基于多目标遗传算法的吸声覆盖层参数优化设计

上海交通大学学报（自然版） ›› 2013, Vol. 47 ›› Issue (08): 1300-1305.

基于多目标遗传算法的吸声覆盖层参数优化设计

陶猛1,2,王广玮1

(1.贵州大学机械工程学院，贵阳 550025; 2.上海交通大学海洋工程国家重点实验室，上海 200240)

收稿日期:2012-11-16 出版日期:2013-08-29 发布日期:2013-08-29
基金资助:
上海交通大学海洋工程国家重点实验室开放课题（1113），上海交通大学机械系统与振动国家重点实验室开放课题（MSV201301），贵州省科学技术基金（黔科合J字［2012］2113）

Parameter Optimization of a Sound Absorption Layer Based on Multi-Objective Genetic Algorithm

TAO Meng1,2，WANG Guangwei1

(1. School of Mechanical Engineering, Guizhou University, Guiyang 550025, China；2. State Key Laboratory of Ocean Engineering, Shanghai Jiaotong University, Shanghai 200240, China)

Received:2012-11-16 Online:2013-08-29 Published:2013-08-29

摘要/Abstract

摘要：

针对圆柱空腔吸声覆盖层低、高频吸声的机制不同，提出了一种基于多目标遗传算法的吸声覆盖层参数优化设计方法.利用有限元软件ANSYS建立了平面波垂直入射吸声覆盖层的分析模型，并采用多目标遗传算法NSGA-II得到了多目标吸声问题的Pareto最优解集.结果表明：多目标优化设计全面考虑了圆柱空腔吸声覆盖层的低、高频吸声之间的耦合，可按需选择其满意的优化结果；比起仅优化吸声覆盖层的材料属性，考虑材料属性和结构参数的综合优化能够获得更佳的宽频吸声性能.

关键词: 多目标, 遗传算法, 吸声覆盖层, 圆柱空腔, 参数优化

Abstract:

Since the sound absorption mechanism of cylindrical-hole layer at low-frequency and at high-frequency are different, an optimization and design method for the sound absorption layer based on the multi-objective genetic algorithm was developed. First, the calculation procedure of the plane wave normally impinging on the sound absorption layer was modeled by using the ANSYS software. Next, the non-dominated sorting genetic algorithm II (NSGA-II) was adopted to solve this optimization problem and get the Pareto optimization. The results indicate that the coupled relationship between the low-frequency and the high-frequency can be both considered during the multi-objective optimization process, and designers can choose the satisfactory optimization results according to their demands. Besides, compared to the optimization of the material properties only, the simultaneous optimization of material properties and structural parameters may result in a higher sound absorption coefficient in a wider frequency range.

Key words: multi-objective, genetic algorithm；sound absorption layer； cylindrical hole； parameter optimization

中图分类号:

TB 56

陶猛1,2,王广玮1. 基于多目标遗传算法的吸声覆盖层参数优化设计[J]. 上海交通大学学报（自然版）, 2013, 47(08): 1300-1305.

TAO Meng1,2，WANG Guangwei1.

Parameter Optimization of a Sound Absorption Layer Based on Multi-Objective Genetic Algorithm

[J]. Journal of Shanghai Jiaotong University, 2013, 47(08): 1300-1305.

参考文献

［1］Hennion A C, Decarpigny J N. Analysis of the scattering of a plane acoustic wave by a doubly periodic structure using the finite element method: Application to Alberich anechoic coatings［J］. Journal of the Acoustical Society of America, 1991, 90(6): 33563367.

［2］汤渭霖, 何世平, 范军. 含圆柱形空腔吸声覆盖层的二维理论［J］. 声学学报, 2005, 30(4): 289295.

TANG Weilin, HE Shiping, FAN Jun. Twodimensional model for acoustic absorption of viscoelastic coating containing cylindrical holes［J］. Acta Acustica, 2005, 30(4): 289295.

［3］Zhao H G, Liu Y Z, Wen J H, et al. Tricomponent phononic crystals for underwater anechoic coatings

［J］. Physics Letters A, 2007, 367(3): 224232.

［4］王仁乾, 马黎黎. 吸声材料的物理参数对消声瓦吸声性能的影响［J］. 哈尔滨工程大学学报, 2004, 25(3): 288294.

WANG Renqian, Ma Lili. Effects of physical parameters of the absorption material on absorption capability of anechoic tiles［J］. Journal of Harbin Engineering University, 2004, 25(3): 288294.

［5］Panigrahi S N, Jog C S, Munjal M L. Multifocus design of underwater noise control linings based on finite element analysis［J］. Applied Acoustics, 2008, 69(12): 11411153.

［6］Wang Y, Pham H. A multiobjective optimization of imperfect preventive maintenance policy for dependent competing risk systems with hidden failure［J］. IEEE Transactions on Reliability, 2011, 60(4): 770781.

［7］Fonseca C M, Fleming P J. Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization ［C］// Proceedings of the Fifth International Conference on Genetic Algorithms. San Mateo, California: Morgan Kauffman Publishers, 1993: 416423.

［8］Srinivas N, Deb K. Multiobjective optimization using nondominated sorting in genetic algorithms［J］.Evolutionary Computation, 1994, 2(3): 221248.

［9］Deb K, Pratap A, Agarwal S. A fast and elitist multiobjective genetic algorithm: NSGAII［J］. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182197.

［10］陶猛, 汤渭霖. Alberich型吸声覆盖层的低频吸声机理分析［J］. 振动与冲击, 2011, 30(1): 5660.

TAO Meng, TANG Weilin. Mechanism analysis of Alberich sound absorption coating at lowfrequency［J］. Journal of Vibration and Shock, 2011, 30(1): 5660.

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