Propeller synchrophasing control with a cylindrical scaled fuselage based on an improved data selection algorithm

Abstract

Propeller synchrophasing control is an active noise control method which can effectively reduce the noise in the cabin of a turboprop aircraft. The propeller signature model identified by the measured acoustic noise data is easily affected by flight speed, altitude, and the existence of the fuselage. Meanwhile, the noise excited by the propellers is nonstationary signal, which often fluctuates greatly, thus affecting the accuracy of the identification of the model. In this paper, a synchrophasing control experimental platform with a cylindrical scaled fuselage on ground is firstly established to validate the actual noise reduction in the cabin. Then, a minimum fluctuation data selection method based on wavelet filtering and three-parameter sinusoidal fitting is proposed to improve the identification accuracy of the propeller signature model. This method extracts the high-precision propeller blade passing frequency signal from the noise signal by using a wavelet filtering algorithm and selects the minimum fluctuation data segment by using a three-parameter sinusoidal fitting algorithm. The experimental results firstly show the significant noise attenuation achieved in the cabin using propeller synchrophasing control. Secondly, the propeller signature model improved by the minimum fluctuation data selection method has higher accuracy than that identified by the traditional method.