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Fan noise, comprising both tonal and broadband components, is a predominant source of noise in aero-engines. Calculating duct acoustic modes for random broadband noise is more complex than that for tonal noise. This paper proposes a probabilistic method for identifying duct acoustic modes based on non-synchronous measurements from microphone arrays. The method overcomes the challenge of acoustic mode aliasing and accurately identifies duct modes in the presence of broadband fan noise. By rotating a circular duct with a microphone array, the proposed method equivalently increases the number of non-synchronous microphone measurement points. The completion of the cross-spectral matrix is realized by the Fast Iterative Shrinkage Thresholding Algorithm (FISTA) to obtain the sound pressure signals from more measurement points. The mode coefficients are estimated using a hierarchical a priori probability method. The inverse problem of mode identification is represented by a Bayesian framework based on a Gaussian-scale mixture prior model. Simulations and experiments validate the method's effectiveness in suppressing mode aliasing, providing accurate results for duct mode identification with broadband noise, eliminating the influence of interfering modes on identification results, and offering improved observation of mode characteristics across a wide frequency and rotational speed range. • The non-synchronous measurements technique is employed to increase the number of measurement points. • A probabilistic model with a hierarchical prior is utilized to characterize the sound field. • The iterative parameter adjustment process is adaptive. • Experiments were performed on an axial compressor test rig to validate the proposed method. [ABSTRACT FROM AUTHOR]

The Non-synchronous Measurements (NSM) was proposed to enhance the accuracy of reconstruction and reduce the measurement cost. However, the investigated object of conventional NSM are only sparsely distributed sound sources, and little emphasis placed on the reconstructed accuracy of the spatially extended sound source. In this article, the traditional NSM is further investigated in the spatially extended sound source, and the sound pressure information is reconstructed by Bayesian compressive sensing algorithm (BCS). Firstly, the spatially extended sound source is scanned by moving the rectangular microphone array, and the measurement effects of the array with large aperture and high density are approximately obtained by using NSM technique. Then, BCS is applied to reconstruct sound field by the NSM of sound pressure. The results of numerical simulations provide evidence for the feasibility of applying the proposed investigation to the spatially extended sound source. NSM has advantages in reconstruction accuracy compared with the single measurement. At the same time, the reconstructed sound pressure under NSM can replace the simultaneous measurement (SM) results of multi-array, which avoids the limitation of using reference microphone in increasing the number of channels and spatial layout. The experimental results of the driven steel plate provide additional evidence to support the effectiveness and feasibility of the investigation. • The application problem of the NSM in the spatially extended sound source like the driven steel plate is investigated. • The NSM increases the number of microphones and avoids the limitation of using reference microphone in increasing the number of channels and spatial layout. • The traditional NSM is further investigated in the spatially extended sound source, and the sound pressure is reconstructed by BCS. [ABSTRACT FROM AUTHOR]