Analysis of Tire Acoustic Cavity Resonance Energy Transmission Characteristics in Wheels Based on Power Flow Method

Author:

Zhao WeiORCID,Liu Yuting,Liu Xiandong,Shan Yingchun,Hu Xiaojun

Abstract

As a kind of low-frequency vehicle interior noise, tire acoustic cavity resonance noise plays an important role, since the other noise (e.g., engine noise, wind noise and friction noise) has been largely suppressed. For the suspension system, wheels stand first in the propagation path of this energy. Therefore, it is of great significance to study the influence of wheel design on the transmission characteristics of this vibration energy. However, currently the related research has not received enough attention. In this paper, two sizes of aluminum alloy wheel finite element models are constructed, and their modal characteristics are analyzed and verified by experimental tests simultaneously. A mathematically fitting sound pressure load model arising from the tire acoustic cavity resonance acting on the rim is first put forward. Then, the power flow method is applied to investigate the resonance energy distribution and transmission characteristics in the wheels. The structure intensity distribution and energy transmission efficiency can be described and analyzed clearly. Furthermore, the effects of material structure damping and the wheel spoke number on the energy transmission are also discussed.

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Research on acoustic-structural coupling model and tire parameters of tire acoustic cavity resonance noise;Journal of Low Frequency Noise, Vibration and Active Control;2023-12-09

2. Attenuation of Tire Cavity Noise Using Active Noise Cancellation in a Car;Transaction of the Korean Society of Automotive Engineers;2022-10-01

3. Vehicle Interior Noise Prediction Based on Elman Neural Network;Applied Sciences;2021-08-30

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