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Prediction and Analysis of Diesel Engine Combustion Noise Using Transfer Function Method

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Abstract

With the increasingly strict emission regulations, the combustion noise has become an important factor restricting diesel engine development. Therefore, it is necessary to study the transfer function and prediction of combustion noise and to control it from the design stage. This paper proposes a method to calculate the combustion noise transfer function. An electronically controlled common rail diesel engine (D19TCI) was selected as the research object. It was tested for in-cylinder pressure and sound pressure across different engine operating conditions. The original signal was preprocessed using time synchronization and least squares polynomial fitting, and the diesel engine combustion noise transfer function was obtained based on motor tests and different injection strategies. The research verified the existence of a transfer relationship between the in-cylinder pressure and surface radiation combustion noise in all directions in the same engine. In addition, the prediction accuracy of the combustion noise obtained using the transfer function was verified. The experimental results show that the combustion noise transfer function obtained using the multiple linear regression main-injection strategy has better consistency result than other strategies, and the results also provide new ideas and methods for optimization design of diesel engine noise reduction.

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Abbreviations

MIT:

the main-injection timing

BMIT:

the base value of main-injection timing

MIP:

the main-injection pressure

PIT:

the pre-injection timing

BPIT:

the base value of pre-injection timing

PIA:

the pre-injection amount

BPIA:

the base value of pre-injection amount

TDC:

top dead center

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Acknowledgement

This research was supported by SMDERI, China.

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Correspondence to Wenzhi Gao.

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Wei, Z., Zheng, L., Huang, L. et al. Prediction and Analysis of Diesel Engine Combustion Noise Using Transfer Function Method. Int.J Automot. Technol. 22, 665–676 (2021). https://doi.org/10.1007/s12239-021-0062-y

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  • DOI: https://doi.org/10.1007/s12239-021-0062-y

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