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Numerical Assessment of Rectangular Straight/Reverse Mufflers at High-Order-Modes

Published online by Cambridge University Press:  14 December 2016

Y. C. Chang,
D. Y. Lin,
H. C. Cheng and
M. C. Chiu
Y. C. Chang
Affiliation:
Department of Mechanical Engineering Tatung University Taipei, Taiwan
D. Y. Lin
Affiliation:
Department of Mechanical Engineering Tatung University Taipei, Taiwan
H. C. Cheng
Affiliation:
Department of Mechanical and Automation Engineering Chung Chou University of Science and Technology Changhua, Taiwan
M. C. Chiu*
Affiliation:
Department of Mechanical and Automation Engineering Chung Chou University of Science and Technology Changhua, Taiwan
*
*Corresponding author (minchie.chiu@msa.hinet.net)

Abstract

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There has been wide-spread use of plane wave theory in muffler design in industry. However, This has led to an underestimation of acoustical performances at higher frequencies. To overcome the above drawback, the finite element and boundary element methods have been developed. Nevertheless, the time consumed in calculating the noise level is unacceptable. Moreover, considering the acoustical effect and necessity of space-constrained situation in industry, a compact design of reverse mufflers which may improve the acoustical efficiency is then proposed.

In this paper, a numerical assessment of rectangular mufflers hybridized with straight/reverse chambers using eigen function, four-pole matrix, and genetic algorithm under limited space is developed. Before the optimization is performed, an accuracy check of the mathematical models for the muffler will be carried out. Results reveal that the noise reduction will increase when the number of chambers increases. In addition, the acoustical performance of the mufflers is reversely proportional to the diameter of the inlet/outlet tubes. Also, the TL of the mufflers will be improved when using more number of target tones in the objective function. Consequently, a successful approach in searching optimal shaped rectangular straight/reverse mufflers using an eigen function and a genetic algorithm method within a constrained space has been demonstrated.

Keywords

BiasEigen functionHigher order modeOptimizationReverse

Information

Type
Research Article
Information
Journal of Mechanics , Volume 34 , Issue 3 , June 2018, pp. 325 - 336
Copyright
Copyright © The Society of Theoretical and Applied Mechanics 2018 

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