Wake structures behind a swimming robotic lamprey with a passively flexible tail

The swimming motions of fishes are the result of an intricate balance of forces. The axial musculature produces forces that bend the body, which has elastic properties that may resist or aid the muscular forces. The body motion then moves the fluid around the animal, producing forces back on the body. The swimming motion we observe comes from a balance between the internal muscle and body forces and the external fluid forces (Gray, 1933; Jordan, 1996).

In particular, the stiffness of the body may be important for effective swimming. Recently, Tytell et al. (Tytell et al., 2010) examined the coupling between the body of an anguilliform swimmer and the fluid in a computational model, and found different optimal body stiffnesses for high-speed swimming, high accelerations and low-power consumption. Similarly, McHenry et al. (McHenry et al., 1995) found that changes in body stiffness could alter both body wavelength and wave speed in rubber models of fish. Other work suggests that fishes can actively control the stiffness of the body; properly timed muscle activation could increase body stiffness by up to a factor of seven (Long, 1998).

Passive stiffness may have a particularly strong effect near the tip of the tail. Analytical, experimental and robotic approaches indicate that the bulk of the thrust force is produced at the tip of the tail (Lighthill, 1971; Leftwich and Smits, 2011; DuBois and Ogilvy, 1978). The fin rays that make up the caudal fin are actively controlled (Flammang and Lauder, 2008). Nevertheless, the bulk of the axial musculature is located more anteriorly, so the force that the caudal fin can produce to resist fluid forces may be relatively small. Therefore, passive interactions between the fluid and the fin are considered important.

We investigated of the role of tail flexibility in anguilliform swimming using the robotic lamprey described by Hultmark et al. (Hultmark et al., 2007) and Leftwich and Smits (Leftwich and Smits, 2011). The stiff, actively controlled tail used in these earlier studies was replaced by a passively flexible tail with a morphology modeled on computer tomography (CT) scans of the silver lamprey. The tail was constructed of flexible PVC gel, and nylon inserts were used to change its degree of flexibility.

Body flexibility is likely to be particularly important for anguilliform swimmers, which is the reason we designed the robot to mimic this mode of swimming. In general, anguilliform swimmers are more flexible than other fishes (Aleyev, 1977). Similarly, body curvature tends to be higher for anguilliform swimming (Aleyev, 1977; Gillis, 1998). Thrust production of flexible bodies was considered numerically in two recent papers: Masoud and Alexeev found that at low Reynolds numbers, oscillating flexible wings at resonance greatly enhanced the thrust production (Masoud and Alexeev, 2010), and Ferreira de Sousa and Allen (Ferreira de Sousa and Allen, 2011) investigated flexible membranes of flapping wings over the time of a flapping cycle.

The anguilliform mode of swimming also has several distinct hydrodynamic features. Anguilliform swimmers tend to produce a wake with two separate vortex rings per full tailbeat (Müller et al., 2001; Tytell and Lauder, 2004), referred to as a 2P wake in the terminology of Williamson and Roshko (Williamson and Roshko, 1988). In contrast, most carangiform swimmers produce a single vortex ring per cycle that links up with the one from the previous cycle (e.g. Nauen and Lauder, 2002), called a 2S wake. Recent computational work indicates that the 2P wake structure is related to the Strouhal number, which can be calculated as St=ω_fA/U_∞, where ω_f and A are the tailbeat frequency and amplitude, respectively, and U_∞ is the swimming speed (Borazjani and Sotiropoulos, 2008). Strouhal number, in turn, can be altered by changing body stiffness (Tytell et al., 2010).

Here, we examine the importance of passive flexibility in the tail of a robotic lamprey. Passive flexibility in swimming has been investigated computationally by Zhu and Shoele (Zhu and Shoele, 2008) and Shoele and Zhu (Shoele and Zhu, 2009), and experimentally by Tangorra et al. (Tangorra et al., 2010). These studies, like the present study, investigated the connections between force, wake structure and flexibility. In this study, we used three different degrees of tail flexibility and the resulting swimming speeds, wake structures and momentum estimations are reported. In addition, we investigated the role of resonance in the wake structure for a given flexibility.

The experiments were carried out in the wake of a robotic swimming lamprey. The robot was adapted from that used by Hultmark et al. (Hultmark et al., 2007) and Leftwich and Smits (Leftwich and Smits, 2011) by replacing the original stiff tail with a custom-made flexible tail. Fig. 1 shows the entire robot with the padding, waterproof skin and tail removed. The tail was attached to the robot by inserting the last two motors into the anterior third of the molded tail. To keep the overall length of the robot constant, the total number of servo motors was reduced from 13 to 11. The servo motors were controlled by a BasicX Stamp microcontroller (Parallax, Inc., Rocklin, CA, USA) to create the anguilliform swimming motion of the lamprey. Reducing the number of motors had an almost negligible effect on the resulting waveform of the lamprey body posterior to the flexible tail. Three different degrees of tail flexibility were tested. The robot was mounted in a recirculating water channel (Fig. 2). This apparatus is well characterized and has a 3% variation of the mean velocity at higher velocities (0.285 m s^-1) (Buchholz, 2006). Further details of the robot construction, operation and kinematics can be found in Hultmark et al. (Hultmark et al., 2007) and Leftwich and Smits (Leftwich and Smits, 2011).

The Reynolds number [Re=LU_∞/v, based on body length (L)=0.9 m, U_∞=0.1 m s^-1 and the kinematic viscosity of the fluid (ν)=1.00×10^-6] was approximately 85,000 (this varied slightly as the flow speed was tuned to hold Strouhal number constant), and the Strouhal number (St) was 0.55. For each of the flexible tails, the flow speed was tuned to reach a Strouhal number of 0.55. The incoming flow speed, i.e. swimming speed (U_∞), was adjusted to compensate for any changes in amplitude (A) due to flexibility. For all but the resonant frequency experiment, the frequency was constant at 0.56 Hz. This Strouhal number is higher than that of the silver lamprey in steady swimming, which is close to 0.35 (Hultmark et al., 2007).

When determining the swimming speed of the robot with the various flexible tails, the Strouhal number was not set a priori. An air-bearing carriage allowed the robot to move freely in the streamwise direction. The robot was powered on and began to swim upstream. The incoming flow speed was then adjusted until the robot was able to hold its station. This speed was considered the ‘free swimming speed’.

The velocity field in the half wake behind the swimming robot was measured using particle image velocimetry (PIV). In these experiments, the flow was seeded with silver coated hollow ceramic spheres with a diameter of 100 μm and an average specific gravity of 1.01 (Conduct-O-Fil AGSL-150-30 TRD, Potters Industries Inc., Valley Forge, PA, USA). The particles were illuminated by a Spectra Physics 2020 argon-ion laser with a wavelength of 488 nm (Newport Corporation, Mountain View, CA, USA). The laser was then used to create a light sheet with an optical fiber delivery system and a Powell lens (Oz Optics Ltd, Ottawa, ON, Canada). The sheet thickness was typically 1.5 mm. The sheet was oriented parallel to the robot in the plane in which the robot was swimming. A Redlake MotionXtra HG-LE camera (Redlake, IDT, Tallahassee, FL, USA) was used to capture the images. A Stanford Research Systems DG535 Digital Delay/Pulse Generator (Stanford Research Systems, Inc., Sunnyvale, CA, USA) was used to control the camera timing. Image pairs were captured with a time delay of 11 ms between images. Ten image pairs were captured each second, with typical exposure times of 3 ms. The camera was mounted above the water channel at a 90 deg angle to the laser sheet. The experimental configuration for PIV is shown in Fig. 2. The results presented here represent instantaneous velocity and vorticity contours.

The tail was made using a moldable PVC polymer (M-F Manufacturing, Fort Worth, TX, USA). The shape of the tail was modeled on CT scan data of a 29 cm silver lamprey, Ichthyomyzon unicuspis, provided by Dr Frank Fish (personal communication), which consisted of data slices taken every 2 mm. ImageJ software (National Institutes of Health, Bethesda, MD, USA) allowed us to extract the body points from each slice. The mold was scaled using the coordinates of the posterior third of the animal. The anterior third of the mold was hollow, to allow insertion of the last two motors, so that this part was active. The remaining part of the tail was passive, and a thin nylon insert of constant planform but different thicknesses was used to control the degree of flexibility (the insert is seen in orange in Fig. 3). The length of the flexible part of the tail, l_pass, corresponded to approximately 20% of the total length of the robot.

To quantify the relative coherence of the various wakes, we also calculated the average turbulence intensity, I, over a cycle of motion for each case. This is defined as I=u′/U, where u′ is the variance of the turbulent velocity fluctuations [u′=√1/2(u′²+v′²), where u′ and v′ are the fluctuations of the streamwise and spanwise velocity, respectively] and U is the Reynolds-averaged mean velocity (U=√U²+V², where U and V are the mean components of the streamwise and spanwise velocity, respectively).

To investigate the role of material resonance, the frequency response of tail 2 was examined by actuating it over a range of frequencies from 0.1 to 0.65 Hz while submerged in quiescent water. The tail motion was filmed, and from the video images, the peak-to-peak amplitude of the tip of the tail was measured. The results are shown in Fig. 6. A resonant plateau is found in the range of frequencies from 0.22 to 0.34 Hz. The center of this plateau was chosen as the resonant frequency for this tail, indicated by the vertical line in the figure. Experiments with this tail were performed at the resonant frequency (0.28 Hz) and at twice the resonant frequency (0.56 Hz). The Strouhal number was kept constant for these two cases by reducing the swimming speed appropriately. It is not expected that the incoming flow will greatly affect the resonant frequency given the broad resonant curve and large plateau seen in Fig. 6.

The net force produced by the lamprey was estimated from the velocity fields (obtained from PIV experiments) using a two-dimensional control volume analysis. This analysis is a standard application of the momentum equation. Details of the control volume method and all equations used here can be found in most fluid dynamics textbooks (e.g. Smits, 2000). The control volume contained the entire robot, as shown in Fig. 7. The incoming velocity is known for each experiment, and was assumed to be uniform across the inflow area. The PIV data was obtained only over the left side of the wake, so the full wake was constructed by mirroring the data and offsetting it in time by half a cycle. The portion of the control volume along the robot is neglected as reliable data is not available.

Table 1 shows the characteristics of the flexible tails. Tail 1 is the least flexible tail, with a flexural stiffness (EI) of 1.9 N m², corresponding to an average Young's modulus (E) of 0.23 MPa. The most flexible tail, tail 3, is extremely flexible and deforms almost completely under its own weight. The damped natural frequency (in air) decreases by 12% from tail 1 to tail 2, and by 35% from tail 1 to tail 3. The damping coefficient decreases by 58% from tail 1 to tail 2 and by 96% from tail 1 to tail 3 (see Table 1); in other words, oscillations persist much longer in tail 3 than in tail 1.

The disorganized vortex structure in the wakes of the flexible tails is seen clearly by direct visualization of the flow patterns, as shown in Fig. 8. At this Strouhal number (0.55), all three tails produce two pairs of vortical regions per tailbeat, but the vorticity becomes more disorganized as tail flexibility increases. In Fig. 8A (tail 1), the pair of vortices from the most recent half cycle is visible at the top of the image, near the center. The red vortex is the secondary vortex from the previous cycle, and the blue vortex is the starting-stopping vortex from the most recent change in tail direction – it will separate into two vortices. The pair is aligned in the flow direction and induces a velocity between them in the perpendicular direction. The wake behind the lamprey robot fitted with an actively flexing tail is shown in Fig. 9 for comparison. In this case, the pair of vortices is much more distinct. In the flexible case, however, the larger vortices are composed of several small vortices of the same sign. They convect together downstream for a short distance, and then begin to break up.

The wake produced by tail 2 (Fig. 8B) is similar to that of tail 1 (Fig. 8A). Again, the pair of vortices on the visible half of the wake is in the upper center of the image. The red vortex represents the secondary vortex from the previous stopping-starting motion, and the blue vortex is the primary vortex from this half cycle. The main vortex cores, however, are smaller than those in the wake of tail 1, and are composed of several vortex structures with the same sign. These small vortices convect downstream, but do not retain coherence for as long as with the stiffer tail.

The most flexible tail, tail 3, has the least coherent wake structure (Fig. 8C). The oppositely signed vortex pair for this half cycle is visible at the top of Fig. 8C. In this case, the areas of positive and negative vorticity are not as distinct as they are in the wakes of tails 1 and 2. The vortex structures, smaller and more numerous, loosely form a pair of vortices. However, the lack of organization and mixture of positive and negative vorticity in each member of the pair causes the overall structure to break down very quickly. Instead of convecting downstream as a cohesive element, only these small vortices are present in the wake. Their similar strengths and uniform distribution work to cancel each other out quickly. Hence, the overall strength of the circulation decreases at high flexibility.

For comparison, previous data from the same robot with the actively flexing tail are shown (Fig. 9) [after Hultmark et al. (Hultmark et al., 2007)]. Unlike in the present study, the tail flexes substantially but actively, and does not deform appreciably in response to fluid forces. Nevertheless, the wake is similar to those from passively flexible tails (shown in Fig. 8), with two pairs of vortices shed per cycle, but the vortices are substantially more coherent.

In addition to imaging the wake structures at a constant Strouhal number, an air-bearing carriage above the water channel was used to determine the free swimming speed of the lamprey robot with each flexible tail. Previous work (Hultmark et al., 2007) determined this for the robot with the stiff, active tail. From this data, the steady swimming speed and Strouhal number were determined for each tail (Fig. 10). Fig. 10A shows the swimming speeds (m s^-1) of the robot with each tail. We see that the active tail produces the fastest swimming whereas the most flexible tail is the slowest (discounting tail 2 at resonance, which is operating at half the frequency; see Resonance frequency below). From the active tail to tail 1, there is a 11% decrease in swimming speed; the swimming speed of tail 2 decrease a further 17% from tail 1, and that of tail 3 decreases from tail 2 by 29%.

Swimming speeds were used to calculate the steady-swimming Strouhal numbers of the tails. The results, given in Fig. 10B, show an increase in Strouhal number from the active tail to tail 1 and a continued increase to tail 2 (indicating decreased performance). Tail 3, however, shows a slightly lower Strouhal number than tail 2 (3.4%), primarily because of a decrease in tip-to-tip amplitude.

To compare the force production of the flexible tails, the axial force (i.e. the force in the swimming direction) for an entire cycle was estimated using Eqn 4, and then summed over an entire cycle. Fig. 11 shows the outward momentum flux for the three flexible tails. These are the average values for 20 cycles of each tail.

When flapping at the same frequency and amplitude (0.56 Hz and 9.8 cm, corresponding to St=0.55), the net force decreases with increasing flexibility, becoming more and more negative (Fig. 11). This general trend agrees with available previous work (e.g. Ferreira de Sousa and Allen, 2011; Masoud and Alexeev, 2010). Tail 1 (the least flexible tail) is the closest to zero momentum flux, meaning that it is closest to the self-propelled state, whereas tail 3 (the most flexible tail) has the most negative flux. In all cases, the overall momentum flux per meter is negative, indicating that there is more drag than thrust being generated. In other words, if the robot was not tethered, it would be expected to move backwards in the tank. At this frequency and amplitude, therefore, the more flexible tails result in a lower self-propelled swimming speed, as shown in Fig. 10.

Given that with any of the flexible tails the robot is no longer swimming under a self-propelled condition, we expect that the wake structures (Fig. 8) will differ from the active tail configuration show in Fig. 9. For the flexible tails to achieve steady swimming conditions, the incoming flow, U_∞, would need to be decreased, which would result in an increased Strouhal number. Despite this change, the results presented above show that the main features of the wake are preserved in this deceleration situation.

Thrust and lateral forces were also estimated based on the observed kinematics using the analytical model of Lighthill (Lighthill, 1971) (see our Eqn 5). Fig. 12 shows the kinematics and the mean forces. Increasing passive flexibility tends to decrease thrust. Note that these calculations do not include an estimate of drag, and so are not directly comparable to the net forces shown in Fig. 11. Lateral forces were similar for all of the passive tails. The actively flexing tail, however, produced the lowest thrust. Because its net force was closest to zero (Fig. 11), this suggests that its drag is also lower than the other tails.

As can be observed visually from the flow fields (Fig. 8), the degree of turbulence in the wake increased as tail stiffness decreased. We calculated average turbulence intensity in the wakes of each tail (Fig. 17). For the three stiffnesses, flapping at 0.56 Hz, turbulence intensity increased as tail stiffness decreased. For tail 2, the turbulence intensity decreased when it was flapping at resonance (0.28 Hz), indicating the greater coherence of the wake in the resonant condition. Finally, the actively flexing tail produced the most coherent wake by far, as indicated by the much lower turbulence intensity (Fig. 17).

Corresponding to the decrease in organization of vortices, the momentum flux also becomes less organized in time as tail flexibility increases. The instantaneous net force was estimated using Eqn 4. Fig. 13 shows the instantaneous force per meter during three cycles of motion. The mean value follows the same trend as the per cycle total momentum flux per meter presented in Fig. 11. Additionally, the frame-to-frame fluctuations are larger for tail 1 than the two more flexible tails, reflecting the fact that it generates more coherent vortices. In general, there is a maximum and minimum during each cycle of motion for tail 1 (this is also seen in Fig. 14). However, such organization is not as apparent in tails 2 or 3. Although there are peaks and minima present in these signals, they are less regular and distinct. The green line in Fig. 13 is the instantaneous momentum flux for the active tail. It contains a clear peak approximately halfway through the cycle and a minimum just before the cycle's end. There is also a small secondary peak 75% of the way through the cycle. Although tail 1 also has a large peak during the tailbeat cycle, it occurs later in the cycle than the peak for the active tail.

The regularity of the maxima and minima in the instantaneous momentum flux of the robot with tail 1 allows for comparison with the measured velocity fields. In Fig. 14A, five cycles of the instantaneous momentum flux per meter are shown. Frames of minimum and maximum values are indicated by circles on the plot. The corresponding instantaneous vorticity fields (with velocity vectors) are shown in Fig. 14B,C. The schematics above the flow fields indicate the tail position and direction of motion at that time in the cycle. In Fig. 14B, which corresponds to a low point in the instantaneous momentum flux per meter signal, a large structure is seen moving into the control volume in the upper left corner of the frame. This corresponds to a negative momentum flux out of the control volume. The flow field for the point with a high momentum flux (Fig. 14C) shows a large structure moving out of the control volume (right top corner), increasing the net momentum flux out of the control volume.

To examine the effects of body resonance on the wake structure, tail 2 was driven at its resonant frequency (0.28 Hz). The Strouhal number, however, was matched to previous experiments (St=0.55) by decreasing U_∞. Fig. 15 shows the vorticity contours computed from the measured velocity field. The overall field is much cleaner than that of the same tail flapping at twice the natural frequency (cf. Fig. 8B). There are fewer smaller structures present in the wake away from the main vortices, but there are nevertheless smaller vortices with the same sign that make up the two large oppositely signed vortical structures. Those constitute the 2P structure, which is visible in the upper right portion of the image. The location of those vortices is more predictable from cycle to cycle than they were when the tail was driven above its resonant frequency (0.56 Hz).

As seen in Fig. 11, when the tail is flapped at its resonant frequency, the momentum flux per meter is much higher (there is a 96% increase). Net force is approximately zero, meaning that the robot is nearly propelling itself, much like robot swimming with the active tail. However, when tail 2 is flapped at twice the natural frequency, the momentum flux in this plane drops significantly (if the robot were untethered, it would move backwards in the flow). This increase in capability is also seen in the steady-swimming Strouhal number (Fig. 10B). By flapping at the resonant frequency, the Strouhal number decreases by 25%, moving closer to St≈0.3, which is generally considered to be peak efficiency for undulatory swimmers (Triantafyllou et al., 1993).

Fig. 16 shows a comparison of the instantaneous moment flux for tail 2 at these two frequencies. In addition to an increased mean value, the fluctuations are larger for flapping at resonant frequency. The curve at the resonant frequency has more distinct maxima and minima than the curve at the higher frequency, but the peaks are not as distinct as with tail 1 (see Fig. 13).

In summary, a robotic anguilliform swimmer with a passively flexible tail produces qualitatively the same wake structure as the same robot with an actively flexing tail (Hultmark et al., 2007) and the same as swimming eels (Tytell and Lauder, 2004). Although the wake is qualitatively the same, it is less coherent than those of eels or the robot with the active tail. The wakes of the flexible tails have a higher turbulence intensity than that of the active tail (see Fig. 17). Eels have very flexible bodies (Aleyev, 1977), but they actively control body flexion (Gillis, 1998) and most likely control fin ray motion, like other fishes (Flammang and Lauder, 2008).

The disorganized wake structures may be a consequence of vortex-induced vibration in the passive tails, particularly along the edges that correspond to the location of the caudal, dorsal and anal fins. This vibration was less than 2 cm in all cases, but was particularly noticeable for the most flexible tail, which may account for its especially disorganized wake (Fig. 8C). The high flexibility of fish bodies suggests that they also have the potential to develop such vibrations. However, the coherence of fish wakes (Lauder and Tytell, 2006) suggests that they may be able to damp out vibrations. It is also likely that fish swim ‘in tune’ with their own natural frequencies, helping them to produce coherent wakes. In walking animals, muscle is used to damp out such high-frequency oscillations (Wakeling et al., 2003; Wilson et al., 2001), and although such an effect has not be observed directly in fishes, perhaps fin ray muscles could serve to filter out vortex-induced vibrations.

Additionally, the fact that the robot was, in essence, being towed (i.e. the net force was negative), may be another cause of the disorganized wake structure. The speed of the incoming flow can cause instabilities in the boundary layer along the body (Shen et al., 2003). This can decrease the cohesiveness of the vortices as they are shed into the wake. Therefore, more small structures – both in the main vortex pairs and away from these features – are likely to be present in the wake. This is seen when we compare Fig. 8B with the resonant wake of tail 2. Both the resonant and non-resonant conditions had St=0.55, but the frequency was lower in the resonant case, so U_∞ was also lower. In the resonant case, we see far fewer small vortex structures away from the main vortex pair (Fig. 15) and a slight decrease in turbulence intensity (Fig. 17). However, structures are still present within that pair, suggesting that passive flexibility (as described above) also plays an important role in decreasing wake coherence.

Data from fishes suggest that they produce relatively well-organized wakes (Lauder and Tytell, 2006), but a disorganized wake may sometimes be helpful. The wakes of swimming fishes can persist for long periods of time (Hanke and Bleckmann, 2004), and predators are able to follow the residual flow patterns (Schulte-Pelkum et al., 2007). Disorganized wakes, such as those produced by the passively flexible tails, tend to decay more rapidly than well-organized wakes (like that of the active tail), leaving a smaller signature for predators to detect. In particular, the wake behind tail 2, flapping at its resonant frequency, showed very little structure in the far wake (y<0.1 in Fig. 15), but nevertheless produced enough thrust to propel itself (Figs 13, 16). Fishes differ in the amount of time their wakes persist (Hanke and Bleckmann, 2004); perhaps they can control this duration by changing the coherence of the wake.

Nevertheless, our results suggests that, when other conditions are the same, increasing the passive flexibility of an anguilliform propulsor results in an increasingly large negative (i.e. drag-like) net force, either by decreasing the forward thrust production or increasing the drag. We find that net momentum flux is more negative for increasingly flexible tails (Figs 11, 13).

A simple model (Eqn 5) based on Lighthill's elongated body theory (Lighthill, 1971) suggests that a decrease in thrust (Fig. 12) may be the most important factor, rather than an increase in drag. Elongated body theory suggests that thrust is maximized when tail angle is relatively large when tail speed is high. For tail 1 (EI=1.9 N m²), tail angles are close to 45 deg when tail speed is high, but the tail angles are smaller in general for the more flexible tail (EI=1.0 N m²; Fig. 12A), and are at a less-optimal phase relative to the tail motion. Thus, the changes in kinematics due to increasing flexibility may account for the decrease in thrust and the more negative momentum flux (Fig. 11). Interestingly, the active tail also has a low mean thrust (Fig. 12B) but a net force close to zero. Together, these results suggest that the robot with the active tail may have a lower drag than when it has a passive tail.

Our results are generally consistent with previous computational studies. Tytell et al. (Tytell et al., 2010) developed a model of fluid-structure interaction for a swimming lamprey and found that swimming speed increased with increasing stiffness (similar to our Fig. 10), except at the very highest stiffness. Similarly, Ferreira de Sousa and Allen (Ferreira de Sousa and Allen, 2011) simulated a flexible rod and found, in general, that thrust increased with increasing stiffness, except at the very lowest stiffness. Both of these previous models cannot be compared directly with our results because they kept the driving force constant, resulting in different tailbeat amplitudes and thus different Strouhal numbers for models with different stiffness, whereas we altered the driving amplitude in order to match Strouhal number for the different values of stiffness. Nevertheless, the general patterns are consistent. Our observation of increased thrust at resonance (Fig. 11) is also similar to the simulation results of Masoud and Alexeev (Masoud and Alexeev, 2010), who found a dramatic increase in lift force for a flapping wing at resonance.

Fishes also actively control tail bending. The lamprey has muscle present all the way to the tip of its tail and, like other fishes, has muscles that control fin motion. The fin musculature is known to be active during pharmacologically stimulated fictive swimming (Mentel et al., 2008), but it is not known how the fins are activated during natural swimming. In bluegill sunfish, the caudal fin musculature is active even at the slowest swimming speeds when the fish does not use the caudal fin for propulsion (Flammang and Lauder, 2008). Thus, fishes most likely actively flex their tails even at slow speeds. However, the fin muscles are quite small and the forces they can produce are also small. Thus, fluid pressure on the fin is likely to have a strong effect on fin motion.

In this study and a previous one (Hultmark et al., 2007), we have investigated two extremes: completely active motion, in which internal forces (i.e. the motors) completely dominate external fluid forces (Hultmark et al., 2007), and the completely passive motions described here, in which the tail bending is entirely determined by the fluid forces. The motion of fish tails probably lies somewhere between these two extremes; we postulate that they are closer to the passive extreme investigated here than the completely active extreme from Hultmark et al. (Hultmark et al., 2007).

Increasing the flexibility of the tailof an anguilliform swimmer decreases the coherence of the vortices in the wake. However, even with a very flexible tail, a 2P structure is still visible. The wake of the most flexible tail also breaks down faster. The net force is lower for more flexible tails, most likely because of decreased thrust production. However, by flapping the tail at its resonant frequency, the net force becomes less negative, approaching the self-propelled state, and the wake regains much of its coherence.