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First published online June 13, 2008
Journal of Experimental Biology 211, 2087-2100 (2008)
Published by The Company of Biologists 2008
doi: 10.1242/jeb.016279
Propulsion performance of a skeleton-strengthened fin
Department of Structural Engineering, University of California, San Diego, La Jolla, CA 92093, USA
* Author for correspondence (e-mail: qizhu{at}ucsd.edu)
Accepted 8 April 2008
| Summary |
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Key words: fish locomotion, flexible fin, skeleton-reinforced membrane, fluid–structure interaction
| INTRODUCTION |
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The exact function of the anisotropic flexibility of skeleton-strengthened
membranes in insect flying and fish swimming remains to be fully understood.
Despite a limited number of investigations
(Cheng et al., 1998
;
Pedley and Hill, 1999
),
existing studies in fish swimming and insect flying are predominantly based
upon the assumption that the wings and fins are rigid
(Liu and Kawachi, 1998
;
Ramamurti and Sandberg, 2002
;
Ramamurti and Sandberg, 2007
;
Wang, 2000
;
Pullin and Wang, 2004
) or with
prescribed deformations (Liu and Bose,
1997
; Ramamurti et al.,
2005
; Mittal et al.,
2006
). It has always been speculated that the structural
flexibility of these composite bio-structures, with their stiffness determined
by the coupling of the embedded skeletons and the surrounding materials,
attributes to the highly efficient force generation through flapping motions.
Evidence of the beneficial effects of structural flexibility in the generation
of lift and propulsion forces comes from different sources, in particular the
analytical, numerical and experimental investigations of flapping foils, as
discussed below.
Unlike conventional aero- and hydro-foils, which utilize steady fluid
dynamics for force generation, flapping foils are modeled on fish fins and
insect wings so that they produce aerodynamic or hydrodynamic lift, propulsion
or maneuvering forces using unsteady foil oscillations
(Triantafyllou et al., 1991
;
Tuncer and Platzer, 1996
).
Through various investigations, it has been shown that structural flexibility,
per se, increases the propulsion efficiency in many cases. For
example, by experimentally studying the performance of a semi-flexible
flapping foil, Yamamoto et al. reported up to a 27% increase in propulsion
efficiency compared with a rigid foil
(Yamamoto et al., 1995
).
Similar performance enhancement was observed by Heathcote et al. in their
investigation of a thin steel plate undergoing periodic heaving motion in
still water (Heathcote et al.,
2004
). By theoretically examining the oscillation of a
two-dimensional flexible plate, Katz and Weihs were able to obtain a 20%
increase in propulsion efficiency (Katz
and Weihs, 1978
). The performance of a flexible foil, by contrast,
has been found to depend not only on its elasticity but also on its inertia
(Pederzani and Haj-Hariri,
2006
). This conclusion was confirmed in our recent work
(Zhu, 2007
). In that study, we
investigated the three-dimensional fluid–structure interaction of a
flapping foil. A number of extreme, yet representative, cases were examined,
including chordwise vs spanwise flexibility and fully-coupled (the
foil deformation is driven mostly by the fluid) vs partially
decoupled (the foil deformation is driven mostly by its own inertia)
interactions. By using a boundary-element model coupled with a two-dimensional
plate model, we were able to show that both the structural flexibility and the
inertia of a flapping foil had significant effects on its capability for
thrust generation. Among all the numerical tests we have conducted, two cases
– one with chordwise flexibility and fluid-driven deformation and the
other with spanwise flexibility and inertia-driven deformation – yield
higher propulsion performance than rigid foils. The former case shows
increased efficiency (by 20%, consistent with previous studies) and the latter
shows increased thrust (by almost twofold in some cases) without any
significant loss in efficiency.
Structural flexibility has also been found to effectively reduce fluid
drags. Studying the two-dimensional soap film flow around an elastic glass
fiber as an example, Alben et al. characterized the drag reduction effect of
structural flexibility, illustrating the transition from a rigid-body drag
scaling law of U2 to U4/3 (U
is the speed of incoming flow) when the fiber flexibility exceeds a threshold
(Alben et al., 2002
;
Alben et al., 2004
).
Despite the abovementioned efforts to characterize dynamics of flexible foils or fibers, the hydrodynamic significance of three-dimensional anisotropic flexibility is still not illustrated. In particular, comprehensive studies are required to understand the coupled fluid–structure interactions of bio-membranes with morphological relevance, e.g. those with mechanical properties similar to skeleton-reinforced fins or wings.
We herein present a numerical investigation by coupling fluid dynamics and structural mechanics to examine the propulsion performance of a skeleton-reinforced fin that is geometrically and structurally similar to the caudal fin of a fish. We study cases in which the unsteady motions of the rays are individually controlled by imposing sway–yaw motions at their basal ends. The rest of the ray may deform under the combined effect of its own inertia, the hydrodynamic load and constraint from the membrane. Our approach includes an Euler–Bernoulli beam model for the strengthening rays, as well as a boundary-integral equation representation for the surrounding fluid. To account for large three-dimensional deformations, the beam model is fully nonlinear and allows stretching, bending and twisting. Via systematic simulations, dynamics of a flexible fin and that of a rigid one are compared with each other using the performance metrics, including the capacity and efficiency of the fin to generate thrust and the reduction of transverse force. In addition, we investigate both the homocercal mode, in which the fin deformation is symmetric across the span, and the heterocercal mode, in which the deformation of the dorsal side of the fin is different from that of the ventral side.
The rest of the paper will be organized as follows. In the next section we define the geometry and internal structure of the fin, together with the mathematical formulations and the numerical methods to solve the coupled fluid–structure interaction problem. Following that, numerical results, including the thrust force, the transverse force and the propulsion efficiency of the flexible fin in comparison with a rigid fin, will be presented. Also included will be comparisons between the homocercal and the heterocercal modes. Finally, conclusions will be drawn.
| MATERIALS AND METHODS |
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|
We consider a three-dimensional flapping motion of the fin in combination
with a constant forward speed, U, in the –x direction.
The flapping motion is actuated by imposing sway–yaw motions on each
ray. By assumption, the rays share the same sway motion
y(t)=y0sin(
t) following
the motion of the caudal peduncle. The yaw motion with respect to the
z-axis of Ray i is
(t)=
isin(
t–
i),
i=1,..., 9. The Strouhal number, a non-dimensional parameter
characterizing the unsteady fluid-dynamic effects, is defined to be
St=2y0f/U, where
f=1/T=
/2o is the frequency of the motion and
T is the period.
In the following, we elaborate the mathematical formulations of the ray motion and the fluid dynamics.
Mathematical formulations and numerical algorithms
We apply a three-dimensional nonlinear formulation and model each ray as an
Euler–Bernoulli beam that could be stretched, bent and twisted
(Tjavaras et al., 1998
). Based
upon the Euler–Lagrangian dual-coordinate approach and the robust Euler
parameters, this method achieves fully-nonlinear simulation of arbitrary ray
deformations. A finite difference algorithm, the modified box method, is then
employed to solve these nonlinear equations (see Appendix A for details).
The flow around the fin is assumed to be irrotational except for
zero-thickness sheets of vorticity shed from the trailing edge so that it can
be described by a flow potential
(x,t), where
x=(x, y, z) is the position vector. The near-body flow field
as well as the hydrodynamic loading on the fin are then predicted using a
boundary-element approach based upon the boundary-integral framework by
segmenting the fin surface Sb into Nb
panels Sbj, j=1,..., Nb
(Appendix B).
By solving the potential flow around the body and determining the flow
potential
, the hydrodynamic pressure p on the fin surface
Sb is readily obtained through Bernoulli's equation. We have:
![]() | (1) |
is the density of water (103 kg m–3).
Integrating p over the fin surface, we obtain the hydrodynamic force
as:
![]() | (2) |
The power required to drive the fin is given as:
![]() | (3) |
By definition, the thrust force, Ft, is the
–x component of F. The propulsion efficiency,
, is
defined as:
![]() | (4) |
t and
represent the thrust force and power
consumption averaged over one period T, respectively. The fin
oscillation also creates a transverse force Fr, which is
the y component of F. In the heterocercal mode, there also
exists a non-zero lifting force, Fz, leading to a mean
lifting coefficient
z/
U2c2.
We thus characterize the performance of the fin by its mean thrust
coefficient,
t/
U2c2,
the propulsion efficiency,
, the mean lifting coefficient, and the
transverse force coefficient,
Fr(1)/
U2c2,
where:
![]() | (5) |
As mentioned before, the structural function of the membrane in which the rays are embedded is simplified as linear spring-dampers spanning between neighboring rays. Specifically, in the following calculations, we chose the spring constant to be 20 N m–2, and the damping constant to be 2 Ns m–2 (both are measured per unit length). With such a constraint, in the following simulations the maximum variation of the surface area of the fin is around 5–10%.
We employ an iteration method to solve the fully coupled fluid–structure interaction problem. Below we list the primary steps of this algorithm.
, of the configuration of the
fin.
.
Sbi
(
Sbi is the area of the panel), is transferred
equally to four grid points that are closest to the panel on the two
neighboring rays (two points on each ray). Summing up the forces on all the
panels, the hydrodynamic loads, Fh, on the nine rays are
thus determined.
, we set
and repeat
Steps 2–4.
To eliminate the well-known added mass induced instability, we apply an
implicit added mass scheme. In this approach, the added mass effects are
subtracted from both sides of Eqn
A9 during time integration. As long as convergence of the
iteration is achieved, the exact values of the added mass will not affect the
results. A detailed description of this method is provided in Connell and Yue
(Connell and Yue, 2007
).
Convergence tests
The validity and accuracy of both the Euler–Bernoulli beam solver and
the boundary integral solver have been extensively tested and documented
through numerous convergence studies and comparisons with experiments or other
numerical/theoretical predictions
(Tjavaras et al., 1998
;
Zhu et al., 2002
;
Zhu et al., 2006
;
Zhu, 2007
). These are omitted
here for brevity. Instead, we will concentrate primarily on the convergence of
the algorithm with respect to the time step, as it reflects the accuracy of
our iteration scheme discussed above, which is the core of the interaction
solver.
Convergence tests are performed by examining the propulsive force generated by a fin with leading-edge span s0=0.02 m, chord length c=0.1 m, and thickness d=0.002 m. The rays are structurally identical except for their lengths. Each has a cross-sectional diameter of 1 mm, a Young's modulus, E, of 0.4 GPa, and a hysteretic (material) damping coefficient, D, of 0.4 sGPa. The density of the rays is chosen to be the same as that of the water, i.e. 103 kg m–3.
Kinematically, we set the forward speed to be U=0.1
ms–1. The sway amplitude,
y0=0.5c. The Strouhal number,
St=0.2. We also assume that the nine rays shear the same
yaw motion, with
0i=
0=100 and
i=90° (i=1,..., 9).
Table 1 shows the mean
thrust coefficient,
t/
U2c2,
as a function of the number of body elements, Nb, and the
time step
t. Linear convergence is achieved with respect to
both parameters.
|
| RESULTS |
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By specifying the motion of each individual reinforcing ray, in our model
we are able to achieve controlled anisotropic deformations, both homocercal
and heterocercal, of the fin. We assume that the basal ends of the rays share
the same sway motion following the undulation of the anterior part of the fish
body. The yaw motions, i.e. the rotation of the rays around their basal ends,
can be varied individually. This imitates the muscle action that controls the
ray kinematics (Lauder and Drucker,
2004
). In the following, we will present our numerical results of
the force generation by a rigid fin and a flexible fin with identical
geometries. The flexible fin has the same geometric and structural properties
as the one applied for the convergence tests. In addition, we assume that in
all the simulations the forward speed, U, is 0.1 m
s–1, the sway amplitude, y0, is 0.05 m,
and the phase angle between sway and yaw,
i, is 90°
(i=1,..., 9). The focus of our numerical simulation will be the
comparison between force generation by a rigid fin and that by a flexible fin.
We will also study the differences and similarities between homocercal and
heterocercal modes.
Numerically, 1000 boundary elements are applied on the fin surface. On each
ray, the number of grids is Nr=32. Finally, a time step of
t=T/128 is chosen.
Homocercal modes
In the homocercal motion, we assume that the yaw amplitudes of all the rays
are the same, i.e.
0i=
0 (i=1,...,
9). A typical sequence of the deformation of a flexible fin over
period is displayed in Fig. 2.
At t=0, when the fin is at its mean sway position (y=0) and
maximum yaw angle (
=10°), we observe that there is no pronounced
spanwise (dorso-ventral) bending. As t increases towards T/8
and T/4, accompanying the increasing sway, significant dorso-ventral
bending is observed, creating a bow-shaped trailing edge so that the central
part of the fin bends into the flow. If we plot the trajectories of the
trailing ends of Ray 5 (at the center) and Ray 1 (at the upper edge), it is
clear that the upper and lower edges of the fin undergo smaller and
lagged-behind undulations compared with the center part
(Fig. 3). In
Fig. 2, we also observe that
the dorso-ventral bending reverses its direction near the upper and lower
edges. There are consequently two inflection points.
|
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|
The bow-shaped trailing edge of the fin, as well as the existence of
inflection points near the upper and lower edges, is qualitatively consistent
with a recent report about kinematics of the caudal fin of a bluegill sunfish
(Tytell, 2006
) (see
fig. 3 in that paper).
|
0<40°. From
Fig. 5 we see that the flexible
fin generates a larger thrust when
–10°<
0<
5°. Otherwise, the rigid fin
creates a larger thrust. With regard to the propulsion efficiency, the
flexible fin outperforms the rigid fin through the whole range
(Fig. 6). The maximum
propulsion efficiencies achieved by the rigid fin and the flexible fin are
found to be 0.48 and 0.54, respectively. More importantly, the flexible fin
achieves good performance within a much larger parameter range than the rigid
fin. Indeed, at certain places (e.g. St=0.3,
0=–10°), almost a 100% increase in propulsion
efficiency is observed (also refer to Fig.
8). Thus, we conclude that by using a flexible fin, we can greatly
reduce the dependence of its propulsion performance on kinematic
parameters.
|
|
Both the increase in propulsion efficiency and the decrease in transverse force indicate performance enhancement through three-dimensional structural flexibility. These characteristics are more clearly illustrated in Fig. 8, where cross-sections of Figs 5, 6, 7 are plotted.
Heterocercal modes
In our simulations, the heterocercal mode, i.e. a mode with dorso-ventral
asymmetry, is achieved by varying the amplitudes (but not the frequency and
phase) of yaw motions of the rays. Specifically, we assume that the ray at the
upper edge on the dorsal side (Ray 1) performs no yaw motion
(
01=0°). For the rest of the rays, the yaw amplitude
increases linearly with the largest yaw motion occurring at the lower edge
(Ray 9), i.e.
01=i/9x
0.
Hereafter, we take a closer look at a flexible fin.
As shown in Fig. 9, the ray
at the upper edge (Ray 1) leads both the central ray (Ray 5) and the ray at
the lower edge (Ray 9). This trend is more clearly shown in
Fig. 10, where the
trajectories of the trailing ends of these three rays are plotted. Such
behavior is consistent with experimental observations of bluegill sunfish as
reported by Lauder (Lauder,
2000
).
|
|
0<50°. As we see, compared with the
homocercal mode, the heterocercal mode yields a slightly smaller propulsion
efficiency. For example, the maximum propulsion efficiency achieved in the
heterocercal case is 0.51, in comparison with 0.54 in the homocercal case.
|
Fig. 11D shows the existence of a mean lifting force as generated by the heterocercal motion of the fin. Although this force is 1–2 orders of magnitude smaller than the thrust, it may be an important source of maneuvering force in the vertical direction.
Visualization of three-dimensional flow field
To further investigate the fluid–structure interaction problem
involved in the sway/yaw motion of the fin, and to compare the homocercal mode
with the heterocercal mode, we numerically visualized both the near-body flow
field and the vorticity distribution in the wake.
As illustrated in previous studies, an important phenomenon concerning
flexible objects in flow is that these objects often bend their surfaces to
match the incoming stream. This has been confirmed in various experiments,
including, for example, the bending of a glass fiber by a soap film flow
(Alben et al., 2004
) and the
meandering motion of a dead fish between incoming vortices
(Liao et al., 2003
). To
confirm if this is the case in our problem, in
Fig. 12 we checked the inplane
streamlines within two horizontal planes, z=0 and
z=0.3c, around a rigid fin and a flexible fin undergoing
sway motions. It was found that at the mid-span (z=0), the fin is
bent just a little towards the incoming flow. This results from the fact that
the central ray (Ray 5) is the shortest and least deformable. On the plane
z=0.3c, however, two different tendencies are observed. The
front part of the fin tends to align with the incoming flow, echoing features
of the two-dimensional fluid-driven foil deformations
(Zhu, 2007
). The hind part of
the fin, however, undergoes much smaller reorientation. This is caused by the
three-dimensional effect, in particular interactions with rays closer to the
center via the membrane.
|
Fig. 13A,B shows the
iso-surfaces of vorticity in the wake behind a rigid fin and a flexible fin
undergoing a homocercal flapping motion, respectively. It is evident
(especially from the insets of Fig.
13A,B) that the wake consists of a sequence of vortex rings
symmetric in the dorsal/ventral direction (in two-dimensional experimental
visualizations or numerical simulations, these vortex rings appear as pairs of
vortices). The wakes of the rigid fin and the flexible fin resemble each other
except for a subtle difference at the connection between neighboring rings.
Specifically, in the flexible case, the figure shows elongated forklike
structures between neighboring rings (Fig.
13B). These structures resemble the hairpin structures proposed by
Tytell (Tytell, 2006
) based
upon analysis of PIV data obtained from the near-body flow around a bluegill
sunfish. The hairpins were attributed to the cupping of the fin during
flapping, as is also shown in our simulations (see
Fig. 2).
|
|
| DISCUSSION AND CONCLUSIONS |
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Our results show that compared to rigid fins with the same geometry, a fin with anisotropic flexibility yields higher propulsion efficiency. The structural flexibility also reduces the transverse force, another beneficial effect in fish swimming. Further reduction of transverse force is observed by applying the heterocercal mode.
An interesting phenomenon is that in terms of propulsion efficiency, the fluid–structure coupling greatly reduces the sensitivity of the fin performance to kinematic parameters such as the Strouhal number and the amplitude of yaw motion. Even in cases when these parameters are not optimized, the flexible fin is able to function well as a propeller. This property may have significant effects on fish locomotion. The biological implication is that it may not be critical for a fish to control the motion of its caudal fin accurately to achieve high propulsion performance. In biomimetic applications, this suggests that in the future development of mechanical devices imitating the swimming mechanism of fishes, the controlling system could be significantly simplified provided that the structural properties of the propeller are designed to mimic the ray-reinforced fins.
In addition to the locomotion modes examined in our work, there may exist
other combinations of fin structures and kinematics, leading to different fin
deformations in the swimming process. For example, by observing the fin
actuation of a dace (Leuciscus leuciscus), Bainbridge concluded that
the maximum dorso-ventral curvature occurred when the caudal fin is close to
its maximum sway displacements (Bainbridge,
1963
). His observations also show that the upper and lower edges
of the fin usually precede the center part in their lateral motions. This
contradicts what we found in the homocercal mode when the upper/lower edges
are behind the central part. Similarly, although the fin kinematics predicted
by our model appear to resemble the experimental observations of Tytell
(Tytell, 2006
), subtle
differences, such as the relationship between the center part and the
upper/lower edges in terms of amplitudes of deflection and phases, suggest
more complicated fin kinematics in his experiment than in our simulations. In
species with heterocercal tails, such as spiny dogfish (Squalus
acanthias), a sophisticated ring-within-a-ring vortex structure has been
observed (Wilga and Lauder,
2004
). This was attributed to the asymmetry in vortex formation at
the upper and the lower lobes. A more sophisticated ray model, with actively
controlled curvatures, is also required. To study cases with closer biological
relevance, and to fully understand the benefits of different fin architectures
and locomotion modes, interdisciplinary investigations including fish
morphology and physiology, fish behavior, control, as well as
fluid–structure interactions are required.
The perspective gained from our work and previous studies makes it appropriate to envisage future development of biomimetic propellers imitating flexible flapping fins made of skeleton-reinforced membranes. Compared with the conventional rigid foil design, these novel biomimetic propellers possess the following advantages.
It is necessary to point out the limitations of the current model. First,
our work concerns a geometrically and structurally simplified caudal fin.
Effects of the fish body and attachments such as dorsal, pectoral or anal fins
are not considered. As illustrated by Wilga and Lauder
(Wilga and Lauder, 2002
), in
leopard sharks (Triakis semifasciata) and bamboo sharks
(Chiloscyllium punctatum), the inclination angle of the body plays a
pivotal role in balancing the torque created by the tail. In addition, as
shown by Tytell (Tytell,
2006
), vortices shed from the dorsal and the anal fins of a
bluegill sunfish may contribute significantly to the near-body flow. Recent
experiments with a mechanical fish have shown that the fish body itself might
also generate a sequence of weaker vortex rings in the wake
(Brucker and Bleckmann, 2007
).
The dynamic effects of the vortices generated upstream of the caudal fin
depend not only on their strength but also on their phase with respect to the
caudal fin (Zhu et al., 2002
).
This, in turn, is determined by the exact morphology of the fish as well as
its kinematics. Another effect not considered in the present study is
vorticity generation from the fin surface in locations other than the trailing
edge, in particular the upper and lower edges. This vorticity generation will
not only change the local dynamics (e.g. the pressure distribution) around the
upper/lower edges but will also affect the overall performance of the fin in
propulsion. To fully account for this effect, a Navier–Stokes solver
capable of studying three-dimensional fluid–structure interactions is
required [for a possible method to achieve this, see Connell
(Connell, 2006
)]. A fully
viscous study will not only illustrate effects of vortex generation from
places other than the trailing edge but will also provide more accurate
predictions of the flow field and the hydrodynamic forces, especially in cases
when the Reynolds number is low. These Navier–Stokes simulations,
however, are computationally expensive. In that respect, a potential flow
method like ours provides an alternative way to conduct systematic tests over
a wide range of parameters. To improve the accuracy of our current method,
investigations are underway to develop methods to study vortex generation from
the leading edge area or the upper/lower edges of the fin. The knowledge
gained from these studies will pave the way for future simulations with more
comprehensive numerical tools and experiments using mechanical devices. These
studies may eventually contribute to the creation of biomimetic apparatus that
will be installed on AUVs (autonomous underwater vehicles) or other vehicles
(see Tangorra et al.,
2007
).
Finally, we would like to mention that, in addition to insect wings and
fish fins, there exist a large number of bio-structures in nature with similar
structural characteristics, which can thus be categorized as
skeleton-reinforced membranes. A typical example is found in the membrane of
the erythrocyte (red blood cell), which features a composite structure
including a lipid bilayer and a protein skeleton consisting primarily of actin
and spectrin (Mohandas and Evans,
1994
). The combined visco-elasticity of the bilayer and the
skeleton, as well as their dynamic inter-connectivity, impart remarkable
stability and deformability essential for the cell functionality when it
circulates around the body and squeezes through capillaries half its own
diameter. Similar structures are found in the biopolymer membranes within the
shells of mollusks such as the red abalone (Haliotis rufescens) and
the nautilus (Nautilus pompilius). Remarkable mechanical properties
of these structures (e.g. structural strength, stability, durability and
deployability) suggest biomimetic applications and justify concentrated
research efforts to understand the detailed structural mechanics and
fluid–structure interactions of these bio-structures.
| APPENDIX A. MODEL OF RAYS VIA NONLINEAR BEAM DYNAMICS |
|---|
|
|
|---|
, n and b in the
tangential, normal and bi-normal directions of the ray, respectively. The two
coordinate systems are related by:
![]() | (A1) |
about the principal unit vector of C, i.e. l.
Correspondingly, four Euler parameters – β0,
β1, β2 and β3 – are
defined as:
![]() | (A2) |
![]() | (A3) |
Dynamic equations of the ray movement are derived by considering the
conservation of translational and angular momenta of an infinitesimal segment
of the ray. We have:
![]() | (A4) |
![]() | (A5) |

+Vnn+Vbb
and
(s,t)=

+
nn+
bb
are the translational and angular velocities, respectively.
T(s,t)=T
+Tnn+Tbb
is the internal force inside the ray.
M(s,t)=M
+Mnn+Mbb
is the internal moment.
(s,t) is the strain.
(s,t)=

+
nn+
bb
is the Darboux vector measuring the material torsion and curvatures of the
ray. Fc is the constraining force from the membrane that
controls the distance between neighboring rays. Fh is the
hydrodynamic force to be defined later.
The compatibility relations, which state that the ray's configuration must
be continuous in both space and time, should also be satisfied as:
![]() | (A6) |
The internal forces and moments are related to the strain
and the
Darboux vector
through the constitutive relations
T
=EA
,
M
=GJ
,
Mn=EI
n,
Mb=EI
b. By definition,
A is the cross-sectional area. EI and GJ are the
bending and torsional stiffnesses, respectively. Our current simulations do
not consider cases with ray twisting; therefore, the value of GJ is
irrelevant. At this point, a hysteretic (material) damping can be incorporated
by replacing the Young's modulus E with
E+D
/
t, where D is the damping
coefficient. In addition, the angular velocity
is expressed in terms
of the Euler parameters by:
![]() | (A7) |
The governing equations are closed by applying the spatial derivative of
the Euler parameters. We have:
![]() | (A8) |
Finally, the system of equations is summarized into a vector form, i.e.:
![]() | (A9) |
TnTbV
VnVbβ0β1β2β3

n
b]T.
Detailed description of the matrices H and P is provided by
Tjavaras et al. (Tjavaras et al.,
1998
Overall, 13 boundary conditions are required at the two ends of each ray.
At the basal end, we apply: (1) β0=0; (2) the velocities
V
, Vn and Vb, as
determined by the prescribed sway motion; (3) M
=0; and (4) the
three-dimensional orientation of the ray as determined by the prescribed yaw
motion. In practice, condition 4 is enforced by adding a stiff rotational
spring between the basal end of the ray and a reference direction representing
the yaw motion. This leads to moments Mn and
Mb at the basal end, whose values increase as the ray
deviates from the reference direction. Six additional boundary conditions are
applied at the trailing end, including: (1)
;
(2) the internal force T=0; and (3) the internal moments
Mn and Mb disappear so that
n=
b=0.
To solve Eqn A9 and determine
the dynamics of a single ray, we numerically segment this ray into
Nr–1, each with length
s, by
distributing Nr points sk
(k=1,..., Nr) along its unstretched length. A
modified box method is then applied to integrate the system of equations from
time step ti–1 to
ti=ti–1+
t. We
have:
![]() | (A10) |
An iterative algorithm is employed to solve this system of implicit
equations (Tjavaras et al.,
1998
). The method possesses second-order accuracy in space. The
accuracy of time integration, on the other hand, is first order. The purpose
of selecting a lower-order time integration scheme rather than the more
conventional box method, which possesses second-order accuracy, is to
providing better numerical stability.
APPENDIX B. THE BOUNDARY-INTEGRAL FORMULATION AND THE BOUNDARY-ELEMENT METHOD
The flow around the fin is assumed to be irrotational except for
zero-thickness sheets of vorticity shed from the trailing edge. The flow field
is then easily described by a velocity potential
(x,t)
(the position vector x
(x, y, z) represents an arbitrary
point in space). We further express
as the linear superposition of two
parts,
=
b+
w, where
b
represents the contribution from the fin body and
w represents
the contribution from the wake. The problem is thus decomposed into two
interdependent ones: the boundary value problem for
b and the
evolution of the wake that determines
w.
The boundary-value problem for
b consists of the Laplace
equation
2
b=0 inside the fluid
domain, the far-field condition
|
b|
0 as
|x|
, and the no-flux condition on the fin
surface Sb:
![]() | (B1) |
b is then obtained by invoking Green's theorem.
At any point x on the fin surface we have:
![]() | (B2) |
Note that the term on the right-hand side is known since the value of
n·
b on Sb is
given through Eqn B1. Solving
the integral equation (B2), we
obtain the unknown potential
b on Sb. The
value of
b at any point x inside the fluid is then
evaluated via Green's theorem, i.e.:
![]() | (B3) |
To determine
w, we model the wake as a distribution of
dipoles on a sheet shed from the sharp trailing edge of the fin. With the
continuous generation of vorticity from the trailing edge, this wake sheet
elongates over time. The strength of newly shed wake is determined by the
Kutta condition, i.e. the strength of the newly shed wake
w
equals the instantaneous difference of body influence potential
b between the upper and lower surfaces near the edge so that
the local singularity is eliminated. The rest of the wake, treated as a
material surface, is carried downstream by the collective effect of the
upcoming flow and the self-induced velocity, and its strength is kept
unchangeable due to the absence of dissipation. In order to stabilize the
evolution of the discretely distributed vorticity in the wake, it is necessary
to introduce a desingularization algorithm, in which we assign a finite core
radius
to each (body or wake) panel of dipole distribution
(Krasny, 1986
). It has been
demonstrated that the hydrodynamic forces on the body are not sensitive to the
value of
(Zhu et al.,
2002
). The desingularization also eliminates singularity in the
wake vorticity distribution, allowing us to visualize the near-body flow
field.
A low-order boundary-element algorithm is employed to find the unknown body
influence potential
b on Sb by solving
Eqn B2
(Katz and Plotkin, 1991
). To
achieve this, the fin surface Sb is discretized into
Nb panels. The low-order panel method refers to the fact
that in this algorithm within each panel Sbj
(j=1,..., Nb), the body potential
b and its normal derivative are assumed to be constants. By
collocating at the centroids of the panels,
Eqn B2 is transformed into a
system of linear equations and can be numerically solved. Afterwards, a
forward Euler algorithm is applied for time integrations. As an illustration,
the distribution of boundary elements on the fin surface and the wake is
demonstrated in Fig. B1. The
flow potential
is then determined by summing up the body influence
potential
b and the wake influence potential
w.
|
LIST OF SYMBOLS AND ABBREVIATIONS
t)
)
, Tn,
Tb)
, Vn,
Vb)

t

0i
, n, b)
b
w

i

=(
,
n,
b)
=(
,
n,
b)
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|---|
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