| Citation: | WANG Fei, LI Xingjian, WANG Yanan, et al. Sparse Bayesian based reconstruction of acoustic modes for aircraft engine fans[J]. Journal of Aerospace Power, 2026, 41（5）：20250217 doi: 10.13224/j.cnki.jasp.20250217
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To address the large number of sensors required by uniform circular arrays in duct acoustic mode reconstruction for aero-engines, and the amplitude underestimation problem of traditional L1-norm-based compressed sensing methods, a sparse Bayesian approach for fan noise modal reconstruction was proposed. A hierarchical sparse Bayesian prior model was established and solved using a block coordinate descent algorithm, effectively characterizing and quantifying uncertainties in the measurement process. Furthermore, a non-dominated sorting genetic algorithm was employed to optimize array configuration and enhance reconstruction accuracy. Fan noise modal tests were conducted on a 3.5-stage aero-engine. Results showed that, under the same number of microphones, the sparse Bayesian method achieved lower reconstruction error than the L1-norm regularization method. Under low-speed condition, with an optimized layout of 6 sensors, the reconstruction error for circumferential mode order 5 was 0.01 dB. Under high-speed condition, with 8 optimally placed sensors, the reconstruction errors for mode orders 5 and −12 were 0.50 dB and 0.46 dB, respectively. The study demonstrated that the sparse Bayesian method significantly improved the accuracy of duct acoustic mode reconstruction with fewer sensors.
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