Aerodynamic Characteristics Prediction via Artificial Hair Sensor and Feedforward Neural Network

Fly by feel is a concept in which distributed sensors and actuators are integrated on an aerial system for state awareness or sensation of the environment, and make use of distributed control to increase the system maneuverability, stability and safety. Artificial hair sensors are good candidates as sensors for the fly by feel concept because they are lightweight, have low manufacturing costs and can easily be integrated on the surface of air-vehicle without affecting the flow. We investigate an application of artificial hair sensors considering its capability of measuring the local flow velocity combined with a Feedforward Artificial Neural Network to predict the aerodynamic quantities such as lift coefficient, moment coefficient, angle of attack and free-stream velocity in real-time. These quantities, when combined with the physical and unsteady aerodynamics parameters, will make a framework for designing and implementing an active controller for gust alleviation in a pitch and plunge airfoil system.