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Research Article

Efficient collective swimming by harnessing vortices through deep reinforcement learning

View ORCID ProfileSiddhartha Verma, Guido Novati, and View ORCID ProfilePetros Koumoutsakos
PNAS June 5, 2018 115 (23) 5849-5854; first published May 21, 2018; https://doi.org/10.1073/pnas.1800923115
Siddhartha Verma
aComputational Science and Engineering Laboratory, ETH Zürich, CH-8092 Zürich, Switzerland
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Guido Novati
aComputational Science and Engineering Laboratory, ETH Zürich, CH-8092 Zürich, Switzerland
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Petros Koumoutsakos
aComputational Science and Engineering Laboratory, ETH Zürich, CH-8092 Zürich, Switzerland
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  • ORCID record for Petros Koumoutsakos
  • For correspondence: petros@ethz.ch
  1. Edited by James A. Sethian, University of California, Berkeley, CA, and approved April 25, 2018 (received for review January 22, 2018)

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    Fig. 1.

    Efficient coordinated swimming of two and three swimmers. (A) DNS of two swimmers, in which the leader swims steadily and the follower maintains a specified relative position such that it increases its efficiency by interacting with one row of the vortex rings shed by the leader. The flow is visualized by isosurfaces of the Q criterion (28). (B) DNS of three swimmers, where the two followers maintain specified positions that increase their efficiency by interacting with both rows of the vortex rings shed by the leader. (C) DNS of three swimmers with the follower benefitting from one row of wake vortices generated by each leader. Animations of the 3D simulations are provided in Movies S1–S3.

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    Fig. 2.

    Learning efficient swimming strategies: Differences between 2D and 3D flow fields. (A) The smart swimmer relies on a predefined set of variables to identify its “observed state” (such as range and bearing relative to the leader that are depicted). Additional observed-state parameters are described in Methods. (B) Comparison of vorticity field in the wake of 2D (Upper) and cross-section of the 3D (Lower) swimmers (red, positive; blue, negative). In 2D, the leader’s wake vortices are aligned with its centerline. In contrast, in 3D flows, the wake vortices are diverging, leaving a quiescent region behind the leader. In 2D, smart followers must align with the leader’s centerline. In 3D, they must orient themselves at an angle to harness the wake vortex rings (WRs). Every half a tail-beat period, the smart follower in 2D simulations (ISη) autonomously selects the most appropriate action encoded in policy πη learned during training simulations, which allows it to maximize long-term swimming efficiency (Movie S4). The smart follower is capable of adapting to deviations in the leader’s trajectory (Movie S5), as these situations are encountered when performing random actions during training. (C) Relative horizontal displacement of the smart followers with respect to the leader, over a duration of 50 tail-beat periods starting from rest (solid blue line, ISη; dash-dot red line, ISd). (D) Lateral displacement of the smart followers. (E) Histogram showing the probability density function (PDF; left vertical axis) of swimmer ISη’s preferred center-of-mass location during training. In the early stages of training (first 10,000 transitions; green bars), the swimmer does not show a strong preference for maintaining any particular separation distance. Toward the end of training (last 10,000 transitions; lilac bars), the swimmer displays a strong preference for maintaining a separation distance of either Δx=1.5L or 2.2L. The solid black line depicts the correlation coefficient, with peaks in the black curve signifying locations where the smart follower’s head movement would be synchronized with the flow velocity in an undisturbed wake (see SI Appendix for relevant details). (F) Comparison of body deformation for swimmers ISη (Upper) and ISd (Lower), from t=27 to t=29. Their respective trajectories are shown with the dash-dot lines, whereas the dashed gray line represents the trajectory of the leader. A quantitative comparison of body curvature for the two swimmers may be found in SI Appendix, Fig. S1.

  • Fig. 3.
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    Fig. 3.

    Energetics data for a smart follower maximizing its swimming efficiency. Swimming efficiency (A) and CoT (B) for ISη (solid blue line) and SSη (dash-double-dot black line), normalized with respect to the CoT of a steady solitary swimmer. Four instances of maximum and minimum efficiency, which occur periodically throughout the simulation at times (nTp+0.12), (nTp+0.37), (nTp+0.62), (nTp+0.87), have been highlighted. Tp=1 denotes the constant tail-beat period of the swimmers, whereas n represents an integral multiple. The decline in η at point E (t≈27.7, η=0.86) results from an erroneous maneuver at t≈26.5 (Movie S7), which reveals the existence of a time delay between actions and their consequences.

  • Fig. 4.
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    Fig. 4.

    Flow field and flow-induced forces for ISη, corresponding to maximum efficiency. (A) Vorticity field (red, positive; blue, negative) with velocity vectors shown as black arrows (Upper) and velocity magnitude shown in Lower (bright, high speed; dark, low speed). The snapshots correspond to t=26.12, i.e., point ηmax(A) in Fig. 3A. Demarcations are shown at every 0.2L along the body center line for reference. The wake vortices intercepted by the follower (W1U, W1L, W2U, W2L), the lifted vortices created by interaction of the body with the flow (L1, L2, and L3), and secondary vorticity S1 generated by L1 have been annotated. (B) Flow-induced force vectors (Upper) and body deformation velocity (Lower) at t=26.12. (C and D) Deformation power (C) and thrust power (D) (with negative values indicating drag power) acting on the upper surface of follower. The red line indicates the average over 10 different snapshots ranging from t=30.12 to t=39.12. The envelope signifies the SD among the 10 snapshots. (E and F) Deformation power (E) and thrust power (F) on the lower (left lateral) surface of the swimmer.

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    Fig. 5.

    The 3D swimmer interacting with WRs. (A) Swimming efficiency for a 3D leader (dash-dot red line) and a follower (solid blue line) that adjusts its undulations via a proportional-integrator (PI) feedback controller to maintain a specified position in the wake. After an initial transient, the patterns visible in the efficiency curves repeat periodically with Tp. Time instances where the follower attains its minimum and maximum swimming efficiency have been marked with an inverted red triangle and an upright green triangle, respectively. The sudden jumps at t≈18.3 and 19.3 correspond to adjustments made by the PI controller. (B) An oncoming WR is intercepted by the head of the follower and generates a new LR (C) similar to the 2D case (Fig. 4). As this ring interacts with the deforming body, it lowers the swimming efficiency initially (t≈17.8; A and C), but provides a noticeable benefit further downstream (t≈18.2; A and D).

Data supplements

  • Supporting Information

    • Download Appendix (PDF)
    • Download Movie_S01 (MP4) - 3D simulation of three nonautonomous swimmers, in which the leader swims steadily, and the two followers maintain specified relative positions such that they interact favourably with the leader’s wake. The flow-structures have been visualized using isosurfaces of the Q-criterion.17
    • Download Movie_S02 (MP4) - 3D simulation of two nonautonomous swimmers, in which the leader swims steadily, and the follower maintains a specified relative position to interact favourably with the wake. The energetic-benefit for the follower is similar to that of each of the followers in Supplementary Movie S1.
    • Download Movie_S03 (MP4) - 3D simulation of three nonautonomous swimmers, in which the leaders use a feedback controller to maintain formation abreast of each other, and the follower holds a specified position relative to the leaders. The energetic-benefit for the follower is double that of the followers in Supplementary Movies 1 and 2, as it now interacts profitably with wake-rings generated by both the leaders.
    • Download Movie_S04 (MP4) - 2D simulation of a pair of swimmers, in which the leader swims steadily, and the follower (ISη) takes autonomous decisions to interact favourably with the wake. The upper panel (labelled ‘⍵’) shows the vorticity field generated by the swimmers, whereas the second panel (labelled ‘v’) shows the lateral flow-velocity. The smart-swimmer appears to synchronize the motion of its head with the lateral flow-velocity, which allows it to increase its swimming-efficiency. The lower panels show the energetics metrics, namely, the swimming efficiency η, the thrust-power PThrust, the deformation-power PDef, and the Cost of Transport (CoT).
    • Download Movie_S05 (MP4) - 2D simulation of a pair of swimmers, where the leader performs random actions, and the follower takes autonomous decisions to benefit from the flow-field. The smart-follower, which was trained with a steadily-swimming leader, is able to adapt to the erratic leader’s behaviour without any further training. Remarkably, the follower chooses to interact deliberately with the wake in order to maximize its long-term swimming-efficiency, even though it has the option to swim clear of the unsteady flow-field.
    • Download Movie_S06 (MP4) - A qualitative comparison between swimmer ISη and a real fish following a leader. We observe that the motion of ISη resembles that of the live follower quite well. The leader in the simulation executes random turns after every few tail-beat cycles, and the follower responds to changes in range and bearing, similarly to Supplementary Movie S4.
    • Download Movie_S07 (MP4) - Detailed view of the flow-field around smart-swimmer ISη. The top panel shows the vorticity field in colour and velocity vectors as black arrows. The middle panels show the swimming-efficiency and the deformation-power. The distribution of thrust-power and deformation-power along the swimmer’s left- (‘lower’) and right-lateral (‘upper’) surfaces are shown in the lower panels, and depict how these quantities depend on wake-interactions.
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Efficient collective swimming by harnessing vortices through deep reinforcement learning
Siddhartha Verma, Guido Novati, Petros Koumoutsakos
Proceedings of the National Academy of Sciences Jun 2018, 115 (23) 5849-5854; DOI: 10.1073/pnas.1800923115

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Efficient collective swimming by harnessing vortices through deep reinforcement learning
Siddhartha Verma, Guido Novati, Petros Koumoutsakos
Proceedings of the National Academy of Sciences Jun 2018, 115 (23) 5849-5854; DOI: 10.1073/pnas.1800923115
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