在简单介绍了Beamforming声源识别技术的基本原理和车外气动声源与车内噪声相关性分析的方法之后,在整车气动声学风洞中应用流场外声阵列与车内2个参考麦克风同时进行车内外噪声信号的同步测量,应用频域内声源识别的传统的Beamforming算法和改进的CLEAN-SC算法,识别出了车外的气动噪声源分布,并分析了车外声源与车内噪声的相关性,得出车外噪声源对车内噪声的相对贡献度.结果表明:频域内改进的CLEAN-SC算法可以在很大程度上改善传统Beamforming算法在动态范围和空间分辨率方面的局限性,且算法稳健,使该项技术在风洞内的应用更具实用性.对车内噪声而言,在较多的特征频段,车外后视镜作为声源对车内噪声的贡献度最大.但在一些特征频段,前雨刮和门把手对车内噪声的贡献也不容忽视.%After a brief introduction of the basic principle of Beamforming noise source identification tech-nique and the analysis method of correlation between exterior aerodynamic noise sources and interior noise of vehi-cle, a synchronous measurement of exterior and interior noise signals is conducted in a full scale aero-acoustic wind tunnel with the use of an out-of-flow sound array outside vehicle and two reference microphones inside vehicle. By applying conventional Beamforming algorithm and modified CLEAN-SC algorithm for noise source identification in frequency domain, the distribution of exterior aerodynamic noise sources is identified and the correlation between in-terior noise and exterior noise sources are analyzed, with the relative contribution of exterior noise sources to interior noise obtained. The results show that the modified CLEAN-SC algorithm in frequency domain can relieve the limita-tion in dynamic range and spatial resolution of conventional Beamforming algorithm with good robustness, making that technique has more practicability in its application to wind tunnel. For interior noise, exterior rearview mirror as noise source has the most contribution in many characteristic frequency bands, while at certain frequency bands the contribution of front wiper and door handle can not be neglected as well.
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