Muffler Design for Noise Reduction and Pressure Loss Optimization Using Multiobjective Genetic Algorithm

Abstract

The muffler plays a crucial role in reducing the noise generated by the internal combustion engine exhaust gases. Therefore, an effective muffler design should be capable of significantly reducing noise levels. However, we must also consider backpressure, which can negatively impact engine performance. Backpressure is the additional pressure exerted by the muffler towards the engine, and it can have adverse effects on engine performance, thus requiring minimization. These two objectives often conflict with each other. Hence, in this research, we utilize multiobjective genetic algorithms as a tool to optimize muffler design. Inspired by the natural selection process, genetic algorithms aim to find a muffler design that is not only effective in reducing noise but also produces minimal backpressure. Thus, this study aims to achieve a balance between noise reduction and backpressure minimization in muffler design. The multiobjective genetic algorithm proposes 105 muffler design solutions. These solutions are not dominated by each other against both TL and PL objectives. The design that has the best value in the TL objective is solution 1 with TL and PL values of 26.06 dBA and 2.27 kPa. The design that has the best value in the PL objective is solution 3 with TL and PL values of 8.36 dBA and 1.87 kPa. The muffler compromise design chosen was a solution 41 with TL and PL values of 17.78 dBA and 2.07 kPa.