3.1. Interpretable learning with different state information

In a control system, the choice of sensors usually plays a crucial role. It is thus essential to determine the impact of each sensor as well as their combinations, so as to identify the most influential sensors. First, we explore the effects of different combinations of kinematic variables that provide feedback signals. Using the same hyperparameters listed in table 2 but different random seeds, we performed six groups of trainings and plotted the learning curves in figure 4, where the Reynolds number is all fixed as $100$. Here $\langle {\cdot } \rangle$ indicates an average over one episode. Because the training process involves a certain level of randomness, we conducted three trials for each training in figures 4(a) and 4(b) and observed quite similar learning trends. For the cases in figure 4(c) that will be the focus of the remaining sections, we performed five trials. Therefore, figure 4 consists of $23$ training trials, i.e. 46 000 individual CFD cases in total.

Figure 4.

Training processes using all six types of combinations of sensory-motor cues as the state space. In each training process, three or five independent trials are performed. The associated performances are shown with translucent lines, while the mean data are shown as thick solid lines.

In most trials, the learning starts with a rapidly decreasing trend and then proceeds with a gradually converging trend. All the learning curves eventually reach good convergence. Taking figure 4(c) as an example, the DRL reaches a level of $\langle |y^*|\rangle \approx 0.01$ roughly at the $500$th episode, then progresses rather slowly beyond this point. Although the learning curve sometimes appears to show a non-decreasing trend, the agent can always escape from the locally optimal strategy and find the right search direction again after some unsuccessful trials. This fact reflects the self-adaptivity and reliability of the DRL agent in exploring nonlinear systems.

By comparing the learning curves of different combinations of sensors, it can be observed that using the velocity sensor alone leads to the worst control performance, while the combination of all three kinematic variables as sensors provides the best-converged strategy, where $\langle |y^*|\rangle$ eventually reaches a level of approximately $0.002$, which is a remarkable level of VIV suppression. In addition, the $\ddot {y}^*$ sensor alone can lead to a VIV suppression to approximately $0.01$, yielding a less effective but simpler alternative for the control.

One question that comes to mind is how the DRL agent searches the optimal strategy in one single training. In other words, how does the AI agent think and make decisions during the exploration process? To answer these questions, we select four representative training trials with different state spaces from figure 4 and plot the scatters of the mean drag, vortex lift fluctuation and cross-flow displacement against the AFC forcing strength in figure 5. Although, as we have mentioned, the search path involves a certain level of randomness, it is surprising to see that figure 5 reveals some clear search paths. In particular, by comparing the search paths of $\langle |y^*|\rangle$ in figure 5(a iii), one can observe that the training process can be roughly divided into three stages. In Stage I, the DRL agent explores a strategy that applies larger and larger forcing strength, which does gradually improve the control performance before hitting a plateau at approximately the $150$th episode, when $\langle |y^*|\rangle$ becomes close to $0.08$, with the fluctuation of $\omega ^*$ approaching its saturated value as can be more clearly observed in figure 7(b). In Stage II, a sudden turn appears in the $\langle |C_D|\rangle \sim \langle \omega ^{*2}\rangle ^{1/2}$ trajectory as well as the $\langle |y^*|\rangle \sim \langle \omega ^{*2}\rangle ^{1/2}$ trajectory, occurring roughly at the 150th–200th episode. Unlike in Stage I, although the forcing strength in Stage II gradually decreases, the control performance keeps improving, and reaches a level of $\langle |y^*|\rangle \approx 0.01$ at the end of Stage II around the $1000$th episode. The subsequent Stage III experiences a slower converging stage, in which the DRL agent carefully adjusts its strategy. Eventually, the DRL agent achieves a strategy that almost completely suppresses the VIV. The $\langle C_D \rangle \sim \langle \omega ^{*2}\rangle ^{1/2}$ curve shows a similar trend to that of the $\langle |y^*|\rangle \sim \langle \omega ^{*2}\rangle ^{1/2}$ curve, and the close relationship between $\langle C_D \rangle$ and $\langle |y^*|\rangle$ is further confirmed by figure 6(a). As the main hydrodynamic source that drives the VIV, the root-mean-square (r.m.s.) of the fluctuation part of $C_V$, i.e. $C_V'$, shows a gradually decreasing trend, except for scatter at the very beginning. From the trajectories in figure 5(a), one can see that the DRL agent adopts different strategies in the exploration process, and the fact that changing tactics when proceeding along one direction can no longer improve the performance implies that the decision making of the DRL agent is adaptive.

Figure 5.

Trajectories of the (a) mean drag, (b) r.m.s. of the vortex force and (c) absolute value of the transverse displacement against the AFC forcing strength during training. Four training processes with different combinations of sensory-motor cues are shown in (a–a). The scattered points are coloured with the episode number, ranging from $1$ to $2000$. Five representative cases in subpanels of (a) are marked with white star.

Figure 6.

The trajectories of (a) the mean drag versus the absolute value of the transverse displacement and (b) the momentum coefficient versus power coefficient. Training with the full state space $\{\ddot {y}^*, \dot {y}^*, y^*\}$ is used in this case.

However, one can only find a similar Stage I in figure 5(b iii) and in the first two stages in figure 5(d iii). In contrast, figure 5(c iii) only shows a gradually decreasing trend and eventually reaches a high level of vibration displacement, while exerting increasing AFC forcing and causing increased drag. Recalling figure 4(a), this case is actually a failure. The trajectories illustrated in figure 5 reflect that properly selected sensors play a crucial role in DRL-guided AFC, and can lead to distinct exploration paths during training. Importantly, the revealing of distinct stages implies that while exploring the VIV environment, the DRL agent is smart and capable enough to make radical adjustments to its control strategy when the existing learned strategies cannot make further contributions, which we believe is related to the underlying physics of the system being controlled.

To better understand the physics behind the search path, we draw the $\langle {C_D}\rangle \sim \langle {|y^*|}\rangle$ curve and the $\langle {C_\mu }\rangle \sim \langle {C_P}\rangle$ curve in figure 6. The nearly monotonic variation of $\langle {C_D}\rangle$ against $\langle {|y^*|}\rangle$ implies that the reduction in the mean drag of the controlled cylinder is mainly accompanied by the reduction in the distance swept by the VIV cylinder. This close connection also suggests that $C_D$ could potentially be a good indicator of VIV control. In other words, in future applications, the drag and lift forces exerted on the cylinder could be good alternatives to provide feedback control signals. Figure 6(b) shows the connection between the kinetic energy of the rotary cylinder and the power supplied to the actuator. It is quite surprising to see that the three stages identified in figure 6 are the three edges of a triangle-like closed loop. That is, the initial $\langle {C_\mu }\rangle \sim \langle {C_P}\rangle$ pair roughly overlaps that in the final stage. To unveil more insights, a comparison will be made in § 3.3 between the initial control strategy (taking the 25th episode as a representative) and the final control strategy (the 1996th episode), which consume similar amounts of energy. It is also noted that Stage II proceeds along an energy-saving direction. However, throughout Stage III, $C_\mu$ is almost fixed while the energy supplied to the actuator monotonically increases. Eventually, the performance is further improved at the cost of an almost doubled energy input.

During the exploration process, since the actuation is sampled from a probabilistic distribution determined by the DRL agent, the recorded actuation and state both involve a certain degree of noise. To remove this impact, we utilize the control strategies learned from the $5$th to the $1995$th episode ($ep$ is the episode number), and perform deterministic runs every five episodes. In the second half of theses deterministic runs, the mean value, the fluctuation amplitude and the frequency of actuation are obtained, as depicted in figure 7(a–c). In figure 7(c), because the fast Fourier transformation is performed using the second half of each episode that involves $100T_0$, i.e. 10 000 actuation samples, the finest frequency resolution is $0.01$. This is why the action frequency appears discontinuous. From the case validations illustrated in Appendix A, we show that the actuation frequency follows exactly the same frequency as the kinematic variables, which is consistent with the overlapping frequency of $\omega ^*$ and $y^*$. We also calculate the phase lag between the actuation and the kinematic variables based on correlation analysis and present the results in figure 7(d).

Figure 7.

The variations of the control quantities during a deterministic run from the fifth episode to the $2000$th episode with an interval of five episodes. The (a) mean actuation, (b) fluctuation amplitude of actuation, (c) frequency of actuation and (d) phase lag between the actuation and kinematic variables. The three stages are identified again and classified using different background colours.

From figure 7, one can deduce the following features of the three stages. In Stage I ($ep < 150$), the DRL agent adjusts both the amplitude and frequency of the AFC forcing, both being increased. In Stage II ($150 < ep < 1000$), the DRL agent also adjusts both the amplitude and frequency of the AFC forcing, both being gradually reduced though. In Stage III ($ep > 1000$), the DRL agent gradually adjusts its phase lag against the kinematic variables. A phase lag close to $20^\circ$ between $\omega ^*$ and $\ddot {y}^*$ is finally obtained. In both Stages I and II, action biases, i.e. non-zero mean actions, are observed. It takes a long trial process for the bias to disappear at the end of Stage II. This problem almost vanishes in Stage III.

In the above divisions into three stages, the transition boundary from Stage I and Stage II is quite clear based on either the peak of the action amplitude or the location of the largest action frequency. However, because the change from Stage II to Stage III is relatively vague, in figure 7, the boundary between Stage II and Stage III is only a rough approximation; from the aspect of energy utilization shown in figure 6(b), where $C_\mu$ reaches its minimum and $C_P$ starts to increases again. It also remains a question why Stage I transits to Stage II, which is not simply a consequence of saturated action. This question will be discussed in § 3.3.

It is observed from figure 7(d) that the phase between $\omega ^*$ and $\ddot {y}^*$ is the closest, suggesting that $\ddot {y}^*$ always reacts with changes in $\omega ^*$ faster than the other two sensors. We guess this is a key reason why the $\ddot {y}^*$ sensor is selected from the very beginning, and plays a key role during the whole exploration process.

Note that the above interpretations of the learned control strategy are essentially different from the cluster method (Fernex et al. Reference Fernex, Noack and Semaan2021; Li et al. Reference Li, Fernex, Semaan, Tan, Morzyński and Noack2021), where the state information is projected onto a low-dimensional phase space and different strategies are then clustered. By contrast, the way we interpret the search path is less general but more straightforward from the physics perspective, and benefits from a small number of sensors. In addition, the effect of hyperparameters used in the DRL is examined and the data are shown in Appendix B, where it is indicated that the above findings are less sensitive to the hyperparameters. The fact that different hyperparameters lead to the very similar division of three stages confirm that the above findings are reasonable and solid.

3.2. Sensitivity analysis

For control with three sensor signals, it is worthwhile to know how sensitive each sensor is in the control strategy. Therefore, we perform a sensitivity analysis via the Sobol method (Saltelli Reference Saltelli2002; Sobol Reference Sobol2014), based on SALib – a sensitivity analysis library implemented in Python. Here the ‘actor’ network that establishes the mapping between sensor signals and the control action is utilized, together with randomly selected values within the actuation range. Total sensitivity indices $S_T$ are used to quantify the sensitivity of each control input to the control output. According to the definition in Sobol (Reference Sobol2014), $S_T$ is an invariance-based sensitivity index that measures the contribution of the variance of each input argument to the variance of the output, which including all variance caused by its interactions, of any order, with any other input variables. In a whole training, $S_T$ of each sensory-motor cue is shown in figure 8, where, the same as in the deterministic runs, we perform the sensitivity analysis from the $5$th to $2000$th episode with an interval of five episodes.

Figure 8.

The total sensitivity index for all sensory-motor cues during one training. The sensitivity analysis is conducted from the fifth to the $2000$th episode with an interval of five episodes.

First, it is seen that the $\ddot {y}^*$ sensor maintains a much higher sensitivity level than the other two sensors. This qualitative conclusion is consistent with results in figure 4, where the $\ddot {y}^*$ sensor is the most effective one. Second, the evolution of $S_T$ also exhibits similar features to the three stages identified in § 3.2. As revealed in figure 7, the DRL agent adjusts the actuation amplitude and frequency during Stages I and II, where theoretically one signal among the three kinematic variables is enough. This is why $S_T$ of $\ddot {y}^*$ shows an increasing trend during these two stages. However, because Stage III is a phase-adjusting stage and the $\ddot {y}^*$ sensor alone cannot achieve this, the contributions of the other two sensors start to grow during this stage. This explains why the $S_T$ of both $y^*$ and $\dot {y}^*$ show a dramatically increasing trend in Stage III. From the sensitivity analysis, one can also see that adjustments of the actuation amplitude and frequency are easier than the long-march phase adjustment process.

3.3. Deterministic control using strategies learned at different stages

After the DRL training reaches convergence, we obtain the control law established by the ‘actor’ network. Among the 23 training processes, we select a few cases with a full state space $\{\ddot {y}^*, \dot {y}^*, y^*\}$ as representatives, occurring at the $25$th, the $150$th, the $200$th, the $998$th and the $1996$th episode. We conduct a group of deterministic controls using these strategies learned at different episodes to elucidate the difference of control performance.

Figure 9 demonstrates temporal variations of the rotary forcing recorded in episodes of different stages. From figure 9(e), one can see that the actuation reaches saturation in the early few control cycles, and then gradually becomes quasisteady. To maintain the control performance, a rotational velocity with an amplitude of approximately $0.4$ is required, corresponding to the linear velocity at the edge of the cylinder being less than $U_0/5$. Note that the vortex shedding frequency does not deviate from the natural resonance frequency, as confirmed in Appendix A, suggesting that the main physical mechanism of VIV suppression lies in that the DRL-guided control successfully alters the flow field to an attenuated vortical flow, and that the lift generated by the vortices are balanced by the Magnus effect induced by the rotary forcing. Intriguingly, from all selected cases, as well as the information in figure 7(c), the DRL agent does not proceed with seeking a proper frequency being the vortex shedding frequency in the uncontrolled situation, or its super harmonics or subharmonics, so as to utilize the ‘lock-on’ effect, which is what the AFC community usually adopted.

Figure 9.

(a) Temporal variations of the rotational velocity after the AFC is turned on. (b) The frequency spectrum of $\omega ^*$ in the quasisteady state. Here $f^*$ denotes the frequency normalized by $T_0^{-1}$. The $25$th, $200$th and $1996$th episode are representatives of Stage I, Stage II and Stage III, respectively. The $150$th episode is the boundary between Stage I and Stage II and the $998$th episode the boundary between Stage II and Stage III.

One can now observe another significant fact that at Stage II, the DRL agent gradually learns the control strategy that a large forcing shall be exerted at the first few periods so as to rapidly mitigate the vibration to a new and low-amplitude state. After that, the periodic or quasisteady forcing is applied to fine tune this state. In this sense, the initial stage of each episode that involves a few vibration cycles with higher forcing strength actually plays a vital role in achieving the final VIV suppression objective. Without this initial process, the VIV control can hardly be realized even with a higher-level of forcing, just like cases in Stage I. This finding suggests that the DRL agent is really smart. This type of control strategy can hardly be done in the open-loop manner.

Temporal variations of quantities related to energy consumption, i.e. $C_P$ and $C_\mu$, are depicted in figure 10. Under the converged control strategy at the $1996$th episode, after the initial stage, in which the DRL-guided control strategy requires a relatively large energy input to rapidly reduce the vibration, only a small amount of energy input is needed to maintain the control performance in the quasisteady state. In contrast, the $25$th episode does not show this kind of adaptation. Further quantitative comparisons are based on the power-saving ratio ($PSR$), defined as

(3.1)\begin{equation} PSR = \frac{\langle \Delta P_D \rangle_{T}}{\langle P_C \rangle_{T}}, \end{equation}

where ${\Delta P_D = -\Delta F_DU_0}$ denotes the saved power used to drive the streamwise motion in the situation where the cylinder is cruising with a speed of $U_0$. This quantity is averaged within a time horizon of one vortex shedding period. Here $P_C$ is the power consumed by the AFC. After normalization, we have $PSR = -\langle \Delta C_D \rangle _{T}/\langle {C_P}\rangle _{T}$, where $\Delta C_D$ is the reduced drag coefficient. Therefore, as estimated in the quasiperiodic state, the power saving ratio is $78.7$ if compared with the uncontrolled VIV case, and $1.85$ if compared with the uncontrolled stationary case. In some prior studies (Protas & Wesfreid Reference Protas and Wesfreid2002; Bergmann, Cordier & Brancher Reference Bergmann, Cordier and Brancher2005), where rotary control was used for drag reduction under laminar conditions, $PSR$ did not exceed $1.0$. Therefore, the DRL-guided control presented in this study is energetically efficient.

Figure 10.

Temporal variations of the power coefficient and the momentum coefficient in the deterministic control using controllers learned in selected episodes.

Recalling that in figure 5(a i–iii), similar levels of rotary amplitude were found in the initial and final training stages, it is interesting to see how apparently similar actuations lead to distinct control results. To find the reason, we illustrate the temporal variations of $\omega ^*$, $y^*$, $\dot {y}^*$ and $\ddot {y}^*$ during one quasisteady cycle of the $25$th and the $1996$th episodes in figure 11. Four instantaneous snapshots are selected, when $\ddot {y}^*$, $\omega ^*$, $\dot {y}^*$ and $y^*$ are, in sequence, at their lowest positions. Although a slightly biased $\omega ^*$ appears in the $25$th episode, the amplitudes of $\omega ^*$ in the two cases are very close, i.e. approximately $0.4$. It can be found that the forcing frequency of the $25$th episode is slightly higher than that of the well learned strategy in the $1996$th episode. Furthermore, the $\omega ^*-\ddot {y}^*$ phase lag in the $25$th episode is also slightly smaller, coinciding with the data in figure 7.

Figure 11.

The temporal variation of the rotational forcing as well as the kinematic variables in the quasisteady state. Flow fields at four representative instants are demonstrated. The unsuccessful $25$th episode is shown here for comparison purposes.

In figure 5, although the fluctuation amplitude of the rotary actuation in the two episodes do not show big differences, the two strategies do lead to distinct FSI dynamics. With the control strategy learned in the $25$th episode, the wake exhibits a C(2S) vortex shedding pattern (Williamson & Roshko Reference Williamson and Roshko1988) like in the uncontrolled case, where two single vortices shed within one vibration cycle, and the like-signed vortices coalesce in the downstream region, which is approximately $15$ diameters from the cylinder. On the contrary, a transition to the 2S pattern is observed in figure 11(b). As seen in the VIV phase diagram provided by Williamson & Govardhan (Reference Williamson and Govardhan2004), the 2S pattern is usually associated with a larger wavelength, or smaller vortex shedding frequencies, and this is confirmed by the above results. Intuitively, the near-wall shear layer in figure 11(a) forms an inclined angle using the $25$th episode strategy, which is measured between the centreline of the wake and the boundary of the positive and negative shear layers. This phenomenon appears to be almost symmetrical in figure 11(b). This flow feature also reveals well-controlled structure vibrations.

From instant E to instant F, the negative rotational velocity (clockwise direction) transports momentum from the upper right-hand side of the wake to the lower right-hand side, mitigating the asymmetrical force induced by the near-wall flow structures. In this sense, maintaining the relatively good stability of the upper and lower shear layers is vital for the success of the present DRL-guided VIV control. Now, one can also imagine that if the rotary actuation is performed somewhat earlier (corresponding to a smaller $\omega ^*-\ddot {y}^*$ phase lag) or later (corresponding to a larger $\omega ^*-\ddot {y}^*$ phase lag), the influence of shear layers on the lift could become quite different. Evidence in the later § 3.4 would confirm that the DRL agent does remarkably enhance the stability of this FSI system.

3.4. Deterministic control using the converged control strategy

With the control strategy learned at the $1996$th episode, we illustrate in figure 12 the three-dimensional (3-D) trajectory of $\ddot {y}^*\unicode{x2013}\dot {y}^*-y^*$, with and without the control. In this 3-D space, the uncontrolled trajectory forms a periodic orbit. With a phase lag of $180^\circ$ between $\ddot {y}^*$ and $y^*$, the projection on the two-dimensional (2-D) $\ddot {y}^*\unicode{x2013}y^*$ plane appears to be roughly a straight line. After the control is turned on, this orbit gradually shrinks to its centre and finally reaches the quasiequilibrium state, a tiny closed loop in 3-D space.

Figure 12.

The 3-D scatters of $\ddot {y}^*-\dot {y}^*-y^*$, with projections on the 2-D planes. The scatters are denoted by blue square cubes and pink spheres, respectively, for instants before and after the control is turned on.

Temporal variations of the hydrodynamic force and torque coefficients, as well as the vibration response, are depicted in figure 13. Less than five VIV periods, after the DRL agent starts to mediate the originally periodic FSI system, the FSI system falls into a quasisteady state. In this controlled state, the drag exerted on the cylinder is reduced by $37\,\%$ compared with the uncontrolled VIV case, accompanied by a reduced drag fluctuation of $98\,\%$. Furthermore, the quasisteady drag resembles that in the uncontrolled stationary case. Therefore, the drag reduction mainly stems from the narrowed distance swept by the cylinder, as shown in figure 13(d). In figure 13(b), the lift experiences a sharp change at the beginning of the control, even exceeding that of the uncontrolled VIV case in the first vortex shedding period, then decays quickly as the control continues and is eventually suppressed to almost zero. Meanwhile, a large torque with sharp variations in the beginning is required to drive the rotary motion, as can be seen in figure 13(c). Figure 13(d) demonstrates the successfully suppressed VIV, where the final vibration amplitude is only approximately $0.2\,\%$ of the cylinder diameter (an enlarged view can be found in figure 11), corresponding to a VIV amplitude suppression rate of $99.6\,\%$. Furthermore, for the VIV case, only a $1.3\,\%$ frequency shift is observed after control. This almost unaffected VIV frequency indicates that the system is still in the locked-in condition.

Figure 13.

Time histories of (a) the drag coefficient $C_D$, (b) the lift coefficient $C_L$, (c) the moment coefficient $C_M$ and (d) the transverse displacement $y^*$. The uncontrolled stationary case, the uncontrolled VIV case, and the DRL-controlled case are organized together for comparison purposes.

Comparisons of the three lift force components are depicted in figure 14, where $C_E$ is reduced by a factor of five for the sake of clarity. From the definitions in § 2.1, one can see that the vortex force is the hydrodynamic source that drives the VIV. The non-harmonic variation of $C_V$ stems from the vortex shedding mode. After control, the fluctuation of $C_V$ is reduced by a factor of over $700$. This can be viewed as further evidence of well-suppressed VIV. It is noted that the mean $C_V$ is not zero, which is a consequence of the non-zero mean actuation, as evidenced by the fast Fourier transformation results in figure 9(b).

Figure 14.

Temporal variations of the three lift components of (a) the uncontrolled VIV case and (b) the DRL-controlled VIV case (the vortex force, the added-mass force and the elastic force).

In order to figure out why the VIV can be suppressed in the $1996$th episode, the lift variation in one cycle is shown in figure 15 as well as three instants corresponding to minimum, moderate and maximum lift force, respectively. The pressure field near the cylinder and the vorticity field are also depicted. For the three cases, the vortices that form and shed from the cylinder surface play key roles in lift generation and the resulting VIV.

Figure 15.

Temporal variation of lift coefficient. Pressure field and vorticity field at three representative instants are demonstrated. For the DRL-controlled VIV case, arrows are shown to represent the corresponding rotary direction and amplitude, wherein a full circle means $|\omega ^*|=0.4$.

In the uncontrolled stationary case, when $C_L$ is at its minimum at instant A, the anticlockwise vortex (labelled as ‘acw-vortex’) at its lower surface is forming and the clockwise vortex (labelled as ‘cw-vortex’) is about to shed from the upper surface. Correspondingly, the pressure on its lower surface is much lower than that on the upper surface, resulting in a negative lift force. At instant B, both the acw-vortex and cw-vortex have moved to the downstream. The positive and negative shear layers lead to a balanced pressure distribution on the cylinder.

In the uncontrolled VIV case, at instant D, the cylinder is moving downwards and is near its lowest position. The cw-vortex is about to shed and has a lesser effect on the cylinder. The negative shear layer on the upper surface provides larger pressure than the positive shear layer on the lower surface, resulting in the maximum negative lift. At instant E, the cylinder is near its equilibrium position and is moving upwards with almost its largest transverse velocity. The upward motion creates a high-pressure region on the upper left-hand surface, meanwhile the cw-vortex provides a low-pressure region on the upper right-hand surface, leading to a balanced transverse force.

In the DRL-controlled VIV case, the well-controlled cylinder is nearly stationary. At instant G, since the cylinder is rotating in the anticlockwise direction (i.e. positive $\omega ^*$), the shedding of an acw-vortex is promoted earlier (compared with the uncontrolled stationary case), thus exerting little effect on the pressure distribution over the cylinder surface. The lift force is thus suppressed to a value around zero. Similarly, at instant I, the early shedding of a cw-vortex is induced by the clockwise rotation of the cylinder.

The above results reveal that the DRL knows when to exert the right amount of rotary forcing, so that effective control can be conducted by actively adjusting the phase lag between the rotational forcing and the lift (or the transverse displacement).

To reveal control in a transient process, we present the time histories of the cross-flow displacement $y^*$, the rotational velocity $\omega ^*$ and the rotational acceleration ${\rm d}\omega ^*/{\rm d}t^*$* in figure 16, as well as the pressure and vorticity fields at the selected instants figure 16(a) to figure 16(i). The dark and light grey background marks three cycles, all starting from a zero $y^*$ with upwards motion. The instant in figure 16(a) is the starting point. The control starts at figure 16(a). At instants in figure 16(a,d,f) and figure 16(h), sharp changes of the control forcing occur, whereas at instants in figure 16(c,e,g) and figure 16(i), the oscillation displacement reaches its local maximum or minimum. In these cycles, the control forcing maintains mostly at its maximum allowed value, either positive or negative.

Figure 16.

Temporal variation of the cross-flow displacement $y^*$, the rotational velocity $\omega ^*$ and its temporal derivative ${\rm d}\omega ^*/{\rm d}t^*$. Pressure field and vorticity field at representative instants are demonstrated.

It is observed that the sign of $\omega ^*$ is always opposite to that for $y^*$, suggesting that the DRL agent adopts a simple yet effective opposition control in this initial stage, by utilizing the Magnus effect, i.e. the anticlockwise rotary motion induces a downward force, and vice versa. Therefore, the force induced by rotary motion would counteract the recovery elastic force. As revealed in figure 14, the elastic force is much larger than the lift components. As such, the rotary induced lift force can mitigate the very large recovery elastic force, gradually slowing down the cylinder's transverse motion.

Note that the Magnus effect is only one of the lift components. Another lift component induced by the vortex formation and shedding is nonlinear and complex. The phase difference between the vortex shedding and the cylinder rotation can affect this lift component, which is reflected by the clear phase lags between $y^*$ and $\omega ^*$, at instants in figure 16(b,f). As having been discussed in § 3.1, this type of phase difference also appears in the quasisteady stage, suggesting that the DRL agent has learned a more complicated strategy than the opposition control.

From instant in figure 16(a) to figure 16(b), $\omega ^*$ rapidly increases, which retards the growth of the cw-vortex. At the instant in figure 16(b), $\omega ^*$ reaches its local maximum and then sharply decreases, promoting for the shedding of the cw-vortex and the formation of the following positive shear layer as evidenced at the instant in figure 16(c). Similar phenomena can also be found at instants in figure 16(d,f,h). Comparing instants in figure 16(b,d,f,h), the large low-pressure region gradually becomes smaller and the pressure difference between the upper and lower surfaces is gradually attenuated. This indicates that the DRL-guided control successfully modulates the vortex dynamics. At the instant in figure 16(c), the elastic force is at its negative maximum, the clockwise rotation stimulates the growth of the negative shear layer while retarding the growth of the positive shear layer, leading to an upwards lift force so as to counteract downward elastic force. Similar phenomena can be found at instants in figure 16(e,g,i).

The time-averaged flow fields displayed in figure 17 further reveal the impact of DRL-guided control. Through comparisons, one can note that the low-velocity far downstream region in the uncontrolled VIV case vanishes after control, making the DRL-controlled flow resemble the uncontrolled stationary case, while the length of the recirculation bubble represented by the zero streamwise velocity contour is increased by $16\,\%$. Prior studies (Rabault et al. Reference Rabault, Kuchta, Jensen, Réglade and Cerardi2019; Ren et al. Reference Ren, Rabault and Tang2021a) have shown that the change in the drag force is strongly related to the length of the recirculation bubble, where an elongated recirculation bubble is usually accompanied by attenuated and delayed vortex shedding. Furthermore, the attenuated transverse flow after control indicates a much weaker source of cross-flow vibration.

Figure 17.

Comparisons of the time-averaged streamwise velocity, transverse velocity and pressure field between (a) the uncontrolled stationary cylinder, (b) the uncontrolled VIV cylinder and (c) the DRL-controlled VIV cylinder. In subpanels (a i) and (c i), the recirculation bubble is represented by the $\bar {u} = 0$ contour line.

3.5. Dynamic mode decomposition analysis

To demonstrate changes in the coherent flow structures and flow stability characteristics before and after control, we perform a series of DMD analyses over three flow types, i.e. the uncontrolled stationary case, the uncontrolled VIV case and the DRL-controlled case. Compared with the more popular method of proper orthogonal decomposition, which extracts flow structures based on their energy ranking, DMD provides more clear information related to time or frequencies. With DMD, the unsteady flow field can be reconstructed using limited DMD modes, i.e.

(3.2)\begin{equation} \boldsymbol{v}(\boldsymbol{x}, t) = \sum_{i=0}^{L\leqslant N}{\rm e}^{\mu_it}\boldsymbol{\varPhi}_i(\boldsymbol{x}), \end{equation}

where $N$ is the total number of snapshots. The real and imaginary parts of $\mu _i$ represent the growth rate and angular frequency of the $i$th DMD mode $\boldsymbol {\varPhi }_i(\boldsymbol {x})$, respectively. Note that we have included in $\boldsymbol {\varPhi }_i(\boldsymbol {x})$ the corresponding amplitude, which reflects the influence of the initial data on the DMD mode. Detailed information about the DMD algorithm can be found in Schmid (Reference Schmid2010) and Wang et al. (Reference Wang, Tang, Yu and Duan2017a).

In this study, we extract a series of flow fields with a time interval of $0.02T_0$ within a duration of one vortex shedding period, and the numbers of snapshots used in the three flow systems are $307$, $275$ and $279$, respectively. In our tests, these numbers of snapshots are sufficient to obtain converged DMD results. When conducting the DMD analysis, we select part of the whole domain ranging from $x^* = 0.50$ to $x^* = 14$ and from $y^* = -4$ to $y^* = 4$. It should be noted that the zone that contains the moving cylinder is removed, so as to avoid the effect of moving structure on the DMD analysis. As can be seen from the DMD modes of figure 19, this subdomain is large enough to cover most flow features both around the cylinder and in the wake.

Sorting the DMD modes by their frequencies, we obtain the frequencies (non-dimensionalized as Strouhal numbers) and growth rates of all DMD modes. For the sake of brevity, we list the data for the first four DMD modes in table 3. Here, the Strouhal number of Mode 1 is consistent with the lift frequency extracted in figure 13, and is the dominating fluctuation frequency, i.e. the vortex shedding frequency. The frequencies of Mode 3 and Mode 5 correspond to two and three times the vortex shedding frequency, respectively. The first four growth rates for all three cases are of an order of $10^{-4}$ or smaller, implying that the corresponding modes are neutrally stable.

Table 3.

Comparisons of the amplitude, Strouhal number and growth rate of the first four DMD modes.

In addition to table 3, we plot the growth rate against the Strouhal number for all DMD modes in figure 18. The negative-frequency points are the adjoint modes, making the data symmetrical along the vertical axis. For the uncontrolled stationary case, only a few modes including Mode 1 have positive growth rates. For the uncontrolled VIV case, approximately one-third of the DMD modes have positive growth rates. However, after control, no positive growth rate is found, proving that the DRL-guided control does enhance the flow stability. Moreover, the two peaks in the uncontrolled case suggest that the flow instability can be triggered by either low-frequency or high-frequency perturbations. This potential source of instability is eliminated by the DRL-guided control.

Figure 18.

Scatter plots of the Strouhal number versus growth rate. (a–c) Results for the uncontrolled stationary case, the uncontrolled VIV case and the DRL-controlled VIV case, respectively. (d) A comparison between the three cases involving only the low-frequency modes. The colours determine the mode number. Negative Strouhal numbers represent the corresponding adjoint modes.

Figure 19 shows the dominant DMD modes of the uncontrolled stationary case, the uncontrolled VIV case and the DRL-controlled VIV case. Among these modes, Mode 0 is the mean mode that corresponds to the time-averaged vorticity field, where one can see a pair of symmetrical shear layers. After control, Mode 1 shows a much narrower shear layer compared with figure 19(b i), as well as an elongated shear layer compared with figure 19(a i), consistent with the time-averaged variables illustrated in figure 17. Mode 1 is connected to the lift fluctuation and is thus strongly associated with VIV suppression. In Mode 1, one can see in-line alternating vortices behind the cylinder in figure 19(a ii) and figure 19(c ii), which are much narrower and weaker than those in the uncontrolled VIV case. Vortices whose centre are off the centrelines appear in the uncontrolled VIV case, suggesting that their momentum along the cross-flow direction is larger, gained when interacting with the cylinder. This phenomenon is evidence of larger lift fluctuations of the cylinder exerted by the surrounding fluid. These flow features explain the changes after the VIV is suppressed. In Mode 3, one can see pairs of counter-rotating vortices whose centres are symmetrical along the centreline of the wake flow, which is the main cause of drag fluctuation. Comparing figures 19(b iii) and 19(c iii), the attenuated near-wall flow structures after control explain the remarkably mitigated drag fluctuation.

Figure 19.

The three dominant DMD modes calculated from the uncontrolled stationary case, the uncontrolled VIV case and the DRL-controlled VIV case.

Therefore, the DMD analysis explains the remarkable enhancement of flow stability achieved with DRL-guided control. Furthermore, changes in the coherent structures show that DRL-guided control mediates the flow to a state resembling that of the stationary case while generating an elongated recirculation bubble as well as weak small-scale flow structures.

3.6. Robustness at different Reynolds numbers

In this section, we test the robustness of the DRL-guided control strategy within a Reynolds number range of $[40, 300]$. Instead of directly applying the learned strategy to the off-design conditions, we introduce the idea of transfer learning to further improve performance. We retain almost all of the same set-ups as in the $Re = 100$ case but reinitialize the ‘actor’ and ‘critic’ neural networks with well-trained model parameters as in the control strategy of § 3.2. The training then continues until reaching convergence. The converging trends for various Reynolds numbers are shown in figure 20. Note that in figure 20(a), the actuation range of $\omega ^* \in [-1, 1]$ is retained. However, due to the saturated forcing occurring around $Re = 180$, we enlarge the actuation range and choose an interval of $[-2, 2]$ for the $Re = 200$ case. For the $Re = 300$ case, we choose a larger actuation interval of $[-3, 3]$ and start the transfer learning via inheriting the strategy learned at $Re = 200$. Instantaneous vorticity fields, as well as the vibration responses and AFC forcing at selected Reynolds numbers, are organized into figure 21.

Figure 20.

Transfer learning for cases at Reynolds numbers ranging from $40$ to $300$. In panels (a,b), the first episode starts from the learned strategy at $Re = 100$. For the $Re = 300$ case in (b), the DRL agent inherits the converged strategy from the $Re = 200$ condition.

Figure 21.

The flow fields, vibration response and rotational forcing from DRL-guided control at selected Reynolds numbers. Instantaneous snapshots of (a) the uncontrolled VIV case and (b) the DRL-controlled VIV case. (c) Temporal variations of the vibration amplitude before and after the control is turned on, as well as the AFC forcing. In panels (a,b), instants are selected when the cylinder is crossing its equilibrium position with upward velocity.

At $Re = 40$, the VIV is under the subcritical Re condition (Mittal & Singh Reference Mittal and Singh2005). When the DRL-guided control is turned on at $t^* = 0$, the flow becomes steady and only slight rotary forcing is needed to sustain the suppressed VIV. For $Re \leqslant 120$, the starting point of the transfer learning shows that the learned strategy at $Re = 100$ is already good, capable of suppressing the VIV to a level around $0.001$ times the cylinder diameter. In the following transfer learning process, the curve only shows a very slow decreasing trend, indicating that there is almost no more space for the DRL agent to improve its performance. As Re is further increased to $140$, a larger VIV amplitude is noted in the beginning, suggesting that the learned strategy cannot be directly applied under this condition. However, with transfer learning, the control performance can be further improved to a level of roughly $\langle |y^*|\rangle = 0.003$. This case vigorously verifies the advantage of transfer learning.

As Re is further increased to $160$, a similar trend is observed as in the case of $Re = 140$, while showing a higher starting point and less effective control performance. This change suggests that the increased Re starts to make the VIV control more difficult. The DRL-guided control experiences a failure when Re is increased to $180$, and the performance is only slightly improved as the learning proceeds. The finally converged strategy shows unsuccessful VIV suppression, as depicted in figure 21(c iv). Meanwhile, we also note that the actuators have already reached saturation at this point, explaining why the DRL can hardly improve the performance during transfer learning. Therefore, we expand the actuation range in cases when a higher Reynolds number is involved.

From the perspective of the flow field, one can also observe that in figure 21(a ii,iii,b ii,iii,c ii,iii), a transition from the C(2S) vortex shedding pattern to the 2S pattern occurs. The phenomenon that the DRL-controlled flow field shows a larger centre-to-centre distance between two adjacent and like-signed vortices suggests that the vortex shedding period is enlarged by the rotary control. The less successful case, the $Re = 180$ case, does not show a pattern transition but the distance between the parallel-moving vortices is narrowed. At $Re = 200$ and $Re = 300$, a dramatic transition of the alternating 2S vortex shedding pattern to the C(2S) pattern is observed, which can be explained by the high-frequency rotary forcing in figures 21(c v) and 21(c vi). This frequency shift in the rotary forcing reveals a distinct mechanism of VIV control, which, as concluded by Du & Sun (Reference Du and Sun2015), is due to the fact that the vortex shedding frequency is attracted to the forcing frequency. In other words, for the cases when $Re = 200$ and $Re = 300$, the control strategy discovered by the DRL actually utilizes the ‘lock-on’ effect. A similar vortex shedding pattern of the $Re = 300$ case can also be found in the work of Protas & Wesfreid (Reference Protas and Wesfreid2002), where harmonic rotary control with a frequency of two times the vortex shedding frequency was utilized. In fact, this frequency ratio matches the forcing frequency over the vibration frequency in figure 21(c vi) well.

It should be noted that in this section, while performing the transfer learning, we do not follow the prior practice in § 3.1, in which a group of three or five trials was conducted so as to avoid the possible effects of training randomness, and thus we do not intend to imply that the strategies showcased in figure 21 are the only optimal ones. Our main objective is to demonstrate the robustness of DRL-guided control in terms of different Re, and these cases indeed prove that the DRL-guided control is qualified. In addition, we understand that the VIV at $Re=300$ involves the 3-D effect. Here we still choose the 2-D simulations so as to keep consistent with the other cases. In the future, 3-D and even the fully turbulent conditions would be thoroughly studied. Note that in higher-Reynolds-number conditions, our choice of state space, action and reward for VIV control would not be affected, thus we anticipate that the model-free and data-driven DRL could possibly be still effective.

3.7. Robustness with a perturbed upstream flow

In the Introduction, we mentioned that current studies related to DRL-guided AFC lacks sufficient tests and confirmations with perturbed environments, which is the more realistic situation. In this section, we further verify the robustness of the DRL-guided control strategy in a perturbed upstream flow. We superpose a harmonic perturbation to the uniform velocity $U_0$ at the inlet (Konstantinidis & Bouris Reference Konstantinidis and Bouris2009; Zhao et al. Reference Zhao, Kaja, Xiang and Yan2013; Ma et al. Reference Ma, Zhou, Wang, Yang, Li and Cao2021), and thus the inlet velocity becomes

(3.3)\begin{equation} U_0' = U_0(1+\epsilon_{p}\sin(2{\rm \pi}f_pt)), \end{equation}

where $\epsilon _p$ is the perturbation amplitude and $f_p$ the perturbation frequency. In the study of Konstantinidis & Bouris (Reference Konstantinidis and Bouris2009), both the harmonic (as (3.3)) and the non-harmonic upstream perturbations induced complicated flow patterns and hydrodynamic force responses. In a recent study that focused on the effect of a sinusoidal streamwise gust on the vortex-induced force on an oscillating rectangular cylinder using both wind tunnel experiments and a CFD approach (Ma et al. Reference Ma, Zhou, Wang, Yang, Li and Cao2021), it was found that the flow structure was sensitive to the upstream flow condition. For demonstration purposes, we fix the perturbation amplitude to quite a challenging level, $\epsilon _p = 0.5$ (a value of $0.325$ is used in Konstantinidis & Bouris (Reference Konstantinidis and Bouris2009) and Zhao et al. (Reference Zhao, Kaja, Xiang and Yan2013)). In this sense, the robustness is essentially different from that of § 3.4, due to the fact that the Reynolds number of the incoming velocity dynamically varies within a range of $[50, 150]$. We then focus on the effect of perturbation frequency on the DRL-guided control and test a series of $f_p$, with the dimensionless frequencies $f_pT_0$ being $1$, $0.5$, $0.25$ and $0.125$, respectively. For consistency, the DRL-guided control uses the same learned strategy that was tested in §§ 3.2 and 3.3. The instantaneous snapshots, as well as the temporal response of the vibration amplitude and the AFC forcing, are depicted in figure 22.

Figure 22.

Comparisons of the instantaneous streamwise velocity between (a) the uncontrolled cases and (b) the DRL-controlled cases. (c) The vibration responses and rotational forcing both before and after the AFC is turned on. Cases with four perturbation frequencies are illustrated from subpanel (i) to subpanel (iv).

In cases where the perturbation frequency is large, the streamwise velocity is easier to mix up, causing a less clear streamwise wave as shown in figure 22(a). As shown by the distributions of the high-velocity and low-velocity regions, which can be regarded as signatures of the vibrating cylinder, low-frequency perturbations generally have a stronger impact on the flow field. After control, the DRL-controlled flow field appears to have well-attenuated flow signatures for all perturbation frequencies. Furthermore, as can be seen from figure 22(c), the vibrations are all well suppressed. After the dynamical vibration reaches convergence, the average $\langle |y^*|\rangle$ become $0.0013$, $0.0027$, $0.0141$ and $0.0028$, respectively. In addition, when perturbations are introduced to the FSI system, one can see remarkable reactions from the variations in $\omega ^*$. In figure 22(c i), the rotary control is almost unaffected by the perturbation. However, in figure 22(c ii–iv), the AFC forcing almost reaches saturation. When the perturbation frequency approaches the VIV resonance frequency (see table 1), we see the most challenging case with $f_pT_0$ being $1/4$, as shown in figure 22(c iii). In this case, the asynchronous perturbation and vibration cause difficulties for the DRL in making decisions and induce high-frequency and saturated actuations.

The onset of complicated frequency features reflects the significant impact of the perturbed upstream flow on the flow and vibration response, whereas the DRL agent can still successfully realize the vibration suppression objective. Therefore, the above robustness tests prove that the DRL-guided control is not only efficient under uniform upstream flow conditions, but is also successful when the environment is experiencing a certain level of perturbation.

In our prior study (Ren et al. Reference Ren, Wang and Tang2021b) targeted at eliminating the wake signatures of a circular cylinder, it was observed that the attenuated wake signature is closely related to the hydrodynamic forces exerted on the cylinder, as well as the vibration characteristics. The findings here, on the opposite side, show that the suppression of vibration also facilitates the attenuation of wake signatures, even with strong upstream perturbations.