【作者】 贾宏杰；

【导师】 张俊红；

【作者基本信息】 天津大学 ， 动力工程及工程热物理， 2021， 硕士

【摘要】 随着社会科技的不断发展,人们生活水平的日益提高,在对出行工具的选择上不仅仅参考车辆的动力性、稳定性、经济型,而是更加关注车辆的乘坐品质。针对车辆的乘坐舒适性一般用NVH来进行衡量,一辆汽车的NVH性能好坏往往决定了车辆的品质高低。对于轻型客车这类以乘员为核心的运输车辆,乘员乘坐轻型客车的时间往往高达几小时,这就对轻型客车的乘坐舒适性提出了更高的要求。因此,开展轻型客车全频段噪声识别与优化具有十分重要的意义。在基于传递路径分析对车内低频噪声源进行识别的过程中,为了削减OPAX在信号采集过程干扰噪声的影响,提高分析精度,本文提出一种基于自适应变分模态分解和巴氏距离的优化OPAX方法。考虑到多尺度模糊熵能够较好地表征非稳态复杂信号,将其作为适应度函数;采用模拟退火粒子群算法进行信号自适应变分模态分解,最后通过巴氏距离计算原始信号和分解信号概率密度函数的相似性进行相关模态筛选,实现信号去噪。结果表明,优化后OPAX计算值的信噪比提高了71.2%,均方根误差减小了66.9%,在峰值频率处的误差均控制在5%以内。最后,提出一种利用相干功率谱识别主要噪声频率,以确定车辆低频噪声源。在基于统计能量法的车内中高频噪声识别过程中,在前围、地板的结构等效子模型的基础上建立整车的统计能量分析模型。在统计能量分析模型中对车内三个目标点进行噪声贡献度分析,分析结果表明车内噪声主要由直达声和透射声导致,占比高达70%。在中高频噪声优化过程中,从直达声和透射声两个方面开展优化,依据分析结果针对各位置的透射声,通过增加EVA材料实现噪声的降低。根据车辆低频、中高频提出的噪声优化方案,在实车上进行相应的改进优化,经过实车测试对优化结果进行验证,实验结果表明,车内三个目标点的噪声值由原先的76.98 d B、76.13 d B、76.93 d B下降到70.34 d B、69.84 d B、69.82 d B,降噪效果显著。更多还原

【Abstract】 With the continuous development of social science and technology and the increasing improvement of people’s living standards,the choice of travel tools does not only refer to the power,stability,and economy of the vehicle,but pay more attention to the ride quality of the vehicle.The ride comfort of a vehicle is generally measured by NVH.The NVH performance of a car often determines the quality of the vehicle.For light-duty passenger vehicles such as passenger-centric transportation vehicles,the occupants often take up to several hours,which puts forward higher requirements for the ride comfort of light-duty passenger vehicles.Therefore,it is of great significance to carry out the optimization and control of the full-frequency noise of light passenger vehicles.In the process of identifying low-frequency noise sources in the car based on the transmission path analysis,in order to reduce the influence of OPAX’s interference noise during the signal acquisition process and to improve the analysis accuracy,an optimized OPAX method based on adaptive variational modal decomposition and Bhattacharyya distance is proposed.Considering that the multi-scale fuzzy entropy can better characterize the unsteady complex signal,it is used as the fitness function;the simulated annealing particle swarm algorithm is used to carry out the signal adaptive variational modal decomposition,and finally the original signal and decomposition are calculated by the Bhattacharyya distance The similarity of the signal probability density function is screened in the relevant modal to achieve signal denoising.The results show that the signal-to-noise ratio of the OPAX calculated value after optimization is improved by 71.2%,the root mean square error is reduced by 66.9%,and the error at the peak frequency is controlled within 5%.Finally,a method is proposed to identify the main noise frequency by using the coherent power spectrum to determine the lowfrequency noise source of the vehicle.In the process of identifying mid-and high-frequency noise in the vehicle based on the statistical energy method,a statistical energy analysis model of the entire vehicle is established on the basis of the structural equivalent sub-models of the front wall and the floor.In the statistical energy analysis model,the noise contribution degree of the three target points in the car is analyzed.The analysis results show that the noise in the car is mainly caused by direct sound and transmitted sound,which accounts for up to70%.In the mid-and high-frequency noise optimization process,optimization is carried out from two aspects of direct sound and transmitted sound.According to the analysis results,the noise is reduced by adding EVA material for the transmitted sound at each position.According to the noise optimization scheme proposed by the low frequency and high frequency of the vehicle,the corresponding improvement and optimization are carried out on the actual vehicle,and the optimization results are verified through the actual vehicle test.The experimental results show that the noise value of the three target points in the vehicle has the original 76.98 d B,76.13 d B,76.93 d B down to 70.34 d B,69.84 d B,69.82 d B.The noise reduction effect is significant.更多还原