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Numerical optimization and experimental research on listening environment of crew based on neural networks

机译：基于神经网络的机组听音环境数值优化与实验研究

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摘要

With greater noises in the cabin, a greater impact would be acted on the listening system of crew who lived in the cabin for a long time. The neural network and the listening environment of the cabin were optimized in the paper to improve the listening capacity of crew. Firstly, the periodic sound insulation package was proposed to make a comparison with the traditional sound insulation package to verify its advantage for sound insulation performance of the structure in mid-low frequency. Later, numerical simulation was applied to analyze the influence of duty cycle and periodic number on the transmission loss of the structure. In order to obtain the structure with an optimal transmission loss, the neural network was adopted to predict and optimize the periodic sound insulation package. In the meanwhile, boundary element method was adopted to predict the interior noise in the cabin. In order to analyze noises in the cabin, panel contribution analysis was conducted for the panel of the cabin. Finally, the periodic sound insulation package was applied on these panels to improve the noise in the cabin. It was found that the periodic sound insulation package can effectively control the vibration energy of these panels at these frequency points and reduce noise in the cabin. Finally, the noise in the improved cabin was tested to compare with the result of the numerical computation and obtain good consistency. It indicated that the numerical simulation was reliable. The low frequency noise in the cabin could really be reduced effectively through laying the periodic sound insulation package on the cabin panel. Finally, the listening capacity and environment of crew were obviously improved within the cabin. (C) 2016 Elsevier B.V. All rights reserved.
机译：随着机舱中较大的噪声，将对长时间居住在机舱中的机组的听觉系统产生更大的影响。本文对客舱的神经网络和聆听环境进行了优化，以提高机组的聆听能力。首先，提出了周期性隔声包与传统隔声包进行比较，以验证其在中低频结构隔声性能上的优势。随后，通过数值模拟分析了占空比和周期数对结构传输损耗的影响。为了获得具有最佳传输损耗的结构，采用神经网络来预测和优化周期性隔音包。同时，采用边界元法对舱室内噪声进行了预测。为了分析机舱中的噪声，对机舱的面板进行了面板贡献分析。最后，在这些面板上应用了周期性隔音套件，以改善机舱中的噪音。已经发现，周期性的隔音包可以有效地控制这些面板在这些频率点处的振动能量，并减少机舱中的噪声。最后，对改进型客舱内的噪声进行了测试，以与数值计算结果进行比较，并获得良好的一致性。表明数值模拟是可靠的。通过在机舱面板上放置定期隔音包，可以有效地降低机舱中的低频噪声。最后，机舱内人员的聆听能力和环境得到明显改善。 （C）2016 Elsevier B.V.保留所有权利。

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  • 来源
    《Neurocomputing》 |2017年第12期|244-253|共10页
  • 作者

    Hu He-xuan; Tang Bo; Ding Zheng-yin; Shi Chun-lai; Shi Bang-wen; Guo Zheng-hong;

  • 作者单位

    Hohai Univ, Coll Energy & Elect Engn, Nanjing 211100, Jiangsu, Peoples R China|Tibet Univ, Agr & Anim Husb Coll, Tibet 860000, Peoples R China;

    Hohai Univ, Coll Energy & Elect Engn, Nanjing 211100, Jiangsu, Peoples R China|Tibet Univ, Agr & Anim Husb Coll, Tibet 860000, Peoples R China;

    Zhejiang Univ, Coll Energy Engn, Hangzhou 310027, Zhejiang, Peoples R China;

    Qing Hai Hua Dian Da Tong Power Generat Co Ltd, Planning & Operating Dept, Xining 810000, Peoples R China;

    SPIC Gan Su New Energy Power Co, Lanzhou 730000, Peoples R China;

    Hebei North Univ, Zhangjia Kou 075000, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Neural network; Periodic sound insulation package; Mid-low frequency; Transmission loss; Cabin panels;

    机译：神经网络;周期性隔音包;中低频;传输损耗;机舱面板;

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