| 面向多层级目标的汽车前围声学包优化研究 |
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| 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,2023,58(2):287-295. |
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| 作者姓名： | 黄海波 郑志伟 张思文 吴昱东 杨明亮 丁渭平 |
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| 作者单位： | 1.西南交通大学机械工程学院，四川 成都 6100312.西南交通大学先进驱动节能技术教育部工程研究中心，四川 成都 6100313.长安汽车工程研究总院，重庆 401120 |
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| 基金项目： | 国家自然科学基金（51905408） |
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| 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%.
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| 关 键 词： | 噪声、振动及声振粗糙度 声学包 吸隔声 SVR-DBNs 优化设计 |
| 收稿时间： | 2022-01-06 |
Optimization of Automobile Firewall Acoustic Package for Multi-level Goals |
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| 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,2023,58(2):287-295. |
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| Authors: | HUANG Haibo ZHENG Zhiwei ZHANG Siwen WU Yudong YANG Mingliang DING Weiping |
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| Affiliation: | 1.School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China2.Engineering Research Center of Advanced Driving Energy-Saving Technology, Ministry of Education , Southwest Jiaotong University, Chengdu 610031, China3.Changan Auto Globe R & D Center, Chongqing 401120, China |
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| Abstract: | To study the influence of automotive acoustic package design parameters on its multi-performance objectives, firstly, the traditional DBNs (deep belief networks) method was modified, and the SVR-DBNs (support vector regression-deep belief networks) model was proposed to improve the accuracy of model mapping. Secondly, from the perspective of vehicle noise transfer relationship and hierarchical target decomposition, a multi-level target prediction and analysis method was proposed. Finally, the proposed method was applied to the multi-objective prediction and optimization analysis of the MTL (mean transmission loss), weight and cost of the acoustic package for a real vehicle.The results show that the accuracy of SVR-DBNs method for the MTL, weight and cost target prediction of the acoustic package is higher than 0.975, which is better than that of the traditional BPNN（back propagation neural network）, SVR and DBNs models. The optimization results based on the SVR-DBNs model are appropriate to the measured results, the comprehensive relative error of the predicted and tested targets is 1.09% (the absolute values of the relative errors of MTL, weight and cost are 1.44%, 1.04% and 0.71%, respectively). Compared with the original status, the MTL, weight and cost of the acoustic package have increased by 5.51%, 9.01% and 4.40%, respectively. |
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