基于DOE和改进模拟退火算法的消声器优化设计

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摘要为提高消声器的优化效率，在考虑流速对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，结合改进模拟退火算法分别建立消声器在排气噪声峰值频率处传递损失的单目标和多目标优化模型，开展消声器的优化设计研究。研究结果表明：DOE方法能有效辨识出各参数对消声器传递损失贡献度的大小，简化了消声器的优化模型。消声器内气体的平均流速对消声器的优化结果影响较大。单目标优化可使相应峰值频率处的传递损失达到最大值，多目标优化能使全频段整体优化效果较好，优化设计后排气噪声最大降低31.73dB，优于单目标优化结果。本研究为消声器的优化设计提供了新思路。

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	张俊红
	朱传峰
	毕凤荣
	王健
	李忠鹏

关键词 ：试验设计, 模拟退火算法, 消声器, 优化设计

Abstract：To improve the efficiency of muffler optimization，based on the effect of flow rate on the transmission loss numerical modeling of mufflers, whose parameters were analyzed by Latin hypercube design in experimental design (DOE), Combined with improved simulated annealing algorithm, the single objective and multi-objective optimization model were established respectively in the exhaust noise of peak frequency as the goal of transmission loss, then a research on muffler was launched. The result shows that the DOE method can effectively identify the parameters which affect muffler performance, and simplify the optimization model of muffler. The average velocity of the gas in muffler has great impact on the optimization results. The transmission loss of muffler corresponding to the peak frequency through single objective optimization can reach maximum, while multi-objective optimization can make overall optimization results better, and the maximum reduction of exhaust noise can be 31.73dB, which is better than the results of single objective optimization. This study provides a new way of optimization design of the muffler.

Key words： experimental design simulated annealing algorithm muffler optimization design

收稿日期: 2014-05-15 出版日期: 2015-07-15

引用本文:

张俊红，朱传峰，毕凤荣，王健，李忠鹏. 基于DOE和改进模拟退火算法的消声器优化设计[J]. 振动与冲击, 2015, 34(13): 169-175.
Zhang Jun-hong，Zhu Chuan-feng,Bi Feng-rong，Wang Jian， Li Zhong-peng. Optimization Design of Muffler Based on DOE and Improved Simulated Annealing Algorithm. JOURNAL OF VIBRATION AND SHOCK, 2015, 34(13): 169-175.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2015/V34/I13/169

[1] Yeh L J, Chang Y C, Chiu M C. GA optimization on multi-segments muffler under space constraints[J]. Applied Acoustics, 2004, 65(5): 521-543.
[2] 张翠翠, 吴伟蔚, 陈浩. 基于多目标遗传算法的消声器优化设计[J]. 噪声与振动控制, 2010， 38(3)： 141-143.
ZHANG Cui-cui,WU Wei-wei,CHEN Hao.Optimal Design of Mufflers Based on Muti-objective Genetic Algorithm [J].Noise and Vibration Control, 2010, 38 (3): 141-143.
[3] Goldberg D E, Holland J H. Genetic algorithms and machine learning[J]. Machine learning,1988,3(2):95-99.
[4] Munjal M L. Plane wave analysis of side inlet/outlet chamber mufflers with mean flow[J]. Applied Acoustics, 1997, 52(2): 165-175.
[5] 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
[6] Selamet A, Ji Z L, Radavich P M. Acoustic attenuation performance of circular expansion chambers with offset inlet/outlet[J]. Journal of Sound and Vibration, 1998, 213(4): 619-641.
[7] 胡玉梅，许响林，褚志刚.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.
HU Yu-Mei, XU Xiang-Lin,CHU Zhi-Gang. Exhaust Muffler Design of Automotive Based on Acoustic Transfer Matrix[J]. Journal of Chongqing University: Natural Science Edition,2005,28(1):15-18.
[8] 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, 20(3):22-26.
NI Ji-Min, XIE Nan,DU Qian-Ying. Optimization Design of Vehicle based on DOE[J]. Automobile Technology,2012, 20(3): 22-26
[9] 叶年业, 刘洁, 倪计民. 车用汽油机流动过程模拟及基于 DoE 的配气相位优化[J]. 内燃机工程, 2011, 32(4): 72-75.
YE Nian-ye,LIU Jie,NI Ji-min. Flow process modelling of gasoline engine and optimization of valve timing based on DoE[J]. Chinese Internal Combustion Engine Engineering,2011, 32(4): 72-75.
[10] Ye K Q, Li W, Sudjianto A. Algorithmic construction of optimal symmetric Latin hypercube designs[J]. Journal of statistical planning and inference, 2000, 90(1): 145-159.
[11] 王建宏, 朱永红, 肖绚. 线性回归模型中的递推辨识及其应用[J]. 振动与冲击, 2012, 31(15): 19-25.
WANG Jian-hong,ZHU Yong-hong,XIAO Xu. Application of recursive approach in parameters identification of linear regression model[J]. Journal of Viration and Shock, 2012, 31(15): 19-25.
[12] 张俊红, 何文运, 刘海. 基于支持向量机的柴油机噪声品质客观评价参量权重分析[J]. 振动与冲击, 2013, 32(11) : 112-116.
ZHANG Jun-hong,He Wen-yun,LI Hai. Weight analysis of objective evaluation parameters for a diesel engine noise quality with support vector machine[J]. Journal of Viration and Shock, 2013, 32(11) : 112-116.
[13]Bouleimen K, Lecocq H. A new efficient simulated annealing algorithm for the resource-constrained project scheduling problem and its multiple mode version[J]. European Journal of Operational Research, 2003, 149(2): 268-281.
[14]Cakir B, Altiparmak F, Dengiz B. Multi-objective optimization of a stochastic assembly line balancing: A hybrid simulated annealing algorithm[J]. Computers & Industrial Engineering, 2011, 60(3): 376-384.
[15] Damodaran P, Vélez-Gallego M C. A simulated annealing algorithm to minimize makespan of parallel batch processing machines with unequal job ready times[J]. Expert Systems with Applications, 2012, 39(1): 1451-1458.