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Low-noise structure optimization of a heavy commercial vehicle cab based on approximation model

机译:基于近似模型的重型商用车箱的低噪声结构优化

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摘要

Structure design for reducing noise is essential and widely studied because cab interior noise seriously affects the ride comfort of the driver and passengers, especially for heavy commercial vehicles. This paper proposes an enhancement investigation on the low-noise structure performance of a cab in a heavy commercial vehicle based on an approximation model. A series of vibroacoustic tests with varying running speeds are implemented first to acquire the vibration signals at four cab suspensions and the sound pressure signal at the right ear of the driver. The finite element model and structure-acoustic coupled model of the cab of a heavy commercial vehicle are established sequentially. When the vibration accelerations measured in the tests are converted into excitation signals using the cab structure-acoustic coupled model, the sound pressure of the right ear of the driver is predicted, and the accuracy of the cab structure- acoustic coupled model is then verified. After panel acoustic contribution analysis, an approximation model of critical panel thickness and the peak noise near the right ear of the driver is established in accordance with the radial basis function. Genetic algorithm is utilized to solve an optimization model to obtain optimal panel thicknesses. By comparing the right ear sound pressure before and after optimization, the results confirm that optimized panel thicknesses can reduce sound pressure level of the critical frequency.
机译:降低噪声的结构设计是必不可少的和广泛的研究,因为驾驶室内部噪音严重影响了驾驶员和乘客的乘坐舒适,特别是为重型商用车辆。本文提出了基于近似模型的重型商业车辆中驾驶室的低噪声结构性能的增强研究。首先实现具有不同运行速度的一系列具有不同运行速度的Vibro声学测试,以在驾驶员的右耳处的四个驾驶室悬架和声压信号处获取振动信号。依次建立重型商业车辆驾驶室的有限元模型和结构 - 声学耦合模型。当在测试中测量的振动加速器使用驾驶室结构声学耦合模型转换成激励信号时,预测驾驶员的右耳的声压,然后验证驾驶室结构声耦合模型的精度。在面板声学贡献分析之后,根据径向基函数建立临界面板厚度的近似模型和驾驶员右耳附近的峰值噪声。利用遗传算法来解决优化模型以获得最佳面板厚度。通过在优化前后的右耳声压进行比较,结果证实优化的面板厚度可以降低临界频率的声压级。

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