轿车车身低频声振特性分析与车内噪声多目标优化

发布时间：2019-02-09 17:39

【摘要】：随着汽车性能的不断提高,乘坐舒适性越来越受到人们的重视。汽车NVH特性作为评价乘坐舒适性的重要指标,也越来越多地引起汽车厂商和研究人员的关注。车内低频噪声主要来源于车身结构的振动,因此分析车身结构振动特性和有效控制低频噪声显得格外重要。本文为了有效控制车内低频噪声,结合产学研合作项目,以某轿车为研究对象,对轿车车身声固耦合系统进行了频率响应分析,研究了阻尼复合结构的振动特性,通过模态应变能方法确定阻尼层敷设位置及区域,最后通过多目标优化来有效降低车内低频噪声。首先,建立了某轿车车身的有限元模型,并进行了结构模态分析,在此基础之上建立了封闭车身的有限元模型,并与试验对比确定了模型的有效性。此外还构建了声腔有限元模型并进行了模态分析,将封闭车身有限元模型与声腔有限元模型进行耦合,最终建立了车身声固耦合模型。在不同典型工况下对车身进行了频率响应分析,并对驾驶员及后排右侧乘员耳右旁声压级响应进行了仿真结果与试验结果的对比,从而验证了所建模型的有效性。其次,论述了阻尼复合结构的动力学特性分析方法,分别对自由阻尼结构和约束阻尼结构的有限元建模方法进行了讨论,并研究了不同阻尼结构参数对结构振动特性的影响,对比了两种阻尼结构的耗能效果。通过计算车身关键板件的模态应变能,提取模态应变能较大的区域进行阻尼结构的敷设,对比了新方案与原方案车内噪声控制情况,证明了基于模态应变能敷设阻尼结构的方案是有效和可行的。最后,为了确定车身关键板件敷设阻尼复合结构的最佳厚度,通过拉丁超立方试验设计方法,建立了阻尼厚度与驾驶员右耳旁声压级响应均方根值和敷设阻尼质量的近似模型,分析比较了近似响应面模型、Kriging模型和径向基神经网络模型的预测精度,基于径向基神经网络近似模型通过遗传算法进行了车内噪声多目标优化,根据优化结果对车身结构进行阻尼结构的敷设,并在不同工况下对优化前后车内驾驶员右耳旁声压级响应优化前后进行了对比,对比结果表明了车内低频噪声都有所降低,有效地改善了车内噪声环境。
[Abstract]:With the improvement of automobile performance, people pay more and more attention to ride comfort. As an important index to evaluate ride comfort, automobile NVH characteristics have attracted more and more attention from automobile manufacturers and researchers. The low frequency noise in the vehicle mainly comes from the vibration of the body structure, so it is very important to analyze the vibration characteristics of the body structure and to control the low frequency noise effectively. In this paper, in order to effectively control the low frequency noise in the car body, the frequency response of the acousto-solid coupling system of the car body is analyzed, and the vibration characteristics of the damping composite structure are studied. The location and area of damping layer are determined by modal strain energy method, and the low frequency noise is effectively reduced by multi-objective optimization. Firstly, the finite element model of a car body is established, and the structural modal analysis is carried out. On the basis of this, the finite element model of the closed body is established, and the validity of the model is determined by comparing with the experiment. In addition, the sound cavity finite element model is constructed and modal analysis is carried out. The closed body finite element model is coupled with the sound cavity finite element model, and finally the body acoustic structure coupling model is established. The frequency response of the car body is analyzed under different typical working conditions, and the simulation results of the sound pressure level response of the driver and the right ear of the rear row crew are compared with the experimental results, which verifies the validity of the proposed model. Secondly, the dynamic characteristic analysis method of damping composite structure is discussed, the finite element modeling method of free damping structure and constrained damping structure is discussed, and the influence of different damping structure parameters on structural vibration characteristics is studied. The energy dissipation effects of two damping structures are compared. By calculating the modal strain energy of the key parts of the body, the damping structure is laid in the area where the modal strain energy is high, and the noise control situation of the new scheme is compared with that of the original scheme. It is proved that the scheme based on modal strain energy is effective and feasible. Finally, in order to determine the optimum thickness of damped composite structure, an approximate model of damping thickness, RMS value and damping mass of driver's right ear sound pressure level response is established by using Latin hypercube design method. The prediction accuracy of the approximate response surface model, the Kriging model and the radial basis function neural network model are analyzed and compared. Based on the radial basis function neural network approximation model, the vehicle interior noise multi-objective optimization is carried out by genetic algorithm. According to the optimization results, the damping structure of the body structure is laid, and the sound pressure level response of the driver's right ear is compared before and after the optimization under different working conditions. The comparison results show that the low frequency noise in the vehicle has been reduced. The noise environment in the vehicle is improved effectively.
【学位授予单位】：吉林大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：U463.82

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