车身阻尼材料的布置优化方法

doi:10.16183/j.cnki.jsjtu.2017.09.003

摘要/Abstract

摘要： 通过建立车身的声固耦合有限元模型，分析了车身在低频段的声压频响函数，截取了20~200Hz范围内的4个主要峰值频率以分析壁板贡献度；对车身地板的阻尼材料敷设位置进行优化，并以阻尼材料的厚度为设计变量，以阻尼材料的总质量最小为目标，将 Kriging近似建模技术与粒子群算法相结合对车身地板的阻尼材料敷设厚度进行优化，提出了一种基于壁板贡献度的分析方法和基于改进的粒子群优化算法的车内阻尼材料布置优化方法.结果表明，在保证车内峰值声压不增大的情况下，采用所提优化方法能够达到阻尼材料质量降低52.0%的效果，并通过简易车身装置的实验验证了优化方法的有效性.

关键词: , 车内噪声；阻尼材料；壁板贡献度；优化

Abstract: In order to consider the vehicle interior noise reduction, a finite element method (FEM) model was established for a simple car. Sound pressure level (SPL) frequency response function was analyzed at low frequency phase. Four main peak frequencies in the range of 20Hz to 200Hz and those body panels contributions were focused. The layout of the damping materials was optimized. With the objective of minimizing the mass of damping material, and variables of the thickness of damping materials, by using Kriging metamodeling technique and particle swarm optimization (PSO), optimization method of damping configuration was proposed based on panel contribution. The mass of damping materials was reduced by 52.0% under the requirement that the SPL of peak frequencies did not increase, and the experiment was done to verify the effectiveness of the method.

Key words: vehicle noise, damping material, panel contribution, optimization

中图分类号:

TB 533

赵建轩，王增伟，刘钊，朱平. 车身阻尼材料的布置优化方法[J]. 上海交通大学学报（自然版）, 2017, 51(9): 1036-1042.

ZHAO Jianxuan,WANG Zengwei,LIU Zhao,ZHU Ping. Optimal Design of Damping Material Configuration in Vehicle[J]. Journal of Shanghai Jiaotong University, 2017, 51(9): 1036-1042.

参考文献

［1］梁新华. 汽车车身薄壁件阻尼复合结构振动声学分析与优化［D］. 上海: 上海交通大学机械与动力工程学院, 2007.
［2］张志飞, 倪新帅, 徐中明, 等. 基于结构优化的商用车驾驶室模态特性改进［J］. 汽车工程, 2011, 33(4): 290293.
ZHANG Zhifei, NI Xinshuai, XU Zhongming, et al. Modal characteristics improvement of a commercial vehicle cab based on structural optimization［J］. Automotive Engineering, 2011, 33(4): 290293.
［3］AKANDA A, GOETCHIUS G M. Representation of constrained/unconstrained layer damping treatments in FEA/SEA vehicle system models: A simplified approach［C］∥Proceedings of the 1999 Noise and Vibration ConferenceP342. USA: SAE, 1999: 342356. DOI: 10.4271/1999011680.
［4］郑玲, 谢熔炉, 王宜, 等. 基于优化准则的约束阻尼材料优化配置［J］. 振动与冲击, 2010, 29(11): 156160.
ZHENG Ling, XIE Ronglu, WANG Yi, et al. Optimal configuration of constrained damping materials based on optimization criterion［J］. Journal of Vibration and Shock, 2010, 29(11): 156160.
［5］杨德庆. 动响应约束下阻尼材料配置优化的拓扑敏度法［J］. 上海交通大学学报, 2003, 37(8): 12091212.
YANG Deqing. Topological sensitivity method for the optimal placement of unconstrained damping materials under dynamic response constraints［J］. Journal of Shanghai Jiao Tong University, 2003, 37(8): 12091212.
［6］郭中泽, 陈裕泽, 侯强, 等. 阻尼材料布局优化研究［J］. 兵工学报, 2007, 28(5): 638640.
GUO Zhongze, CHEN Yuze, HOU Qiang, et al. Damping material optimal placement in damping structure design［J］. Acta Armamentarii, 2007, 28(5): 638640.
［7］张一麟, 廖毅, 莫品西, 等. 基于车身模态和板块贡献分析的阻尼优化降噪方法研究［J］. 振动与冲击, 2015, 34(4): 153157.
ZHANG Yilin, LIAO Yi, MO Pinxi, et al. Damping optimization noise reduction technique based on modal and panel contribution analysis［J］. Journal of Vibration and Shock, 2015, 34(4): 153157.
［8］郑玲, 唐重才, 韩志明, 等. 车身结构阻尼材料减振降噪优化设计［J］. 振动与冲击, 2015, 34(9): 4247.
ZHENG Ling, TANG Zhongcai, HAN Zhiming, et al. Optimal design of damping material topology configuration to suppress interior noise in vehicle［J］. Journal of Vibration and Shock, 2015, 34(9): 4247.
［9］SACKS J, WELCH W J, MITCHELL T J, et al. Design and analysis of computer experiments［J］. Statistical Science, 1989, 4(4): 409423.
［10］LIU Z, LU J H, ZHU P. Lightweight design of automotive composite bumper system using modified particle swarm optimizer［J］. Composite Structures, 2016, 140(5): 630643.

[1]	王聚团, 戚晓宁, 黄志明. 水下生产管汇测试技术及其改进研究[J]. 海洋工程装备与技术, 2022, 9(2): 43-49.
[2]	袁振钦, 邹科, 孙亚峰, 刘刚, 屈衍, 李居跃. 基于时域分析法的动态电缆疲劳分析[J]. 海洋工程装备与技术, 2022, 9(2): 50-55.
[3]	王娟, 杨明旺, 郑茂尧, 刘凌云, 赵立君. 高强钢在大型半潜式平台组块建造中的应用[J]. 海洋工程装备与技术, 2022, 9(1): 27-31.
[4]	陈欣, 赵晓磊, 王立坤, 肖德明, 张腾月. 深水大型吸力锚建造技术研究[J]. 海洋工程装备与技术, 2022, 9(1): 32-36.
[5]	尹彦坤, 易涤非. 半潜式生产平台船体结构关键节点工程临界评估[J]. 海洋工程装备与技术, 2022, 9(1): 52-57.
[6]	MA Qunsheng (马群圣), CEN Xingxing (岑星星), YUAN Junyi (袁骏毅), HOU Xumin (侯旭敏). Word Embedding Bootstrapped Deep Active Learning Method to Information Extraction on Chinese Electronic Medical Record[J]. J Shanghai Jiaotong Univ Sci, 2021, 26(4): 494-502.
[7]	ZHANG Shengfa (张胜发), TANG Na (唐纳), SHEN Guofeng (沈国峰), WANG Han (王悍), QIAO Shan (乔杉). Universal Software Architecture of Magnetic Resonance-Guided Focused Ultrasound Surgery System and Experimental Study[J]. J Shanghai Jiaotong Univ Sci, 2021, 26(4): 471-481.
[8]	安庆升, 孙立东, 武秋生. 碳纤维增强复合材料发射筒设计研究[J]. 空天防御, 2021, 4(2): 13-.
[9]	KONG Xiangqiang (孔祥强), MENG Xiangxi (孟祥熙), LI Jianbo (李见波), SHANG Yanping (尚燕平), CUI Fulin (崔福林) . Comparative Study on Two-Stage Absorption Refrigeration Systems with Different Working Pairs[J]. J Shanghai Jiaotong Univ Sci, 2021, 26(2): 155-162.
[10]	ZHUANG Weimin (庄蔚敏), WANG Pengyue (王鹏跃), AO Wenhong (熬文宏), CHEN Gang (陈刚) . Experiment and Simulation of Impact Response of Woven CFRP Laminates with Different Stacking Angles[J]. J Shanghai Jiaotong Univ Sci, 2021, 26(2): 218-230.
[11]	ZHOU Xuhui (周旭辉), ZHANG Wenguang (张文光), XIE Jie (谢颉). Effects of Micro-Milling and Laser Engraving on Processing Quality and Implantation Mechanics of PEG-Dexamethasone Coated Neural Probe[J]. J Shanghai Jiaotong Univ Sci, 2021, 26(1): 1-9.
[12]	HUANG Ningning (黄宁宁), MA Yixin (马艺馨), ZHANG Mingzhu (张明珠), GE Hao (葛浩), WU Huawei (吴华伟). Finite Element Modeling of Human Thorax Based on MRI Images for EIT Image Reconstruction[J]. J Shanghai Jiaotong Univ Sci, 2021, 26(1): 33-39.
[13]	WANG Xianjin, GAO Xu, YU Kuigang . Fixture Locating Modelling and Optimization Research of Aluminum Alloy Sidewall in a High-Speed Train Body[J]. J Shanghai Jiaotong Univ Sci, 2020, 25(6): 706-713.
[14]	QIAO Xing, MA Dan, YAO Xuliang, FENG Baolin. Stability and Numerical Analysis of a Standby System[J]. J Shanghai Jiaotong Univ Sci, 2020, 25(6): 769-778.
[15]	WU Jin, MIN Yu, YANG Xiaodie, MA Simin . Micro-Expression Recognition Algorithm Based on Information Entropy Feature[J]. Journal of Shanghai Jiao Tong University(Science), 2020, 25(5): 589-599.