Biomim. 5:035004

Coombs S, Bleckmann H, Fay RR, Popper AN, eds. 2014. The Lateral Line System. New York: SpringerCoombs S, Conley RA. 1997. Dipole source localization by mottled sculpin. I. Approach strategies. J. Comp.

Physiol. A 180:387–99Corey DP, Garcıa-Anoveros J, Holt JR, Kwan KY, Lin SY, et al. 2004. TRPA1 is a candidate for the

mechanosensitive transduction channel of vertebrate hair cells. Nature 432:723–30Curcic-Blake B, van Netten SM. 2006. Source location encoding in the fish lateral line canal. J. Exp. Biol.

209:1548–59Dagamseh AMK, Wiegerink RJ, Lammerink TSJ, Krijnen GJM. 2012. Towards a high-resolution flow camera

using artificial hair sensor arrays for flow pattern observations. Bioinspir. Biomim. 7:046009Dagamseh AMK, Wiegerink RJ, Lammerink TSJ, Krijnen GJM. 2013. Imaging dipole flow sources using an

artificial lateral-line system made of biomimetic hair flow sensors. J. R. Soc. Interface 10:20130162Dehnhardt G. 2001. Hydrodynamic trail-following in harbor seals (Phoca vitulina). Science 293:102–4Dehnhardt G, Mauck B, Bleckmann H. 1998. Seal whiskers detect water movements. Nature 394:235–36Domenici P. 2011. Webb scales fast-start maneuvers. J. Exp. Biol. 214:875–77Domenici P, Blake R. 1997. The kinematics and performance of fish fast-start swimming. J. Exp. Biol.

200:1165–78Dusek J, Kottapalli AGP, Woo ME, Asadnia M, Miao J, et al. 2013. Development and testing of bio-inspired

microelectromechanical pressure sensor arrays for increased situational awareness for marine vehicles.Smart Mater. Struct. 22:014002

Eberhardt W, Shakhsheer Y, Calhoun B, Paulus J, Appleby M. 2011. A bio-inspired artificial whisker for fluidmotion sensing with increased sensitivity and reliability. In 2011 IEEE Sensors, pp. 982–85. New York:IEEE

Epps BP, Techet AH. 2007. Impulse generated during unsteady maneuvering of swimming fish. Exp. Fluids43:691–700



Ann

u. R

ev. F

luid

Mec

h. 2

016.

48. D

ownl

oade

d fr

om w

ww

.ann

ualr

evie

ws.

org

Acc

ess

prov

ided

by

Mas

sach

uset

ts I

nstit

ute

of T

echn

olog

y (M

IT)

on 0

7/21

/15.

For

per

sona

l use

onl

y.


Fernandez VI, Dusek J, Schulmeister J, Maertens A, Hou S, et al. 2011a. Pressure sensor arrays to optimize thehigh speed performance of ocean vehicles. In 11th Int. Conf. Fast Sea Transp., ed. TJ Peltzer, pp. 363–70.Alexandria, VA: Am. Soc. Nav. Eng.

Fernandez VI, Maertens A, Yaul FM, Dahl J, Lang JH, Triantafyllou MS. 2011b. Lateral-line-inspired sensorarrays for navigation and object identification. Mar. Technol. Soc. J. 45:130–46

Gao A, Triantafyllou MS. 2012. Bio-inspired pressure sensing for active yaw control of underwater vehicles.In 2012 Oceans, pp. 1–7. New York: IEEE

Gazzola M, Van Rees W, Koumoutsakos P. 2012. C-start: optimal start of larval fish. J. Fluid Mech. 698:5–18Ginter CC, DeWitt TJ, Fish FE, Marshall CD. 2012. Fused traditional and geometric morphometrics demon-

strate pinniped whisker diversity. PLoS ONE 7:e34481Ginter CC, Fish FE, Marshall CD. 2010. Morphological analysis of the bumpy profile of phocid vibrissae.

Mar. Mammal. Sci. 26:733–43Hanke W, Witte M, Miersch L, Brede M, Oeffner J, et al. 2010. Harbor seal vibrissa morphology suppresses

vortex-induced vibrations. J. Exp. Biol. 213:2665–72Hans H, Miao J, Weymouth G, Triantafyllou MS. 2013. Whisker-like geometries and their force reduction

properties. In 2013 MTS/IEEE OCEANS - Bergen, pp. 1–7. New York: IEEEHarper DG, Blake RW. 1991. Prey capture and the fast-start performance of northern pike Esox lucius. J. Exp.

Biol. 155:175–92Hassan ES. 1993. Mathematical description of the stimuli to the lateral line system of fish, derived from a

three-dimensional flow field analysis. III. The case of an oscillating sphere near the fish. Biol. Cybern.69:525–38

Huffard CL. 2006. Locomotion by Abdopus aculeatus (Cephalopoda: Octopodidae): walking the line betweenprimary and secondary defenses. J. Exp. Biol. 209:3697–707

Klein A, Bleckmann H. 2011. Determination of object position, vortex shedding frequency and flow velocityusing artificial lateral line canals. Beilstein J. Nanotechnol. 2:276–83

Kottapalli AGP, Asadnia M, Barbastathis G, Triantafyllou MS, Miao J, Tan C. 2012a. Polymer MEMSpressure sensor arrays for fish-like underwater sensing applications. Micro Nano Lett. 7:1189–92

Kottapalli AGP, Asadnia M, Hans H, Miao J, Triantafyllou MS. 2014a. Harbor seal inspired MEMS artificialmicro-whisker sensor. In 2014 IEEE 27th Int. Conf. Micro Electro Mech. Syst. (MEMS), pp. 741–44. NewYork: IEEE

Kottapalli AGP, Asadnia M, Miao JM, Barbastathis G, Triantafyllou MS. 2012b. A flexible liquid crystalpolymer MEMS pressure sensor array for fish-like underwater sensing. Smart Mater. Struct. 21:115030

Kottapalli AGP, Asadnia M, Miao JM, Triantafyllou MS. 2013. Electrospun nanofibrils encapsulated in hy-drogel cupula for biomimetic MEMS flow sensor development. In 2013 IEEE 26th Int. Conf. Micro ElectroMech. Syst. (MEMS), pp. 25–28. New York: IEEE

Kottapalli AGP, Asadnia M, Miao J, Triantafyllou MS. 2014b. Touch at a distance sensing: lateral-line inspiredMEMS flow sensors. Bioinsp. Biomim. 9:046011

Kottapalli AGP, Tan CW, Olfatnia M, Miao JM, Barbastathis G, Triantafyllou MS. 2011. A liquid crystalpolymer membrane MEMS sensor for flow rate and flow direction sensing applications. J. Micromech.Microeng. 21:085006

Kriegseis J, Kinzel M, Rival DE. 2013. On the persistence of memory: Do initial conditions impact vortexformation? J. Fluid Mech. 736:91–106

Liao JC. 2006. The role of the lateral line and vision on body kinematics and hydrodynamic preference ofrainbow trout in turbulent flow. J. Exp. Biol. 209:4077–90

Liao JC. 2007. A review of fish swimming mechanics and behaviour in altered flows. Philos. Trans. R. Soc. B362:1973–93

Liao JC, Beal DN, Lauder GV, Triantafyllou MS. 2003. Fish exploiting vortices decrease muscle activity.Science 302:1566–69

Liu C. 2007. Micromachined biomimetic artificial haircell sensors. Bioinspir. Biomim. 2:S162–69Maertens AP, Triantafyllou MS. 2014. The boundary layer instability of a gliding fish helps rather than

prevents object identification. J. Fluid Mech. 757:179–207McConney ME, Anderson KD, Brott LL, Naik RR, Tsukruk VV. 2009a. Bioinspired material approaches to

sensing. Adv. Funct. Mater. 19:2527–44



Ann

u. R

ev. F

luid

Mec

h. 2

016.

48. D

ownl

oade

d fr

om w

ww

.ann

ualr

evie

ws.

org

Acc

ess

prov

ided

by

Mas

sach

uset

ts I

nstit

ute

of T

echn

olog

y (M

IT)

on 0

7/21

/15.

For

per

sona

l use

onl

y.


McConney ME, Chen N, Lu D, Hu HA, Coombs S, et al. 2009b. Biologically inspired design of hydrogel-capped hair sensors for enhanced underwater flow detection. Soft Matter 5:292–95

McHenry MJ, Liao JC. 2014. The hydrodynamics of flow stimuli. See Coombs et al. 2014, pp. 73–98Miao J, Kottapalli AGP, Asadnia M, Triantafyllou MS. 2013. Assessing flow for sightless underwater surveil-

lance. SPIE Newsroom, March 6. doi:10.1117/2.1201302.004734Miersch L, Hanke W, Wieskotten S, Hanke FD, Oeffner J, et al. 2011. Flow sensing by pinniped whiskers.

Philos. Trans. R. Soc. B 366:3077–84Mogdans J, Bleckmann H. 1998. Responses of the goldfish trunk lateral line to moving objects. J. Comp.

Physiol. A 182:659–76Montgomery JC, Baker CF, Carton AG. 1997. The lateral line can mediate rheotaxis in fish. Nature 389:960–63Montgomery JC, Coombs S, Baker CF. 2001. The mechanosensory lateral line system of the hypogean form

of Astyanax fasciatus. In The Biology of Hypogean Fishes, ed A Romero, pp. 87–96. New York: SpringerMontgomery JC, Coombs S, Halstead M. 1995. Biology of the mechanosensory lateral line in fishes. Rev. Fish

Biol. Fish. 5:399–416Muller HM, Fleck A, Bleckmann H. 1996. The responses of central octavolateralis cells to moving sources.

J. Comp. Physiol. A 179:455–71Muller UK, van den Boogaart JGM, van Leeuwen JL. 2008. Flow patterns of larval fish: undulatory swimming

in the intermediate flow regime. J. Exp. Biol. 211:196–205Niesterok B, Hanke W. 2012. Hydrodynamic patterns from fast-starts in teleost fish and their possible rele-

vance to predator–prey interactions. J. Comp. Physiol. A 199:139–49Packard A. 1969. Jet propulsion and the giant fibre response of Loligo. Nature 221:875–77Pitcher T, Partridge B, Wardle C. 1976. A blind fish can school. Science 194:963–65Plachta DTT, Hanke W, Bleckmann H. 2003. A hydrodynamic topographic map in the midbrain of goldfish

Carassius auratus. J. Exp. Biol. 206:3479–86Rapo MA, Jiang H, Grosenbaugh MA, Coombs S. 2009. Using computational fluid dynamics to calculate the

stimulus to the lateral line of a fish in still water. J. Exp. Biol. 212:1494–505Ren Z, Mohseni K. 2012. A model of the lateral line of fish for vortex sensing. Bioinspir. Biomim. 7:036016Ristroph L, Liao JC, Zhang J. 2015. Lateral line layout correlates with the differential hydrodynamic pressure

on swimming fish. Phys. Rev. Lett. 114:018102Salumae T, Kruusmaa M. 2013. Flow-relative control of an underwater robot. Proc. R. Soc. A 469:20120671Schulte-Pelkum N, Wieskotten S, Hanke W, Dehnhardt G, Mauck B. 2007. Tracking of biogenic hydrody-

namic trails in harbour seals (Phoca vitulina). J. Exp. Biol. 210:781–87Sichert A, Bamler R, van Hemmen J. 2009. Hydrodynamic object recognition: when multipoles count. Phys.

Rev. Lett. 102:058104Solomon JH, Hartmann MJ. 2006. Biomechanics: robotic whiskers used to sense features. Nature 443:525Spagnolie SE, Shelley MJ. 2009. Shape-changing bodies in fluid: hovering, ratcheting, and bursting. Phys.

Fluids 21:013103Spedding GR. 2014. Wake signature detection. Annu. Rev. Fluid Mech. 46:273–302Stocking JB, Eberhardt WC, Shakhsheer YA, Calhoun BH, Paulus JR, Appleby M. 2010. A capacitance-based

whisker-like artificial sensor for fluid motion sensing. In 2010 IEEE Sensors, pp. 2224–29. New York:IEEE

Streitlien K, Triantafyllou GS, Triantafyllou MS. 1996. Efficient foil propulsion through vortex control. AIAAJ. 34:2315–19

Tao J, Yu XB. 2012. Hair flow sensors: from bio-inspiration to bio-mimicking—a review. Smart Mater. Struct.21:113001

Taylor G. 1953. Formation of a vortex ring by giving an impulse to a circular disk and then dissolving it away.J. Appl. Phys. 24:104–5

Triantafyllou MS. 2012. Survival hydrodynamics. J. Fluid Mech. 698:1–4Tytell ED, Lauder GV. 2008. Hydrodynamics of the escape response in bluegill sunfish, Lepomis macrochirus.

J. Exp. Biol. 211:3359–69Valdivia P, Bhat S. 2014. Whisker-like sensors with tunable follicle sinus complex for underwater applications.

In Bioinspiration, Biomimetics, and Bioreplication 2014, ed. A Lakhtakia, Art. 90550C. Bellingham, WA:SPIE



Ann

u. R

ev. F

luid

Mec

h. 2

016.

48. D

ownl

oade

d fr

om w

ww

.ann

ualr

evie

ws.

org

Acc

ess

prov

ided

by

Mas

sach

uset

ts I

nstit

ute

of T

echn

olog

y (M

IT)

on 0

7/21

/15.

For

per

sona

l use

onl

y.


Valdivia P, Subramaniam V, Triantafyllou MS. 2012. Design of a bio-inspired whisker sensor for underwaterapplications. In 2012 IEEE Sensors, pp. 1–4. New York: IEEE

Valdivia P, Subramaniam V, Triantafyllou MS. 2013. Performance analysis and characterization of bio-inspiredwhisker sensors for underwater applications. In 2013 IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 5956–61.New York: IEEE

van Netten SM. 2006. Hydrodynamic detection by cupulae in a lateral line canal: functional relations betweenphysics and physiology. Biol. Cybern. 94:67–85

Venturelli R, Akanyeti O, Visentin F, Jezov J, Chambers LD, et al. 2012. Hydrodynamic pressure sensingwith an artificial lateral line in steady and unsteady flows. Bioinspir. Biomim. 7:036004

von Campenhausen C, Riess I, Weissert R. 1981. Detection of stationary objects by the blind cave fishAnoptichthys jordani (Characidae). J. Comp. Physiol. 143:369–74

Wakeling JM, Johnston I. 1998. Muscle power output limits fast-start performance in fish. J. Exp. Biol.201:1505–26

Webb JF. 2014a. Lateral line morphology and development and implications for the ontogeny of flow sensingin fishes. See Bleckmann et al. 2014, pp. 247–70

Webb JF. 2014b. Morphological diversity, development, and evolution of mechanosensory lateral line system.See Coombs et al. 2014, pp. 17–72

Webb P. 1976. The effect of size on the fast-start performance of rainbow trout Salmo cairdneri, and aconsideration of piscivorous predator-prey interactions. J. Exp. Biol. 65:157–77

Weihs D. 1973. The mechanism of rapid starting of slender fish. Biorheology 10:343–50Weihs D, Webb PW. 1984. Optimal avoidance and evasion tactics in predator-prey interactions. J. Theor.

Biol. 106:189–206Wells M. 1990. Oxygen extraction and jet propulsion in cephalopods. Can. J. Zool. 68:815–24Weymouth GD, Subramaniam V, Triantafyllou MS. 2015. Ultra-fast escape maneuver of an octopus-inspired

robot. Bioinspir. Biomim. 10:016016Weymouth GD, Triantafyllou MS. 2012. Global vorticity shedding for a shrinking cylinder. J. Fluid Mech.

702:470–87Weymouth GD, Triantafyllou MS. 2013. Ultra-fast escape of a deformable jet-propelled body. J. Fluid Mech.

721:367–85Wieskotten S, Dehnhardt G, Mauck B, Miersch L, Hanke W. 2010. Hydrodynamic determination of the

moving direction of an artificial fin by a harbour seal (Phoca vitulina). J. Exp. Biol. 213:2194–200Wieskotten S, Mauck B, Miersch L, Dehnhardt G, Hanke W. 2011. Hydrodynamic discrimination of wakes

caused by objects of different size or shape in a harbour seal (Phoca vitulina). J. Exp. Biol. 214:1922–30Windsor SP, Norris SE, Cameron SM, Mallinson GD, Montgomery JC. 2010. The flow fields involved in

hydrodynamic imaging by blind Mexican cave fish (Astyanax fasciatus). Part II: gliding parallel to a wall.J. Exp. Biol. 213:3832–42

Wu G, Yang Y, Zeng L. 2007. Routine turning maneuvers of koi carp Cyprinus carpio koi: effects of turningrate on kinematics and hydrodynamics. J. Exp. Biol. 210:4379–89

Yang Y, Chen J, Engel J, Pandya S, Chen N, et al. 2006. Distant touch hydrodynamic imaging with an artificiallateral line. PNAS 103:18891–95

Yang Y, Klein A, Bleckmann H, Liu C. 2011. Artificial lateral line canal for hydrodynamic detection. Appl.Phys. Lett. 99:023701

Zhu Q, Wolfgang M, Yue DKP, Triantafyllou MS. 2002. Three-dimensional flow structures and vorticitycontrol in fish-like swimming. J. Fluid Mech. 468:1–28



Ann

u. R

ev. F

luid

Mec

h. 2

016.

48. D

ownl

oade

d fr

om w

ww

.ann

ualr

evie

ws.

org

Acc

ess

prov

ided

by

Mas

sach

uset

ts I

nstit

ute

of T

echn

olog

y (M

IT)

on 0

7/21

/15.

For

per

sona

l use

onl

y.

Documents

Biomimetic Survival Hydrodynamics and Flow Sensing · onlineandin print.) Changes may still occur before final publication online and in print Annu. Rev. Fluid Mech. 2016.48. Downloaded