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Optimization of a Porous Ducted Air Induction System Using Taguchi's Parameter Design Method

Journal Article
2014-01-0887
ISSN: 1946-3995, e-ISSN: 1946-4002
Published April 01, 2014 by SAE International in United States
Optimization of a Porous Ducted Air Induction System Using Taguchi's Parameter Design Method
Sector:
Citation: Zhang, W., Khurana, R., Likich, M., and Lynch, M., "Optimization of a Porous Ducted Air Induction System Using Taguchi's Parameter Design Method," SAE Int. J. Passeng. Cars - Mech. Syst. 7(2):816-821, 2014, https://doi.org/10.4271/2014-01-0887.
Language: English

Abstract:

Taguchi method is a technology to prevent quality problems at early stages of product development and product design. Parameter design method is an important part in Taguchi method which selects the best control factor level combination for the optimization of the robustness of product function against noise factors. The air induction system (AIS) provides clean air to the engine for combustion. The noise radiated from the inlet of the AIS can be of significant importance in reducing vehicle interior noise and tuning the interior sound quality. The porous duct has been introduced into the AIS to reduce the snorkel noise. It helps with both the system layout and isolation by reducing transmitted vibration. A CAE simulation procedure has been developed and validated to predict the snorkel noise of the porous ducted AIS. In this paper, Taguchi's parameter design method was utilized to optimize a porous duct design in an AIS to achieve the best snorkel noise performance. The virtual experiments based on an orthogonal array in the parameter design method were conducted by the developed simulation procedure and the optimized design was recommended. Furthermore, the parts based on the optimized design are manufactured and tested to verify if the intended performance and other high priority requirements for the AIS are met. It was concluded that a traditional CAE analysis enhanced with robustness technique is an efficient tool to optimize the AIS design in this case study.