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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.

生物体，如鱼、鸟、昆虫，在执行集体任务时往往会将自己组织成明确的模式。目前的项目旨在了解环境对鱼群中可以观察到的游泳模式和模式的作用，包括运动的步态，几何组织和同步。当鱼在粘性流体介质中游动和相互作用时，它们会经历流动介导的阻力，这会影响它们的效率和能量消耗。目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，集体游泳单元提供最低的能量消耗或最高的运动速度。基本的问题是，这些模式是否、如何以及为什么根据手头的任务而不同或不不同，以及流体介质中的扰动，如尾流、电流等，会影响他们。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生物启发机器人车辆。作为教育和推广计划的一部分，将开发一个互动软件Virtual Robofish，向高中生演示鱼群模式形成的原理。目前的项目旨在将联合收割机完全解析的流体动力学模拟与无梯度优化程序相结合，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。其中要考虑的目标函数是游泳效率，游泳速度，和一个集体群的声学签名。通过完全解析粘性流模拟发现的最佳模式将与低阶有限偶极势流模型进行比较，以了解形态学，运动学，惯性和粘性效应的重要性，省略在低阶模型中，集体游泳的动力学。完整的Navier-Stokes模型和低阶势流模型都将得到增强，以引入流动扰动的影响，如涡尾流、速度流等。将研究这种扰动对优化游泳模式的影响。所产生的知识将导致更好地理解生物系统中的自组织原理，并在国家安全和健康应用中的水下机器人群的设计，工程和控制方面取得实际进展。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。