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(a) Live yellowfin tuna (Thunnus albacares) swimming (with nine finlets indicated by letters A to I). (b) Illustration of the dorsal and ventral finlets of a yellowfin tuna. (c) Dorsal and ventral finlets of a mackerel during free swimming. (d ) Caudal peduncle region of a yellowfin tuna with finlets. (e) A single tuna finlet to show the attached base and free posterior region. ( f ) Tuna finlet overlapped by the computational finlet model (red outline).
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Finlets are a series of small non-retractable fins common to scombrid fishes (mackerels, bonitos and tunas), which are known for their high swimming speed. It is hypothesized that these small fins could potentially affect propulsive performance. Here, we combine experimental and computational approaches to investigate the hydrodynamics of finlets i...
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Identical tandem flippers of plesiosaurs, which are unique among all animals, have been a source of debate regarding the role of hind flippers in their locomotion. Here, inspired by the kinematics of plesiosaur flippers, the effect of amplitude ratio on the propulsive performance of in-line tandem pitching foils is investigated through a series of...
Citations
... Current methods to generate respiratory airway models include computer-aided design (CAD), the segmentation of medical images, and algorithm-based morphing [8]. CAD-based approaches have historically included Gambit and SolidWorks to generate new geometries, and HyperMorph, MAYA, and/or Blender to modify existing geometries [9]. Zhao et al. [10] and Talaat et al. [11] used user-defined functions (UDFs) to control the opening/closing of the glottis and the expansion-contraction of the alveoli following tidal breathing waveforms. ...
Even though inhalation dosimetry is determined by three factors (i.e., breathing, aerosols, and the respiratory tract), the first two categories have been more widely studied than the last. Both breathing and aerosols are quantitative variables that can be easily changed, while respiratory airway morphologies are difficult to reconstruct, modify, and quantify. Although several methods are available for model reconstruction and modification, developing an anatomically accurate airway model and morphing it to various physiological conditions remains labor-intensive and technically challenging. The objective of this study is to explore the feasibility of using an adjoint–CFD model to understand airway shape effects on vapor deposition and control vapor flux into the lung. A mouth–throat model was used, with the shape of the mouth and tongue being automatically varied via adjoint morphing and the vapor transport being simulated using ANSYS Fluent coupled with a wall absorption model. Two chemicals with varying adsorption rates, Acetaldehyde and Benzene, were considered, which exhibited large differences in dosimetry sensitivity to airway shapes. For both chemicals, the maximal possible morphing was first identified and then morphology parametric studies were conducted. Results show that changing the mouth–tongue shape can alter the oral filtration by 3.2% for Acetaldehyde and 0.27% for Benzene under a given inhalation condition. The front tongue exerts a significant impact on all cases considered, while the impact of other regions varies among cases. This study demonstrates that the hybrid adjoint–CFD approach can be a practical and efficient method to investigate morphology-associated variability in the dosimetry of vapors and nanomedicines under steady inhalation.
... Nauen and Lauder 135 showed that these finlets help enhance the thrust generation at the fish's tail by directing the flow in the direction of the caudal fin vortex. Recently, Wang et al. 136 have shown that the finlets undergo pitching and heaving motion, and there is an increasing phase difference between the kinematics of finlets as we go downstream along the body. Lagopoulos et al. 101 showed that the transition of drag to thrust occurs when the Strouhal number based on the cycle-averaged swept trajectory instead of the maximum path traveled by trailing edge approaches one. ...
A review of recent literature on thrust generation mechanisms by a hydrofoil, bioinspired from fish locomotion is presented. The present work considers fish-inspired periodic kinematics of three types: pitching, heaving, and undulations along with the combination of some of these motions. The pitching corresponds to the tail of the fish while heaving and undulation correspond to that of the body. The undulation also corresponds to the surface of the body; for certain fishes. Both numerical and experimental studies in this arena have been reviewed. The present review follows the classification of oscillatory and undulatory motion. We discuss oscillatory motion with emphasis on pitching, heaving, and the combination of these two motions. In undulatory motion, we cover body undulation and surface undulation motion as a propulsive mechanism. We compare and contrast wake signatures, thrust, and propulsive efficiencies for different motion types. A future outlook, which may help researchers to identify open questions, has been provided.
... Interactions between airway morphology and other factors (aerosols, breathing, etc.) can also be important [4]. 2 Current methods to generate respiratory airway models include computer-aided design (CAD), segmentation of medical images, and algorithm-based morphing [5]. The CAD-based approaches historically include Gambit and SolidWorks to generate new geometries, and HyperMorph, MAYA, and/or Blender to modify existing geometries [6]. Zhao et al. [7] and Talaat et al. [8] used user-defined functions (UDFs) to control the opening/closing of the glottis and expansion-contraction of the alveoli following the tidal breathing waveforms. ...
Even though inhalation dosimetry is determined by three factors (i.e., breathing, aerosols, and the respiratory tract), the first two categories have been more widely studied than the last. Both breathing and aerosols are quantitative variables that can be easily changed, while respiratory airway morphologies are difficult to reconstruct, modify, and quantify. Despite several methods are available for model reconstruction and modification, developing an anatomically accurate airway model and morphing it to various physiological conditions remains labor-intensive and technically challenging. The objective of this study is to explore the feasibility of using an adjoint-CFD model to understand airway shape effects on vapor deposition and control vapor flux into the lung. A mouth-throat model was used, with the shape of the mouth and tongue being automatically varied via adjoint morphing, and the vapor transport being simulated using ANSYS Fluent coupled with a wall absorption model. Two chemicals with varying adsorption rates, Acetaldehyde and Benzene, were considered, which exhibited large differences in dosimetry sensitivity to airway shapes. For both chemicals, large shape deformations were explored to find the maximal possible morphing; both the maximal and intermediate deformations were simulated for morphology parametric studies. Results show that changing the mouth-tongue shape can alter the oral filtration by 3.2% for Acetaldehyde and 0.27% for Benzene under a given inhalation condition. This study demonstrates that the hybrid adjoint-CFD approach can be a practical and efficient method to investigate morphology-associated variability in inhalation dosimetry of vapors and nanomedicines.
... Central to their swimming capabilities is the caudal fin which serves as their primary hydrodynamic surface for both propulsion and maneuvers [4][5][6]. The role of the caudal fin is most pronounced in species of the Body and/or Caudal Fin (BCF) form which represents more than 85% of fish species [7]. ...
... The results showed that the caudal fin stiffness played a significant role in determining the optimal motor control program and the swimming gaits, and there existed an optimal caudal fin stiffness that maximized both the speed in forward swimming and the final heading change in turning maneuver. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 A c c e p t e d M a n u s c r i p t 6 ...
In animal and robot swimmers of Body and Caudal Fin (BCF) form, hydrodynamic thrust is mainly produced by their caudal fins, the stiffness of which has profound effects on both thrust and efficiency of swimming. Caudal fin stiffness also affects the motor control and resulting swimming gaits that correspond to optimal swimming performance; however, their relationship remains scarcely explored. Here using magnetic, modular, undulatory robots (μBots), we tested the effects of caudal fin stiffness on both forward swimming and turning maneuver. We developed six caudal fins with stiffness of more than 3 orders of difference. For a μBot equipped with each caudal fin (and μBot absent of caudal fin), we applied reinforcement learning (RL) in experiments to optimize the motor control for maximizing forward swimming speed or final heading change. The motor control of μBot was generated by a Central Pattern Generator (CPG) for forward swimming or by a series of parameterized square waves for turning maneuver. In forward swimming, the variations in caudal fin stiffness gave rise to three modes of optimized motor frequencies and swimming gaits including no caudal fin (4.6 Hz), stiffness<10-4 Pa·m4 (~10.6 Hz) and stiffness>10-4 Pa·m4 (~8.4 Hz). Swimming speed, however, varied independently with the modes of swimming gaits, and reached maximal at stiffness of 0.23×10-4 Pa·m4, with the μBot without caudal fin achieving the lowest speed. In turning maneuver, caudal fin stiffness had considerable effects on the amplitudes of both initial head steering and subsequent recoil, as well as the final heading change. It had relatively minor effect on the turning motor program except for the μBots without caudal fin. Optimized forward swimming and turning maneuver shared an identical caudal fin stiffness and similar patterns of peduncle and caudal fin motion, suggesting simplicity in the form and function relationship in μBot swimming.
... Freshwater sunfish (genus Lepomis), for example, have a symmetrical arrangement of the anal and dorsal fin, whereas the dorsal fins of trout (genus Salvelinus) and salmon (genus Salmo) are located farther forward on the body than the anal fins. Many fish that swim at higher speeds, such as tuna (tribe Thunnini), have caudal fins that are stiff, dorsal and anal fins that are elongated but thin, and finlets along the tail that help guide flow [12]. Additionally, fish can vary the morphology and kinematics of fins depending on the locomotory task, and even modulate a fin's stiffnesses as swimming speeds change [6,[13][14][15][16]. ...
Fish coordinate the motion of their fins and body to create the time-varying forces required for swimming and agile maneuvers. To effectively adapt this biological strategy for underwater robots, it is necessary to understand how the location and coordination of interacting fish-like fins affect the production of propulsive forces. In this study, the impact that phase difference, horizontal and vertical spacing, and compliance of paired fins had on net thrust and lateral forces was investigated using two fish-like robotic swimmers and a series of computational fluid dynamic simulations. The results demonstrated that the propulsive forces created by pairs of fins that interact through wake flows are highly dependent on the fins’ spacing and compliance. Changes to fin separation of less than one fin length had a dramatic effect on forces, and on the phase difference at which desired forces would occur. These findings have clear implications when designing multi-finned swimming robots. Well-designed, interacting fins can potentially produce several times more propulsive force than a poorly tuned robot with seemingly small differences in the kinematic, geometric, and mechanical properties.
... In addition, an in-depth study of how the adhesive movement of fish increases friction and resists shear, and how adhesive movement, detachment, and sliding behavior is achieved on this basis, requires more specialized systems to test underwater contact behavior. It can also be used for fluid force visualization and mechanical modeling using digital particle image velocimetry systems or computational hydrodynamics [120][121][122][123][124][125][126][127][128][129][130]. With the development of unsteady flow fields and surface attachment physics, the modeling of kinematic and kinetic models for adherent fish, and the refinement of interfacial force theory at the liquid-solid interface, further progress in underwater adhesion kinematics will be achieved [131,132]. ...
In nature, some fish can adhere tightly to the surface of stones, aquatic plants, and even other fish bodies. This adhesion behavior allows these fish to fix, eat, hide, and migrate in complex and variable aquatic environments. The adhesion function is realized by the special mouth and sucker tissue of fish. Inspired by adhesion fish, extensive research has recently been carried out. Therefore, this paper presents a brief overview to better explore underwater adhesion mechanisms and provide bionic applications. Firstly, the adhesion organs and structures of biological prototypes (e.g., clingfish, remora, Garra, suckermouth catfish, hill stream loach, and goby) are presented separately, and the underwater adhesion mechanisms are analyzed. Then, based on bionics, it is explained that the adhesion structures and components are designed and created for applications (e.g., flexible gripping adhesive discs and adhesive motion devices). Furthermore, we offer our perspectives on the limitations and future directions.
... Direct numerical simulations (DNS) were implemented to resolve the inspiratory flows with high-frequency uvula oscillations. The immersed boundary method (IBM) was used to control the uvula kinematics based on a Cartesian-grid finite-difference approach (Figure 1d) [49], which has undergone extensive testing in simulations of flapping propulsion for insects [50,51], birds [52,53], fish [54,55], and breathing [56]. In summary, the airway surface with tetrahedral meshes was immersed in a structured hexahedral grid ...
... Direct numerical simulations (DNS) were implemented to resolve the inspiratory flows with high-frequency uvula oscillations. The immersed boundary method (IBM) was used to control the uvula kinematics based on a Cartesian-grid finite-difference approach ( Figure 1d) [49], which has undergone extensive testing in simulations of flapping propulsion for insects [50,51], birds [52,53], fish [54,55], and breathing [56]. In summary, the airway surface with tetrahedral meshes was immersed in a structured hexahedral grid (Figure 1c), with the boundary conditions specified on the immersed surfaces via the ghost-cell approach [57]. ...
This study presents a data-driven approach to identifying anomaly-sensitive parameters through a multiscale, multifaceted analysis of simulated respiratory flows. The anomalies under consideration include a pharyngeal model with three levels of constriction (M1, M2, M3) and a flapping uvula with two types of kinematics (K1, K2). Direct numerical simulations (DNS) were implemented to solve the wake flows induced by a flapping uvula; instantaneous vortex images, as well as pressures and velocities at seven probes, were recorded for twelve cycles. Principal component analysis (PCA), wavelet-based multifractal spectrum and scalogram, and Poincaré mapping were implemented to identify anomaly-sensitive parameters. The PCA results demonstrated a reasonable periodicity of instantaneous vortex images in the leading vector space and revealed distinct patterns between models with varying uvula kinematics (K1, K2). At higher PCA ranks, the periodicity gradually decays, eventually transitioning to a random pattern. The multifractal spectra and scalograms of pressures in the pharynx (P6, P7) show high sensitivity to uvula kinematics, with the pitching mode (K2) having a wider spectrum and a left-skewed peak than the heaving mode (K1). Conversely, the Poincaré maps of velocities and pressures in the pharynx (Vel6, Vel7, P6, P7) exhibit high sensitivity to pharyngeal constriction levels (M1–M3), but not to uvula kinematics. The parameter sensitivity to anomaly also differs with the probe site; thus, synergizing measurements from multiple probes with properly extracted anomaly-sensitive parameters holds the potential to localize the source of snoring and estimate the collapsibility of the pharynx.
... Liao et al. [12,13] found that, by introducing vortices upstream of a live trout, the trout would adopt a gait synchronized to the upstream vortex shedding and trace the path of the vortices, a locomotion pattern that is associated with energy recovery. Experimental and computational studies of hydrofoils in tandem formation undergoing either pure pitching or oscillation motions found that the performance of the downstream foil is affected by the vortex pattern it encounters [15,16,18,[20][21][22][23][24][25][26][27]. In the study by Newbolt et al. [24] in which tandem hydrofoils were assigned uncoordinated flapping motions, the follower in the wake of the leader could fall into various stable positions controlled by changes in amplitude, phase, and frequency. ...
The leading-edge vortex (LEV) formation on the caudal fin (CF) has been identified as playing a key role in efficient lift-based thrust production of fish-like propulsion. The enhancement of the CF LEV through its interaction with vortices formed upstream due to a median fin with a distinct shape is the focus of this paper. High-speed, high-fidelity videos and particle imaging velocimetry (PIV) were obtained from rainbow trout during steady forward swimming to visualize the undulatory kinematics and two-dimensional flow behavior. Body kinematics are quantified using a traveling-wave formulation that is used to prescribe the motion of a high-fidelity three-dimensional surface model of the fish body for a computational fluid dynamics (CFD) study. The pressure field of the CFD result is compared and validated with the PIV result from the experiment. Using CFD, the vortex forming and shedding behaviors of the anal fin (AF) and their capturing and interaction with the trunk (TK) and the CF are visualized and examined. Coherent AF-bound LEVs are found to form periodically, leading to thrust production of the AF. The vortices subsequently shed from the AF are found to help stabilize and reinforce the LEV formation on the CF by aiding LEV initiation at stroke reversal and enhancing LEV during a tail stroke, which leads to enhancement of lift-based thrust production. The CF is found to shed vortex tubes (VTs) that create backward-facing jets, and the ventral-side VT and the associated backward jets are both strengthened by vortices shed by the AF. An additional benefit of the AF is found to be reduction of body drag by reducing the lateral crossflow that leads to loss of beneficial pressure gradient across the body. Through varying AF-CF spacing and AF height, we find that CF thrust enhancement and TK drag reduction due to the AF are both affected by the position and size of the AF. The position and area of the AF that led to the most hydrodynamic benefit are found to be the original, anatomically accurate position and size. In this paper, we demonstrate the important effect of vortex interaction among propulsive surfaces in fish-like propulsion.
... This method has been successfully employed in previous biological swimming studies [44][45][46] and bio-inspired canonical problems, [47][48][49][50][51] and has been previously validated extensively. 48,52,53 More details can be found in Refs. 54 and 55. ...
Mechanisms for hydrodynamic benefit via fluid interactions in large planar fish schools ( n ≥ 10) are investigated by two-dimensional numerical simulations of carangiform fish swimming. It is observed that the average swimming efficiency of the 10-fish school is increased by 30% over a single swimmer, along with a thrust production improvement of 114%. The performance and flow analyses characterize the associated hydrodynamic interaction mechanisms in large dense schools leading to enhanced performance. First, anterior body suction arises from the proximity of the suction side of the flapping tail to the head of the following fish. Next, the block effect is observed as another fish body blocks the flow behind a fish. Finally, the wall effect enhances the flow of momentum downstream where the body of a neighboring fish acts as a wall for the flapping of a fish tail moving toward it. Because these primary body–body interactions are based on the arrangement of surrounding fish, a classification of the individual fish within the school is presented based on the intra-fish interactions and is reflected in the performance of the individuals. It is shown that the school can be separated as front fish, middle fish, edge fish, and back fish based on the geometric position, performance, and wake characteristics. Finally, groupings and mechanisms observed are proven to be consistent over a range of Reynolds numbers and school arrangements.
... An immersed-boundary-based DNS solver [33] was applied to resolve the respiratory flow dynamics (Fig. 2a). This DNS solver has been extensively validated in flapping propulsion simulations for fishes [34,35], birds [36,37], and insects [38,39]. Unstructured cells were used to mesh the airway surface, which was then applied to a structured Cartesian grid using the multiscale ghost-cell method [40]. ...
Snoring and obstructive sleep apnea (OSA) are often associated with uvula vibrations and pharynx constrictions. However, successful treatment of snoring or accurate diagnosis of OSA has been proven challenging. This study aimed to identify acoustic indexes that were sensitive to underlying airway structural or kinematic variations. Six physiologically realistic models were developed that consisted of three pharynx constriction levels (M1-3) and two uvula-flapping kinematics (K1-2). Direct numerical simulations (DNS) were performed to resolve spatial and temporal flow dynamics, and an immersed boundary method was used to approximate the uvula vibrations. Time-varying acoustic pressures at six points in the pharynx were analyzed using different algorithms in frequency- or frequency–time domains. Signature flow structures formed near the uvula for different uvula motions and in the pharynx for different pharyngeal constriction levels. The fast Fourier transform showed that the acoustic energy was mainly distributed in four peaks (flapping frequency and three harmonics) with descending magnitudes. Their amplitudes and distribution patterns differed among the six models but were not substantial. The continuous wavelet transforms showed clearly separated acoustic cycles (in both frequency and time) in the uvula-induced flows and revealed a cascading bifurcation pattern in the input–output semblance map. Specifically, the multifractal spectrum was sensitive to uvula flapping kinematics but not pharynx constrictions. By contrast, the input–output cross-correlation and Hilbert phase space showed high sensitivity to pharynx constrictions but low sensitivity to uvula kinematics. The frequency–time analyses of DNS-predicted pressures offered insight into the acoustics signals that were not apparent in original signals and could be used individually or in combination in diagnosis or treatment planning for snoring/OSA patients.
Graphic abstract
