Water-based friction modifiers (FMs) are widely used in wheel-rail friction management. They may suffer from suspension stability degradation, but the influence of this on their tribological performance has not been systematically clarified. In this study, the performance evolution of a water-based FM during suspension stability degradation was investigated using a twin-disc rolling-sliding wear tester. Friction control performance, wear rates, and rolling contact fatigue (RCF) damage were evaluated for undegraded and degraded FM samples collected from the top and bottom layers of their container post ”settling”. The results show that all FM samples reduced the friction coefficient compared with the dry condition, while the corresponding wear and RCF responses varied markedly with degradation severity and sampling layer. The undegraded FM exhibited a balanced performance, reducing both wear and RCF damage. Under the slightly degraded stage, the top- and bottom-layer samples exhibited similar friction control and wear behaviour to each other, but both produced severe RCF damage. Under the moderately and severely degraded stages, pronounced differences emerged between the top-layer and bottom-layer samples, and these differences became more significant with increasing degradation severity. In general, the tribological balance of the FM deteriorated after degradation, and in some cases led to increased wear and aggravated RCF damage.

High-speed rail grinding train operates at high speed. If grinding wheel faults such as cracking and chipping exist, they may seriously compromise the grinding quality and safety. In the past, fault identification accuracy of real-time online monitoring of grinding wheels based on local mean decomposition (LMD) of vibration signal was limited by the end effects of traditional LMD. To identify grinding wheel faults timely and accurately, an improved LMD method for grinding wheel was proposed. First, grinding experiments using grinding wheels with typical faults, including single crack, two cracks, and single chipping, were conducted to obtain the vibration characteristics of damaged grinding wheels. Then, a signal extension method based on waveform matching was established to solve the end effects problem of traditional LMD. Finally, fault diagnosis of high-speed rail grinding wheels was realized based on the improved LMD and support vector machine (SVM). The results show that the improved fault diagnosis method solved the fault underreporting problem that exists in traditional method. The comprehensive accuracy of recognizing the three typical faults increases from 84.62% to 94.87%. Specifically, the recognition accuracy for the most commonly seen fault type of single crack increases by 15.38%, which significantly alleviates the low recognition accuracy problem of traditional methods and ensures the safety and quality of high-speed grinding.

In order to reduce turnout rail wear, the paper establishes a coupled dynamics model and a turnout rail wear model that consider the true profile of the turnout rail, the vehicle’s continuous traction force while passing, and the operational resistance. Comparative analysis of various models for predicting turnout rail wear indicates that the wear energy model is better suited for this purpose. The ideal profile update step for the turnout rail is 0.05 mm, and the adaptive filtering algorithm, tailored to the turnout characteristics, smooths wear distribution effectively while retaining crucial data features. The wear model developed in the paper predicts wear depth that is 89.91% consistent with the measured data. The grinding model introduced in the paper significantly enhances the wheel-rail contact conditions in the turnout, with lateral and vertical vibration accelerations of the vehicle reduced by 52.56% and 30.43%, respectively, during turnout passage. The research offers theoretical support for mitigating rail wear in high-speed turnouts.

In order to study the influence of rack railway irregularity on the rack railway engineering vehicle, the dynamic model of the rack railway engineering vehicle is established, and the simulation data are compared with the experimental data to verify the accuracy of the dynamic model. With the split and integral models respectively established for the rack railway, the influence of the rack railway modeling method is studied, and the dynamic response law of the rack railway engineering vehicle under the rack railway irregularity and different running speeds is analyzed. The results show that compared with the integral model, the split model is more accurate in simulating the dynamic characteristics of the rack railway engineering vehicle. The step irregularity of the rack railway will increase the radial impact force of the rack and gear, leading to more serious unloading of the wheelset.

Taking into account the talent demand in the equipment manufacturing sector within Sichuan's "5+1" and Chengdu's "5+5+1" industrial systems, Southwest Jiaotong University (SWJTU), leveraging its academic strengths and educational assets in rail transit, and embracing the concept of "open education," has embarked on the exploration and implementation of the industry-university-research-application model for rail transit construction equipment with leading enterprises in the rail transit industry. SWJTU fully collaborates with industries and enterprises in the top-level design of talent training, faculty involvement, curriculum development, practical resources, scientific research collaboration, and the implementation of research outcomes. By aligning with industry needs through adjusting professional focuses, optimizing the curriculum framework, forming a team of dual-qualified teachers, engaging enterprise mentors fully in curriculum development and teaching, establishing joint research facilities, fostering research collaborations, and facilitating the exchange of achievements between the university and enterprises, SWJTU has established a talent training model for rail transit construction equipment that integrates education, industry, employment, and entrepreneurship.

Sanding with hard particles is an effective method to improve wheel-rail adhesion under low-adhesion conditions. However, the lack of unified standards for sanding parameters necessitates further investigation into their optimization. This study examined the effects of sanding application parameters on adhesion restoration and surface damage using a twin-disc wheel-rail rolling contact testing machine. The results showed that particle distribution density as the most critical factor influencing adhesion restoration, outweighing the effects of particle size and material. With the increase in particle distribution density, the wheel-rail adhesion coefficient (adhesion restoration amplitude), wear rate and material damage increased sharply at first and then stabilized after surpassing a threshold (approximately 0.607 g/m). Additionally, the restoration duration (adhesion coefficient remained in a proper amplitude) increased almost linearly with an increase in particle distribution density. The influence of particle size on adhesion restoration amplitude depended on particle distribution density, affecting the sensitivity of the adhesion coefficient to density changes. While Alumina exhibited better adhesion restoration effect (restoration amplitude and duration) than Silica sand, it resulted in significantly greater surface damage. Furthermore, to facilitate field applications, an empirical equation was developed to evaluate adhesion restoration amplitude. Based on this equation, a graded control strategy for sanding amounts (0.6-2.2 kg/min) was proposed, related to train operating speeds ranging from 20 to 120 km/h.

The operation of rail grinding vehicle is complex, involving theories of different disciplines such as dynamics, tribology, contact mechanics, hydraulics and control theory. To study the rail grinding process, a multi-software co-simulation model is established for the rail grinding vehicle is established in this paper. It includes four sub-models: dynamic sub-model of grinding trolley dynamics, rail-grinding wheel contact sub-model, hydraulic sub-model and control sub-model. The dynamic sub-model of grinding trolley can present the vertical displacement of grinding wheel, and the hydraulic system can present the contact pressure change according to the vertical displacement. According to the pressure change, the contact force and friction force between rail and grinding wheel are fed back to the dynamic sub-model to realize data circulation feedback. Finally, the dynamic parameters and hydraulic parameters are analyzed to verify that the model can reflect the real rail grinding process.

To study the tread surface hardness difference that caused by wheel diameter difference of wheelsets, an investigation on corresponding factors of wheelset hardness was done. A group of data including wear rate, wheel diameter, wheel rim hardness and tread hardness was collected to serve as the dataset for neural network. Improved extreme learning machine (ELM) based on particle swarm optimization (PSO) algorithm was chosen to be the main method, trained by dataset and used to predict the tread surface hardness difference. The result shows that PSO-ELM is able to describe the changing trend of tread surface hardness difference, and reaches the best correlation level compared to the original ELM and BP network. Finally, the trained network was applied to analyze the relation between tread surface hardness difference, revealing that the rolled steel is a less sensitive material than the casted steel when meeting hardness or diameter difference.

To mitigate steel rail wear in turnouts, this study examines wear mechanisms and influencing factors in high-speed turnouts. We establish a coupled dynamics model to analyze vehicle-turnout interactions, incorporating actual turnout rail profiles, continuous traction forces from passing vehicles, and running resistance. This study evaluates the suitability of various wear models, update procedures, wear superposition methods, and filtering techniques for predicting turnout rail wear. A predictive model for turnout rail wear is developed, and the influence of different turnout parameters on rail wear is investigated. Computational results indicate that the wear power model provides superior accuracy and efficiency in predicting turnout rail wear. A depth update step of 0.05mm proves optimal for turnout steel rail wear, while an adaptive filtering algorithm tailored to turnout wear characteristics effectively filters rail wear. Compared to traditional models, our dynamic model, which accounts for traction-resistance, closely aligns with actual measured data, demonstrating higher computational accuracy. Excessive longitudinal and lateral stiffness during vehicle passage through turnouts is found to be detrimental to reducing turnout steel rail wear. Conversely, appropriately increasing anti-snaking damper stiffness and lateral damper damping can mitigate rail wear. Based on the distribution of rail wear in the turnout area, an holistic grinding scheme was formulated. This scheme effectively improved the wheel-rail contact conditions in the turnout area, reducing the lateral vibration acceleration of vehicles passing through the turnout by 52.56% and the vertical vibration acceleration by 30.43%. The holistic grinding scheme can effectively improve the smoothness of the turnout and reduce vehicle vibrations when passing through. This research provides a theoretical basis for suppressing rail wear in high-speed turnouts, effectively reducing the maintenance costs of turnout rails, and has significant implications for improving the operational safety and economic benefits of high-speed trains.

With railways transitioning into the maintenance era, this study proposes a method to ensure structural safety and control maintenance costs. We develop a Peridynamics (PD) fatigue crack growth prediction model to determine the safe crack threshold for rail steel material under cyclic loading, known as the fatigue damage tolerance size. By conducting fatigue tests, we obtain the PD parameters for the rail steel and rigorously validate the model's reliability. Using both the compact tensile (CT) specimen model and a transient wheel-rail contact model, we analyze factors influencing crack propagation and fatigue damage tolerance. The results reveal that higher cyclic loads and smaller stress ratios result in shorter fatigue life and smaller damage tolerance sizes. Additionally, larger initial crack angles, faster train speeds, and heavier axle loads reduce fatigue crack propagation life and damage tolerance sizes. Ultimately, based on damage tolerance analysis, it is recommended to limit crack propagation in U71Mn rail steel within a 3.8-millimeter safety range for reliable operation.

The fatigue crack growth-rate test of rail head, waist, and bottom material for U71Mn welded rail was carried out. Digital image correlation (DIC) was used to capture the full-field displacement. The crack-tip position was accurately obtained based on the full-field displacement data, and an accurate crack-tip opening displacement (CTOD) measurement point was found. The CTOD values of the welded rail head under overloaded and unloaded condition were extracted, and the area size of elastic CTOD and plastic CTOD was obtained. According to COD data under different experimental conditions, the corresponding crack opening force was extracted, the crack opening function introduced based on the Elber model, and a calculation method of effective stress-intensity factors (SIFs) considering the plasticity-induced crack closure proposed. The results in this paper provide some references for accurately assessing the fatigue life of welded rail.

我国建设中的都江堰至四姑娘山齿轨铁路位于地震频发地带,齿轨车辆运行中很有可能遭遇地震.为研究地震情况下护轨对齿轨车辆运行安全性的影响,通过多体动力学软件 SIMPACK 对地震时不设护轨、仅单侧设置护轨以及两侧均设置护轨等情况下齿轨车辆进行仿真.仿真结果表明,在地震激励下,当不设护轨或仅单侧设置护轨时,齿轨车辆车轮均爬上钢轨而脱轨;在钢轨两侧均设置护轨时,齿轨车辆未发生脱轨.说明在两侧钢轨内侧同时设置护轨可有效增加地震情况下齿轨车辆运行安全性,降低车辆脱轨风险.

针对多边形车轮运行过程中的曲线工况和轨道激励问题,建立多边形磨耗模型,分析了曲线工况和轨道激励对多边形演变趋势的影响.仿真结果表明:相较于基于局部法的Archard模型,基于全局法的磨耗功模型计算效率更高,两种模型多边形磨耗演变趋势相同;轨道激励会显著增加轮轨垂向力;曲线工况和轨道激励不会改变车轮多边形演变的趋势.

27 t轴重的侧架交叉支撑转向架和副构架径向转向架是中国最近研制的两种重载货车转向架.为研究比较两转向架的曲线性能,分析曲线几何参数、轨道谱激励对不同类型转向架轮轨动力的影响特性,综合考虑转向架结构形式、技术参数和重载曲线轨道相关要求,建立重载货车-轨道耦合动力学模型和曲线参数化模型.结果表明,副构架径向转向架曲线性能在小半径曲线(≤800 m)线路上具有相对优势,曲线半径越小,优势越明显,但增大曲线半径和施加线路谱激励均会弱化其优势;两种转向架对外轨超高和缓和曲线长度变化的动力响应趋势基本一致,都在欠超高(0～15 mm)范围内轮轨综合响应较小;缓和曲线长度对两者均存在拐点,且拐点近乎相同,如当速度为80 km/h,曲线半径为800 m时,计算拐点都是约50 m,与TB 10627—2017《重载铁路设计规范》标准中规定的缓和曲线长度最小取值一致.

车辆在运行过程中,车轮的圆周廓形并非一直保持不变,在各种激励作用下,逐渐呈现多边形化,并可能出现多边形相位差现象.为研究车轮多边形的相位差对动力学性能的影响,借助磨耗仿真的手段研究了多边形车轮存在相位差时的磨耗演变行为.计算结果表明:同一轮对存在单侧多边形现象时,多边形的影响会传递到轮对的另一侧没有多边形的车轮;当轮对两侧均有多边形现象,且车轮的一侧波峰与另一侧的波谷相对应时,两侧轮轨垂向力均达到最小;当轮对两侧车轮都有多边形现象且存在相位差时,一侧车轮的圆周最大磨耗速度加剧,且随着相位差的增加磨耗加剧的车轮在轮对两侧交替出现,并当多边形车轮的一侧多边形波峰与另一侧车轮的波谷相对应时,两侧车轮磨耗速率均达到最大.

为研究普速铁路12号单开道岔在列车载荷下关键区域的动力学响应特征,结合岔区动力学性能的特点,对渝怀铁路一组普速道岔进行现场试验,通过在道岔转辙器区尖轨、导曲线部分和辙叉区叉心位置布置测点,利用动态响应采集系统采集应力-应变和振动加速度信号,并通过轮轨力标定系统反演轮轨作用力,从时域和频域两方面揭示轮轨作用力和振动加速度响应特征.试验结果表明,列车机车通过道岔转辙器区和辙叉区时会产生轮轨作用力和振动加速度峰值,其中直向过岔时辙叉区轮轨垂向力峰值约为150 kN,叉心处垂向振动加速度峰值约为400 m/s2,主频为295 Hz,振动能量较为集中;侧向过岔时转辙器区轮轨横向力峰值约为70 kN,尖轨处横向振动加速度峰值约为60 m/s2,主频不明显,振动能量在频域内分布较分散,叉心处振动加速度主频集中在450～500 Hz内.脱轨系数呈现先降低后升高的趋势,最大值为0.64,出现在转辙器区.

基于车辆-轨道耦合动力学理论,根据27 t轴重C80E型重载货车和重载铁路曲线轨道的结构特点,建立重载货车-轨道耦合动力学模型和曲线轨道参数化模型.通过分析各悬挂刚体部件受力及其相对位移,推导出相应悬挂力/力矩的表达式,对重载货车通过曲线时的悬挂力、斜楔摩擦力和轮轨力进行仿真计算,并比较分析摇枕侧滚运动对货车悬挂及轮轨动力作用的影响.结果表明:在直缓点、缓圆点等曲线连接点附近,曲线点头角和侧滚角的突变会引起货车垂向冲击振动;货车以80 km/h速度通过R800 m曲线时,垂向悬挂力和轮轨力增载率分别为5.94％和8.53％;摇枕侧滚运动对货车纵向和横向悬挂力的影响较小,可一定程度上衰减货车垂向冲击振动,但对其最大峰值影响不大.

针对过度磨耗钢轨的打磨,提出一种以圆弧切点为关键参数的钢轨廓形设计方法;以轮轨接触位置为优化区域,以钢轨磨耗和打磨材料去除量作为优化目标函数,以廓形边界范围、凹凸性、脱轨系数和轮轨横向力为约束条件,建立磨耗钢轨打磨设计廓形多目标函数;集成多元模拟退火寻优算法进行求解;为了得到能代表重载线路曲线区段的钢轨廓形,作为优化的输入数据,采用最小二乘距离算法、算术平均算法、加权平均算法和散点重构算法得出4种钢轨代表廓形;使用Pearson相关系数、Kendall秩相关系数和Spearman秩相关系数计算出4种算法的钢轨代表廓形与实测廓形接触点概率分布曲线的相关性,取相关性最高的代表廓形为等效重载线路曲线区段的实际廓形;对某重载线路过度磨耗钢轨的经济性打磨廓形以及采用圆弧型廓形设计方法的优化廓形进行分析.分析结果表明:优化廓形与现场打磨廓形相较,截面廓形磨削量减少69.56 mm2,下降64.98％,脱轨系数小幅增大,轮轨横向力基本不变,轮对横移变化较小,曲线通过性能相近,80万次通过量下的磨耗面积增加2.19 mm2,钢轨的磨耗速率略微增大,整体仍延长了钢轨寿命.

针对目前轮轨接触斑及接触应力分布难以有效检测的问题,基于接触面的准静态弹簧模型与超声波反射法,设计一种适用于静态轮轨接触状态检测的系统.系统采用水浸式点聚焦超声探头进行检测,主要由机械结构部分、超声波激励采集系统和接触斑及应力分析显示系统组成.机械结构部分包括轮轨加载机构和两轴扫描机构,分别实现对轮轨的固定与加载,以及夹持超声探头进行平面扫描运动;超声波激励采集系统实现对扫描机构的运动控制以及超声波信号的激励、采集和传输,并与接触斑及应力分析显示系统通过以太网通信,实现命令接收与超声波数据的上传;接触斑及应力分析显示系统实现超声数据的接收与处理,并实时显示和存储检测结果.利用所搭建的检测系统首先进行标定实验,建立超声声压反射系数与接触应力之间的关系,然后进行轮轨接触斑与应力分布检测实验,获得了20～70 kN载荷下车轮试件与钢轨的接触斑及应力分布云图,最后采用3次样条插值处理优化了检测效果.实验结果表明:所提出的检测方法与系统能够有效检测静态轮轨接触斑几何形状与接触应力的分布情况,反映真实的轮轨接触状态.对于轮轨的优化设计、寿命预测和轨道维护等研究都具有重要作用和意义.