Smart Fixed Wing Aircraft (SFWA) aims to develop and test passive and active flow control technologies to improve the high lift performance of a wing.
The purpose of this research was to develop and demonstrate a closed loop flow control to minimise the separation on a trailing-edge flap. The active flow control improved the aerodynamic performance by delaying the trailing-edge separation on a single element trailing-edge flap. The flow control was pulsed blowing applied near the leading-edge region of the flap. The pulsed blowing was through spanwise segmented slots. A closed loop flow control system was designed to demonstrate flow control on a mid-scale wind tunnel model.
The Topic Description called for two directions of closed loop flow control methods. The first was a pre-modelled approach which would allow the calibrating of the algorithm. This algorithm development was devoted for future use in flight test activities. The second method was an adaptive algorithm, which was adjusted by a “learning-by-doing” system. This approach was demonstrated in mid-scale wind tunnel tests.
In this work, active flow control using pulsed air jets was investigated in order to delay flow separation on a two-element high-lift wing. The method was validated experimentally. A novel iterative learning control (ILC) algorithm was developed that uses position based pressure measurements to update the actuation. The method was experimentally tested on a wing model in a 0.9 m x 0.6 m wind tunnel initially and then the R. J. Mitchell wind tunnel at the University of Southampton. Compressed air and fast switching solenoid valves were used as actuators to excite the flow and the pressure distribution around the chord of the wing was measured as a feedback control signal for the ILC controller.
Experimental results showed that the actuation was able to delay the separation and increase the overall lift by approximately 15% to 20%. By using the ILC algorithms, the controller was able to track the target lift and using the optimum control algorithm with an extended reference, the controller was able to maximize the lift enhancement. In the second wind tunnel test session, open loop tests were completed to generate data which was used to create a system model.
A two-dimensional model function was then fitted using locally weighted scatter-plot smoothing and the model was applied in a model based iterative learning optimization algorithm. Wind tunnel experimental results showed that the method was able to optimise the performance with two variables and an overall lift enhancement of approximately 20% could be achieved.