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高速列车低噪声设计中的部件声学指标分解方法
引用本文:张捷,姚丹,王瑞乾,肖新标.高速列车低噪声设计中的部件声学指标分解方法[J].交通运输工程学报,2021,21(3):248-257.
作者姓名:张捷  姚丹  王瑞乾  肖新标
作者单位:1.四川大学 高分子材料工程国家重点实验室,四川 成都 6100652.西南交通大学 牵引动力国家重点实验室,四川 成都 6100313.常州大学 机械与轨道交通学院,江苏 常州 213164
基金项目:国家自然科学基金项目52002257国家自然科学基金项目U1934203国家自然科学基金项目U1834201四川省应用基础研究项目2021YJ0531高分子材料工程国家重点实验室自主课题sklpme2020-3-12
摘    要:提出了一种基于整车噪声仿真分析的部件声学指标分解方法;将高速列车的部件声学指标按类型分为声源指标和路径指标2种主要形式,分别基于声线法和统计能量分析方法建立了高速列车的车外噪声预测模型和车内噪声预测模型,通过选定的初始参数作为计算输入,预测车外、车内噪声,并与车辆顶层声学指标进行差异化对比分析;基于声源贡献、路径贡献与参数灵敏度分析,考虑多目标优化,确定了声源部件和路径部件的声学指标。研究结果表明:噪声源的指标分解,基于整车车外噪声仿真分析,当车外噪声预测结果满足声学设计目标且设计裕量在可接受范围之内时,此时的声源参数输入即可作为一组声源指标分解结果;对于传声路径的指标分解,基于整车车内噪声仿真分析,当车内噪声满足声学设计目标且设计裕量在可接受范围之内时,此时的路径参数输入即可作为一组路径指标分解结果;当声源指标或路径指标不满足整车噪声要求时,则需要进行声源或路径的贡献分析,计算主要贡献声源或路径的参数灵敏度,通过对主要贡献声源或者路径进行修正迭代,使之最终满足声学设计目标;低噪声设计需要不断综合多项指标的反馈,合理地调整部件声学指标,确保声学指标分解满足顶层目标,且具有可行性。 

关 键 词:车辆工程    高速列车    低噪声设计    声学指标    统计能量分析    声线法
收稿时间:2020-12-26

Decomposition method to determine acoustic indexes of components in low-noise design procedure of high-speed trains
ZHANG Jie,YAO Dan,WANG Rui-qian,XIAO Xin-biao.Decomposition method to determine acoustic indexes of components in low-noise design procedure of high-speed trains[J].Journal of Traffic and Transportation Engineering,2021,21(3):248-257.
Authors:ZHANG Jie  YAO Dan  WANG Rui-qian  XIAO Xin-biao
Institution:1.State Key Laboratory of Polymer Materials Engineering, Sichuan University, Chengdu 610065, Sichuan, China2.State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, Sichuan, China3.School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, Jiangsu, China
Abstract:A method for determining the acoustic indexes of the components based on vehicle noise simulation analysis was proposed. The acoustic indexes of the high-speed train components were divided into noise source and sound transfer path indexes according to the types. The prediction models of high-speed train exterior and interior noise were established respectively by using ray acoustics and statistical energy analysis methods. A set of initial parameters were selected as the model inputs to predict the exterior and interior noise, and the predicted results were compared with the top-level design targets of the vehicle. Based on the noise source, sound transfer path contribution, parameter sensitivity, and multi-objective optimization, the acoustic indexes of the noise source components and sound transfer path components were determined. Analysis results show that according to the exterior noise simulation analysis, the inputs of the noise source parameters can be regarded as a set of determination results of noise source indexes when the predicted exterior noise meets the top-level acoustic design targets and the design margin is within the acceptable range. In determining the sound transfer path indexes based on the interior noise simulation analysis, the inputs of the sound transfer path parameters can be regarded as a set of determination results of sound transfer path indexes when the predicted interior noise meets the top-level acoustic design targets and the design margin is within the acceptable range. However, when the noise source indexes or sound transfer path indexes do not satisfy the vehicle noise requirements, it is necessary to analyze the noise source or sound transfer path contribution, calculate the parameter sensitivity of the main contributing noise source or sound transfer path, and make the main contributing noise source or sound transfer path achieve the top-level acoustic design targets through correction iteration. Low-noise design procedure needs to integrate the feedback of multiple indicators continuously. The acoustic indexes of the components should be adjusted reasonably to ensure that the acoustic indexes of the components not only satisfy the top-level acoustic design targets but also show feasibility. 10 figs, 31 refs. 
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