Directivity identification method for noise sources of engine and parts thereof

Abstract

The invention discloses a directivity identification method for noise sources of an engine and parts thereof, and belongs to the technical field of noise source separation of aero-engines and parts thereof. The sound source noise is experimented through a microphone array, and if the spectral characteristics of the noise of each part of the sound source are obtained, the sound power spectrum matrix about the intensity, namely directivity, of each sound source can be constructed only by using a sufficient number of microphones; by applying an SODIX-Bes inversion algorithm, the sound source intensity and directivity can be obtained by solving from a benign linear algebraic equation set. The sound source directivity simulation technology (SODIX) based on a cross-spectrum matrix developed by the DLR of German is improved. According to the invention, the noise directivity and sound intensity of the engine and each part thereof can be separated from the experimental data of the whole engine noise or the part noise thereof.

Description

technical field [0001] The invention belongs to the technical field of noise source separation of aero-engines and components thereof, and in particular relates to a method for identifying the directionality of noise sources of engines and components thereof. Background technique [0002] Aeroengine noise includes multiple noise sources, such as fan inlet noise, fan outlet noise, combustion noise, turbine noise, and jet flow noise; while engine component noise also includes rotor noise, stator noise, front noise, rear noise, And blade leading edge noise, trailing edge noise, etc. Not only that, as figure 1 Another important characteristic of the aero-engine noise shown is that these noise sources have very strong directivity. [0003] The traditional microphone array measurement technology can separate the noise sources at the inlet and outlet of the engine, but this is only an average noise separation within the aperture scale of the microphone array, and the directional ...

AI Technical Summary

Problems solved by technology

However, there are three major shortcomings in the application of the SODIX method: (1) The number and location of sound sources need to be known in advance before the application of the SODIX method. For this reason, the SODIX method divides the space into spatially continuous sound source distribution according to the wavelength characteristics of the sound wave , this will inevitably produce a false sound source, for example, the casing from the engine inlet to the outlet also does not know the sound source, especially for high-frequency noise sources, due to the reduction of the wavelength, when the number of sound sources increases to more than the number of microphones, resulting in an unsolvable math problem

(2) SODIX uses the cross-spectrum matrix fitting of the full-array microphone signal, thus forming a complex and huge nonlinear algebraic equation system solution, especially because the cross-spectrum matrix of the full-array microphone signal is used, the separation distance included in SODIX The cross-spectrum of microphone signals with poor correlation further brings about the stability problem of solving m

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