Archives of Acoustics, 41, 1, pp. 43–53, 2016
10.1515/aoa-2016-0004

Shape Optimization of Multi-chamber Acoustical Plenums Using the BEM, Neural Networks, and the GA Method

Ying-Chun CHANG
Tatung University
Taiwan, Province of China

Ho-Chih CHENG
Chung Chou University of Science and Technology
Taiwan, Province of China

Min-Chie CHIU
Chung Chou University of Science and Technology
Taiwan, Province of China

Yuan-Hung CHIEN
Tatung University
Taiwan, Province of China

Most researchers have explored noise reduction effects based on the transfer matrix method and the boundary element method. However, maximum noise reduction of a plenum within a constrained space, which frequently occurs in engineering problems, has been neglected. Therefore, the optimum design of multi-chamber plenums becomes essential. In this paper, two kinds of multi-chamber plenums (Case I: a two-chamber plenum that is partitioned with a centre-opening baffle; Case II: a three-chamber plenum that is partitioned with two centre-opening baffles) within a fixed space are assessed.

In order to speed up the assessment of optimal plenums hybridized with multiple partitioned baffles, a simplified objective function (OBJ) is established by linking the boundary element model (BEM, developed using SYSNOISE) with a polynomial neural network fit with a series of real data – input design data (baffle dimensions) and output data approximated by BEM data in advance. To assess optimal plenums, a genetic algorithm (GA) is applied. The results reveal that the maximum value of the transmission loss (TL) can be improved at the desired frequencies. Consequently, the algorithm proposed in this study can provide an efficient way to develop optimal multi-chamber plenums for industry.
Keywords: boundary element method; plenum; centre-opening baffle; polynomial neural network model; group method of data handling; optimisation; genetic algorithm.
Full Text: PDF
Copyright © Polish Academy of Sciences & Institute of Fundamental Technological Research (IPPT PAN).

References

Alley B.C., Dufresne R.M., Kanji N., Reesal M.R. (1989), Costs of workers’ compensation claims for hearing loss, Journal of Occupational Medicine, 31, 134–138.

Bie D.A., Hansen C.H. (1988), Engineering noise control: theory and practice, Unwin Hyman, London.

Blair G.P., Coates S.W. (1973), Noise produced by unsteady exhaust efflux from an internal combustion engine, SAE, 73160.

Chang Y.C., Yeh L.J., Chiu M.C. (2004), Numerical studies on constrained venting system with side inlet/outlet mufflers by GA optimisation, Acta Acustica united with Acustica, 1, 1–11.

Chang Y.C., Yeh L.J., Chiu M.C. (2005), Shape optimisation on double-chamber mufflers using Genetic Algorithm, Proc. ImechE Part C: Journal of Mechanical Engineering Science, 10, 31–42.

Chang Y.C., Yeh L.J., Chiu M.C., Lai G.J. (2005), Shape optimisation on constrained single-layer sound absorber by using GA method and mathematical gradient methods, Journal of Sound and Vibration, 286, 4–5, 941–961.

Cheremisinoff P.N., Cheremisinoff P.P. (1977), Industrial noise control handbook, Ann Arbor Science, Michigan.

Chiu M.C. (2010), Shape optimisation of one-chamber Mufflers with reverse-flow ducts using a genetic algorithm, Journal of Marine Science and Technology, 18, 1, 12–23.

Chiu M.C., Chang Y.C. (2008), Numerical studies on venting system with multi-chamber perforated mufflers by GA optimisation, Applied Acoustics, 69, 11, 1017–1037.

Chiu M.C., Chang Y.C. (2010), Numerical assessment of a space-constrained venting system with multi-chamber plug mufflers by GA method, Journal of Marine Science and Technology, 18, 3, 317–332.

Ffowcs J.E., Howe M.S. (1975), The generation of sound by density inhomogenities in low mach number nozzle flows, J. of Fluid Mechanics, 70, 3, 605–622.

Holland J. (1975), Adaptation in natural and artificial system, Ann Arbor, University of Michigan Press.

Ivakhnenko A.G. (1971), Polynomial theory of complex system, IEEE Trans. Syst. Man. Cyber, 1, 4, 364–368.

Jong D. (1975), An analysis of the behavior of a class of genetic adaptive systems, Doctoral Dissertation, Department of Computer and Communication Sciences, Ann Arbor, University of Michigan, USA.

Ko S.H. (1971), Sound attenuation in lined rectangular ducts with flow and its application to the reduction of aircraft engine noise, J. Acoust. Soc. Am., 50, 6, 1418–1432.

Laurence W. (1998), Integer programming, John Wiley & Sons, New York.

Li, X., Hansen C.H. (2005), Comparison of models for predicting the transmission loss of plenum chambers, Applied Acoustics, 66, 7, 810–828.

Liu J., Herrin D.W. (2010), Enhancing micro-perforated panel attenuation by partitioning the adjoining cavity, Applied Acoustics, 71, 120–127.

Mccormick A.M. (1975), The attenuation of sound in lined rectangular ducts containing uniform Flow, Journal of Sound and Vibration, 39, 1, 35–41.

Munjal M.L. (1997), Plane wave analysis of side inlet/outlet chamber mufflers with mean flow, Applied Acoustics, 52, 165–175.

Patrikar A., Provence J. (1996), Nonlinear system identification and adaptive control using polynomial networks, Mathl. Comput. Modeling, 23, 1/2, 159–173.

Rardin R.L. (1998), Optimisation in operations research, Prentice Hall, New Jersey.

Yeh L.J., Chang Y.C., Chiu M.C. (2006), Numerical studies on constrained venting system with reactive mufflers by GA optimisation, International Journal for Numerical Methods in Engineering, 65, 1165–1185.

Yeh L.J., Chang Y.C., Chiu M.C., Lai G.J. (2004), GA optimisation on multi-segments muffler under space constraints, Applied Acoustics, 65, 5, 521–543.




DOI: 10.1515/aoa-2016-0004