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

生物，如鱼、鸟、昆虫，在执行集体任务时倾向于将自己组织成定义良好的模式。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，它们会受到流动介导的阻力，这影响了它们的效率和能量消耗。目前的项目致力于寻找最适合特定任务的特定游泳模式，例如，在给定某些约束条件下，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、如何以及为什么不同，取决于手头的任务，以及流体介质中的干扰，如尾迹、电流等，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，并协助工程和设计自主的生物机器人车辆，用于各种任务。作为教育和推广计划的一部分，将开发一个交互式软件虚拟机器鱼，向高中学生展示鱼类学校的模式形成原理。目前的项目旨在将在粘性流体介质中自我推进的柔性游泳体的完全解析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功能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。通过完全分辨的粘性流动模拟发现的最佳模式将与低阶有限偶极子势流模型进行比较，以了解形态学，运动学，惯性和粘性效应的重要性，在低阶模型中省略，对集体游泳动力学。对全Navier-Stokes模型和低阶势流模型进行了改进，引入了涡旋尾迹、速度流等流动扰动的影响。本文将研究这些扰动对优化游泳模式的影响。所产生的知识将导致对生物系统中自组织原理的更好理解，并在水下机器人群的设计、工程和控制方面取得实际进展，用于国家安全和健康应用。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.