Predicting rolling contact fatigue crack hot spots or regions with increased local driving forces in rails is challenging due to the wide range of factors that influence crack initiation. Rail sections experience fluctuating creepage conditions, contact positions, and loads throughout their lifespan, influencing the development and location of fatigue cracks. A new computational method is proposed that predicts the orientation and regions prone to rolling contact fatigue cracks under realistic service loading. It combines multi-body simulations, finite element analysis, and critical plane approaches. A novel multi-variable sampling technique simplifies loading spectra into representative traction profiles, which are then analyzed using finite element analysis and the Smith–Watson–Topper damage indicator parameter (DIP_SWT). The maximum DIP_SWT value identifies the critical plane and potential crack orientation. A case study on the Swedish heavy haul train line (Malmbanan) considers measured traffic and loading conditions, analyzing the wheel load spectrum for a 384 m long section of a R = 450 m curve. Results show that the DIP_SWT is highest for the locomotive with a loaded payload configuration, with a maximum value of 3.84 × 10⁻⁸ located at 38.59 mm from the lower gauge face corner. The DIP_SWT critical plane aligns with experimental measurements of RCF cracks orientations near the gauge corner. This computational method, when combined with other predictive tools, can efficiently identify conditions that lead to RCF cracks and determine their possible locations and orientations in railway tracks.

This paper explores how wicked problems are perceived and operationalised in engineering education. Focusing on a particular technical university in Northern Europe, this study employs a whole-institution action research approach spanning policy, program, and course levels, combining analysis of a top-down policy intervention, qualitative discourse analysis of program evaluations across the institution, and development and piloting of a novel framework with teachers in problem- and project-based courses. The findings reveal that wicked problems are widely acknowledged in the program evaluations, however, often reframed into more familiar concepts, leading to limited and implicit rather than systematic consideration, and that related learning progression through programs is assumed rather than explicitly designed for. The developed framework demonstrates analytical and practical value by enabling differentiation between different dimensions and levels of “wickedness” in problem- and project-based courses, across dimensions such as problem complexity, students and stakeholder diversity and relations, and sustainability. Overall, the study highlights the need and provides tools for deliberate pedagogical design and coordinated strategies at course, study program, and university levels, for enhancing the relevance of engineering education with regard to wicked problems.

Active wheelset steering can improve the curving performance of railway vehicles and thus reduce wear. Several control strategies have been proposed to achieve a perfect steering condition which may require feedback signals that are difficult to measure. This study proposes a control strategy for an active wheelset steering system via wheelset angular velocity measurements aimed to minimise longitudinal creepages in curves. The desired wheelset angular velocity is derived from the relation of longitudinal creepages and the wheelset movement in curves. Co-simulations with a conventional railway vehicle with two two-axle bogies are carried out for 24 cases. First, the strategy based on actual wheel-rail geometry is used to evaluate the effectiveness of the proposed control strategy; in the second step, a simplified strategy is tested using the approximated equivalent rolling radius approach. Curving performance indicators, including wheelset movements and wheel-rail wear number, are used to evaluate the performance of the control system. The results indicate the effectiveness of the proposed control strategies. Challenges and practical considerations are also discussed.

The Centre for ECO2 Vehicle Design (ECO2) is a Swedish multilateral competence centre based at KTH Royal Institute of Technology with a focus on the development of resource effcient vehicle for sustainable transport. This report covers the first 18 months of ECO2’s new agreement period (Jul 2024 – Dec 2029), marking a period of organizational renewal, research excellence, and strategic positioning for future growth. During this period, ECO2 successfully completed three PhD defences, produced 33 peer-reviewed journal publications, 50 conference papers, and strengthened its research portfolio with 12 projects multiple project applications. The new five-year centre agreement was finalized, securing partner and KTH financing co-financing and establishing enhanced governance structures. ther highlights include the holding of the second Resource Efficient Vehicles conference in Graz with 10 ECO2 contributions, establishing a collaboration agreement with Fraunhofer IAO, and expanding industrial partnerships through new doctoral projects. ECO2’s research continues to address critical attributes of vehicles as part the transport system – effciency, accessibility and resilience, which contribute sustainable transport and other major societal challenges.

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.

A promising approach to control wear and rolling contact fatigue (RCF) on wheel and rail profiles is to optimise the rail geometry for improved wheel-rail contact conditions in order to minimise contact pressures, creepages, and creep forces by achieving a better match between the wheel and rail profiles. Typically, optimised profiles are implemented through rail grinding. However, achieving a substantially different target profile requires greater grinding depth and material removal, which shortens rail life, making it essential to account for these limitations in rail profile optimisation processes. This study presents how to include grinding limitations in rail profile optimisation processes and studies its effect by optimising two rail profiles, one considering grinding limitations and another without such consideration, to assess their comparative impact on a multi-objective optimisation process and resulting profiles. Furthermore, the degree of conformality of the wheel-rail contact between wheel and rail are analysed and its relationship with contact pressure, wear, and rolling contact fatigue (RCF) is investigated.

Active wheelset steering using wheelset angular velocity feedback has been proposed and proven effective in achieving one of the goals for perfect steering condition, i.e. minimising longitudinal creep forces and thus wear. This control strategy relies on the contact parameters of wheel-rail pairs to generate a desired angular velocity by using the wheel rolling radii. Some wheel-rail combinations give a non-monotonic function of the average rolling radius with respect to curve radius, which therefore poses a difficulty in controlling active steering with the angular velocity feedback. This study aims to investigate the effect of different wheel-rail combinations and wheel wear. Worn wheels in general tend to cause a non-unique function whereas the original profile gives a monotonic one. Two countermeasures are then investigated to cope with the raised challenges. First, the reference signal generated from the approximated average rolling radius method has proven to be effective for both the original and the least worn profile; however, a wheel with higher wear depths would require a new tuned scaling factor. The control strategy based on equal wheelset angular velocity is then investigated with two schemes: equal wheelset angular velocity within the same running gear and equal angular velocity of all wheelsets within the vehicle. Both schemes provide significant wear number reduction, but the second approach results in better distribution of wheel-rail lateral forces among all wheelsets. This control strategy can also relax the demanded knowledge of wheel-rail contact properties and vehicle travelling speed.

This study presents a systematic framework for optimising wheel and rail profiles and its application in a North American heavy-haul railway. The network suffers from severe low-rail RCF in tight curves with widened gauge, while high-rail damage remains limited due to effective friction management. To address this imbalance, a two-phase optimisation strategy was developed in order to consider the demands and constraints imposed by the stakeholders: first, a wheel profile was designed to reduce low-rail damage across varying gauge conditions while maintaining or reducing high-rail damage in sharp curves; second, two new rail profiles were introduced for mild curves and tangent track to ensure compatibility with the new optimised wheel profile and to reduce contact stresses in these sections. Wheel profile optimisation markedly reduces low-rail subsurface stress, but tread modifications compromise its performance on larger-radius curves. Optimising rail profiles for wide curves and tangent track resolves this shortcoming. Overall, integrated wheel-rail optimisation outperforms wheel-only strategies and is practically achievable via adjustments to existing rail grinding templates, underscoring the value of a system-level approach to managing RCF and wear in heavy-haul operations.

Automating human-oriented tasks and complementing humans in their work with rail vehicles, may it be in relation to management, maintenance, or network operation, can be facilitated by the use of artificial intelligence (AI), which carries the promise to make human-oriented tasks more efficient and less error-prone. It is the firm belief of the authors, given the global lack of skilled rail expertise and the increased complexity and efficiency requirements of the operational environment that AI will become a complementary companion to humans in that work environment with the potential for making railways more efficient. The aim of this chapter is therefore to provide a backgrounding regarding some of the beneficial aspects of AI and, based on concrete examples, give recommendations on how to avoid typical pitfalls when using and implementing AI in your organization.

Vehicles are complex systems composed of multiple interdependent subsystems. A design change in one subsystem can lead to either beneficial or detrimental effects on others, making it essential to understand how design choices propagate through the system. This paper focuses on the traction chain in rail vehicles, specifically the interaction between two critical subsystems: the traction motor and the inverter. Since the inverter supplies the current and voltage required by the motor, changes in motor design can affect the inverter’s thermal behaviour. We analyse this interaction to understand how motor design influences the temperature evolution of inverter power electronic components. To achieve this, we apply a framework that integrates network theory, structural equation modelling (SEM), and sensitivity analysis. First, analytical models of a three-phase induction motor and an inverter thermal model are developed. A reverse breadth-first search is used to identify all input parameters that influence the inverter temperature. Global sensitivity analysis (GSA) isolates the most influential inputs, enabling the construction of a reduced network graph. Then, SEM and local sensitivity analysis (LSA) are applied to quantify the relationship between motor design parameters and inverter temperature, yielding a coefficient that captures the strength of the dependency. This approach provides an alternative representation of the multidisciplinary interactions between subsystems. It also helps identify key change propagation paths, making it easier for designers to anticipate and manage the consequences of design changes. By reducing analytical complexity and clarifying subsystem interdependencies, the framework supports more efficient early-stage design, potentially reducing the number of iterations needed to reach a satisfactory solution.