闫国华1,2,李成晨1,王玺臻1,刘勇1.基于声学处理的风扇噪声预测模型改进[J].航空发动机,2021,47(S1):19-24
基于声学处理的风扇噪声预测模型改进
Improvement of Fan Noise Prediction Model Based on Acoustic Treatment
  
DOI:
中文关键词:  Heidmann模型  声学处理  风扇噪声  衰减系数  均方声压  涡扇发动机
英文关键词:Heidmann model  acoustic treatment  fan noise  attenuation coefficient  mean-square acoustic pressure  turbofan engine
基金项目:天津市教委科研计划(2020KJ017)、中国民航大学科研启动基金(2020KYQD76)资助
作者单位E-mail
闫国华1,2,李成晨1,王玺臻1,刘勇1 中国民航大学航空工程学院1基础实验中心2:天津300300 ghyan@cauc.edu.com 
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中文摘要:
      Heidmann风扇噪声模型没有考虑风扇外涵道中的声学处理对于噪声抑制的影响,导致风扇噪声的预测结果普遍大于 试验结果。为了提高预测结果精度,利用风扇噪声抑制模型分别求出风扇进口衰减系数和出口衰减系数,然后将其应用于Heid? mann模型中,计算修正后的风扇进口噪声和出口噪声的均方声压。将MATLAB软件作为风扇噪声预测模型的开发平台,以某型 涡扇发动机为例进行预测。结果表明:相较于原模型的预测结果,改进模型的风扇噪声明显降低,最大降幅达到7 dB;通过对比风 扇噪声在各工况下的预测结果和试验结果发现,改进模型预测值与实测值的平均误差从原模型的5 dB降低到3 dB以下。该改进 方法有效改善了Heidmann模型预测结果偏大的情况,使风扇噪声的预测结果更加准确。
英文摘要:
      It was not considered Heidmann fan noise modelin as the influence of the acoustic treatment in the fan duct on the noise suppression,which led to the prediction results of fan noise generally larger than the experimental results. In order to improve the accuracy of the prediction results,the fan noise suppression model was used to calculate the fan inlet attenuation coefficient and the fan outlet atten? uation coefficient respectively,and it was applied to the heidmann model to calculate the modified mean square acoustic pressure of the fan inlet noise and the fan outlet noise. Matlab software was used as the development platform of fan noise prediction model,and a turbofan engine was taken as an example to predict. The results show that:comparing with the prediction results of the original model,the fan noise of the improved model is significantly reduced,and the maximum reduction is 7 dB. By comparing the predicted and experimental results of fan noise under various working conditions,it is found that the average error between the predicted and measured values of the improved model is reduced from 5 dB of the original model to less than 3 dB. The improved method can effectively improve the situation that the pre? diction result of heidmann model is too large,and make the prediction result of fan noise more accurate.
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