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面向多层级目标的汽车前围声学包优化研究

黄海波 郑志伟 张思文 吴昱东 杨明亮 丁渭平

黄海波, 郑志伟, 张思文, 吴昱东, 杨明亮, 丁渭平. 面向多层级目标的汽车前围声学包优化研究[J]. 西南交通大学学报. doi: 10.3969/j.issn.0258-2724.20211086
引用本文: 黄海波, 郑志伟, 张思文, 吴昱东, 杨明亮, 丁渭平. 面向多层级目标的汽车前围声学包优化研究[J]. 西南交通大学学报. doi: 10.3969/j.issn.0258-2724.20211086
HUANG Haibo, ZHENG Zhiwei, ZHANG Siwen, WU Yudong, YANG Mingliang, DING Weiping. Optimization of Automobile Firewall Acoustic Package for Multi-level Goals[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20211086
Citation: HUANG Haibo, ZHENG Zhiwei, ZHANG Siwen, WU Yudong, YANG Mingliang, DING Weiping. Optimization of Automobile Firewall Acoustic Package for Multi-level Goals[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20211086

面向多层级目标的汽车前围声学包优化研究

doi: 10.3969/j.issn.0258-2724.20211086
基金项目: 国家自然科学基金(51905408)
详细信息
    作者简介:

    黄海波(1989—),男,讲师,博士,研究方向为汽车NVH现代设计理论与方法,E-mail:huanghaibo214@swjtu.edu.cn

    通讯作者:

    丁渭平(1968—),男,教授,博士,研究方向为汽车NVH现代设计理论与方法,E-mail:dwp@swjtu.edu.cn

  • 中图分类号: U462

Optimization of Automobile Firewall Acoustic Package for Multi-level Goals

  • 摘要:

    为了研究汽车声学包设计参数对其多性能目标的影响,首先,改进了传统的深度信念网络(DBNs)方法,并提出SVR-DBNs (support vector regression- deep belief networks)模型,提升了模型映射的准确度;其次,从车辆噪声传递关系与层级目标分解角度出发,提出了一种多层级目标预测与分析方法;最后,将所提方法应用于具体车型的前围声学包性能、重量与成本多目标预测与优化分析. 研究结果表明:SVR-DBNs方法对前围声学包性能、重量与成本目标预测准确度均在0.975以上,优于传统的反向传播神经网络(BPNN)、SVR与DBNs模型;基于SVR-DBNs模型的优化结果与实测结果接近,两者加权目标相对误差为1.09%(平均传递损失(MTL)、重量和成本相对误差绝对值分别为1.44%、1.04%与0.71%),优化后的实测结果较前围声学包原始状态性能、重量和成本分别提升了5.51%、9.01%与4.40%.

     

  • 图 1  前围声学包多层级目标分解架构

    Figure 1.  Multi-level goals decomposition architecture of firewall acoustic package

    图 2  DBNs拓扑结构

    Figure 2.  DBNs topology

    图 3  SVR-DBN训练流程

    Figure 3.  SVR-DBN training flowchart

    图 4  前围声学包系统及零部件测试环境

    Figure 4.  Firewall acoustic package system and parts test environment

    图 5  模型$ {f}_{1}^{\left[1\right]} $$ {f}_{3}^{\left[1\right]} $的训练与测试结果

    Figure 5.  Training and test results of model $ {f}_{1}^{\left[1\right]} $$ {f}_{3}^{\left[1\right]} $

    表  1  前围声学包系统及零部件原始状态

    Table  1.   Original state of firewall acoustic package system

    部件名称材料名称厚度/
    mm
    面积
    占比/%
    密度/
    (kg·m−3
    单价/
    (元·m−3
    前围钣金高强度钢① 1.0② 30782039100
    ③ 1.2④ 70
    外前围
    隔音垫
    玻璃纤维⑤ 10.0⑥ 201005800
    ⑦ 20.0⑧ 80
    内前围
    隔音垫
    PU 泡沫⑨ 5.0⑩ 35647200
    ⑪ 10.0 ⑫ 65
    棉毡⑬ 3.0⑭ 201134000
    ⑮ 5.0⑯ 80
    前围覆盖率⑰ 99.5%±0.1%, ±25 元
    前围泄漏量⑱ 0.20%±0.05%, ±35 元
    注:①~⑱表示构建预测模型的设计变量.
    下载: 导出CSV

    表  2  多层级分解模型在测试集上的准确度

    Table  2.   Accuracy of multi-level model on test set

    目标第一~二层级(R2第二~三层级(R2
    MTL0.9750.970、0.974、0.968
    重量0.9830.979、0.984、0.981
    成本0.9860.988、0.978、0.980
    下载: 导出CSV

    表  3  声学包多目标优化结果

    Table  3.   Multi-goals optimization results of acoustic package

    预测模型MTL重量成本
    R2MSE/dBR2MSE/kgR2MSE/元
    BPNN0.9170.7710.9241.2670.93113.457
    SVR0.9550.3640.9590.8460.9617.950
    DBNs0.9710.3410.9790.6110.9747.491
    SVR-DBNs0.9750.2960.9830.5790.9864.360
    下载: 导出CSV

    表  4  前围声学包多目标优化设计参数

    Table  4.   Multi-goals optimization design parameters of firewall acoustic package

    部件名称材料名称厚度/mm面积占比/%
    前围钣金高强度钢1.032
    1.268
    外前围隔音垫玻璃纤维14.028
    20.072
    内前围隔音垫PU 泡沫6.037
    10.063
    棉毡4.028
    5.072
    前围覆盖率/%99.6
    前围泄露量/%0.20
    下载: 导出CSV

    表  5  前围声学包多目标优化结果与实测结果

    Table  5.   Multi-goals optimization results and measured results of firewall acoustic package

    状态MTL/dB重量/kg成本/元加权目标
    原始状态45.421.4281.61.0247
    优化后预测值48.619.3267.30.9588
    实测值47.919.5269.20.9693
    下载: 导出CSV
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  • 收稿日期:  2022-01-06
  • 修回日期:  2022-06-16
  • 网络出版日期:  2023-01-18

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