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鱼类启发的波动稳定游动分段模型。

Fish-inspired segment models for undulatory steady swimming.

机构信息

Department of Computer Science, Aberystwyth University, Ceredigion, SY23 3FL, United Kingdom.

Division of Functional Morphology, Department of Zoology, Stockholm University, Stockholm, Sweden.

出版信息

Bioinspir Biomim. 2022 May 24;17(4). doi: 10.1088/1748-3190/ac6bd6.

Abstract

Many aquatic animals swim by undulatory body movements and understanding the diversity of these movements could unlock the potential for designing better underwater robots. Here, we analyzed the steady swimming kinematics of a diverse group of fish species to investigate whether their undulatory movements can be represented using a series of interconnected multi-segment models, and if so, to identify the key factors driving the segment configuration of the models. Our results show that the steady swimming kinematics of fishes can be described successfully using parsimonious models, 83% of which had fewer than five segments. In these models, the anterior segments were significantly longer than the posterior segments, and there was a direct link between segment configuration and swimming kinematics, body shape, and Reynolds number. The models representing eel-like fishes with elongated bodies and fishes swimming at high Reynolds numbers had more segments and less segment length variability along the body than the models representing other fishes. These fishes recruited their anterior bodies to a greater extent, initiating the undulatory wave more anteriorly. Two shape parameters, related to axial and overall body thickness, predicted segment configuration with moderate to high success rate. We found that head morphology was a good predictor of its segment length. While there was a large variation in head segments, the length of tail segments was similar across all models. Given that fishes exhibited variable caudal fin shapes, the consistency of tail segments could be a result of an evolutionary constraint tuned for high propulsive efficiency. The bio-inspired multi-segment models presented in this study highlight the key bending points along the body and can be used to decide on the placement of actuators in fish-inspired robots, to model hydrodynamic forces in theoretical and computational studies, or for predicting muscle activation patterns during swimming.

摘要

许多水生动物通过波动身体运动来游泳,了解这些运动的多样性可能为设计更好的水下机器人提供潜力。在这里,我们分析了一组不同鱼类物种的稳定游泳运动学,以研究它们的波动运动是否可以用一系列相互连接的多节模型来表示,如果可以,确定驱动模型节段配置的关键因素。我们的研究结果表明,鱼类的稳定游泳运动学可以成功地用简约模型来描述,其中 83%的模型具有少于五个节段。在这些模型中,前节段比后节段长得多,并且节段配置与游泳运动学、身体形状和雷诺数之间存在直接联系。代表具有细长身体的鳗鱼状鱼类和在高雷诺数下游泳的鱼类的模型比代表其他鱼类的模型具有更多的节段和更少的节段长度沿身体变化。这些鱼类更多地募集其前部身体,从而更靠前地发起波动波。两个与轴向和整体身体厚度有关的形状参数,以中等至高的成功率预测了节段配置。我们发现头部形态是其节段长度的良好预测指标。尽管头部节段有很大的变化,但所有模型的尾部节段长度相似。由于鱼类表现出可变的尾鳍形状,尾节段的一致性可能是为了实现高推进效率而对进化约束的结果。本研究中提出的基于生物启发的多节模型突出了身体上的关键弯曲点,并可用于在鱼类启发机器人中决定执行器的位置、在理论和计算研究中模拟水动力、或预测游泳时肌肉激活模式。

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