Energy efficiency of flapping flight: understanding and exploitation

扑翼飞行是低雷诺数飞行最有效的形式，在自然和人工研究中都有体现。例如，它允许蝴蝶每年在北美和墨西哥西南部之间迁徙多达3000英里，主要是通过拍打翅膀来利用不稳定的空气动力学机制。其他更大的扑翼物种，如鸟类和蝙蝠，在更高的雷诺数下工作，在狩猎、迁徙和躲避捕食者时使用类似的机制来实现飞行。然而，由于非线性运动学和形态学的复杂性，以及处理和充分测量活体标本的困难，这仍然是最不被理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，用于在各种参数空间和物种之间进行可重复的扑翼飞行风洞测试。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-隧道系统的高机动性使我们能够更深入地了解翼尖如何“扫出”特定的扑动飞行路径，从而实现非定常结构的最佳气动控制，从而增加升力，超出典型的固定翼失速攻角。同步分量速度场和表面压力测量将用于表征非定常气动结构的形成和脱落，目的是将全局流场行为与机翼表面感知到的压力联系起来。这代表了一个“部分可观察系统”，与各种感兴趣的物种的自然感知能力一致——例如，蝙蝠可以通过翅膀膜上的微小毛发感知气流，大型海鸟的鼻孔用于在湍流条件下寻找阵风。因此，该实验寻求一种生物启发的途径，在最小可观察的流场中对运动机翼进行节能气动控制。对这些机制的进一步研究提出了一种新的控制方法，通过实时流动观察来驱动臂翼系统，并使用强化学习神经网络控制机翼的运动学。这些观测结果将通过机翼上的表面压力测量和机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，控制输入用于改善基于升力最大化框架的扑动行为，以最大限度地提高能源效率，例如，增加航程。从风洞流中提取的能量与机器人提供的执行该过程的能量相平衡，从而确定了一个“最佳扑翼飞行”路径。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，以便更好地理解自然界中的这种运动，从而为节能空气动力学的持续进展提供信息。该方法还提供了较高自由度的良好可扩展性，是使用人工智能实现实时流量控制的真正新颖方法。有了这些工具，一个多学科的工程方法来理解背后的扑翼飞行的能源效率是寻求，在一个原始的途径建立英国小组在生物学和动物学学科。该项目允许未来在空气动力学、机器人和FWMAVs方面的应用，实现向零污染的过渡。

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.