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

• Autorzy: Chang Y.-C. , Chiu M.-C. , Xie J.-L.

Identyfikatory

DOI

10.1515/aoa-2017-0021

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

finite element method; polynomial neural network model; genetic algorithm; group method of data handling; reciprocating compressor; optimization

• Wydawca: Instytut Podstawowych Problemów Techniki PAN ; Komitet Akustyki PAN ; Polskie Towarzystwo Akustyczne
• Czasopismo: Archives of Acoustics
• Rocznik: 2017
• Tom: Vol. 42, No. 2
• Strony: 189--197
• Opis fizyczny: Bibliogr. 14 poz., rys., tab., wykr.

Twórcy

autor

Chang Y.-C.

• Department of Mechanical Engineering, Tatung University, Taiwan, R.O.C.

autor

Chiu M.-C.

• Department of Mechanical and Automation Engineering, Chung Chou University of Science and Technology, No. 6, Lane 2, Sec.3, Shanchiao Rd., Yuanlin, Changhua 51003, Taiwan, R.O.C.

autor

Xie J.-L.

• Department of Mechanical Engineering, Tatung University, Taiwan, R.O.C.

Bibliografia

• 1. Chang Y. C., Chiu M. C., Cheng M. M. (2009), Optimum design of perforated plug mufflers using neural network and genetic algorithm, Proc. ImechE Part C: Journal of Mechanical Engineering Science, 223, 935–952.
• 2. Chang Y. C., Yeh L. J., Chiu M. C. (2004), Numerical studies on constrained venting system with side inlet/outlet mufflers by GA optimization, Acta Acustica united with Acustica, 1, 1–11.
• 3. Cheremisinoff P. N., Cheremisinoff P. P. (1977), Industrial noise control handbook, Ann Arbor Science, Michigan.
• 4. Chiu M. C., Chang Y. C. (2009), Application of neural network and genetic algorithm to the optimum design of perforated tube mufflers, J. of Mechanics, 25, N7–N16.
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• 11. Roozen N. B., Oetelaar V. D. J., Geerlings A., Vliegenthart T. (2009), Source identification and noise reduction of a reciprocating compressor; a case history, International Journal of Acoustics and Vibration, 14, 2, 90–98.
• 12. Suh K. H., Lee H., Oh W. S., Jung W. H. (1998), An analysis of the hermetic reciprocating compressor acoustic system, International Compressor Engineering Conference.
• 13. Wang C. N. (1992), The application of boundary element method in the noise reduction analysis for the automotive mufflers, Ph. D Thesis, Taiwan University.
• 14. Yeh L. J., Chang Y. C., Chiu M. C. (2006), Numerical studies on constrained venting system with reactive mufflers by GA optimization, International Journal for Numerical Methods in Engineering, 65, 1165–1185.

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).