Wheel-rail contact friction coefficient is often assumed to be constant through the entire contact patch for the calculation of surface traction. In reality, however, the friction value in a certain point decreases when transitioning from adhesion to slip regimes. Including this friction coefficient behaviour in the estimations of surface traction on the contact patch can potentially provide more accurate calculations of wear and rolling contact fatigue (RCF). In the present work, a slip velocity-dependent friction coefficient is implemented in the tangential contact solver using the concept of ‘Friction Memory’. The effect of this implementation on traction estimations and on the prediction of wear and RCF is analysed by comparing the results with a case with constant friction coefficient in the contact patch. Furthermore, the slip velocity-dependent friction coefficient provides a creep curve with a maximum creep forces value, and a decreasing creep force for higher creepages. This is commonly known as one of the possible mechanisms of curve squeal noise generation. The results provide insights into the likelihood of curve squeal generation, and an on-set curve squeal noise detection technique is proposed that also accounts for the influence of profile changes due to wear.

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.

This compendium is mainly intended for the MSc education in rail vehicle engineering at KTH Royal Institute of Technology. It now appears in its 4th edition. Together with the compendium "Part 2: Rail Vehicles", they comprise 20 chapters and more than 600 pages.

This compendium is mainly intended for the MSc education in rail vehicle engineering at KTH Royal Institute of Technology. It now appears in its 4th edition. Together with the compendium "Part 1: Rail Systems", they comprise 20 chapters and more than 600 pages.

In railway signalling systems, trackside equipment and signalling devices are controlled by wayside object controllers (OC). With the application of radio communication to wirelessly transmit command control and signals, the cost of cabling and installation has been significantly reduced. However, for non-electrified regional lines in rural areas, dedicated cables are still installed along the track to drive the wayside object controllers, which increases both capital and operational costs. With the development of renewable energy sources (RES) and wayside energy storage systems (ESS), a concept of smart wayside object controllers (SWOC) is proposed, which are autonomous, off-grid and powered by local renewable energy sources to reduce the related costs. To assist future system development, this work develops methods to not only calculate power and energy demand for system configuration but also estimate the life cycle cost in long-term operation. A case study based on a railway test site in Sweden is performed. This study shows that it is sufficient to use 100% local renewable energy sources to power the SWOC, trackside equipment and signalling devices. Compared with the existing system, the SWOC shows a significant cost saving in long-term operation by removing cabling and installation, reducing trackside maintenance and replacing the power supply with renewable energy power sources for decarbonisation. Therefore, the SWOC shows its significant benefits over the existing system from both economical and environmental aspects. In the end, some suggestions for future development and implementation of SWOC are given.

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.