Bayesian compressive sensing for identifying tonal acoustic modes of fan noise in the duct

Fan noise is a significant noise source for large bypass ratio engines, and its propagatable modes increase with frequency, making it difficult to be measured by a sufficient number of microphones. A Bayesian compressive sensing method for fan tonal noise mode identification is proposed to solve the problem of insufficient number of microphones in acoustic mode identification. The sound field in the duct is described by a probabilistic model, and the inverse problem of compression perception for mode identification is represented by a Bayesian framework. The mode identification method based on Bayesian compressive sensing can sparsely recover unknown mode coefficient solutions and achieve parameter adaption. Numerical simulations and experimental tests verify the effectiveness of the Bayesian compressive sensing method in mode identification. The results show that the proposed method of mode identification can accurately identify the target modes with 56.3% fewer microphones than the conventional method.