摘要
为研究钝体绕流的气动噪声源特性,采用Realizablek-ε湍流模型与宽频带噪声源模型相结合的方法模拟钝体的声功率级和表面声功率级,比较并分析来流风速、钝体截面形式及尺寸对气动噪声源强度及其分布特性的影响规律,探讨气动噪声源的影响机制.结果表明:钝体绕流气动噪声源主要位于气流发生分离、湍流运动比较剧烈的地方,且钝体的外形越趋近于流线型,其气动噪声源强度越低;四极子噪声源对总噪声的贡献比偶极子噪声源的贡献小得多;柱体表面声功率级最大值与来流风速对数之间呈线性正相关,与截面尺寸之间呈线性负相关.最后提出了表面声功率级的数学预测模型,为工程结构的声环境设计及气动噪声控制提供参考.
To investigate the characteristics of aerodynamic noise sources induced by flows around bluff bodies, the acoustic power level and surface acoustic power level of the bluff bodies were numerical simulated based on combination of the Realizable k-εturbulence model and broadband noise sources model method. And the effects of oncoming wind speed, cross-section and characteristic size of the bluff bodies on the magnitude and distribution of aerodynamic noise sources were analyzed, and the influence mechanism of aerodynamic noise sources was further discussed. The results show that, the aerodynamic noise sources are quite significant in the regions where flow separation occurs with intensive turbulence, and they show a decreasing trend for the bluff body with a more streamlined cross-section. And the contribution of quadrupole sources to the total noise is much less than that of dipole sources, and thus the surface acoustic power level, which corresponds to the dipole sources, is used to analyze the characteristics of aerodynamic noise sources. Furthermore, the maximum surface acoustic power level is positively linear correlated with the logarithm of the oncoming wind speed, and is negatively linear correlated with the characteristic size. Finally, the proposed mathematical prediction model for the surface acoustic power level provides references for the acoustic environment design and aerodynamic noise control in engineering applications.
出处
《哈尔滨工业大学学报》
EI
CAS
CSCD
北大核心
2017年第12期146-151,共6页
Journal of Harbin Institute of Technology
基金
国家自然科学基金(51578186)
中国建筑股份有限公司科研基金(CSCEC-2015-Z-39
CSCEC-2010-Z-01-02)
关键词
钝体绕流
气动噪声源
宽频带噪声源模型
影响机制
预测模型
flows around bluff bodies
aerodynamic noise source
broadband noise sources model
influence mechanism
prediction model