Modelling and reducing wear resulting from wheel-rail interaction constitute fundamental aspects in the railway field, primarily associated with ensuring running stability and safety while reducing maintenance interventions and costs. The main focus of this study is to conduct a comprehensive investigation into the development of a stable wheel profile that effectively reduces wear and essentially maintains its initial shape throughout the operation of the vehicle. The primary objective is to enhance the vehicle’s dynamic performance, improve ride comfort for passengers, and ultimately reduce maintenance costs. In addition to these goals, the study also aims at examining the wear depth associated with the proposed wheel profile and analyse its impact on the vehicle’s dynamic behaviour.

Equivalent conicity (EC) in-service has been required in TSI INF and TSI LOC & PAS for several years. However, due to implementation difficulties, only a few infrastructure managers and railway undertakings currently include EC requirements in their maintenance programmes. A novel method called the Gradient Index Profile (GIP), which combines gradient index for wheel (GIPw) and gradient index for rail (GIPr), has been developed to complement and support EC. In this study, based on a large number of on-track measured rail profiles, we investigate the distribution functions of GIPr on tangent tracks. We also examine the correlations of GIPr with track EC with the nominal S1002 wheel profile (EC S1002) and in-service EC with a reference worn wheel profile (EC ref. worn), respectively. Finally, we propose maintenance limit values for GIPr, which should be useful for rail surface management related to vehicle running stability.

High axle load poses several challenges for infrastructure management. The introduction of 30-tonne axle load wagons on the Swedish iron ore line exacerbated rolling contact fatigue challenges. While infrastructure managers have effectively controlled rolling contact fatigue on the high rail of curves through the adoption ofwear-resistant rail profiles and optimized rail grinding practices, mitigating rollingcontact fatigue on the low rail remains a significant challenge. Particularly, tight curves with radii up to 850 meters are prone to spalling defects under widened gaugeconditions. Therefore, this study investigates the impact of gauge widening and wheel profile wear on wheel-rail interaction and rail damage. A multi-body dynamic model of an iron ore wagon is implemented in the GENSYS software environment. Practical degradation parameters relevant to wheel-rail interaction are incorporated for both thevehicle and track. Simulations are conducted under normal and widened gauge conditions to assess the differences in severe gauge widening scenarios. Thesimulation results demonstrate that under widened gauge conditions, rolling contactfatigue on the low rail exhibit considerable increase compared to normal gaugeoperations. The combination of increased wheel hollowness and gauge widening further exacerbates rolling contact fatigue. Moreover, the effect of running speedindicates that reducing speed is advisable to minimize rail damage in widened gauge conditions

Innovations in high-speed rail (HSR) have had substantial effects on different stakeholders within and outside the railway system. As part of the European Shift2Rail research programme, several innovative solutions are developed for, among others, improving the HSR infrastructure. The Joint Undertaking behind this research program has set objectives for these innovations in terms of punctuality, capacity, and life cycle costs. With a focus on infrastructure-related innovations for HSR, this paper aims at assessing their impacts in relation to these targets. We review the relevant research literature about the effects of HSR innovations and their assessment. The paper presents a hybrid assessment methodology combing different approaches to assess capacity, punctuality, and cost effects. This contributes to reducing the existing gap that is found in the research literature. Based on a reference scenario for HSR line and collected data from different stakeholders, the results indicate that infrastructure innovations in HSR, being developed within the European Shift2Rail research programme, can contribute to reaching the target set for punctuality. Further innovations in HSR infrastructure and/or other railway assets may be needed to reach additional targets and for more accurate improvement values giving more insights into their impacts.

This paper is an outcome of an international collaborative research initiative. Researchers from 24 institutions across 12 countries were invited to discuss the state-of-the-art in railway train air brake modelling with an emphasis on freight rains. Discussed models are classified as empirical, fluid dynamics and fluid-empirical dynamics models. Empirical models are widely used, and advanced versions have been used for train dynamics simulations. Fluid dynamics models are better models to study brake system behaviour but are more complex and slower in computation. Fluid-empirical dynamics models combine fluid dynamics brake pipe models and empirical brake valve models. They are a balance of model fidelity and computational speeds. Depending on research objectives, detailed models of brake rigging, friction blocks and wheel-rail adhesion are also available. To spark new ideas and more research in this field, the challenges and research gaps in air brake modelling are discussed.

Intelligently identifying rail vehicle faults instigating running instability from carbody floor acceleration is essential to ensure operational safety and reduce maintenance costs. However, the vehicle-track interaction's nonlinearities and scarcity of running instability occurrences complicate the task. The running instability is an anomaly in the vehicle-track interaction. Thus, we propose unsupervised anomaly detection and clustering algorithms based iVRIDA framework to detect and identify running instability and corresponding root cause. We deploy and compare the performance of the PCA-AD (baseline), Sparse Autoencoder (SAE-AD), and LSTM-Encoder-Decoder (LSTMEncDec-AD) model to detect the running instability occurrences.

Furthermore, we deploy a k-means algorithm on latent space to identify clusters associated with root causes instigating instability. We deployed the iVRIDA framework on simulated and measured accelerations of European high-speed rail vehicles where SAE-AD and LSTMEncDec-AD models showed 97% accuracy. The proposed method contributes to smart maintenance by intelligently identifying anomalous vehicle-track interaction events.

The air spring is the main part of the secondary suspension of passenger railway vehicles. The aim of this paper is to review existing modelling techniques for air springs in order to check if challenges set in the past decade for available models have been met. The advantages and disadvantages of different air spring models (phenomenological/mechanical, thermodynamic, analytic, FEM) are summarised and discussed from the point of view of: model accuracy, multiphysics interaction, influence of structural and material non-linearities, obtention of parameters, frequency range and the balance between accuracy and computational effort. The first conclusion is that current research is mainly focused on the vertical behaviour with less attention paid to the lateral performance. Moreover, it is concluded that further research is needed to include non-linearities of the bellow and to consider fluid-structural interaction; this  would allow improving the model of vertical behaviour and evaluating better the lateral performance of the pneumatic system. FEM models might be an interesting tool that allows performing a more complete analysis of air springs (combining different physics, including material non-linearities, considering the real shape of the bellow and reinforcing fibres, etc) favouring the comfort analysis and including the lateral dynamics of the air spring.

Powerful electric locomotives with high traction performance are foreseen to be used to boost the overall performanceof freight transport. However, they would exert extra burden on the power supply system, so the power peak demandwould be a bottleneck for future freight transport. To avoid large-scale modifications to the existing systems but ensureoperational reliability, this study investigates the formation of power peaks and explores power peak shaving concepts tolet the existing systems be more reliable and accommodate more freight traffic. Different from many previous studieswhich focus on energy saving, this study aims at lowering the power peak demand by “smart train operation”, i.e.altering the train speed profile without compromising running time. This study is mainly performed by simulation basedon a standardized freight operation with full regenerative braking used. But this study also shows a real case study basedon measurement data of power history from an onboard energy meter. The study shows the formation of power peaksin different conditions and suggests some possible measures to shave the power peak demand. The study also showsthat there is a compromise between power peak shaving and energy saving, to which more attention is needed in futurestudies

The friction coefficient in the calculation of surface traction between wheel and rail is often considered to be constant. However, the true friction coefficient starts to fall as passing from adhesion area to slip area. Inclusion of such behaviour in the estimation of tractions on the contact patch can make the calculations more accurate. In the presented work, the slip dependent coefficient of friction is implemented in the tangential contact solution by using ‘Friction Memory’ concept [1] and the effect of such consideration on the prediction of wear and rolling contact fatigue (RCF) is analysed. Furthermore, an on-set curve squeal noise detection technique has been proposed.

Condition Monitoring (CM) of dynamic vehicle track interaction is an important research topic in rail vehicle dynamics. The most cost-effective method for CM is through carbody floor mounted accelerometers because this is most safe and reliable location for onboard accelerometers onboard inservice train. However, the dynamic response of carbody is influenced not only by excitations coming from track but also by various nonlinearities such as wheel-rail interface and vehicle suspension elements. Thus, it is very challenging to accurately monitor track subsystems via carbody floor accelerations. In this article, two feature extraction algorithms are proposed with the objective of obtaining crucial information on the stability of vehicle using carbody floor accelerations. The first algorithm is based on spectral analysis and the latter is on adaptive signal processing technique. The first algorithm calculates transfer function between track irregularities and carbody floor acceleration using Multiple Input Multiple Output (MIMO) system identification method. The later method analyses the carbody floor accelerations with Empirical Mode Decomposition followed by Singular Value Decomposition (EMD+SVD). These algorithms are evaluated on simulated carbody floor accelerations obtained with vehicle dynamic simulations. In this investigation, it is observed that the first method extracts more crucial information from carbody floor acceleration in comparison to EMD+SVD method. These features are planned to be used in future research to develop machine learning based intelligent fault identification algorithm for identification of root cause of vehicle running instability occurrence.