Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-26T06:21:10.787Z Has data issue: false hasContentIssue false
  • English
  • Français

Application of Neural Network and Genetic Algorithm to the Optimum Design of Perforated Tube Mufflers

Published online by Cambridge University Press:  05 May 2011

M.-C. Chiu  and
Y.-C. Chang
M.-C. Chiu*
Affiliation:
Department of Automatic Control Engineering, Chungchou Institute of Technology, Changhua, Taiwan 51003, R.O.C.
Y.-C. Chang*
Affiliation:
Department of Mechanical Engineering, Tatung University, Taipei, Taiwan 10452, R.O.C.
*
* Assisstant Professor, corresponding author
** Associate Professor
Get access

Abstract

Research on new techniques of perforated silencers has been well addressed. However, the research work on shape optimization for a volume-constrained silencer within a constrained machine room is rare. Therefore, the optimum design of mufflers becomes an essential issue. In this paper, to simplify the optimum process, a simplified mathematical model of the muffler is constructed with a neural network using a series of input design data (muffle dimensions) and output data (theoretical sound transmission loss) obtained by a theoretical mathematical model (TMM). To assess the optimal mufflers, the neural network model (NNM) is used as an objective function in conjunction with a genetic algorithm (GA). Moreover, the numerical cases of sound elimination with respect to pure tones (500, 1000, 2000Hz) are exemplified and discussed.

Before the GA operation can be carried out, the accuracy of the TMM is checked by Crocker's experimental data. In addition, both the TMM and NNM are compared. It is found that the TMM and the experimental data are in agreement. Moreover, the TMM and NNM confirm.

The results reveal that the maximum value of the sound transmission loss (STL) can be optimally obtained at the desired frequencies. Consequently, it is obvious that the optimum algorithm proposed in this study can provide an efficient way to develop optimal silencers.

Keywords

Generalized decoupling techniquePerforated mufflerNeural network modelOptimizationGenetic algorithm
Type
Technical Note
Information
Journal of Mechanics , Volume 25 , Issue 3 , September 2009 , pp. N7 - N16
Copyright
Copyright © The Society of Theoretical and Applied Mechanics, R.O.C. 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Davis, D. D., Stokes, J. M. and Moorse, L., “Theoretical and Experimental Investigation of Mufflers with Components on Engine Muffler Design, NACA Report, p. 1192(1954).Google Scholar
2.Prasad, M. G., “A Note on Acoustic Plane Waves in a Uniform Pipe with Mean Flow,” Journal of Sound and Vibration, 95, pp. 284–290 (1984).CrossRefGoogle Scholar
3.Munjal, M. L., Acoustics of Ducts and Mufflers with Application to Exhaust and Ventilation System Design, John Wiley and Sons, New York (1987).Google Scholar
4.Kim, Y. H., Choi, J. W. and Lim, B. D., “Acoustic Characteristics of An Expansion Chamber with Constant Mass Flow and Steady Temperature Gradient (Theory and Numerical Simulation),” Journal of Vibration and Acoustics 112, pp. 460–467 (1990).CrossRefGoogle Scholar
5.Sullivan, J. W. and Crocker, M. J., “Analysis of Concentric Tube Resonators Having Unpartitioned Cavities,” Acous. Soc. Am., 64, pp. 207–215 (1978).CrossRefGoogle Scholar
6.Sullivan, J. W., “A Method of Modeling Perforated Tube Muffler Components I: Theory,” Acous. Soc. Am., 66, pp. 772–778 (1979).CrossRefGoogle Scholar
7.Sullivan, J. W., “A Method of Modeling Perforated Tube Muffler Components II: Theory,” Acous. Soc. Am., 66, pp. 779–788 (1979).CrossRefGoogle Scholar
8.Thawani, P. T. and Jayaraman, K., “Modeling and Applications of Straight-through Resonators,” Acous. Soc. Am., 73, pp. 1387–1389 (1983).CrossRefGoogle Scholar
9.Rao, K. N. and Munjal, M. L., “A Generalized Decoupling Method for Analyzing Perforated Element Mufflers,” Nelson Acoustics Conference,Madison(1984).Google Scholar
10.Jayaraman, K. and Yam, K., “Decoupling Approach to Modeling Perforated Tube Muffler Component,” Acous. Soc. Am., 69, pp. 390–396 (1981).CrossRefGoogle Scholar
11.Munjal, M. L., Rao, K. N. and Sahasrabudhe, A. D., “Aeroacoustic Analysis of Perforated Muffler Components,” Journal of Sound and Vibration, 114, pp. 173–88 (1987).CrossRefGoogle Scholar
12.Peat, K. S., “A Numerical Decoupling Analysis of Perforated Pipe Silencer Elements,” Journal of Sound and Vibration, 123, pp. 199–212 (1988).CrossRefGoogle Scholar
13.Yeh, L. J., Chang, Y. C., Chiu, M. C. and Lai, G. J., “GA Optimization on Multi-segments Muffler Under Space Constraints,” Applied Acoustics, 65, pp. 521–543 (2004).CrossRefGoogle Scholar
14.Chang, Y. C., Yeh, L. J. and Chiu, M. C., “Numerical Studies on Constrained Venting System with Side Inlet/outlet Mufflers by GA Optimization,” Acta Acustica, 1, pp. 1–11 (2004).Google Scholar
15.Chang, Y. C., Yeh, L. J. and Chiu, M. C., “Shape Optimization on Double-chamber Mufflers Using Genetic Algorithm,” Proc. ImechE Part C: Journal of Mechanical Engineering Science, 10, pp. 31–42 (2005).CrossRefGoogle Scholar
16.Yeh, L. J., Chang, Y. C. and Chiu, M. C., “Numerical Studies on Constrained Venting System with Reactive Mufflers by GA Optimization,” International Journal for Numerical Methods in Engineering, 65, pp. 1165–1185 (2006).CrossRefGoogle Scholar
17.Chiu, M. C., “Shape Optimization of Double-Chamber Side Mufflers with Extended Tube by Using Four-Pole Matrix and Simulated Annealing Method,” Journal of Mechanics, 24, pp. 31–43 (2008)CrossRefGoogle Scholar
18.Ivakhnenko, A. G., “Polynomial Theory of Complex System,” IEEE Trans. Syst. Man. Cyber., 1, pp. 364–368 (1971).CrossRefGoogle Scholar
19.Kondo, T., The Learning Algorithms of the GMDH Neural Network and Their Application to Medical Image Recognition, SICE (1988).Google Scholar
20.Patrikar, A. and Provence, J., “Nonlinear System Identification and Adaptive Control Using Polynomial Networks,” Mathl. Comput. Modeling, 23, pp. 159–173 (1996).CrossRefGoogle Scholar
21.Holland, J., Adaptation in Natural and Artificial System, Ann Arbor, University of Michigan Press (1975).Google Scholar
22.Jong, D., “An Analysis of the Behavior of a Class of Genetic Adaptive Systems,” Doctoral Dissertation, Dept. Computer and Communication Sciences, Ann Arbor, University of Michigan (1975).Google Scholar
23.Chang, Y. C. and Chiu, M. C., “Shape Optimization of One-Chamber Cross-flow Mufflers by GA Optimization,” Journal of Mechanics, 24, pp. 13–29 (2008).CrossRefGoogle Scholar
24.Rao, K. N. and Munjal, M. L., “Experimental Evaluation of Impedance of Perforates with Grazing Flow.,” Journal of Sound and Vibration, 123, pp. 283–295 (1986).Google Scholar