地铁列车车内低频结构噪声仿真

建立某地铁列车车体结构和车内声腔有限元模型,进行声固耦合模态分析,得到车体结构和车内声腔的模态特征。将车体动力学模型计算得到的车体振动激励施加于声固耦合模型中,分析地铁列车车内低频噪声和车身板件声压贡献量,得到对观察点声压贡献较大的板件,有针对性地提出车体结构改善方案,降低观察点处的噪声,为地铁列车车内噪声优化提供指导。     

开闭件声腔对噪声传递函数分析的影响

为了准确地仿真分析汽车的NVH特性,通常需要准确获取声腔的声学特性参数。以某内饰车身为研究对象,以车内声学特性机理为基础,为探索开闭件声腔模型对噪声传递函数仿真分析的影响,分别建立了传统车内声腔模型的声固耦合系统和附加开闭件声腔的车内声固耦合系统。采用以声腔模态分析、板件贡献量分析、原点动刚度分析三种CAE仿真分析方法并结合所选车型的仿真NTF曲线特点,有针对性地分析了开闭件声腔在声腔建模时需要被考虑的原因。并通过试验验证了模型的准确性和方法的有效性。结果表明,附加开闭件声腔的建模仿真更接近实际情况,使得仿真更准确。     

车身结构与车内空腔流固耦合系统的模态分析

为研究汽车的振动噪声特性,利用ANSYS软件采用板壳、梁和流体单元建立某车身结构及车内空腔的有限元模型,其中,将车身结构作为弹性体,乘员室内的空气作为流体.通过对车身结构、车内空腔流体及车身与车内空腔流体的耦合结构进行模态分析,并对比计算结果,得到它们之间的相互关系,有利于整车振动和噪声特性的研究.     

基于声灵敏度的车身拓扑优化

为探讨板件结构对车内噪声的影响,建立某商务车的白车身有限元模型,分析其模态和车身声灵敏度,确定对汽车低频振动和噪声影响较大的板件;结合变密度法在整车环境下对板件进行减振降噪的拓扑优化,根据拓扑优化结果对板件进行结构修改. 优化后的车身声灵敏度曲线与原车身声灵敏度曲线相比,低频范围内的峰值都有不同程度的降低,表明结合整车动态响应的汽车板件结构拓扑优化可以有效降低车内低频振动噪声.     

G70B列车油罐液固耦合模态分析

针对列车油罐因列车提速导致液固耦合作用突出的问题,运用ANSYS软件对轻型油罐车G70B型罐体的液固耦合模态响应进行实例分析和计算;建立车载油罐的液固耦合系统有限元模型;对轻型油罐车G70B型罐体进行在不同贮液比时的液固耦合模态分析,得出固有频率和模态振型图.结果表明:不同的贮液比对罐体结构的模态特性影响很大,油罐内液体质量越多,油罐结构耦合振动频率越低.分析结果可以为油罐的结构设计提供参考.     

高速列车车厢结构声-振耦合响应数值分析

为研究高速列车运行时结构表面产生的强声压对乘坐环境和结构破坏的影响,针对某型高速列车建立车厢声-振耦合有限元模型,研究车厢的结构模态、室内声场模态及结构-声场耦合系统模态;针对其所处特殊动态环境,计算耦合系统谐响应,考察其振动特点及室内噪声分布情况.计算结果表明,车厢结构低阶模态显示出良好的整体性,在较高频段内以局部模...     

多传感器耦合随机共振子系统的协同响应及其应用

提出了耦合随机共振子系统的协同响应和频谱特性概念,解析和数值地分析了系统参数、噪声强度和耦合系数对单一共振子、耦合和无耦合共振子系统的灵敏度、功率谱放大率和信噪比的影响,构建了基于多传感器耦合随机共振子的微弱信号检测模型.理论分析和数值模拟结果表明多传感器耦合系统能产生更强烈的共振效应,其功率谱放大率和信噪比都能显著提高,并通过轴承故障诊断实例证明,所构建的检测模型对噪声淹没的微弱信号具有较强的检测能力,具有良好的应用前景.     

考虑推进剂晃动的火箭液固耦合分析的比拟算法

对于弹性容器与不可压无黏液体之间的线性耦合问题,已有缩聚对称形式的液固耦合系统有限元方程.利用比拟算法获得液固耦合系统的系统矩阵,将问题转化为通用有限元程序可以解决的问题.以包含贮箱的火箭模型为例,求解火箭的模态特性,其中包括由液体晃动所引起的火箭振动模态.结果表明此类模态与重力加速度有关,频率随重力加速度的增大而增大.     

A型地铁车内噪声分析和优化

为研究轨道车辆轻量化带来的车内振动噪声问题,基于某A型地铁车辆的有限元模型,建立其声学计算模型,以车体板件频率响应的振动位移结果作为声学激励,在车内布置ISO标准场点进行车内噪声分析.结果显示,车内各点声压级在频域上分布极不均匀,且普遍存在几个较大的峰值.分析目标板件的振动,提出几种减振降噪的优化方案.对比各方案发现,增加板件强度后振动和噪声都相应地减小;减振降噪需综合考虑优化后车体的整体强度和动力学性能.     

某轿车前后桥有限元模型分析及试验验证

利用轿车前、后桥的Pro/E三维数模建立其有限元模型,将CAD和CAE有机地结合在一起,提高建模效率. 同时,为了验证模型分析结果的精确度,对前、后桥进行自由模态以及静态加载试验,使该有限元模型能够作为其他工况分析的模型.     

Research on the virtual reality of vibration characteristics in vehicle cabin based on neural networks

A finite element model of commercial vehicles was firstly built in the paper to study the virtual reality of vibration characteristics, and the top 6-order modal was then computed and compared with the experimental results to verify the reliability of the computational model. Then, a neural network model of the cabin was built. Through road tests, the excitation signal at the cabin suspension point and the response signal of interior vibration noise were measured under the idle condition and constant speed condition. The measured excitation signal was applied to the prediction model for frequency response analysis, in order to compute the interior noise within the range 20–200 Hz. The obtained simulation result of the vibration noise was compared with the experimental result and analyzed. As indicated from the analysis, the influence of excitation spectrum and the model can be reflected by the simulation response spectrum, which is consistent with the experimental result. The higher precision can be also obtained when the model is applied to predict the interior noise.

     

基于CDH/EXEL的多孔吸声材料在内饰车身噪声传递函数分析中的应用

在汽车内饰车身噪声频响分析中,通过有限元实体单元和RBE3单元建立多孔吸声材料模型,通过MSC Nastran和结合CDH/EXEL的MSC Nastran计算采用多孔吸声材料的内饰车身的噪声进行计算,并比较二者的计算结果.结果表明：CDH/EXEL能使有效提高分析曲线与试验曲线的对标结果的准确性,从而更好地指导车身结构设计.     

高速列车车内低频气动噪声预测

为研究气动载荷下高速列车的车内低频噪声,建立高速列车空气动力学模型,采用大涡模拟(Large Eddy Simulation,LES)法计算中间车的表面脉动压力.将脉动压力加载到高速列车的有限元模型上,通过瞬态分析得到车体的振动位移响应;将位移响应作为边界条件,采用边界元法(Boundary Element Method,BEM)分析车内噪声.结果表明：车窗振动位移最大,车顶和车底次之;中间车车厢的两端声压比中部大;在低频范围内,车厢内声压呈强弱交替分布,声场强弱界限较明显,且随着频率的增大,沿车体纵向和横向干涉条纹增多;车内低频气动噪声随速度二次方的增大而增加.     

一类非线性系统的自适应滑模模糊控制

针对一类具有多个子系统的欠驱动非线性系统提出了一种自适应滑模模糊控制方法. 首先通过分析模糊控制与边界层滑模控制的相似性,提出了滑模模糊控制方法;然后根据滑模 面斜率和各子系统控制对于系统动态性能的影响,分别采用模糊推理根据系统状态自动地实时 调节滑模面斜率和各子系统在系统控制中的作用;最后通过简单的滑模模糊控制器实现对具有 多个子系统的欠驱动非线性系统的控制.将该方法应用于吊车的运输控制中,仿真结果证明了 其有效性和鲁棒性.     

70%低地板轻轨车车内噪声分析和降噪优化

针对城市轨道交通振动噪声问题突显、制造平稳低噪的城市轨道列车的需求较大的问题,研究70%低地板轻轨车车内噪声.建立无内装70%低地板轻轨车体有限元模型及其声学计算模型;以车体板件频率响应的位移振动结果作为声学激励,在车内布置ISO标准场点,获得车内噪声分析结果.将声压峰值处的频率作为目标频率,分析该频率处各低场点的板件贡献量,并确定目标板件;针对目标板件的振动,制定简便易行的降噪优化方案;对比降噪前后的车内声压值,归纳出一套可行的无内装70%低地板轻轨车体降噪方法.     

Development of a noise prediction software NASEFA and its application in medium-to-high frequency ranges

For the analysis of noise problems in medium-to-high frequency ranges, the energy flow boundary element method (EFBEM) has been studied. EFBEM is numerical analysis method of energy flow analysis (EFA), and solves energy governing equations using a boundary element method in complex structures. Based on EFBEM, a noise prediction software, “noise analysis system by energy flow analysis” (NASEFA), was developed. For effective maintenance, NASEFA is composed of three main modules: the translator, the model converter, and the main solver. The translator changes the FE model to the NASEFA BE model, and the model converter changes the BE model to an EFBE model, including various data, such as structural materials, medium properties, sources, and boundary conditions. NASEFA then solves the acoustic energy density and intensity on boundary and in the field. Moreover, it analyzes interior and exterior noise problems for single and multiple domains in two and three dimensions. Finally, for the validation of the software developed, interior and exterior noise predictions of various structures were performed. The results obtained with NASEFA were compared with those of the commercial SEA program and experiment. From these comparative studies, the usefulness of NASEFA was established.     

噪声环境下的窄带音频信号快速分类方法

提出了一种基于线性判别分析和高斯混合模型的窄带音频快速分类方法,该方法在白噪声、街道噪声和车内噪声环境下都能有效区分语音、音乐和噪声。实验结果表明,该方法在保证分类时间不大于1 s的情况下,分类准确率能达到95%以上。     

基于VA One的飞机舱内噪声预计方法研究

利用基于统计能量分析方法的VA One软件,建立了机身声学试验平台的舱内噪声预计模型。利用该模型,开展了声振激励下不同噪声处理方式的舱内噪声预计。通过仿真及实验结果的对比,验证了模型的有效性,并以此为基础提出了飞机结构子系统划分方法及飞机舱内噪声预计的建模方法,为进一步进行飞机舱内噪声预计提供借鉴。     

Virtual reality of interior noises of vehicles based on boundary element and neural networks

Interior noises of vehicles would be caused when the vibration of body panels excites the indoor air. In the paper, the vibration load of engine was obtained firstly through experiments. Secondly, the engine load was applied in the finite element model of body in white to compute the vibration velocity and realize virtual reality, indicating that the front support of the body had large vibration velocity when the frequency was lower than 60 Hz. The boundary element was then adopted to compute the interior noise and extract the sound pressure at a point near the driver’s head. Two obvious peaks were shown in sound pressure curves, at 270 and 310 Hz, respectively. The body panels that had obvious impact on the interior peak noise were determined through the panel contribution analysis, and the interior peak noise was remarkably reduced after applying sound absorption materials on these panels. Nevertheless, many more additional sound absorption materials were not always better. If a multilayer of sound absorption materials was needed, an optimal value was existed in the thickness of sound absorption material. And a great impact would be played toward the interior noise of the cabin by the reasonable selection of different sound absorption materials and their thicknesses. Finally, the neutral network (NN) was also used to predict interior noises, which was compared with the result of the boundary element. The maximum difference between the prediction values of NN and boundary element was within 5 dB, indicating that the neural network was feasible to predict the interior noise. Subsequently, the neural network method would be applied to conduct the optimization analysis for the interior noise.