Passive hydrodynamic interactions in minimal fish schools

Chang, Wei; Hu, Qiao; Zhang, Tangjia; Zeng, Yangbin

doi:10.1016/j.oceaneng.2022.110574

Cited by 17 publications

(4 citation statements)

References 69 publications

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“…Fish school: The key to understanding fish-schooling behavior lies in making clear the role of fluid medium or flow structures [120][121][122][123]. A single fish can be a good analytical entry point.…”

Section: Intermediate Viewmentioning

confidence: 99%

Research Development on Fish Swimming

Jiang

2022

Chin. J. Mech. Eng.

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Fishes have learned how to achieve outstanding swimming performance through the evolution of hundreds of millions of years, which can provide bio-inspiration for robotic fish design. The premise of designing an excellent robotic fish include fully understanding of fish locomotion mechanism and grasp of the advanced control strategy in robot domain. In this paper, the research development on fish swimming is presented, aiming to offer a reference for the later research. First, the research methods including experimental methods and simulation methods are detailed. Then the current research directions including fish locomotion mechanism, structure and function research and bionic robotic fish are outlined. Fish locomotion mechanism is discussed from three views: macroscopic view to find a unified principle, microscopic view to include muscle activity and intermediate view to study the behaviors of single fish and fish school. Structure and function research is mainly concentrated from three aspects: fin research, lateral line system and body stiffness. Bionic robotic fish research focuses on actuation, materials and motion control. The paper concludes with the future trend that curvature control, machine learning and multiple robotic fish system will play a more important role in this field. Overall, the intensive and comprehensive research on fish swimming will decrease the gap between robotic fish and real fish and contribute to the broad application prospect of robotic fish.

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Section: Intermediate Viewmentioning

confidence: 99%

Research Development on Fish Swimming

Jiang

2022

Chin. J. Mech. Eng.

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“…Things become worse when the rotation is also set free. Bergmann and Iollo [41], Novati et al [42] and Wei et al [43] studied the 3-DoF schooling of two self-propelled swimmers arranged in different initial configurations, including in-line, side-by-side, and staggered formation. The results show that the swimmers could not maintain stable configurations for a long time when only relying on the passive interactions, and they will eventually end up in collision or divergence [43].…”

Section: Introductionmentioning

confidence: 99%

“…Bergmann and Iollo [41], Novati et al [42] and Wei et al [43] studied the 3-DoF schooling of two self-propelled swimmers arranged in different initial configurations, including in-line, side-by-side, and staggered formation. The results show that the swimmers could not maintain stable configurations for a long time when only relying on the passive interactions, and they will eventually end up in collision or divergence [43]. Those studies highlight how deeply the restriction in the individual body movement influences the behavior of the schooling foils, and how difficult it is to maintain 3-DoF stable configurations in the absence of active control.…”

Section: Introductionmentioning

confidence: 99%

“…Those studies highlight how deeply the restriction in the individual body movement influences the behavior of the schooling foils, and how difficult it is to maintain 3-DoF stable configurations in the absence of active control. Thus, it is necessary to introduce appropriate control algorithms relying on the environmental information to impel swimmers to form a stable configuration [43].…”

Section: Introductionmentioning

confidence: 99%

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Learning to school in dense configurations with multi-agent deep reinforcement learning

et al. 2022

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Fish are observed to school in different configurations. However, how and why fish maintain a stable schooling formation still remains unclear. This work presents a numerical study of the dense schooling of two freely-swimming swimmers by a hybrid method of the multi-agent deep reinforcement learning and the immersed boundary-lattice Boltzmann method. Active control policies are developed by synchronously training the leader to swim at given speed and orientation and the follower to hold close proximity to the leader. After training, the swimmers could resist the strong hydrodynamic force to remain in stable formations and meantime swim in desired path, only by their tail-beat flapping. The tail movement of the swimmers in the stable formations are irregular and asymmetrical, indicating the swimmers are carefully adjusting their body-kinematics to balance the hydrodynamic force. In addition, a significant decrease in the mean amplitude and cost of transport is found for the follower in the latter two cases, indicating these swimmers could maintain the swimming speed with less efforts. The results also show that the side-by-side formation is hydrodynamically more stable but energetically less efficient than other configurations, while the full-body staggered formation is energetically more efficient as a whole.

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