Vehicle road noise prediction using component-based transfer path analysis from tire test-rig measurements on a rolling tire

Almirón, Jesús Ortega; Bianciardi, Fabio; Corbeels, Patrick; Pieroni, Nicola; Kindt, Peter; Desmet, Wim

doi:10.1016/j.jsv.2021.116694

Cited by 11 publications

(2 citation statements)

References 19 publications

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“…The surface of the dynamometer rollers was equipped with a special type of uniquely designed road shells (Stone mixture composition) as shown in Figure 2 to excite the low-frequency broadband excitation. The importance of road roughness to a car vibroacoustic response was extensively studied [5][6][7][8][9]. Installing impact bars on the rollers is also possible (this replicates real-world obstacles, see Figure 3).…”

Section: Excitationmentioning

confidence: 99%

Acoustic Dynamometer Measurements and Virtual Prototyping of a Vehicle to Address the Low Frequency Noise in a Passengers Cabin

Palled,

Chmielewski,

Nieradka

et al. 2024

Proceedings of the 10th Convention of the European Acoustics Association Forum Acusticum 2023

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Section: Excitationmentioning

confidence: 99%

Acoustic Dynamometer Measurements and Virtual Prototyping of a Vehicle to Address the Low Frequency Noise in a Passengers Cabin

Palled,

Chmielewski,

Nieradka

et al. 2024

Proceedings of the 10th Convention of the European Acoustics Association Forum Acusticum 2023

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“…A primary challenge emanates from the intricate interplay among the road surface, tires, suspension, body, and other systems inherent in road noise generation. Owing to the multitude of influencing factors and complex transmission paths [16], the direct construction of an approximation model for underlying parameters and vehicle interior noise bears the risk of underfitting. Such an approach is counterproductive to the precise identification of principal transmission paths and the rectification of road noise-related issues.…”

Section: Introductionmentioning

confidence: 99%

Improving Electric Vehicle Structural-Borne Noise Based on Convolutional Neural Network-Support Vector Regression

Jia,

Zhou,

Huang

et al. 2023

Electronics

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In order to enhance the predictive accuracy and control capabilities pertaining to low- and medium-frequency road noise in automotive contexts, this study introduces a methodology for Structural-borne Road Noise (SRN) prediction and optimization. This approach relies on a multi-level target decomposition and a hybrid model combining Convolutional Neural Network (CNN) and Support Vector Regression (SVR). Initially, a multi-level target analysis method is proposed, grounded in the hierarchical decomposition of vehicle road noise along the chassis parts, delineated layer by layer, in accordance with the vibration transmission path. Subsequently, the CNN–SVR hybrid model, predicated on the multi-level target framework, is proposed. Notably, the hybrid model exhibits a superior predictive accuracy exceeding 0.97, surpassing both traditional CNN and SVR models. Finally, the method and model are deployed for sensitivity analysis of chassis parameters in relation to road noise, as well as for the prediction and optimization analysis of SRN in vehicles. The outcomes underscore the high sensitivity of parameters such as the dynamic stiffness of the rear axle bushing and the large front swing arm bushing influencing SRN. The optimization results, facilitated by the CNN–SVR hybrid model, align closely with the measured outcomes, displaying a negligible relative error of 0.82%. Furthermore, the measured results indicate a noteworthy enhancement of 4.07% in the driver’s right-ear Sound Pressure Level (SPL) following the proposed improvements compared to the original state.

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A novel formulation for transfer path identification and vibration prediction in the over-track building induced by trains

Liu

2023

Environ Sci Pollut Res

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Vehicle road noise prediction using component-based transfer path analysis from tire test-rig measurements on a rolling tire

Cited by 11 publications

References 19 publications

Acoustic Dynamometer Measurements and Virtual Prototyping of a Vehicle to Address the Low Frequency Noise in a Passengers Cabin

Acoustic Dynamometer Measurements and Virtual Prototyping of a Vehicle to Address the Low Frequency Noise in a Passengers Cabin

Improving Electric Vehicle Structural-Borne Noise Based on Convolutional Neural Network-Support Vector Regression

A novel formulation for transfer path identification and vibration prediction in the over-track building induced by trains

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