This presentation was made at CAASE18, The Conference on Advancing Analysis & Simulation in Engineering. CAASE18 brought together the leading visionaries, developers, and practitioners of CAE-related technologies in an open forum, to share experiences, discuss relevant trends, discover common themes, and explore future issues.

Resource Abstract

3-pass exhaust mufflers are successfully used in exhaust systems to reduce the noise caused by exhaust gases from engines. Optimized geometric design of the muffler has always been a challenge and an active research area in the industry. Muffler should be designed to satisfy conflicting demands of two major design objectives - transmission loss and back pressure on engine. Transmission loss represents the exhaust noise attenuation by muffler and back pressure represents the additional pressure drop which is created by insertion of muffler in the exhaust system. Designers are tasked with maximizing the noise attenuation with minimum increase in back pressure. Various geometric and operating design parameters are considered to obtain a trade-off between these two conflicting design objectives. These geometric design parameters include porosity of perforated elements, lengths of end cavities, expansion chamber diameter etc. Number of studies can be found where deterministic approaches were taken to find an optimum design between the targeted noise attenuation and backpressure. But all the previous studies are performed separately with calculation of backpressure in CFD and transmission loss in FEM/BEM. However, a complete multi-objective multi-disciplinary optimization approach to find the optimum geometric configuration of muffler is still in need where two conflicting objectives i.e. transmission loss and backpressure can be balanced at the same time. In the present study, transmission loss and backpressure drop are simultaneously optimized to find an optimum geometric design and configuration of a 3-pass muffler. The results are compared with a deterministic case study to show the effectiveness of the stochastic design optimization approach. Additionally, tuned muffler is always a challenge to design. An attempt has been made in this paper to tune the current muffler model by benchmarking the transmission loss spectrum with a target design spectrum. A field meta-modelling based optimization technique is used to minimize the root-mean-square error (RMSE) between transmission loss frequency spectrum curve of current model and design target. This current approach will typically reduce the muffler design development time to couple of weeks from months.