• Proceedings of the KSME Conference
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• 2000.04b
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• pp.892-898
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• 2000

This study is on the development of a new muffler composed of a valve system using an elasticity or spring. The valve system using the elasticity of spring is set along the exhaust-gas flow and designed to work itself alone the driving condition of a engine. By that reason the engine capacity is so enlarged that a muffler with controller sensing exhaust-gas pressure is able to be satisfied to noise reduction and- power enlargement more than conventional muffler. The purpose of this study is to develope the new muffler which has more noise reduction and power enlargement than conventional muffler and electric-control muffler.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.570-575
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• 2019

Active muffler is implemented by applying active noise control technique to reduce exhaust noise of automobile muffler. Conventional Filtered_x LMS algorithm has a problem that the degree of control filter becomes very large and convergence deteriorates when acoustic feedback is present. The recursive LMS algorithm can compensate for this problem because it can be easily diverted in the adaptive filter adaptation process. In this paper, the structure of the primary path and the secondary path transfer function is designed as the IIR filter to improve the convergence performance and the computational burden, and the stabilization filter algorithm is applied to secure stability which is a disadvantage of the IIR filter structure. The stabilization filter algorithm plays a role of pulling the pole into the unit circle to prevent the pole of the transfer function corresponding to the acoustic feedback from diverging during the adaptation process. In this way, the computational burden of the active muffler system and the convergence performance can be improved. In order to show the usefulness of the proposed system, we compared the performance of the proposed Filtered_x LMS algorithm with the performance of the proposed system for the exhaust sound of a diesel engine, which is a variable environment. Compared to conventional algorithm, proposed algorithm's computational burden is less than half, and convergence performances are more than 4 times.