CFD Studies of the Effects of Waveform on Swimming Performance of Carangiform Fish

Cui, Zhe; Gu, Xiaofeng; Li, Kangkang; Jiang, Hongzhou

doi:10.3390/app7020149

Cited by 22 publications

(9 citation statements)

References 37 publications

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“…The structures of the vortex street are double-row, and there are no obvious changes in Figure 10. This result is consistent with the conclusion in our previous study (Cui, Gu, Li, & Jiang, 2017), in which we found that the wave number of body motions has little effect on swimming performance.…”

Section: Vortex Analysissupporting

confidence: 94%

Sharp interface immersed boundary method for simulating three-dimensional swimming fish

Cui

Yang

Jiang

2020

Engineering Applications of Computational Fluid Mechanics

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A second order finite-difference numerical method is used to solve the Navier-Stokes equations of incompressible flow, in which the solid body with complex geometry is immersed into the fluid domain with orthogonal Cartesian meshes. To account for influences of the solid body, interactive forces are applied as boundary conditions at Cartesian grid nodes located in the exterior but in the immediate vicinity of the solid body. Fluid flow velocities in these nodes are reconstructed to track and control the deformation of the solid body, in which the local direction normal to the body surface is employed using the level-set function. The capabilities of this method are demonstrated by the application to fish swimming, and the computed behaviors of swimming fish agree well with experimental ones. The results elucidate that the ability of swimming fish to produce more thrust and high efficiency is closely related to the Reynolds number. The single reverse Kármán street tends to appear when both the Strouhal number and tail-beating frequency are small, otherwise the doublerow reverse Kármán street appears. The algorithm can capture the geometry of a deformable solid body accurately, and performs well in simulating interactions between fluid flow and the deforming and moving body. ARTICLE HISTORY

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Section: Vortex Analysissupporting

confidence: 94%

Sharp interface immersed boundary method for simulating three-dimensional swimming fish

Cui

Yang

Jiang

2020

Engineering Applications of Computational Fluid Mechanics

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“…This makes it challenging to directly link our model of locomotion behaviour with biomechanical (e.g. Bandyopadhyay, 2002; Cui et al., 2017; Lighthill, 1971) or behavioural models (e.g. Oteiza et al., 2017) that describe movement of the migrant's body.…”

Section: Discussionmentioning

confidence: 99%

Merging computational fluid dynamics and machine learning to reveal animal migration strategies

et al. 2021

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Understanding how migratory animals interact with dynamic physical environments remains a major challenge in migration biology. Interactions between migrants and wind and water currents are often poorly resolved in migration models due to both the lack of high‐resolution environmental data, and a lack of understanding of how migrants respond to fine‐scale structure in the physical environment. Here we develop a generalizable, data‐driven methodology to study the migration of animals through complex physical environments. Our approach combines validated computational fluid dynamic (CFD) modelling with animal tracking data to decompose migratory movements into two components, namely movement caused by physical forcing and movement due to active locomotion. We then use a flexible recurrent neural network model to relate local environmental conditions to locomotion behaviour of the migrating animal, allowing us to predict a migrant's force production, velocity and trajectory over time. We apply this framework to a large dataset containing measured trajectories of migrating Chinook salmon through a section of river in California's Sacramento‐San Joaquin Delta. We show that the model is capable of describing fish migratory movements as a function of local flow variables, and that it is possible to accurately forecast migratory movements on which the model was not trained. After validating our model, we show how our framework can be used to understand how migrants respond to local‐flow conditions, how migratory behaviour changes as overall conditions in the system change and how the energetic cost of migratory movements depends on environmental conditions in space and time. Our framework is flexible and can readily be applied to other species and systems.

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“…Note that in anguilliform studies it is common to use the product of f and a peak-to-peak undulation amplitude, A, as the characteristic velocity in the Reynolds number calculation. However, since undulation amplitude is an output of the model and cannot be known a priori, I elected to use a characteristic velocity seen in fish literature as an input velocity scale, f L [45,46]. Illustrations of the peak-to-peak amplitude, A are given in Figures 1 and 2.…”

Section: Methodsmentioning

confidence: 99%

“…Since it was desired to have a fluid scaling parameter as an input to the model, an input Reynolds number was defined in Eq. 2, based on a characteristic length that was the swimmer's body (L) and a frequency based characteristic velocity that was the product of the undulation frequency and the swimmer's body length, f L [45,46]. Many anguilliform swimming studies use the characteristic velocity as f A, where A is the peakto-peak undulation amplitude.…”

Section: Symmetry Within Each Half-stroke Cyclementioning

confidence: 99%

Diving into a simple anguilliform swimmer's sensitivity

Battista¹

2020

Preprint

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Computational models of aquatic locomotion range from individual modest simple swimmers in 2D to sophisticated 3D individual swimmers to complex multiswimmer models that attempt to parse collective behavioral dynamics. Each of these models contain a multitude of model input parameters to which the model outputs are inherently dependent, i.e., various swimming performance metrics. In this work, the swimming performance's sensitivity to parameters is investigated for an idealized, simple anguilliform swimming model in 2D. The swimmer considered here propagates forward by dynamically varying its body curvature, similar to motion of a C. elegan. The parameter sensitivities were explored with respect to the fluid scale (Reynolds number, Re), stroke (undulation) frequency, as well as a kinematic parameter controlling the velocity and acceleration of each upstroke and downstroke. In total, 5000 fluid-structure interaction simulations were performed, each with a unique parameter combination selected via a Sobol sequence. Thus, global sensitivity analysis was performed using Sobol sensitivity analysis. Results indicate that the swimmer's performance is most sensitive to variation in its stroke frequency. Trends in swimming performance were discovered by projecting the performance metrics from the overall 3D parameter space onto particular 2D subspaces and Pareto-like optimal fronts were identified for swimming efficiency. This work is a natural extension of the parameter explorations of the same model from [1].

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CFD Studies of the Effects of Waveform on Swimming Performance of Carangiform Fish

Cited by 22 publications

References 37 publications

Sharp interface immersed boundary method for simulating three-dimensional swimming fish

Sharp interface immersed boundary method for simulating three-dimensional swimming fish

Merging computational fluid dynamics and machine learning to reveal animal migration strategies

Diving into a simple anguilliform swimmer's sensitivity

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