Motion Optimistion Of Plunging Airfoil Using Swarm Algorithm

Abstract

Micro Aerial Vehicles (MAVs) are battery operated, remote controlled miniature flying vehicles. MAVs are required in military missions, traffic management, hostage situation surveillance, sensing, spying, scientific, rescue, police and mapping applications. The essential characteristics required for MAVs are: light weight, maneuverability, ease of launch in variety of conditions, ability to operate in very hostile environments, stealth capabilities and small size. There are three main classes of MAVs : fixed, rotary and flapping wing MAV’s. There are some MAVs which are combinations of these main classes. Each class has its own advantage and disadvantage. Different scenarios may call for different types of MAV. Amongst the various classes, flapping wing class of MAVs offer the required potential for miniaturisation and maneuverability, necessitating the need to understand flapping wing flight. In the case of flapping winged flight, the thrust required for the vehicle flight is obtained due to the flapping of the wing. Hence for efficient flapping flight, optimising the flap motion is necessary. In this thesis work, an algorithm for motion optimisation of plunging airfoils is developed in a parallel framework. An evolutionary optimisation algorithm, PSO (Particle Swarm Optimisation), is coupled with an unsteady flow solver to develop a generic motion optimisation tool for plunging airfoils. All the unsteady flow computations in this work are done with the HIFUN1 code, developed in–house in the Computational Aerodynamics Laboratory, IISc. This code is a cell centered finite volume compressible flow solver. The motion optimisation algorithm involves starting with a population of motion curves from which an optimal curve is evolved. Parametric representation of curves using NURBS is used for efficient handling of the motion paths. In the present case, the motion paths of a plunging NACA 0012 airfoil is optimised to give maximum flight efficiency for both inviscid and laminar cases. Also, the present analysis considers all practically achievable plunge paths, si- nusoidal and non–sinusoidal, with varying plunge amplitudes and slopes. The results show promise, and indicate that the algorithm can be extended to more realistic three dimension motion optimisation studies.