Optimization of Screw Mufflers Equipped with Two Inlets and One Outlet Using Neural Network Model, Finite Element Method, and Genetic Algorithm

Min-Chie Chiu, Ying-Chun Chang, Tian-Syung Lan, Ho-Sheng Chen and Laxminarayan Sahoo

Mathematical Problems in Engineering, 2022, vol. 2022, 1-11

Abstract: Noise abatement by using efficient mufflers is compulsory, as venting noise, a type of huge noise in the industry, has a serious impact on human hearing. In order to reduce the noise abatement cost, the idea of using a muffler to suppress two kinds of venting noise sources arises. In this study, a muffler internally inserted with a screwed plate was proposed with two inlets and an outlet for exhaust venting. An analysis of the finite element method (FEM) was performed to estimate the muffler’s acoustical performance using the COMSOL program. A simplified objective function established by an artificial neural network (ANN) was trained to shorten the optimization procedure and linked to a genetic algorithm (GA). During the muffler analysis, both Rx (the outline dimension of the muffler) and D (the diameter of a straight and perforated tube) were chosen as design parameters. In addition, two target frequencies (1500 Hz and 2000 Hz) were specified during the optimization process. Consequently, the result reveals that the optimization of a screw muffler having two inlets and one outlet was efficiently assessed.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/mpe/2022/7727561.pdf (application/pdf)
http://downloads.hindawi.com/journals/mpe/2022/7727561.xml (application/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:7727561

DOI: 10.1155/2022/7727561

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().