Collaborative Research: Scaling of Unsteady Locomotor Performance and Maneuverability

鲸鱼是最大的动物，必须能够在其水生环境中灵活地捕捉猎物，躲避捕食者，在复杂的环境中航行，并争夺配偶。机动能力(如加速、快速和紧凑)通常随着质量的增加而降低，这表明机动水生动物有一个大小上限。这项研究将评估海洋中最大的动物--短吻鲸--在野外执行自然行为的机动性的大小依赖关系。放置在自由活动鲸鱼身上的定制设计的可拆卸传感器将记录它们的运动，而带有专门相机的空中无人机将测量鲸鱼及其在水面上的附属物(即鳍状肢、吸虫)的大小和形状。通过将这些数据与水动力的数学模型相结合，可以比较不同大小的鲸鱼物种，以确定大小作为世界生态和进化的驱动力施加的机械约束-S最大的捕食者。该项目将包括对研究生和博士后学者进行将工程学和物理学与生物学相结合的跨学科研究的培训。此外，研究结果可能为具有增强机动性能的自主水下机器人的仿生设计提供见解。运动是动物生活的一个基本方面。量化个体的精细运动对于理解生理、生态和进化过程具有重要意义。机动能力也至关重要，因为它决定着动物捕获猎物、躲避捕食者和障碍物、生活在复杂环境中以及争夺配偶的能力。关于动物如何在水生环境中活动的调查仍然知之甚少，特别是对于动物体重达到极限的大型动物。这项建议开发了一种生物记录和遥感方法，通过计算建模，分析体重按数量级变化的自由放养的流浪鲸的运动学和机动性。游泳性能(即加速度：游泳速度的变化；敏捷性：转弯速度；机动性：转弯半径)将随着正常物种的身体尺寸的增加而下降，这些由形态尺寸施加的机械约束将明显成为世界上最大的捕食者生态和进化的驱动力。安装在无人机上的定制设计的多传感器动物携带标签和流体透镜相机将被用来分析开放海洋中的江豚的运动学和形态。这种方法使我们能够量化进行自然动作的野兽的高分辨率运动学，同时量化被标记的动物的形态维度，包括控制和推进表面。通过将这些数据与可通过计算流体动力学(CFD)和轨迹计算水动力的建模相结合

Whales, the largest animals, must be able to maneuver in their aquatic environment to capture prey, avoid predators, navigate complex environments, and compete for mates. The ability to maneuver (e.g., accelerate, turn quickly and tightly) generally decreases as mass increases, suggesting there is an upper size limit for maneuvering aquatic animals. This study will evaluate the size dependence of maneuverability of the largest animals in the ocean, rorqual whales, performing natural behaviors in the wild. Custom-designed removable sensors placed on the bodies of free-ranging whales will record their movement while aerial drones with specialized cameras will measure the size and shape of the whales and their appendages (i.e., flippers, flukes) at the surface of the water. By combining these data with mathematical modeling of hydrodynamic forces, different sized whale species can be compared to determine mechanical constraints imposed by size as a driving force in the ecology and evolution of the world?s largest predators. This project will include the training of graduate students and postdoctoral scholars in interdisciplinary research that integrates engineering and physics with biology. Further, the results may provide insights into the biomimetic design of autonomous underwater vehicles with enhanced maneuvering performance. Movement is a fundamental aspect of animal life. Quantifying the fine-scale movement of individuals has important consequences for understanding physiological, ecological, and evolutionary processes. Maneuvering capacity is also critically important as it governs an animal's ability to capture prey, avoid predators and obstacles, inhabit complex environments, and compete for mates. Investigations into how animals maneuver in aquatic environments remains poorly understood, particularly for large animals at the extreme of animal body mass. This proposal develops a bio-logging and remote sensing approach, with computational modeling, to analyze the kinematics and maneuverability of free-ranging rorqual whales (Balaenopteridae) ranging in body mass by an order of magnitude. Swimming performance (i.e., acceleration: change in swimming velocity; agility: rate of turning; maneuverability: turning radius) will decrease as body size increases across rorqual species and that these mechanical constraints imposed by morphology size will be evident as a driving force in the ecology and evolution of the world's largest predators. Custom engineered multi-sensor animal-borne tags and Fluid Lensing cameras attached to aerial drones will be used to analyze the kinematics and morphology of rorquals in the open ocean. This approach enables us to quantify the high-resolution kinematics of rorquals engaged in natural maneuvers while simultaneously quantifying the morphological dimensions of tagged animal, including control and propulsion surfaces. By combining these data with modeling where hydrodynamic forces can be calculated via Computational Fluid Dynamics (CFD) and trajectories