Jet noise source identification represents an important research direction in the field of aeroacoustics and has significant importance for aviation engine noise reduction and environmental protection. Jet noise belongs to typical distributed coherent sources, and strong coherence generated by large-scale turbulent structures poses major challenges to traditional acoustic source identification methods. To address this problem, this paper proposes a Bayesian subspace equivalent source method based on proper orthogonal decomposition (POD). The proposed method first extracts the primary coherent structures of acoustic fields through proper orthogonal decomposition, effectively suppressing background noise interference. The method then establishes a Bayesian regularization framework that utilizes marginal likelihood functions to achieve automatic optimization of regularization parameters, effectively avoiding singularities in inverse problems. Algorithm performance was systematically validated through three approaches: line source simulations, large eddy simulation data analysis of Mach 0.9 jets, and free jet wind tunnel experiments. The validation successfully separated and localized the spatial distribution of jet noise sources under different Strouhal number conditions. Results demonstrate that the proposed method has good reconstruction accuracy and directivity characteristics within the large-scale turbulent frequency range. Compared to traditional equivalent source methods, the proposed method exhibits significant advantages in acoustic source distribution characteristic estimation. The advantages are primarily attributed to the synergistic effect of intrinsic coherence between source decomposition techniques and equivalent sources.
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