Research on Prediction and Control of Heavy Commercial Vehicle Interior High Frequency Noise Based on SEA

Tang, Rongjiang; Huang, Li; Tong, Zhe; Li, Shenfang

doi:10.1109/icctec.2017.00245

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…Furthermore, the utilization of large-scale simulation software, such as VA-One, has contributed to the advancement of mid-to-high frequency noise analysis within vehicles. For instance, Tang et al [6] focused on vehicle floor structures and proposed two optimization methods, namely increasing floor mat thickness and modifying their structure, to enhance the sound insulation performance of commercial vehicle floors based on SEA modeling principles. The simulation results demonstrated an error of less than 2 dB(A) when compared to experimental results, affirming the accuracy of the SEA method.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Prediction and Optimization of Vehicle Acoustic Package Based on ResNet Neural Network

Wu,

Liu,

Huang

et al. 2023

Sound&Vibration

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Vehicle interior noise has emerged as a crucial assessment criterion for automotive NVH (Noise, Vibration, and Harshness). When analyzing the NVH performance of the vehicle body, the traditional SEA (Statistical Energy Analysis) simulation technology is usually limited by the accuracy of the material parameters obtained during the acoustic package modeling and the limitations of the application conditions. In order to effectively solve these shortcomings, based on the analysis of the vehicle noise transmission path, a multi-level objective decomposition architecture of the interior noise at the driver's right ear is established. Combined with the data-driven method, the ResNet neural network model is introduced. The stacked residual blocks avoid the problem of gradient disappearance caused by the increasing network level of the traditional CNN network, thus establishing a higherprecision prediction model. This method alleviates the inherent limitations of traditional SEA simulation design, and enhances the prediction performance of the ResNet model by dynamically adjusting the learning rate. Finally, the proposed method is applied to a specific vehicle model and verified. The results show that the proposed method has significant advantages in prediction accuracy and robustness.

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Section: Introductionmentioning

confidence: 99%