基于改进遗传算法的车身板件厚度优化

周伟; 李敏; 丘铭军; 张西龙; 柳江; 张洪波

doi:10.16511/j.cnki.qhdxxb.2021.26.045

清华大学学报（自然科学版） >

2022 , Vol. 62 >Issue 3: 523 - 532

DOI: https://doi.org/10.16511/j.cnki.qhdxxb.2021.26.045

专题：智能交通

基于改进遗传算法的车身板件厚度优化

周伟 ,
李敏 ,
丘铭军 ,
张西龙 ,
柳江 ,
张洪波

展开

1. 青岛理工大学机械与汽车工程学院, 青岛 266520;
2. 工业流体节能与污染控制教育部重点实验室(青岛理工大学), 青岛 266520;
3. 中国重型机械研究院股份公司, 西安 710032

收稿日期: 2021-09-01

网络出版日期: 2022-03-10

基金资助

李敏,讲师,E-mail:minli@qtech.edu.cn

收起

Vehicle body panel thickness optimization by a genetic algorithm

ZHOU Wei ,
LI Min ,
QIU Mingjun ,
ZHANG Xilong ,
LIU Jiang ,
ZHANG Hongbo

Expand

1. College of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China;
2. Key Lab of industrial Fluid Energy Conservation and Pollution Control(Qingdao University of Technology), Ministry of Education, Qingdao 266520, China;
3. China National Heavy Machinery Research Institute Co., Ltd., Xi'an 710032, China

Received date: 2021-09-01

Online published: 2022-03-10

Fold

摘要

为了在车身设计阶段降低车内噪声，以HyperMesh软件建立的车身声固耦合模型为研究对象，提出一种改进的遗传算法优化车身板件厚度。采用Hammersley实验设计方法，建立白车身一阶整体模态、车身质量、车内目标点最大声压级响应面。以目标点最大声压级为性能指标，改进的遗传算法用于车身板件厚度优化。目标点声压级最大值降低4.0 dB，相对遗传算法、全局响应面法、自适应响应面法和可行方向法优化车内噪声的结果，改进的遗传算法分别提高了2.2%、2.2%、2.3%和2.5%。结果表明：对遗传算法的改进，提升了遗传算法的稳定性和寻优能力，改进的遗传算法可用于设计阶段的车身板件厚度优化。

关键词： 车身板件; 遗传算法; 有限元仿真; 声固耦合模型; 声学灵敏度

本文引用格式

周伟 , 李敏 , 丘铭军 , 张西龙 , 柳江 , 张洪波 . 基于改进遗传算法的车身板件厚度优化[J]. 清华大学学报（自然科学版）, 2022 , 62(3) : 523 -532 . DOI: 10.16511/j.cnki.qhdxxb.2021.26.045

Abstract

The interior noise in a vehicle can be reduced by optimizing the body panel thickness. A genetic algorithm was used to optimize the vehicle body panel thickness based on a coupled acoustic structure model of the vehicle body in HyperMesh. The Hammersley experimental design method was used to determine the response surfaces of the first-order global modal of the body-in-white, the body mass and the maximum sound pressure of a target point inside the vehicle. The maximum sound pressure at the target point was then used as the performance index in a genetic algorithm to optimize the vehicle body panel thickness. The optimum thickness reduced the maximum peak sound pressure at the target point by 4.0 dB. This algorithm gave 2.2% lower sound levels than a standard genetic algorithm, 2.2% lower than the global response search method, 2.3% lower than the adaptive response surface method and 2.5% lower than the feasible direction method, respectively. The results show that this genetic algorithm improves the stability and optimization ability of the genetic algorithm and this algorithm can efficiently optimize the vehicle body panel thickness.

Key words： vehicle body panel; genetic algorithm; finite element simulation; coupled acoustic-structure; vehicle sound levels

参考文献

[1] 任栉翔. 基于有限元法的车内噪声控制与结构优化[D]. 镇江:江苏大学, 2017. REN Z X. Interior noise control and structure optimization based on finite element method[D]. Zhenjiang:Jiangsu University, 2017. (in Chinese)
[2] 聂祚兴. 车身噪声传递函数的全局灵敏度分析及稳健优化设计[D]. 长沙:湖南大学, 2012. NIE Z X. Global sensitivity analysis and robust optimal design for car body's noise transfer function[D]. Changsha:Hunan University, 2012. (in Chinese)
[3] KODIYALAM S, YANG R J, GU L, et al. Multidisciplinary design optimization of a vehicle system in a scalable, high performance computing environment[J]. Structural and Multidisciplinary Optimization, 2004, 26(3):256-263.
[4] FANG J G, GAO Y K, SUN G Y, et al. Multiobjective reliability-based optimization for design of a vehicledoor[J]. Finite Elements in Analysis and Design, 2013, 67:13-21.
[5] 张博文, 吴光强, 黄焕军. 基于迭代更新近似模型的车内噪声优化[J]. 计算力学学报, 2016, 33(1):33-38. ZHANG B W, WU G Q, HUANG H J. Optimization of interior noise in vehicle based on iteratively updating approximation model[J]. Chinese Journal of Computational Mechanics, 2016, 33(1):33-38. (in Chinese)
[6] ZHANG Z Y, ZHANG Y B, HUANG C X, et al. Low-noise structure optimization of a heavy commercial vehicle cab based on approximation model[J]. Journal of Low Frequency Noise, Vibration and Active Control, 2018, 37(4):987-1002.
[7] CHEN Y, QIU N N, ZANG L B, et al. Investigation into transmission radiated noise during the acceleration of electric buses based on response surface methodology[J]. International Journal of Vehicle Design, 2020, 82(1-4):205-223.
[8] JIANG Y Z, WU P B, ZENG J, et al. Multi-parameter and multi-objective optimisation of articulated monorail vehicle system dynamics using genetic algorithm[J]. Vehicle System Dynamics, 2020, 58(1):74-91.
[9] 曹银行, 柳贡民, 张龙. 基于遗传算法的分支管路系统动力学优化设计[J]. 振动与冲击, 2021, 40(9):221-227, 253. CAO Y H, LIU G M, ZHANG L. Dynamic optimization design of branch pipeline system based on genetic algorithm[J]. Journal of Vibration and Shock, 2021, 40(9):221-227, 253. (in Chinese)
[10] 曾发林, 刘雅晨, 胡枫. 语音清晰度车身面板贡献量分析与优化[J]. 振动与冲击, 2021, 40(7):68-74. ZENG F L, LIU Y C, HU F. Speech intelligibility auto body panel contribution analysis and optimization[J]. Journal of Vibration and Shock, 2021, 40(7):68-74. (in Chinese)
[11] 王立平, 孔祥昱, 于广. 基于遗传算法的并混联机床电机伺服控制参数整定[J]. 清华大学学报(自然科学版), 2021, 61(10):1106-1114. WANG L P, KONG X Y, YU G. Motor servo control parameter tuning for parallel and hybrid machine tools based on a genetic algorithm[J]. Journal of Tsinghua University (Science and Technology), 2021, 61(10):1106-1114. (in Chinese)
[12] SHEN Y J, JIA M Q, YANG K, et al. Optimal design and dynamic performance analysis based on the asymmetric-damping vehicle ISD suspension[J]. Shock and Vibration, 2021:9996563.
[13] 董志贵, 王福林. 基于改进遗传算法的拖拉机转向梯形优化设计[J]. 农机化研究, 2022, 44(1):248-252, 258. DONG Z G, WANG F L. Optimization design of tractor steering trapezoid based on improved genetic algorithm[J]. Journal of Agricultural Mechanization Research, 2022, 44(1):248-252, 258. (in Chinese)
[14] 陈文, 徐晓龙, 钟晓伟, 等. 基于改进遗传算法的环形倒立摆PID参数整定[J]. 计算机仿真, 2021, 38(3):165-169. CHEN W, XU X L, ZHONG X W, et al. Parameter tuning of PID controller for annular inverted pendulum system based on improved genetic algorithm[J]. Computer Simulation, 2021, 38(3):165-169. (in Chinese)
[15] ECKERT J J, SANTICIOLLI F M, SILVA L C A, et al. Vehicledrivetrain design multi-objective optimization[J]. Mechanism and Machine Theory, 2021, 156:104123.
[16] 袁培佩. 乘用车声固耦合有限元模态分析[D]. 西安:长安大学, 2014. YUAN P P. Element modes analyses of the acoustic and structure coupling system[D]. Xi'an:Chang'an University, 2014. (in Chinese)
[17] 惠巍, 刘更, 吴立言. 轿车声固耦合低频噪声的有限元分析[J]. 汽车工程, 2006, 28(12):1070-1073, 1077. HUI W, LIU G, WU L Y. Finite element analysis on the car interior low-frequency noise with acoustic-structural coupling[J]. Automotive Engineering, 2006, 28(12):1070-1073, 1077. (in Chinese)
[18] 赵彤航. 基于传递路径分析的汽车车内噪声识别与控制[D]. 长春:吉林大学, 2008. ZHAO T H. Vehicle interior noise identification and control based on transfer path analysis[D]. Changchun:Jilin University, 2008. (in Chinese)
[19] 靳晓雄, 张立军, 江浩. 汽车振动分析[M]. 上海:同济大学出版社, 2002. JIN X X, ZHANG L J, JIANG H. Automobile vibration analysis[M]. Shanghai:Tongji University Press, 2002. (in Chinese)
[20] 杜功焕, 朱哲民, 龚秀芬. 声学基础[M]. 3版. 南京:南京大学出版社, 2012. DU G H, ZHU Z M, GONG X F. Fundamentals of acoustics[M]. 3rd ed. Nanjing:Nanjing University Press, 2012. (in Chinese)
[21] 赵静, 周鋐, 梁映珍, 等. 车身板件振动声学贡献分析与优化[J]. 机械工程学报, 2010, 46(24):96-100. ZHAO J, ZHOU H, LIANG Y Z, et al. Vehicle body panel acoustic contribution analysis and optimization[J]. Journal of Mechanical Engineering, 2010, 46(24):96-100. (in Chinese)

Options

文章导航

模态框（Modal）标题

摘要

本文引用格式

Abstract

参考文献

访问统计