Energy efficiency of flapping flight: understanding and exploitation

扑动飞行的能量效率：理解和利用

• 批准号: 2889142
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2889142
• 关键词: Energy; efficiency; flapping; flight; understanding

Flapping flight represents the most efficient form of low Reynolds number flight across natural and manmade studies. For example, it allows butterflies to migrate up to 3000 miles between North America and southwestern Mexico annually, largely exploiting unsteady aerodynamic mechanisms via flapping of their wings. Other larger flapping species such as birds and bats, operating in higher Reynolds number regimes, use similar mechanisms to achieve flight during hunting, migration and evasion of predators. However, this remains one of the least understood and exploited natural forms of locomotion, due to the combined complexity of non-linear kinematics and morphology, as well as the difficulty in working with and sufficiently measuring live specimens. This project proposes a novel wind tunnel platform for investigation into flapping flight kinematics, using a 6-axis industrial articulated robotic arm, for repeatable wind tunnel testing of flapping flight across various parameter spaces, and hence species. The wind tunnel facility is the T1 Wind Tunnel at Imperial College London, an ultra-low turbulence facility capable of maximum wind speeds of up to 42 m/s. The high manoeuvrability of this wing-robot-tunnel system enables a deeper understanding of how the specific flapping flight path 'swept out' by the wing tip allows for optimal aerodynamic control of unsteady structures for augmentation of lift beyond typical fixed-wing stall angles of attack. Synchronous 3 component velocity fields and surface pressure measurements will be used to characterise the formation and shedding of unsteady aerodynamic structures, with the aim of correlating global flow field behaviour to the pressure sensed on the surface of the wing. This represents a 'partially observable system', in line with the natural sensing abilities of various species of interest - for example, bats can sense airflow via microscopic hairs on their wing membranes, and the nostrils of large seabirds are used to find gusts to ride during turbulent conditions. The experiment therefore seeks a bioinspired pathway for energy efficient aerodynamic control of a moving wing in a minimally observable flow field.Further investigations into exploitation of these mechanisms propose a novel control approach for actuating the arm-wing system via real-time flow observation, with the wing kinematics controlled using a Reinforcement Learning Neural Network. The observations are to be achieved via surface pressure measurements on the wing and fixed velocity sensors off-wing (or other biomimicking sensor systems as appropriate), with control inputs used to improve the flapping behaviour based on a lift-maximising framework to maximise energy efficiency, for example, to increase the range. An 'optimal flapping flight' path is sought, as determined by power extracted from the wind tunnel flow balanced against energy supplied to perform the procedure from the robot. The overall aim of the project is therefore to develop a programmable approach to investigation of flapping and unsteady flight control, to enable a better understanding of such locomotion in the natural world and hence inform ongoing progress into energy-efficient aerodynamics. This approach also offers good scalability to higher degrees of freedom and a truly novel approach to real-time flow control using artificial intelligence. With these tools, a multi-disciplinary engineering approach to understanding the energy efficiency behind flapping flight is sought, in an original pathway to established UK groups in Biology and Zoological disciplines. The project allows for future application across aerodynamics, robotics and FWMAVs enabling Transition to Zero Pollution.

在自然界和人工研究中，扑翼飞行是最有效的低雷诺数飞行形式。例如，它允许蝴蝶每年在北美和墨西哥西南部之间迁移3000英里，主要是通过拍打翅膀利用不稳定的空气动力学机制。其他较大的拍动物种，如鸟类和蝙蝠，在较高的雷诺数制度下运作，使用类似的机制来实现狩猎，迁移和逃避捕食者的飞行。然而，由于非线性运动学和形态学的综合复杂性，以及使用和充分测量活体标本的困难，这仍然是人们最不了解和利用的自然运动形式之一。该项目提出了一种新的风洞平台，用于研究扑翼飞行运动学，使用6轴工业关节机械臂，可重复的风洞测试扑翼飞行在不同的参数空间，因此物种。风洞设施是伦敦帝国理工学院的T1风洞，这是一个超低湍流设施，最大风速可达42米/秒。这种机翼-机器人-风洞系统的高机动性使人们能够更深入地了解翼尖“扫出”的特定扑动飞行路径如何允许对非定常结构进行最佳空气动力学控制，以增加超过典型固定翼失速迎角的升力。同步的3分量速度场和表面压力测量将被用于验证非定常空气动力结构的形成和脱落，目的是将总体流场特性与机翼表面上感测到的压力相关联。这代表了一个“部分可观察的系统”，符合各种感兴趣物种的自然感知能力-例如，蝙蝠可以通过翅膀膜上的微观毛发感知气流，大型海鸟的鼻孔用于在湍流条件下寻找阵风。因此，实验寻求一个bioinspired路径的能量有效的气动控制的移动翼在最小可观察的流field.Further调查利用这些机制提出了一种新的控制方法，通过实时流动观察，与机翼运动学控制使用强化学习神经网络驱动的臂翼系统。观察将通过机翼上的表面压力测量和翼外的固定速度传感器（或其他适当的仿生传感器系统）来实现，控制输入用于基于升力最大化框架来改善扑翼行为，以最大化能量效率，例如，增加范围。一个“最佳的扑翼飞行”的路径，寻求确定的功率从风洞流平衡的能量供应，以执行从机器人的程序。因此，该项目的总体目标是开发一种可编程的方法来研究扑翼和非定常飞行控制，以便更好地了解自然世界中的这种运动，从而为节能空气动力学的持续进展提供信息。这种方法还提供了更高自由度的良好可扩展性，以及使用人工智能进行实时流量控制的真正新颖的方法。有了这些工具，寻求一种多学科的工程方法来理解扑翼飞行背后的能源效率，在生物学和动物学学科建立英国团体的原始途径。该项目允许未来应用于空气动力学，机器人和FWMAV，实现零污染过渡。