Fig 3 - available via license: CC BY
Content may be subject to copyright.
Lateral force (F y ). (A & C,) Spatiotemporal distribution of the lateral force on the body for two periods from the simulation. The dashed black line indicates a zero-crossing (phase) of the force. (B & D) Comparison of the phase of the lateral force along the body from CFD (solid black line, the same as the dashed lines in A and C) with the phase of the negation of the velocity and the phase of the negation of the acceleration. A 2π term is added or subtracted to ensure continuity. https://doi.org/10.1371/journal.pcbi.1006883.g003
Source publication
How muscles are used is a key to understanding the internal driving of fish swimming. However, the underlying mechanisms of some features of the muscle activation patterns and their differential appearance in different species are still obscure. In this study, we explain the muscle activation patterns by using 3D computational fluid dynamics models...
Citations
... Meanwhile, the fish model usually uses a slender body or thin plate geometrically, making accurate inertial torque analysis impossible. To eliminate the limitations of these modeling methodologies, Ming et al. used a 3D CFD simulation with a self-propelled fish model to investigate the torque and power output patterns for anguilliform and carangiform swimmers (Ming et al., 2019). They analyze the energy transfer, storage and release within the body, explaining the mechanisms of muscle activation regarding fluid/inertial torque and body elasticity. ...
... Every point on the body at a time instant t is rotated by θ b (t) so that the angular momentum is conserved (Ming et al., 2019). According to the above method, we obtained the middle lines of the fish model for kinematic specification, which are shown in Fig. 1E. ...
... The NPL is also observed in Fig. 4A, as the speed of the torque wave moving backward, ν T , is 3.31 L/T, which is higher than the propagation speed of the body curvature wave ν 0 = 1.0. Ming et al. showed similar results in a mackerel fish model, which shared the same middle line curvature with our model (Fig. 4B) (Ming et al., 2019). In comparison, the speed of the torque wave is 2.11 L/T, which is much lower than that of Carp. ...
... Conservation of linear momentum was achieved by fixing the COM at the origin; next, we assumed that the fish had an angular velocity to ensure that the angular momentum was conserved. The method is described in detail in [21]. The middle-line curves used in this paper were derived from video recordings of a bluegill sunfish escape process [11]. ...
... For each moment, we rotated the curve by θ b (t) to ensure that the angular momentum was conserved. The method is described in detail in [21]. By the above method, we obtained middle-line kinematic input that satisfies the conservation of linear momentum and of angular momentum, as shown in Figure 2e. ...
The fast start of fish is a rapid event that involves fast actuation in musculature and highly unsteady hydrodynamics. Fast-start capability is of great significance for fish to either hunt prey or escape from predators. In this study, we used a three-dimensional CFD model to study the hydrodynamics of a crucian carp during a C-type fast start. This study confirms the previous observations from both experiments and simulations that the jets are induced by the fast start for force generation, and the vortex rings generated in both the preparation and propulsion stages connect to each other. In addition, an obvious vortex ring generated by the head during the propulsion stage was observed, which potentially benefits the rotational motion during the fast start. According to the hydrodynamic information from CFD modeling, we established a model to analyze the internal torque, which represents the muscular actuation. The backward traveling speed of internal torque is 1.56 times the curvature speed, which confirms the existence of neuromechanical phase lag during the fast start of fish. This study potentially benefits the design of robot fish in terms of kinematics and driving mode.
... Ji and Huang (2017) carried out a numerical simulation on the influence of Re on energy extraction efficiency of the wave flexible model. Ming et al. (2019) coupled the three-dimensional CFD model to the motion of fish, calculated the distribution of torque and power. Zhao and Dou (2019a) carried out a numerical simulation on the combined undulating-motion pattern (CUMP) of bionic fish. ...
This paper proposes a prediction strategy for the hydrodynamic performance of bionic fish. The major challenges are meshing and building prediction model. The NACA0012 airfoil is used to replace the fish driven by the body and/or caudal fin (BCF), and a two-dimensional swimming geometric model is constructed. The geometric model is divided into hybrid meshes using the overset mesh method. The classical traveling wave model is studied using Matlab, and an improved self-propelled motion model is established. The hydrodynamic performance of the self-propelled model is numerically simulated based on computational fluid dynamics (CFD). In this strategy, the geometric model and the self-propelled motion model are integrated with user defined functions (UDF). The influences of the parameters such as inflow velocity, frequency, wavelength, and head fluctuation amplitude on the motion performance are studied. The results show that when inflow velocity is uniform, the self-propelled motion will eventually reach a quasi-steady state. According to the numerical simulation results, a hydrodynamic performance prediction model is established based on multilayer perceptron (MLP). The model is used to predict the performance of the optimized traveling wave parameters, and the error is within 3 (Formula presented.). The accuracy and generalization ability of the MLP prediction model is verified. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
... The force generation and power consumption patterns of a solitary 2D swimmer, shown in Figs. 3(a) and 3(b), are similar to those of a three-dimensional mackerel, 36 which is also a carangiform swimmer. Unsurprisingly, the fish snout always suffers drag, while the tail generates most of the thrust, as shown in Fig. 3(a). ...
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.
... In this study, we focused on steady swimming where the thrust produced by the fish balanced the hydrodynamic drag it experiences. For carangiform swimming, thrust is produced by the cyclic beating of the posterior region of the fish body, while the hydrodynamic drag is experienced dominantly in the anterior part of the body (Ming et al., 2019). In our simulation, we implemented a swimming pattern with a pre-determined tail beat amplitude and frequency. ...
Elucidating the hydrodynamics of fish swimming is critical to identifying the processes underlying fish orientation and schooling. Due to their mathematical tractability, models based on potential flow are preferred in the study of bidirectional interactions of fish with their surroundings. Dipole-based models that assimilate fish to pairs of vortices are particularly enticing, but yet to be thoroughly validated. Here, we embark on a computational fluid dynamics (CFD) campaign informed by experimental data to validate the accuracy of dipole-based models. The locomotory patterns of a fish undergoing carangiform swimming are reconstructed from existing experimental data, which are used as inputs to CFD simulations of a fish swimming in a channel flow. We demonstrate that dipole-based models are accurate in capturing key features of the fluid flow, but cannot predict the elongated flow streamlines around the fish that are evident in CFD. To address this issue, we propose an alternative model that replaces each vortex in the pair with a sheet along the fish length. Using a pair of vortex sheets that span approximately 80% of the fish body length with a separation distance of approximately 50% of the body width, the model is successful in predicting the fluid flow around the swimming fish for a range of background flow speeds and channel widths. The proposed model shows improved accuracy at the cost of a mildly increased computational effort, thereby constituting an ideal basis for research on fish hydrodynamics.
... Patel established hydrodynamically resolved computational neuromechanics by combining the neuromechanical model into the CFD method [89]. Ming and Zhao studied muscle activation patterns and muscle-contraction model of pre-strains, respectively [90,91]. Tokić concluded the relationship between muscle efficiency and body size [92]. ...
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.
... Simulation of flow fields from yolk-sac larvae to juveniles shows that larvae need to continuously adjust their sensory, neural, and muscular systems to adapt to changing flow regimes [76]. Furthermore, simulations coupled to the motion of fish explain the traveling wave speeds of the muscle activations [77]. Swimming kinematics are also strongly influenced by the morphology of the particular species [78]. ...
The blind troglobite cavefish Sinocyclocheilus rhinocerous lives in oligotrophic, phreatic subterranean waters and possesses a unique cranial morphology including a pronounced supra-occipital horn. We used a combined approach of laboratory observations and Computational Fluid Dynamics modeling to characterize the swimming behavior and other hydrodynamic aspects, i . e ., drag coefficients and lateral line sensing distance of S . rhinocerous . Motion capture and tracking based on an Artificial Neural Network, complemented by a Particle Image Velocimetry system to map out water velocity fields, were utilized to analyze the motion of a live specimen in a laboratory aquarium. Computational Fluid Dynamics simulations on flow fields and pressure fields, based on digital models of S . rhinocerous , were also performed. These simulations were compared to analogous simulations employing models of the sympatric, large-eyed troglophile cavefish S . angustiporus . Features of the cavefish swimming behavior deduced from the both live-specimen experiments and simulations included average swimming velocities and three dimensional trajectories, estimates for drag coefficients and potential lateral line sensing distances, and mapping of the flow field around the fish. As expected, typical S . rhinocerous swimming speeds were relatively slow. The lateral line sensing distance was approximately 0.25 body lengths, which may explain the observation that specimen introduced to a new environment tend to swim parallel and near to the walls. Three-dimensional simulations demonstrate that just upstream from the region under the supra-occipital horn the equipotential of the water pressure and velocity fields are nearly vertical. Results support the hypothesis that the conspicuous cranial horn of S . rhinocerous may lead to greater stimulus of the lateral line compared to fish that do not possess such morphology.
... These St values fall into the range of the real fish and are located between the viscous regime and inertia regime. 19 The caudal fin of a fish using BCF undulatory locomotion is an effective propeller, and its body usually introduces drag, 1,35 but the body might act as a foil to generate thrust. 13 To quantify the roles of the tail and body, we divide the fish into the body and tail at the peduncle. ...
As an important structure for generating thrust, the shapes of fish tails have adaptively evolved to achieve great swimming performance through natural selection over hundreds of millions of years. The particular optimal tail shape of fish is not universal for all situations but significantly varies with other factors, such as undulatory kinematics. In this study, using a sharp-interface immersed boundary method with a self-propelled model, we investigated the hydrodynamic performance of swimmers that equipped with three different caudal fins in the a common undulatory mode, carangiform locomotion, to determine the optimal shape. The three caudal fins tested are as follows: a round fin emulating that of snakehead fish (channidae), an indented fin emulating that of saithe (pollachius virens), and a lunate fin emulating that of tuna (thunnus thynnus). At the regular undulating amplitude A = 0.1L at the tail tip (L is the body length), the swimmer with the indented tail achieves the highest speed U = 1.45 L/s with a relatively high quasipropulsive efficiency of ηq = 0.324; at a higher undulating amplitude A = 0.15L, the indented tail swimmer achieves slightly lower speed than the lunate fin swimmer, who has the highest speed (2.06L/s vs. 2.07 L/s), but the efficiency of the former is higher than that of the latter. Therefore, the indented fin is believed to perform the best among the three fins tested regarding carangiform undulatory swimming. This finding is consistent with observations made on fish in nature, e.g., carangiform swimming fish that have been evolving for hundreds of million years have a caudal fin similar to the indented caudal fin in the current study.
... . Some of the different types of flapping bodies and motions considered are: rigid or flexible foils (Lighthill 1960;Wu 1971;Anderson et al. 1998;Heathcote & Gursul 2005) undergoing heaving and/or pitching motions (Freymuth 1988;Lewin & Haj-Hariri 2003;Von Ellenrieder, Parker & Soria 2003;Triantafyllou et al. 2004;Buchholz & Smits 2005;); flexible foils oscillated at one point and otherwise bending passively (Alben 2008b(Alben , 2009cMichelin & Smith 2009;Yeh & Alexeev 2014;Hoover et al. 2018;Hess, Tan & Gao 2020), or with an internal driving force distributed all along the foil (Tytell et al. 2016;Ming et al. 2019); foils oscillated transversely to an imposed oncoming flow (Anderson et al. 1998;Lewin & Haj-Hariri 2003), or swimming (translating/rotating) freely under a force balance law (Vandenberghe, Zhang & Childress 2004;Alben & Shelley 2005;Spagnolie et al. 2010;Alben et al. 2012;Yeh & Alexeev 2014). Another large body of work has considered the stability and dynamics of passive flexible flags, plates and membranes in fluid flows (Shelley & Zhang 2011). ...
... However, due to the inability to precisely control the individual characteristic parameters (such as the flapping amplitude, the flapping frequency, the shape of flapping pattern, and the phase lag along the body curve) and the difficulty to measure locomotor characteristics of live fish (such as the thrust, the speed, and the efficiency), there still remains some unanswered questions before the fish swimming mechanism is fully understood. Among those studies about flapping patterns, the fish's body is usually modeled as a rigid/flexible foil [10][11][12][13][14][15][16][17]. One general conclusion is that the thrust rises with the increase of the flapping amplitude and the flapping frequency. ...
The excellent swimming performances of live fish motivate scientists and engineers around the world to study its swimming mechanism and develop fish-like underwater robots, namely, the biomimetic robotic fishes. This paper compares different designs of biomimetic robotic fishes performing Body and/or Caudal Fin (BCF) swimming locomotion, and stresses how the designs evolve. The general trend is to utilize a simpler and more robust mechanism to make biomimetic robotic fishes mimic their counterparts in nature better, at the same time, to exhibit better swimming performances. Representative studies are given and discussed. Challenges of current studies are summarized and future research directions are presented. With state-of-the-art engineering and biological technologies, the biomimetic robotic fishes have great potentials in some areas where the conventional screw propellers are not applicable, like narrow space navigation and eco-friendly environment monitoring.
