基于DOE和MIGA的消声器优化设计

张俊红; 朱传峰; 毕凤荣; 王健; 何文运; 李忠鹏

doi:10.13433/j.cnki.1003-8728.2016.0224

基于DOE和MIGA的消声器优化设计

doi: 10.13433/j.cnki.1003-8728.2016.0224

1.
天津大学内燃机燃烧学国家重点实验室, 天津 300072

基金项目:

国家高技术研究发展计划(863计划)项目(2014AA041501)资助

详细信息

作者简介:
张俊红(1962-),教授,研究方向为动力机械振动与噪声控制及结构优化设计,zhangjh@tju.edu.cn

计量
- 文章访问数: 253
- HTML全文浏览量: 42
- PDF下载量: 6
- 被引次数: 0
出版历程
- 收稿日期: 2014-03-25
- 刊出日期: 2016-02-05

Optimization Design of Muffler Based on Design of Experiment and Multi-Island Genetic Algorithm

1.
State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China

摘要

摘要: 高效率的设计出大消声量的消声器一直是车辆排气噪声控制中面临的难题。考虑到消声器优化过程中涉及参数较多,在消声器传递损失数值建模的基础上,采用试验设计(DOE)中的拉丁超立方设计对消声器参数进行分析,结合多岛遗传算法(MIGA)和传统遗传算法(GA)分别建立消声器在排气噪声单峰值频率和多峰值频率处的传递损失为目标的优化模型,开展消声器传递损失优化设计研究。结果表明:DOE方法能有效的辨识出各参数对消声器传递损失影响的大小,简化了消声器的优化模型。MIGA对消声器在单峰值频率和多峰值频率的优化都优于GA,且多峰值频率的优化好于单峰值频率的优化,能使排气噪声最大降低20.98 dB。
- 试验设计 /
- 多岛遗传算法 /
- 消声器 /
- 传递损失 /
- 优化设计
Abstract: To design muffler efficiently with high capability of noise reduction is always a tough problem in control of vehicle exhaust noise. Considering that there are many parameters in the muffler design optimization, the design parameters of mufflers were analyzed by Latin Hypercube of design of experiment (DOE) based on the numerical modeling of transmission loss. Combining the multi-island genetic algorithm (MIGA) with genetic algorithm (GA), the optimization model of mufflers was established in which the transmission losses at the single peak frequency of exhaust noise and multiple peak frequencies are set as as the optimization objective, respectively. The result shows that the DOE method can effectively identify the parameters which affect muffler performance and simplify the optimization model of muffler. The optimization results of MIGA in both the single peak frequency and multiple peak frequency is better than that of GA, and the results of multiple peak frequency, optimization which is better than that of single peak frequency optimization, can reduce the exhaust noise by 20.98 dB. This study provides a new optimization design method of muffler.
- computer software /
- control /
- design of experiments /
- design /
- efficiency

HTML全文

参考文献(15)

[1]	高红武.噪声控制工程[M].武汉:武汉理工大学出版社,2003:10-60 Gao H G. Noise control engineering[M]. Wuhan:Wuhan University of Technology Press, 2003:10-60(in Chinese)
[2]	黎志勤,黎苏.汽车排气系统噪声与消声器设计[M].北京:中国环境科学出版社,1991:15-40 Li Z Q, Li S. The noise of automobile exhaust system and muffler design[M]. Beijing:China Environmental Science Press, 1991:15-40(in Chinese)
[3]	何渝生.汽车噪声控制[M].北京:机械工业出版社,1999:60-85 He Y S. Automotive noise control[M]. Beijing:Mechanical Industry Press, 1999:60-85(in Chinese)
[4]	倪计民,解难,杜倩颖,等.基于DoE的车用消声器优化设计[J].汽车技术,2012,(3):22-26 Ni J M, Xie N, Du Q Y, et al. Optimization design of vehicle muffler based on DoE[J]. Automobile Technology, 2012,(3):22-26(in Chinese)
[5]	Chiu M C. Shape optimization of one-chamber perforated mufflers filled with wool using simulated annealing[J]. Journal of Marine Science and Technology, 2013,21(4):380-390
[6]	胡玉梅,许响林,褚志刚,等.基于声传递矩阵法的汽车排气消声器设计[J].重庆大学学报(自然科学版),2005,28(1):15-18,31 Hu Y M, Xu X L, Chu Z G, et al. Exhaust muffler design of automotive based on acoustic transfer matrix[J]. Journal of Chongqing University (Natural Science Edition), 2005,28(1):15-18,31(in Chinese)
[7]	孙伟,张呈林.基于多目标遗传算法的直升机总体参数优化设计[J].机械科学与技术,2010,29(2):265-269 Sun W, Zhang C L. Helicopter preliminary parameter optimization based on multi-objective genetic algorithm[J]. Mechanical Science and Technology for Aerospace Engineering, 2010,29(2):265-269(in Chinese)
[8]	Munjal M L. Plane wave analysis of side inlet/outlet chamber mufflers with mean flow[J]. Applied Acoustics, 1997,52(2):165-175
[9]	Chiu M C, Chang Y C. Numerical studies on venting system with multi-chamber perforated mufflers by GA optimization[J]. Applied Acoustics, 2008,69(11):1017-1037
[10]	Chiu M C. Shape optimization of multi-chamber mufflers with plug-inlet tube on a venting process by genetic algorithms[J]. Applied Acoustics, 2010,71(6):495-505
[11]	Selamet A, Ji Z L, Radavich P M. Acoustic attenuation performance of circular expansion chambers with offset inlet/outlet:Ⅱ. comparison with experimental and computational studies[J]. Journal of Sound and Vibration, 1998,213(4):619-641
[12]	Millo F, Badami M, Longhini F, et al. Optimization of a variable geometry exhaust system through design of experiment[R]. SAE Technical Paper 2008-01-0675, 2008
[13]	王群.基于对称拉丁超立方设计的多目标进化算法[D].西安:西安电子科技大学,2011:20-55 Wang Q. Multiobjective evolutionary algorithm based on symmetric latin hypercube designs[D]. Xi'an:Xidian University, 2011:20-55(in Chinese)
[14]	Hong B, Soh T Y, Pey L P. Development of a helicopter blade FE model using MIGA optimization[C]//45th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics & Materials Conference, Palm Springs, California:American Institute of Aeronautics and Astronautics, Inc., 2004:1-8
[15]	Goldberg D E, Holland J H. Genetic algorithms and machine learning[J]. Machine Learning, 1988,3(2-3):95-99