无缝线路模型轨道的流变特性实验

通过对模型轨道的实验，验证了无缝线路（ＣＷＲ）流变理论模型的正确性，并为将来进行实物轨道的流变特性实验提供了具体步骤和确定流变理论参数的方法。     

谐振式轨道电路电气绝缘节在无碴轨道中损耗的研究

无碴轨道谐振式无绝缘轨道电路传输距离的缩短,直接影响行车安全。为达到延长轨道电路传输长度的要求,通过分析和计算对电气绝缘节在轨道电路中损耗进行了研究。     

跨座式单轨交通PC轨道梁预制技术研究及应用

跨座式单轨PC轨道梁为"梁轨合一"结构,线路的平、竖曲线、横向超高以及预拱均在一次浇筑的梁体上实现,其线形及精度直接决定着列车行驶的舒适性.芜湖市单轨项目在国内无成熟经验可借鉴的情况下创新施工方案,设置直曲线梁专用生产线、研发智能三维可调节模板、研究线形动态控制方法,得以成功制造出高标准的轨道梁,并总结出成熟的工艺及方法,具有推广价值.     

Analytical extension of the effective axle characteristics concept for the development of a structured chassis design process

The lateral vehicle dynamics is defined by the effects of side forces at the front and rear axle. These forces are caused by the slip and camber angle at the individual tyres, which are results of the kinematics and compliances of the chassis. This paper extends the approach of the effective axle characteristics by Paceyka to the analytical expression of the axle cornering stiffness and the axle relaxation behaviour with the aim of the development of a chassis design process as it applies in the early design stage. The obtained expression is integrated into a single track model and validated against a full nonlinear two-track model. By this means of these analytical expressions for the axle cornering stiffness and the axle relaxation behaviour it is possible to directly calculate and analyse the effective slip angles for linear quasi-static and dynamic driving manoeuvres.     

Extended applications of track irregularity probabilistic model and vehicle–slab track coupled model on dynamics of railway systems

Track irregularities are inevitably in a process of stochastic evolution due to the uncertainty and continuity of wheel–rail interactions. For depicting the dynamic behaviours of vehicle–track coupling system caused by track random irregularities thoroughly, it is a necessity to develop a track irregularity probabilistic model to simulate rail surface irregularities with ergodic properties on amplitudes, wavelengths and probabilities, and to build a three-dimensional vehicle–track coupled model by properly considering the wheel–rail nonlinear contact mechanisms. In the present study, the vehicle–track coupled model is programmed by combining finite element method with wheel–rail coupling model firstly. Then, in light of the capability of power spectral density (PSD) in characterising amplitudes and wavelengths of stationary random signals, a track irregularity probabilistic model is presented to reveal and simulate the whole characteristics of track irregularity PSD. Finally, extended applications from three aspects, that is, extreme analysis, reliability analysis and response relationships between dynamic indices, are conducted to the evaluation and application of the proposed models.     

New methodology for fast prediction of wheel wear evolution

In railway applications wear prediction in the wheel–rail interface is a fundamental matter in order to study problems such as wheel lifespan and the evolution of vehicle dynamic characteristic with time. However, one of the principal drawbacks of the existing methodologies for calculating the wear evolution is the computational cost. This paper proposes a new wear prediction methodology with a reduced computational cost. This methodology is based on two main steps: the first one is the substitution of the calculations over the whole network by the calculation of the contact conditions in certain characteristic point from whose result the wheel wear evolution can be inferred. The second one is the substitution of the dynamic calculation (time integration calculations) by the quasi-static calculation (the solution of the quasi-static situation of a vehicle at a certain point which is the same that neglecting the acceleration terms in the dynamic equations). These simplifications allow a significant reduction of computational cost to be obtained while maintaining an acceptable level of accuracy (error order of 5–10%). Several case studies are analysed along the paper with the objective of assessing the proposed methodology. The results obtained in the case studies allow concluding that the proposed methodology is valid for an arbitrary vehicle running through an arbitrary track layout.     

A locomotive–track coupled vertical dynamics model with gear transmissions

A gear transmission system is a key element in a locomotive for the transmission of traction or braking forces between the motor and the wheel–rail interface. Its dynamic performance has a direct effect on the operational reliability of the locomotive and its components. This paper proposes a comprehensive locomotive–track coupled vertical dynamics model, in which the locomotive is driven by axle-hung motors. In this coupled dynamics model, the dynamic interactions between the gear transmission system and the other components, e.g. motor and wheelset, are considered based on the detailed analysis of its structural properties and working mechanism. Thus, the mechanical transmission system for power delivery from the motor to the wheelset via gear transmission is coupled with a traditional locomotive–track dynamics system via the wheel–rail contact interface and the gear mesh interface. This developed dynamics model enables investigations of the dynamic performance of the entire dynamics system under the excitations from the wheel–rail contact interface and/or the gear mesh interface. Dynamic interactions are demonstrated by numerical simulations using this dynamics model. The results indicate that both of the excitations from the wheel–rail contact interface and the gear mesh interface have a significant effect on the dynamic responses of the components in this coupled dynamics system.     

转换轨及轮径校准对全自动驾驶地铁出入线平纵断面设计要求

综合考虑转换轨及轮径校准的概念与功能,结合全自动驾驶的作业模式,对全自动驾驶地铁出入线转换轨和轮径校准设置必要性、位置及其对线路平纵断面设计要求进行分析,提出:①对于场段与正线控制权不一致的,需设置转换轨进行控制权交接;②对于场段与正线控制权一致的,理论上无需设置转换轨进行驾驶模式转换,但鉴于备用模式下仍需采用GOA3以下级别的驾驶模式,建议现阶段仍需设置转换轨;③转换轨宜设置在车辆基地一端;④轮径校准段需保证35m的平直坡段;⑤转换轨(轮径校准段以外部分)坡度不宜大于24‰,若坡度大于24‰,则需验算一度停车再启动能力,且坡度不得大于35‰。     

基于机器学习的地铁轨道几何劣化规律个性化建模研究

较高的轨道平顺性是保障地铁列车安全舒适运行的基础,准确掌握地铁轨道的劣化规律对保障轨道质量具有重要意义。根据地铁线路特点,选择影响地铁轨道质量劣化的7类异质性因素,给出赋值模型,并基于机器学习方法建立轨道质量指数(track quality index,TQI)短时预测前馈神经网络模型。为了验证模型,采集了北京地铁1号线的线路设备数据及2016年8月15日至2019年2月18日间的17次TQI检测数据,形成训练数据集和测试数据集,并采取深度学习技术,利用训练数据集对该模型进行训练。基于测试数据集的模型预测值的可决系数为0.938,平均绝对百分比误差为4.80%,结果表明该模型是有效的且具有较高的预测精度。     

地铁车辆噪声现状及分析

随着城市轨道交通车辆的快速发展,乘客对地铁车辆的舒适性提出了更高的要求,地铁车辆运营阶段的噪声问题成为目前广泛关注的热点问题,也是亟待解决的问题。文章从地铁车辆的噪声现状入手,分析轮轨相互作用引起的轮轨噪声问题,车体和薄弱环节的隔声与密封问题,以及隧道运行环境会产生混响声场环境差异问题等,为车辆噪声控制提供依据并指明方向。