Control of structure-borne noise for a vehicle body by using power flow analysis and acoustic path participation method

Yong-liang, Wang; Lu, Chihua; Qin, Xunpeng; Huang, Song; Tan, Xiao; Sun, Yuguang

doi:10.1016/j.apacoust.2019.07.029

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…1), the vibration responses of interest at node R on the structure (e.g. the steering wheel) can be numerical calculated, which can be written as [19,20]  …”

Section: Vibration Sensitivity Methodsmentioning

confidence: 99%

“…Based on the transmissibility concept, Cervantes [18] investigated the design and implementation of a simplified method, which allows a rapid and accurate analysis. By using the power flow analysis and acoustic path participation method, literatures [19] obtained the high energy transfer paths and the dynamic parameters of these paths being modified to reduce noise radiation. Although the TPA or modified TPA have been investigated theoretically (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Analysis and control of vehicle steering wheel vibration based on acceleration transfer path analysis method

Ye,

Wang,

Liu

et al. 2023

Preprint

Self Cite

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This paper is aimed to analyze and control the structure vibration of a vehicle steering wheel by using the acceleration transfer path analysis (TPA) method and vibration sensitivity method. A steering wheel vibration test is conducted to obtain the critical engine speed and corresponding to the frequency. The acceleration-acceleration transfer function from mounts body side to steering wheel is deducted and tested, in conjunction with the actual acceleration spectrums of mounts body side, the acceleration spectrum of steering wheel is synthesized and the accuracy of the synthetic results is evaluated high. Then the vibration transmitted by each path is investigated and the main vibration participation paths are picked out from numerous paths. The interaction mechanisms between the path transfer vibration, path vibration participation and the total vibration are further obtained. The critical parameters (mounting stiffnesses and vibration sensitivity) of the main vibration participation paths are matched, modified and the vibration control effect is experimental verified. The results indicate that, by pertinently matching the stiffness parameters of the mounts and reducing vibration sensitivity of structure, the dynamic force transmitted by the paths are decreased, the vibration transfer functions from mounts body side to steering wheel are decreased, and then the actual steering wheel vibration is controlled.

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“…1), the vibration responses of interest at node R on the structure (e.g. the steering wheel) can be numerical calculated, which can be written as [19,20]  …”

Section: Vibration Sensitivity Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis and control of vehicle steering wheel vibration based on acceleration transfer path analysis method

Ye,

Wang,

Liu

et al. 2023

Preprint

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

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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.

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Vibrational Energy Properties of Twin-Block Ballastless Track with Anti-vibration Structure on Bridge by Power Flow Analysis

Zhao

Liu

et al. 2021

KSCE J Civ Eng

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Control of structure-borne noise for a vehicle body by using power flow analysis and acoustic path participation method

Cited by 5 publications

References 19 publications

Analysis and control of vehicle steering wheel vibration based on acceleration transfer path analysis method

Analysis and control of vehicle steering wheel vibration based on acceleration transfer path analysis method

Vehicle Interior Noise Prediction Based on Elman Neural Network

Vibrational Energy Properties of Twin-Block Ballastless Track with Anti-vibration Structure on Bridge by Power Flow Analysis

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