摘要
在风洞开口实验段,针对不同风速及不同叶尖速比,应用Brükel&Kjær公司60通道轮型声阵列及声信号采集系统对直径为1.4 m的S翼型风轮进行声场测试,并采用统计最优近场声全息(SONAH)技术进行旋转风轮低频噪声源识别及频域特征分析。实验结果表明:最大声强度是旋转叶片产生的基频噪声,其对应总声压级随风速增加呈函数f(x)=-0.0092x^(4)+0.297x^(3)-3.7403x^(2)+23.186x+49.274增加,随叶尖速比增加呈函数f(x)=0.4467x^(4)-10.273x^(3)+87.728x^(2)-328.75x+567.23增加;识别的噪声源最大能量中心集中于翼展位置约0.545 m,相对半径r/R=0.778处,且不随风速和尖速比的改变而改变。
In the open section of the wind tunnel,the sound field of S-wing rotating rotor with1.4-meter diameter was measured by BK60-channel type acoustic array and acoustic signals at different wind speed and different tip speed ratio was acquired.Low-frequency sound sources of rotating wind turbine were located using statistically optimal near-field acoustic holography(SONAH).The experimental results showed that the maximum sound intensity is the fundamental frequency noise generated by the rotating blades,and the corresponding sound pressure level increased as a function f(x)=-0.0092 x^(4)+0.297 x^(3)-3.7403 x^(2)+23.186 x+49.274 with the wind speed,and increased as a function f(x)=0.4467 x^(4)-10.273 x^(3)+87.728 x^(2)-328.75 x+567.23 with the tip speed ratio.The center of the identified noise sources is concentrated on the radial position of about 0.545 m,that is r/R=0.778,and did not change with wind speed and tip speed ratio.
作者
张翠青
高志鹰
陈永艳
代元军
汪建文
候熠
Zhang Cuiqing;Gao Zhiying;Chen Yongyan;Dai Yuanjun;Wang Jianwen;Hou Yi(School of Energy and Power Engineering,Inner Mongolia University of Technology Huhhot 010051,China;Department of Electrical Engineering,Inner Mongolia Technical College of Mechanics and Electrics,Huhhot 010070,China;Key Laboratory of Wind Energy and Solar Energy Technology,Ministry of Education,Huhhot 010051,China;School of Energy Engineering,Xinjiang Institute of Engineering,Urumqi 830091,China)
出处
《太阳能学报》
EI
CAS
CSCD
北大核心
2021年第1期302-307,共6页
Acta Energiae Solaris Sinica
基金
国家自然科学基金(51866012)
国家自然科学基金(51366010)
内蒙古重大自然科学基金(2018ZD08)。
关键词
风力机
噪声控制
声强度
声压级
噪声源
统计最优近场声全息(SONAH)
声源识别
wind turbines
noise control
sound intensity
sound pressure level
noise source
statistically optimal near-field acoustic holography(SONAH)
sound source identification