While localizing sound sources within the shear flow, conventional beamforming faces limitations due to the Rayleigh criterion, restricting its resolution. Moreover, acoustic inversion method encounters challenges in establishing the relationship between source strength and acoustic quantities within the shear flow, considering the effects of convection, refraction, and reflection. This paper introduces a novel approach, acoustic inversion method based on the shear flow Green’s function, to address sound source localization. The function is derived to mathematically describe the propagation of acoustic pressure and particle velocity in the shear flow, accounting for flow-acoustic effects such as convection, refraction, and reflection. Using the derived function, the transfer matrix relating the source strength to the measured acoustic pressure or particle velocity is constructed. The norm constrained minimization and sparse Bayesian learning are then applied to estimate source strength distribution and achieve accurate localization. The acoustic propagation simulations and wind tunnel experiments demonstrate that the method can go beyond the Rayleigh limit, providing higher resolution than conventional beamforming techniques. And the method employs a more reasonable source-to-receiver transfer model, resulting in superior performance to other shear flow correction method. Notably, particle velocity exhibits superior localization accuracy and robustness in the wind tunnel experiments, reducing relative localization errors by up to 26.7% compared to acoustic pressure.

中文翻译：

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基于剪切流格林函数的开放喷射风洞声源定位声学反演方法


在剪切流内定位声源时，传统波束形成由于瑞利准则而面临限制，限制了其分辨率。此外，考虑对流、折射和反射的影响，声波反演方法在建立剪切流内声源强度和声学量之间的关系方面遇到了挑战。本文介绍了一种新颖的方法，即基于剪切流格林函数的声学反演方法，来解决声源定位问题。该函数的推导是为了在数学上描述剪切流中声压和粒子速度的传播，并考虑对流、折射和反射等流声效应。使用导出的函数，构建将源强度与测量的声压或粒子速度相关的传递矩阵。然后应用范数约束最小化和稀疏贝叶斯学习来估计源强度分布并实现精确定位。声传播模拟和风洞实验表明，该方法可以超越瑞利极限，提供比传统波束形成技术更高的分辨率。并且该方法采用了更合理的源到接收器传递模型，从而具有优于其他剪切流校正方法的性能。值得注意的是，粒子速度在风洞实验中表现出卓越的定位精度和鲁棒性，与声压相比，相对定位误差最多可减少 26.7%。