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  • Open Access


    Prediction of Sound Transmission Loss of Vehicle Floor System Based on 1D-Convolutional Neural Networks

    Cheng Peng1, Siwei Cheng2, Min Sun1, Chao Ren1, Jun Song1, Haibo Huang2,*

    Sound & Vibration, Vol.58, pp. 25-46, 2024, DOI:10.32604/sv.2024.046940

    Abstract The Noise, Vibration, and Harshness (NVH) experience during driving is significantly influenced by the sound insulation performance of the car floor acoustic package. As such, accurate and efficient predictions of its sound insulation performance are crucial for optimizing related noise reduction designs. However, the complex acoustic transmission mechanisms and difficulties in characterizing the sound absorption and insulation properties of the floor acoustic package pose significant challenges to traditional Computer-Aided Engineering (CAE) methods, leading to low modeling efficiency and prediction accuracy. To address these limitations, a hierarchical multi-objective decomposition system for predicting the sound insulation performance More >

  • Open Access


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

    Yunru Wu1, Xiangbo Liu1, Haibo Huang1,2,*, Yudong Wu1, Weiping Ding1,2, Mingliang Yang1,2,*

    Sound & Vibration, Vol.57, pp. 73-95, 2023, DOI:10.32604/sv.2023.044601

    Abstract 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, More >

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