Archives of Acoustics, 42, 2, pp. 189–197, 2017
10.1515/aoa-2017-0021

Noise Elimination of Reciprocating Compressors Using FEM, Neural Networks Method, and the GA Method

Ying-Chun CHANG
Tatung University
Taiwan, Province of China

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

Ji-Lin XIE
Tatung University
Taiwan, Province of China

Industry often utilizes acoustical hoods to block noise emitted from reciprocating compressors. However, the hoods are large and bulky. Therefore, to diminish the size of the compressor, a compact discharge muffler linked to the compressor outlet is considered. Because the geometry of a reciprocating compressor is irregular, COMSOL, a finite element analysis software, is adopted. In order to explore the acoustical performance, a mathematical model is established using a finite element method via the COMSOL commercialized package. Additionally, to facilitate the shape optimization of the muffler, a polynomial neural network model is adopted to serve as an objective function; also, a Genetic Algorithm (GA) is linked to the OBJ function. During the optimization, various noise abatement strategies such as a reverse expansion chamber at the outlet of the discharge muffler and an inner extended tube inside the discharge muffler, will be assessed by using the artificial neural network in conjunction with the GA optimizer. Consequently, the discharge muffler that is optimally shaped will decrease the noise of the reciprocating compressor.
Keywords: finite element method; polynomial neural network model; genetic algorithm; group method of data handling; reciprocating compressor; optimization
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DOI: 10.1515/aoa-2017-0021

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