Investigation Of Active Vibration Control Of Trailing Edge Flap Smart Rotor

Vibration problem has been an important issue since helicopter was invented. Evolutions of technologies applied for alleviating vibration accompany the updating of helicopter engineering. Rotor system is the main source of helicopter’s vibration and a large amount of scholars have been making efforts to lower the vibration level of rotor system. Trailing Edge Flap control technology is one of the smart rotor methods which focus on reducing vibration. When the rotor rotates, the flaps revolve around an axis on the trailing edge of each blade by a given pattern and they produce additional aerodynamic loads. The combination of aerodynamic loads of blades and flaps results in the reduction of entire system’s vibration, realizing the control of helicopter’s vibration. This paper aims at searching the optimal solution for the control of trailing edge flap’s motion to reduce the rotor’s vibration load.The purpose of this paper is to establish a process which focuses on searching the optimal flap motion to achieve the minimum hub vibration level. Time cost could be can be largely decreased through this method. Firstly, an aeroelastic model of the rotor involving blades and flaps on the trailing edge was built based on a large deformation blade theory which is to deal with the nonlinearity of the blades. Force product method was applied to calculate the blades’ vibration. Hub vibration value could be obtained while flaps’ motion was imported into this model. Afterwards, the method for searching the optimal flaps’ motion pattern was designed, which was indicated by amplitudes of(Nb–1)?, Nb? and(Nb+1)? motion harmonic frequency of the flaps, for getting the minimum hub vibration level. Latin Hypercube Sampling was used for create numbers of sampling points to gain the basic output via calculating through the previous model. These inputs and outputs then were used by Radial Basis Function to create a surrogate model which can reduce largely computing time comparing to the previous model but cause little errors. Subsequently Particle Swarm Optimization was applied to save much more computing time while searching for the optimal results. Then comparison between the results of the optimal case and the other one where flaps did not move would be made. In the end, the analysis and discussion would be given.