汽车工程 ›› 2021, Vol. 43 ›› Issue (1): 113-120.doi: 10.19562/j.chinasae.qcgc.2021.01.015

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汽车声学包轻量化设计

唐中华1,贺岩松1,马涛1,张志飞1(),蒲弘杰2,李云2,陈钊2   

  1. 1.重庆大学汽车工程学院,重庆 400044
    2.东风柳州汽车有限公司,柳州  545005
  • 收稿日期:2020-05-07 修回日期:2020-07-07 出版日期:2021-01-25 发布日期:2021-02-03
  • 通讯作者: 张志飞 E-mail:z.zhang@cqu.edu.cn
  • 基金资助:
    重庆市技术创新与应用发展专项重点项目(cstc2019jscx-fxydX0013)

Lightweight Design of Automotive Sound Package

Zhonghua Tang1,Yansong He1,Tao Ma1,Zhifei Zhang1(),Hongjie Pu2,Yun Li2,Zhao Chen2   

  1. 1.School of Automotive Engineering,Chongqing University,Chongqing  400044
    2.Dongfeng Liuzhou Motor Co. ,Ltd. ,Liuzhou  545005
  • Received:2020-05-07 Revised:2020-07-07 Online:2021-01-25 Published:2021-02-03
  • Contact: Zhifei Zhang E-mail:z.zhang@cqu.edu.cn

摘要:

本文旨在研究汽车声学包的轻量化设计,要求在保证声学性能的前提下,尽可能实现轻量化的目标。首先运用统计能量法建立了包含车身结构和各声腔的整车模型,提取80 km/h匀速工况下发动机舱的声辐射激励、动力总成激励和车身表面脉动压力激励,并将其施加在该模型上,获得驾驶员头部声腔声压,结果与试验数据吻合良好,验证了模型的有效性。接着根据各子系统对驾驶员头部声腔声压的贡献量分析结果,对内前围和前地板声学包提出了改进方案。最后以改进方案中各层材料厚度为设计变量,以驾驶员头部声腔总声压级和声学包总质量为目标构建Kriging近似模型,采用多目标遗传算法对声学包材料厚度进行优化,优化后的声学包声学性能与原来相当,而总质量减轻了20.76%。

关键词: 汽车声学包, 统计能量法, Kriging代理模型, 多目标优化

Abstract:

This paper aims to study the lightweight design of automotive sound package, requesting its mass as light as possible while ensuring its acoustic performance. Firstly, statistical energy analysis is utilized to build a vehicle model covering vehicle body structure and all sound cavities, and the sound radiation excitation of engine compartment, the powertrain excitation and the pulsation pressure excitation of body surface at an 80 km/h cruising condition are extracted and applied on the model to obtain the sound pressure of driver's head cavity, which agree well with the test results, verifying the validity of the model. Then the contributions of all subsystems to the sound pressure of driver's head cavity are analyzed, and based on which the improvement schemes for the sound packages of inner front bulkhead and front floor panel are proposed. Finally, with the thickness of each panel in improvement schemes as variables, the total sound pressure level of driver's head cavity and the total mass of two sound packages as objectives, the kriging surrogate models are constructed, and the multi-objective genetic algorithm is adopted to optimize the panel thicknesses of two sound packages. After optimization, the total mass of two sound packages reduces by 20.76% without worsening the acoustic performance of two sound packages.

Key words: automotive sound package, statistical energy analysis, Kriging surrogate model, multi-objective optimization