Self-propelled swimming simulations of bio-inspired smart structures

Morphology and motor behavior of endemic fishes in the upper reaches of the Yangtze River basin

Dam construction alters the hydrodynamic conditions, consequently impacting the swimming behavior of fish. To explore the effect of flow hydrodynamics on fish swimming behavior, five endemic fish species in the upper Yangtze River basin were selected. Through high-speed video visualization and computer analysis, these species' swimming patterns under different flow velocities (0.1-1.2 m/s) were investigated. The kinematic and morphological characteristics of the fish were presented. The principal component analysis was used to analyse the main factors influencing the swimming ability of fish and to determine the correlation coefficients among fish behavior indicators. Fish exhibited three different swimming patterns under different flow velocities. Low velocity (0.1-0.3 m/s) corresponds to free motion, middle velocity (0.4-0.7 m/s) corresponds to cruising motion, and high velocity corresponds to stress motion (0.8-1.2 m/s). The fish kinematic index curves were obtained, and four of five fish species showed two extreme points, which means the optimal and adverse swimming strategies can be determined. With the increase in flow velocity, the tail-beat frequency showed an increasing trend, whereas the tail-beat angle and amplitude showed a decreasing trend. Morphological and kinematic parameters were the two main indexes that affect the swimming ability of fish, which accounts for 41.9% and 26.9%, respectively.

Numerical Simulations of Flow around Copepods: Challenges and Future Directions

Copepods are small aquatic creatures which are abundant in oceans as a major food source for fish, thereby playing a vital role in marine ecology. Because of their role in the food chain, copepods have been subject to intense research through different perspectives from anatomy, form-function biology, to ecology. Numerical simulations can uniquely support such investigations by quantifying: (i) the force and flow generated by different parts of the body, thereby clarify the form-function relation of each part; (ii) the relation between the small-scale flow around animal and the large-scale (e.g., oceanic) flow of its surroundings; and (iii) the flow and its energetics, thereby answering ecological questions, particularly, the three major survival tasks, i.e., feeding, predator avoidance, and mate-finding. Nevertheless, such numerical simulations need to overcome challenges involving complex anatomic shape of copepods, multiple moving appendages, resolving different scales (appendage-, animal- to large-scale). The numerical methods capable of handling such problems and some recent simulations are reviewed. At the end, future developments necessary to simulate copepods from animal- to surrounding-scale are discussed.

A non-dimensional parameter for classification of the flow in intracranial aneurysms. I. Simplified geometries

Non-dimensional parameters are routinely used to classify different flow regimes. We propose a non-dimensional parameter, called Aneurysm number (An), which depends on both geometric and flow characteristics, to classify the flow inside aneurysm-like geometries (sidewalls and bifurcations). The flow inside aneurysm-like geometries can be widely classified into (i) the vortex mode in which a vortex ring is formed and (ii) the cavity mode in which a stationary shear layer acts similar to a moving lid of a lid-driven cavity. In these modes, two competing time scales exist: (a) a transport time scale, T t , which is the time scale to develop a shear layer by transporting a fluid particle across the expansion region, and (b) the vortex formation time scale,T v . Consequently, a relevant non-dimensional parameter is the ratio of these two time scales, which is called Aneurysm number: An = T t /T v . It is hypothesized, based on this definition, that the flow is in the vortex mode if the time required for vortex ring formationT v is less than the transport time T t (An ≳ 1). Otherwise, the flow is in the cavity mode (An ≲ 1). This hypothesis is systematically tested through numerical simulations on simplified geometries and shown to be true through flow visualizations and identification of the main vortex and shear layer. The main vortex is shown to evolve when An ≳ 1 but stationary when An ≲ 1. In fact, it is shown that the flows with An ≲ 1 (cavity mode) are characterized by much smaller fluctuations of wall shear stress and oscillatory shear index relative to flows with An ≳ 1 (vortex mode) because of their quasi-stationary flow pattern (cavity mode) compared to the evolution and breakdown of the formed vortex ring (vortex mode).

Synchronisation through learning for two self-propelled swimmers

The coordinated motion by multiple swimmers is a fundamental component in fish schooling. The flow field induced by the motion of each self-propelled swimmer implies non-linear hydrodynamic interactions among the members of a group. How do swimmers compensate for such hydrodynamic interactions in coordinated patterns? We provide an answer to this riddle though simulations of two, self-propelled, fish-like bodies that employ a learning algorithm to synchronise their swimming patterns. We distinguish between learned motion patterns and the commonly used a-priori specified movements, that are imposed on the swimmers without feedback from their hydrodynamic interactions. First, we demonstrate that two rigid bodies executing pre-specified motions, with an alternating leader and follower, can result in substantial drag-reduction and intermittent thrust generation. In turn, we study two self-propelled swimmers arranged in a leader-follower configuration, with a-priori specified body-deformations. These two self-propelled swimmers do not sustain their tandem configuration. The follower experiences either an increase or decrease in swimming speed, depending on the initial conditions, while the swimming of the leader remains largely unaffected. This indicates that a-priori specified patterns are not sufficient to sustain synchronised swimming. We then examine a tandem of swimmers where the leader has a steady gait and the follower learns to synchronize its motion, to overcome the forces induced by the leader's vortex wake. The follower employs reinforcement learning to adapt its swimming-kinematics so as to minimize its lateral deviations from the leader's path. Swimming in such a sustained synchronised tandem yields up to [Formula: see text] reduction in energy expenditure for the follower, in addition to a [Formula: see text] increase in its swimming-efficiency. The present results show that two self-propelled swimmers can be synchronised by adapting their motion patterns to compensate for flow-structure interactions. Moreover, swimmers can exploit the vortical structures of their flow field so that synchronised swimming is energetically beneficial.