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.

The growing demands of modern rail transport generated by increased axle loads and operational speeds have intensified wheel-rail interaction damage such as wear, rolling contact fatigue (RCF), and track geometry degradation. These interrelated damage mechanisms form a self-reinforcing cycle that can compromise safety, increase maintenance costs, and disrupt operations. A key strategy to control these damages is the optimisation of wheel and rail profiles to improve contact conditions. Over the years, a variety of optimisation techniques have emerged, ranging from target-based geometric function methods to advanced evolutionary algorithms. While genetic algorithms and other stochastic algorithms have shown promising results in handling the complex, multi-objective nature of wheel-rail profile design, the influence of algorithm selection on optimisation performance remains underexplored. This study investigates the comparative performance of two prominent stochastic optimisation algorithms, Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimisation (MOPSO), in optimising the wheel profile of a Swedish passenger train.

Increasing axle loads and speeds in heavy-haul railway systems have intensified rail and wheel damage, leading to elevated maintenance costs and reduced operational efficiency. A promising solution to this issue without compromising service demands is enhancing wheel–rail interaction through optimisation of rail profiles. This study introduces a rail profile optimisation framework tailored for a broad-gauge heavy-haul network experiencing excessive rail wear, utilising Non-dominated Sorting Genetic Algorithm II (NSGA-II). The framework is designed to minimise wear and rolling contact fatigue (RCF) while maintaining satisfactory and safe vehicle dynamic performance. The framework includes optimisation of both high and low rail profiles for sharp and mild curves, as well as optimisation of two rail profiles for tangent track to improve contact point distribution and reduce hollow wear. The optimisation process is based on in-service profiles to ensure practical grindability and incorporates multi-body simulations (MBS) to assess wheel and rail damage as well as vehicle dynamic behaviour. The results indicate that the optimised profiles substantially reduce wear and RCF across various track sections. Furthermore, long-term wear and RCF evaluation of rail profiles on sharp and mild curves confirm the superior performance of optimised profiles, thereby validating their potential for integration into maintenance practices.

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: 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. The results demonstrate that combined wheel-rail optimisation delivers superior performance compared to a wheel-only optimisation solution and is feasible in practice through the adaptation of existing rail grinding templates, highlighting the importance of a system-level approach to managing RCF and wear in heavy-haul railway operations.

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 minimising 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.