Acoustical Assessment of Automotive Mufflers Using FEM, Neural Networks, and a Genetic Algorithm

• Autorzy: Chang Y.-C. , Chiu M.-C. , Wu M.-R.

Identyfikatory

DOI

10.24425/123923

• Języki publikacji: EN

Abstrakty

EN

In order to enhance the acoustical performance of a traditional straight-path automobile muffler, a multi-chamber muffler having reverse paths is presented. Here, the muffler is composed of two internally parallel/extended tubes and one internally extended outlet. In addition, to prevent noise transmission from the muffler’s casing, the muffler’s shell is also lined with sound absorbing material. Because the geometry of an automotive muffler is complicated, using an analytic method to predict a muffler’s acoustical performance is difficult; therefore, COMSOL, a finite element analysis software, is adopted to estimate the automotive muffler’s sound transmission loss. However, optimizing the shape of a complicated muffler using an optimizer linked to the Finite Element Method (FEM) is time-consuming. Therefore, in order to facilitate the muffler’s optimization, a simplified mathematical model used as an objective function (or fitness function) during the optimization process is presented. Here, the objective function can be established by using Artificial Neural Networks (ANNs) in conjunction with the muffler’s design parameters and related TLs (simulated by FEM). With this, the muffler’s optimization can proceed by linking the objective function to an optimizer, a Genetic Algorithm (GA). Consequently, the discharged muffler which is optimally shaped will improve the automotive exhaust noise.

Słowa kluczowe

EN

acoustics; finite element method; genetic algorithm; muffler optimization; polynomial neural network model

• Wydawca: Instytut Podstawowych Problemów Techniki PAN ; Komitet Akustyki PAN ; Polskie Towarzystwo Akustyczne
• Czasopismo: Archives of Acoustics
• Rocznik: 2018
• Tom: Vol. 43, No. 3
• Strony: 517--529
• Opis fizyczny: Bibliogr. 26 poz., rys., tab., wykr.

Twórcy

autor

Chang Y.-C.

• Department of Mechanical Engineering, Tatung University, Taiwan, ROC

autor

Chiu M.-C.

• Department of Mechanical and Automation Engineering, Chung Chou University of Science and Technology, 6, Lane 2, Sec.3, Shanchiao Rd., Yuanlin, Changhua 51003, Taiwan, R.O.C.

autor

Wu M.-R.

• Department of Mechanical Engineering, Tatung University, Taiwan, ROC

Bibliografia

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).