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

Zhao, Wei; Liu, Yuting; Liu, Xiandong; Shan, Yingchun; Hu, Xiaojun

doi:10.3390/app11093979

Cited by 3 publications

(2 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Helium gas was adopted by Waisanen et al 30 as the inflation gas to reduce TACR noise, but it proved to be a little more expensive. Subbian et al 31 and Zhao et al 32 exploited the potential of the coupling effect between the tire cavity and wheel in reducing TACR noise. Considering the tire itself, the discussions on the constituent composite material and tire contour design factors were conducted by Yoon et al and Kim et al 33,34 to develop the low-TACR noise tire.…”

Section: Introductionmentioning

confidence: 99%

Research on acoustic-structural coupling model and tire parameters of tire acoustic cavity resonance noise

Bao,

Feng,

Zhao

et al. 2023

Journal of Low Frequency Noise, Vibration and Active Control

Self Cite

View full text Add to dashboard Cite

Tire acoustic cavity resonance (TACR) noise is one kind of low-frequency and narrowband noise that particularly annoys passengers, especially becomes more prominent in electric vehicles. This paper demonstrates a numerical investigation on the TACR noise via the acoustic-structural coupling finite element model. The accuracy of this coupling finite element model is validated both by the analytic method based on the superposition theory of traveling waves and experimental modal tests of tire cavities. According to the simulated models, the influences of external factors including the inflation pressure and road load on the TACR noise are studied. Meanwhile, as the main constituent component in the tire model, the effect of belt cord is also discussed. To design the low-TACR noise tire, various design parameters of the inner contour in tire cavity are analyzed. By the orthogonal test and range analysis, it is found that the contour parameters can slightly affect the modal frequency and sound pressure of TACR noise. Among these, the curvature radius of the tire shoulder ρ 4, tire sidewall ρ 5, and half-section height H have a more obvious influence on the TACR noise than the curvature radius of the tire crown region ρ 2 and transition region ρ 3. Meanwhile, it also illustrates that increasing the curvature radius at the tire shoulder ρ 4 and reducing the half-section height H has a reduction effect on the TACR noise. This study can contribute to the design of low road noise tires.

show abstract

Section: Introductionmentioning

confidence: 99%

Research on acoustic-structural coupling model and tire parameters of tire acoustic cavity resonance noise

Bao,

Feng,

Zhao

et al. 2023

Journal of Low Frequency Noise, Vibration and Active Control

Self Cite

View full text Add to dashboard Cite

show abstract

“…By modifying the body structure parameters to reduce vehicle interior noise, the NVH optimization time of the vehicle during the design phase and the subsequent trial production and mass production phases can be greatly minimized. Previous studies have recognized and reduced vehicle interior noise based on the finite element model of the vehicle body [12][13][14][15][16][17][18]. Kim et al [19] used the progressive quadratic response surface method to optimize the lower arm of the vehicle suspension system.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Interior Noise Prediction Based on Elman Neural Network

Zhou

Liu

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

Vehicle interior noise is an important factor affecting ride comfort. To reduce the noise inside the vehicle at the vehicle body design stage, a finite element model of the vehicle body must be established. While taking the first-order global modal of the body-in-white, the maximum sound pressure level of the target point in the vehicle, the body mass, and the side impact conditions into account, the thickness of the body panel as determined via sensitivity analysis is treated as the input variable, and the sample is determined by following the Hamersley experimental design. Specifically, the Elman neural network predicts the noise value in the vehicle, and a vehicle body structure optimization method that comprehensively considers NVH performance and side impact safety is established. The prediction errors of the Elman neural network algorithm were within 3%, which meets the prediction accuracy requirements. To achieve satisfactory restraint performance, the maximum sound pressure level of the target point in the vehicle is reduced by 5.92 dB, and the maximum intrusions of the two points on the B-pillar inner panel are reduced by 31.1 mm and 33.71 mm, respectively. The side impact performance is improved while the noise inside the vehicle is reduced. This study provides a reference method for multidisciplinary research aiming to optimize the design of vehicle body structures.

show abstract