Energy efficiency of flapping flight: understanding and exploitation

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.