试生产阶段SUV低频噪声识别与改进研究
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摘要
提出一种适用于试生产阶段的SUV低频噪音识别与改进流程。建立白车身有限元模型,通过模态试验验证模型有效性。建立驾驶室声固耦合模型,进行频率响应分析。基于实车噪声与激励力测试及车内响应点的声压值灵敏度,识别板件振动的噪声频率。分析主要峰值频率下的板块单位面积声学贡献量,通过对问题板件加强局部刚度和涂贴阻尼来降低车内噪音。结果表明在整车质量增加较小的情况下车内低频噪音得到有效控制。为试生产阶段的低频噪声识别与改进提供有效的方法。
A method for low frequency noise identification and improvement of the vehicle in pre-production stage was proposed. The FEA model of the BIW body was established and verified by modal experiments. The structure-acoustic coupling model of the cabin was established and used for vehicle frequency response analysis. The noise frequency of the panel vibration was identified by analyzing the acoustic sensitivity curve,the sound pressure of the cabin and the mounting point excitation force of the body. The panel sound pressure contribution of per unit area of the peak frequency was analyzed.By strengthening the local stiffness and adding damping material respectively at noise panels to reduce the interior noise. The result shows that the low-frequency noise of the vehicle was effectively controlled and the weight gain of car body was not significant. This method provides a reference for the low-frequency noise identification and improvement in pre-production stage.
A method for low frequency noise identification and improvement of the vehicle in pre-production stage was proposed. The FEA model of the BIW body was established and verified by modal experiments. The structure-acoustic coupling model of the cabin was established and used for vehicle frequency response analysis. The noise frequency of the panel vibration was identified by analyzing the acoustic sensitivity curve,the sound pressure of the cabin and the mounting point excitation force of the body. The panel sound pressure contribution of per unit area of the peak frequency was analyzed.By strengthening the local stiffness and adding damping material respectively at noise panels to reduce the interior noise. The result shows that the low-frequency noise of the vehicle was effectively controlled and the weight gain of car body was not significant. This method provides a reference for the low-frequency noise identification and improvement in pre-production stage.
引文
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[9]Koruk H,Sanliturk K Y.Optimisation of damping treatments based on big bang-big crunch and modal strain energy methods[J].Journal of Sound&Vibration,2014,333(5):1319-1330.
[2]张丰利.基于汽车NVH正向设计流程的整车模态匹配研究[D].合肥:合肥工业大学,2009.(Zhang Feng-li.Research on Vehicle Modal Frequencies Matching Based on Original NVH Design Process[D].Hefei:Hefei University of Technology,2009.)
[3]庞剑,谌刚,何华.汽车噪声与振动—理论与应用[M].北京理工大学出版社,2006:319-320.(Pang Jian,Chen Gang,He Hua.Automotive Noise and Vibration-Principle and Application[M].Beijing:Beijing Institute of Technology Press,2006:319-320.)
[4]周玮,廖日东.基于模态叠加法的曲轴动态特性研究与结构优化[J].农业工程学报,2015,31(3):129-136.(Zhou Wei,Liao Ri-dong.Dynamic characteristic based on modal superposition method and structure optimization of crankshaft[J].Transactions of the Chinese Society of Agricultural Engineering,2015,31(3):129-136.)
[5]石宏娟,樊文欣,郭常立.基于ATV的内燃机机体结构辐射噪声预测技术研究[J].机械设计与制造,2009(10):123-125.(Shi Hong-juan,Fan Wen-xin,Guo Chang-li.Research on structural radiation noise prediction for ICE block based on ATV technology[J].Machinery Design&Manufacture,2009(10):123-125.)
[6]杨晓涛,谷正气,杨振东.汽车乘员舱多层吸声材料的多目标优化[J].振动与冲击,2013,32(4):21-25.(Yang Xiao-tao,Gu Zheng-qi,Yang Zhen-dong.Multiple-target Optimization of Multilayer Sound Absorption Material Combinations of Passenger Compartment[J].Journal of vibration and shock,2013,32(4):21-25.)
[7]杨雅怡.汽车顶棚振动特性的研究及优化[D].重庆:重庆交通大学,2014.(Yang Ya-yi.Study and Optimization of Vibration Characteristics of Automobile Roof[D].Chongqing:Chongqing Jiaotong University,2014.)
[8]陈宏.汽车内饰材料声学性能与整车NVH性能改进研究[J].汽车技术,2009(12):80-82.(Chen Hong.Investigation on Improvement of Acoustic Performance of Automotive Interior Material and Vehicle NVH Performance[J].Automobile Technology,2009(12):80-82.)
[9]Koruk H,Sanliturk K Y.Optimisation of damping treatments based on big bang-big crunch and modal strain energy methods[J].Journal of Sound&Vibration,2014,333(5):1319-1330.