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Optimization of Exhaust Muffler Design Variables for Transmission Loss Using Coupling of modeFRONTIER and GT-POWER
ISSN: 2641-9637, e-ISSN: 2641-9645
Published August 31, 2021 by SAE International in United States
Citation: Hiwale, D., Bijwe, V., Vaidya, R., and Chavan, Y., "Optimization of Exhaust Muffler Design Variables for Transmission Loss Using Coupling of modeFRONTIER and GT-POWER," SAE Int. J. Adv. & Curr. Prac. in Mobility 4(2):411-418, 2022, https://doi.org/10.4271/2021-01-1042.
Exhaust Noise attenuation is one of the important functions of exhaust muffler. Transmission Loss (TL) is a measure of noise attenuation used in designing exhaust mufflers for NVH. TL is a logarithmic difference between inlet and outlet pressures for unit velocity input at inlet of the muffler and anechoic termination at outlet of the muffler as boundary conditions. TL amplitude and its frequency tuning depends on a combination of various muffler design parameters like volume, length, muffler cross section, pipe cross sections, pipe perforations, number of chambers, baffle perforations, etc. Achieving the desired TL performance with no valleys over a wide frequency range is very challenging. Manual design iterations with large numbers of permutations and combinations of design variables are difficult and time-consuming. It also needs a highly experienced professional to balance TL performance, design variables and design constraints. The current paper discusses an exhaust muffler TL optimization simulation process that couples modeFRONTIER for DOEs, & GT-POWER, for acoustic simulation. All identified design variables are iterated in batch mode within specified design limits. DOEs are set up using Non-dominated Sorting Genetic Algorithm (NSGA) or Multi Objective Genetic Algorithm (MOGA) in modeFRONTIER. Based upon pre-defined iterative simulation cycles, muffler design is optimized to meet design constraints and TL performance. This optimization helps to reduce manual efforts in building simulation models, to carry out more iterations, to reduce solver time, to reduce manual intervention for post processing, and to give optimized TL performance.