Some anguilliform swimmers such as eels and lampreys swim near the ground, which has been hypothesized to have hydrodynamic benefits. To investigate whether swimming near ground has hydrodynamics benefits, two large-eddy simulations of a self-propelled anguilliform swimmer are carried out—one swimming far away from the ground (free swimming) and the other near the ground, that is, midline at 0.07 of fish length (L) from the ground creating a gap of 0.04 L . Simulations are carried out under similar conditions with both fish starting from rest in a quiescent flow and reaching steady swimming (constant average speed). The numerical results show that both swimmers have similar speed, power consumption, efficiency, and wake structure during steady swimming. This indicates that swimming near the ground with a gap larger than 0.04 L does not improve the swimming performance of anguilliform swimmers when there is no incoming flow, that is, the interaction of the wake with the ground does not improve swimming performance. When there is incoming flow, however, swimming near the ground may help because the flow has lower velocities near the ground.
more »
« less
A numerical study of fish adaption behaviors in complex environments with a deep reinforcement learning and immersed boundary–lattice Boltzmann method
Fish adaption behaviors in complex environments are of great importance in improving the performance of underwater vehicles. This work presents a numerical study of the adaption behaviors of self-propelled fish in complex environments by developing a numerical framework of deep learning and immersed boundary–lattice Boltzmann method (IB–LBM). In this framework, the fish swimming in a viscous incompressible flow is simulated with an IB–LBM which is validated by conducting two benchmark problems including a uniform flow over a stationary cylinder and a self-propelled anguilliform swimming in a quiescent flow. Furthermore, a deep recurrent Q-network (DRQN) is incorporated with the IB–LBM to train the fish model to adapt its motion to optimally achieve a specific task, such as prey capture, rheotaxis and Kármán gaiting. Compared to existing learning models for fish, this work incorporates the fish position, velocity and acceleration into the state space in the DRQN; and it considers the amplitude and frequency action spaces as well as the historical effects. This framework makes use of the high computational efficiency of the IB–LBM which is of crucial importance for the effective coupling with learning algorithms. Applications of the proposed numerical framework in point-to-point swimming in quiescent flow and position holding both in a uniform stream and a Kármán vortex street demonstrate the strategies used to adapt to different situations.
more » « less- Award ID(s):
- 1856237
- NSF-PAR ID:
- 10360563
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 11
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
more » « less
Active particles consume energy stored in the environment and convert it into mechanical motion. Many potential applications of these systems involve their flowing, extrusion, and deposition through channels and nozzles, such as targeted drug delivery and out‐of‐equilibrium self‐assembly. However, understanding their fundamental interactions with flow and boundaries remain incomplete. Herein, experimental and theoretical studies of hydrogen peroxide (H2O2) powered self‐propelled gold–platinum nanorods in parallel channels and nozzles are conducted. The behaviors of active (self‐propelled) and passive rods are systematically compared. It is found that most active rods self‐align with the flow streamlines in areas with high shear and exhibit rheotaxis (swimming against the flow). In contrast, passive rods continue moving unaffected until the flow rate is very high, at which point they also start showing some alignment. The experimental results are rationalized by computational modeling delineating activity and rod‐flow interactions. The obtained results provide insight into the manipulation and control of active particle flow and extrusion in complex geometries.
-
Abstract Swimming microorganisms switch between locomotory gaits to enable complex navigation strategies such as run-and-tumble to explore their environments and search for specific targets. This ability of targeted navigation via adaptive gait-switching is particularly desirable for the development of smart artificial microswimmers that can perform complex biomedical tasks such as targeted drug delivery and microsurgery in an autonomous manner. Here we use a deep reinforcement learning approach to enable a model microswimmer to self-learn effective locomotory gaits for translation, rotation and combined motions. The Artificial Intelligence (AI) powered swimmer can switch between various locomotory gaits adaptively to navigate towards target locations. The multimodal navigation strategy is reminiscent of gait-switching behaviors adopted by swimming microorganisms. We show that the strategy advised by AI is robust to flow perturbations and versatile in enabling the swimmer to perform complex tasks such as path tracing without being explicitly programmed. Taken together, our results demonstrate the vast potential of these AI-powered swimmers for applications in unpredictable, complex fluid environments.more » « less
-
more » « less
Abstract Through billions of years of evolution, microorganisms mastered unique swimming behaviors to thrive in complex fluid environments. Limitations in nanofabrication have thus far hindered the ability to design and program synthetic swimmers with the same abilities. Here we encode multi-behavioral responses in microscopic self-propelled tori using nanoscale 3D printing. We show experimentally and theoretically that the tori continuously transition between two primary swimming modes in response to a magnetic field. The tori also manipulated and transported other artificial swimmers, bimetallic nanorods, as well as passive colloidal particles. In the first behavioral mode, the tori accumulated and transported nanorods; in the second mode, nanorods aligned along the toriʼs self-generated streamlines. Our results indicate that such shape-programmed microswimmers have a potential to manipulate biological active matter, e.g. bacteria or cells.
-
more » « less
In this study, we numerically investigate the effects of the tail-beat phase differences between the trailing fish and its neighboring fish on the hydrodynamic performance and wake dynamics in a two-dimensional high-density school. Foils undulating with a wavy-like motion are employed to mimic swimming fish. The phase difference varies from 0° to 360°. A sharp-interface immersed boundary method is used to simulate flows over the fish-like bodies and provide quantitative analysis of the hydrodynamic performance and wakes of the school. It is found that the highest net thrust and swimming efficiency can be reached at the same time in the fish school with a phase difference of 180°. In particular, when the phase difference is 90°, the trailing fish achieves the highest efficiency, 58% enhancement compared with a single fish, while it has the highest thrust production, increased by 108% over a single fish, at a phase difference of 0°. The performance and flow visualization results suggest that the phase of the trailing fish in the dense school can be controlled to improve thrust and propulsive efficiency, and these improvements occur through the hydrodynamic interactions with the vortices shed by the neighboring fish and the channel formed by the side fish. In addition, the investigation of the phase difference effects on the wake dynamics of schools performed in this work represents the first study in which the wake patterns for systems consisting of multiple undulating bodies are categorized. In particular, a reversed Bénard–von Kármán vortex wake is generated by the trailing fish in the school with a phase difference of 90°, while a Bénard–von Kármán vortex wake is produced when the phase difference is 0°. Results have revealed that the wake patterns are critical to predicting the hydrodynamic performance of a fish school and are highly dependent on the phase difference.
