Understanding Bio-Locomotion for Collective Swimming in a Quiet and Disturbed Media

了解在安静和受干扰的介质中集体游泳的生物运动

基本信息

批准号：
1762827
负责人：
Yulia Peet
金额：
$ 30.5万
依托单位：
Arizona State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1762827&HistoricalAwards=false
关键词：
Understanding Bio Locomotion Collective Swimming

项目摘要

Living organisms, such as fish, birds, insects, tend to organize themselves into well-defined patterns while performing collective tasks. The current project seeks to understand the role of the environment on the patterns and modes of swimming that can be observed in schools of fish, including the gaits of locomotion, geometrical organization, and synchronization. When fish swim and interact in a viscous fluid media, they experience the flow-mediated drag forces that affect their efficiency and the energy expenditure. The current project is devoted to a search of specific swimming modes that are best suited for certain tasks, for example, in providing the lowest energy expenditure, or the highest speed of motion, given certain constraints, of a collective swimming unit. The fundamental questions are whether, how and why these modes differ or don't differ depending on the task at hand, and how the disturbances in the fluid media, such as wakes, currents, etc., will affect them. This knowledge will help manage and protect natural fish habitats, and assist in engineering and design of autonomous bio-inspired robotic vehicles for various missions. As a part of an educational and outreach program, an interactive software Virtual Robofish will be developed to demonstrate the principles of pattern formation in fish schools to high-school students.The current project seeks to combine fully-resolved hydrodynamic simulations of flexible swimming bodies that self-propel in a viscous fluid media, with gradient-free optimization procedures, in order to reveal and understand the optimal modes of collective bio-locomotion that optimize certain objective functions. Among the objective functions to be considered are the swimming efficiency, swimming speed, and an acoustic signature of a collective swarm. The optimum patterns found via fully-resolved viscous flow simulations will be compared with a low-order finite-dipole potential flow model in order to understand the importance of morphology, kinematics, inertial and viscous effects, omitted in a low-order model, on dynamics of collective swimming. Both the full Navier-Stokes model and the low-order potential flow model will be enhanced to introduce the effect of flow disturbances, such as vortex wakes, velocity currents, etc. The effect that such disturbances have on the optimized swimming modes will be investigated. The generated knowledge will lead to a greater understanding of the principles of self-organization in biological systems, and to practical advances in design, engineering and control of underwater robotic swarms for national security and health applications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

鱼、鸟、昆虫等生物体在执行集体任务时往往会将自己组织成明确的模式。当前的项目旨在了解环境对鱼群游泳模式和模式的作用，包括运动步态、几何组织和同步。当鱼在粘性流体介质中游泳和相互作用时，它们会受到流动介导的阻力，从而影响它们的效率和能量消耗。当前的项目致力于寻找最适合某些任务的特定游泳模式，例如，在给定某些限制的情况下，为集体游泳单位提供最低的能量消耗或最高的运动速度。基本问题是这些模式是否、如何以及为何根据手头的任务而有所不同或不变，以及流体介质中的扰动（例如尾流、水流等）将如何影响它们。这些知识将有助于管理和保护自然鱼类栖息地，并协助工程和设计用于各种任务的自主仿生机器人车辆。作为教育和推广计划的一部分，将开发交互式软件 Virtual Robofish，向高中生演示鱼群模式形成的原理。当前项目旨在将灵活游泳体的完全解析水动力模拟与在粘性流体介质中自我推进，采用无梯度优化程序，以揭示和理解优化某些目标函数的集体生物运动的最佳模式。要考虑的目标函数包括游泳效率、游泳速度和群体的声学特征。通过完全解析的粘性流模拟找到的最佳模式将与低阶有限偶极子势流模型进行比较，以了解低阶模型中省略的形态、运动学、惯性和粘性效应的重要性。集体游泳的动态。完整的纳维-斯托克斯模型和低阶势流模型将得到增强，以引入流动扰动的影响，例如涡流、速度流等。将研究这些扰动对优化游泳模式的影响。所产生的知识将有助于更好地理解生物系统中的自组织原理，并促进国家安全和健康应用的水下机器人群的设计、工程和控制方面的实际进展。该奖项反映了 NSF 的法定使命，并已通过使用基金会的智力优点和更广泛的影响审查标准进行评估，认为值得支持。