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所属单位：能源与动力学院

发表刊物：Tuijin Jishu

摘要：In propeller synchrophasing noise reduction, the propeller noise model is identified from the measured data under certain flight conditions firstly, and then the optimal synchrophase angle is calculated based on the noise model, finally the optimal synchrophase angle will be used for propeller synchrophasing control. In the process of noise model identification, due to the influence of flight speed, height variation and air flow perturbation, the measured data often fluctuate greatly, which affects the accuracy of the identification model and the optimal synchrophase angle. In this paper, a measured data selection method based on wavelet filtering and three parameter sinusoidal fitting method is proposed to improve the accuracy of noise model identification. The noise Blade-Pass Frequency (BPF) signal is extracted through the wavelet filtering algorithm, from which the three parameter sinusoidal fitting algorithm is adopted to select the minimum fluctuation data for noise model identification. This method can effectively reduce the impact of measured data fluctuations and improve the accuracy of identification model. Experimental results has shown that the noise model identified by the minimum fluctuation data can achieve higher precision compared with the traditional noise model identified by certain data, and the error between the sound pressure level (SPL) predicted by the noise model and the actual measured SPL is less than 1dB, the error between the optimal phase angle predicted by the noise model and the actual optimal phase angle is less than 5°. Moreover, the propeller synchrophasing with the optimal phase angle can achieve 19.5dB noise reduction at the experimental location. © 2018, Editorial Department of Journal of Propulsion Technology. All right reserved.

ISSN号：1001-4055

是否译文：否

发表时间：2018-11-01

合写作者：Cao, Yun-Fei,Sheng, Long,Xia, Tian-Qian

通讯作者：黄向华