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Prediction and energy contribution analysis of interior noise in a high-speed train based on modified energy finite element analysis

机译:基于改进能量有限元分析的高速列车内部噪声预测和能量贡献分析

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Compared with traditional finite element analysis and statistical energy analysis, the energy finite element analysis (EFEA) has the advantages of small computations and solving local responses in large-scale complex structures. In this paper, the structural sound insulation effect is introduced to EFEA for predicting and analyzing interior noise responses of extruded structures in a high-speed train. Firstly, the carriage structure and cavity models are established based on EFEA theory, and model parameters are obtained through experiments and effective simulations. Mechanical and acoustic excitation sources of the high-speed train are extracted through multi-body dynamic simulation, acoustic finite element method and nonlinear acoustic solution. The reliability of predicted excitations is validated by verifying the amplitude peak frequency bands. The interior noise responses at several observations are obtained and in good agreement with on-site test results, and the EFEA prediction model of interior noise is verified accurately. Secondly, the energy contribution of exterior excitation sources to interior noise responses is analyzed based on EFEA model. The results indicate that acoustic excitations dominate the acoustic energy in the frequency bands above 800 Hz, and the energy contribution of mechanical excitations and acoustic excitations is relatively close in the frequency bands below 600 Hz. The contribution of aerodynamic noise excitations is predominantly concentrated in the frequency bands below 500 Hz, while that of wheel-rail noise excitations is concentrated in the frequency bands above 800 Hz. In the frequency bands below 1250 Hz, the energy contribution of rail noise excitations is much more important than that of wheel noise excitations, while their contribution gets gradually close in the frequency bands above 1600 Hz. Besides, the acoustic excitations at the bottom of the carriage are quite important sources. Finally, wheel-rail noise excitations are optimized with spoke-shielding damping wheels and dynamic vibration absorbers, and the sound pressure level in the amplitude peak bands is reduced by about 8 dB. The interior noise responses are reduced by about 3-5 dB in the frequency bands above 800 Hz, and total loudness, sharpness and roughness at different locations are decreasing by more than 1 sone, 0.05 acum and 0.03 asper respectively. Therefore, wheel-rail noise optimizations have good reduction effects on interior noise responses in the high frequency bands. (C) 2019 Elsevier Ltd. All rights reserved.
机译:与传统的有限元分析和统计能量分析相比,能量有限元分析(EFEA)的优点是计算量小并且可以解决大型复杂结构中的局部响应。本文将结构隔音效果引入到EFEA中,以预测和分析高速列车中挤压结构的内部噪声响应。首先,基于EFEA理论建立了滑架的结构和空腔模型,并通过实验和有效的仿真获得了模型参数。通过多体动力学仿真,声学有限元方法和非线性声学解法,提取了高速列车的机械和声学激励源。通过验证振幅峰值频带来验证预测激励的可靠性。获得了几处观测结果的内部噪声响应,并与现场测试结果吻合良好,并准确验证了EFEA内部噪声预测模型。其次,基于EFEA模型分析了外部激励源对内部噪声响应的能量贡献。结果表明,在800 Hz以上的频带中,声激励占主导地位,在600 Hz以下的频带中,机械激励和声激励的能量贡献相对较弱。空气动力学噪声激励的贡献主要集中在500 Hz以下的频段,而轮轨噪声激励的贡献主要集中在800 Hz以上的频段。在1250 Hz以下的频带中,轨道噪声激励的能量贡献比车轮噪声的激励更为重要,而在1600 Hz以上的频带中,它们的贡献逐渐接近。此外,滑架底部的声激励是非常重要的来源。最后,使用辐条屏蔽阻尼轮和动态减振器优化了轮轨噪声激励,并且振幅峰值带中的声压级降低了约8 dB。在高于800 Hz的频带中,内部噪声响应降低了大约3-5 dB,并且在不同位置的总响度,清晰度和粗糙度分别降低了1 sone,0.05 acum和0.03 asper。因此,轮轨噪声优化对高频带的内部噪声响应具有良好的降低效果。 (C)2019 Elsevier Ltd.保留所有权利。

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