摘要
随着人们对轨道车辆振动和噪声舒适性要求的提高,轨道车辆整车噪声的预测显得尤为重要。传统的噪声计算方法如有限元法、边界元法和统计能量法SEA的建模时间较长,计算复杂。该文介绍了一种基于经验公式的整车噪声快速计算方法,对车辆分段成多声学腔体,并采用多腔体耦合模型进行计算。然后,基于整车噪声快速计算方法对轻轨车辆进行整车噪声参数输入和预测计算。计算结果显示:静置工况下客室内噪声平均值为62.5 dB(A),运行工况下前两节车辆客室内噪声平均值分别为67.5 dB(A)和67.2 dB(A),运行车外噪声为78.3 dB(A),均满足轻轨车辆噪声限值标准要求。最后对整车噪声预测结果进行了试验验证。结果表明,轻轨车辆整车噪声平均计算误差小于2 dB(A),达到了行业内较高的计算精度要求。
With the improvement of the comfort requirements of rail vehicle vibration and noise,the prediction of vehicle noise of rail vehicles is particularly important.Traditional noise calculation methods such as finite element method,boundary element method and statistical energy analysis(SEA)method have longer modeling time and complicated calculation.This paper introduced a fast calculation method of vehicle noise based on empirical formula.The vehicle was segmented into multiple acoustic cavities and calculated by multi-cavity coupling model.Then,based on the rapid calculation method of vehicle noise,the vehicle noise parameter input and prediction calculation were performed on the light rail vehicle(LRV).The calculation results showed that the average noise in the passenger room was 62.5 dB(A)under static conditions,and the average noise of the passengers in the first two vehicles was 67.5 dB(A)and 67.2 dB(A)respectively under running conditions.The external noise was 78.3 dB(A).All these predicted values met the noise limit requirements of light rail vehicles.Finally,the vehicle noise prediction results were tested and verified.The results showed that the average calculation error of the whole vehicle noise of light rail vehicles was less than 2 dB(A),which meet the higher calculation accuracy requirements in the industry.
作者
蒋忠城
刘晓波
王先锋
郭冰彬
李登科
JIANG Zhong-cheng;LIU Xiao-bo;WANG Xian-feng;GUO Bing-bin;LI Deng-ke(The State Key Laboratory of Heavy Duty AC Drive Electric Locomotive Systems Integration,Zhuzhou 412001;CRRC Zhuzhou Locomotive Co.,Ltd.,Zhuzhou 412001)
出处
《机械设计》
CSCD
北大核心
2019年第S02期160-164,共5页
Journal of Machine Design
关键词
轻轨车辆
噪声预测
试验验证
频谱特征
LRV
noise prediction
test verification
spectrum characteristics