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.

JO - Archives of Acoustics L1 - http://www.journals.pan.pl/Content/108123/PDF/123923.pdf L2 - http://www.journals.pan.pl/Content/108123 IS - No 3 EP - 517–529 KW - acoustics KW - finite element method KW - genetic algorithm KW - muffler optimization KW - polynomial neural network model ER - A1 - Chiu, Min-Chie A1 - Chang, Ying-Chun A1 - Wu Meng-Ru PB - Committee on Acoustics PAS, PAS Institute of Fundamental Technological Research, Polish Acoustical Society VL - vol. 43 JF - Archives of Acoustics SP - 517–529 T1 - Numerical Assessment of Automotive Mufflers Using FEM, Neural Networks, and a Genetic Algorithm UR - http://www.journals.pan.pl/dlibra/docmetadata?id=108123 DOI - 10.24425/123923