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<inf>SWT</inf>). The maximum DIP<inf>SWT</inf> 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<inf>SWT</inf> 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<inf>SWT</inf> 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.

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.

Increased equivalent conicity of wheels because of hollow wear during long-term operation influences the ride comfort performance of Chinese high-speed trains. To investigate the evolution of wheel wear, a high-speed train operating on the Beijing-Shanghai Railway line at maximum operational speed of 350 km/h is monitored over a time of 1.5 years. An MBS based wear calculation software tool of KTH using stochastic simulation inputs has been used for wear prediction, where the vehicle suspension parameters and global structural modes of car-body and bogie frame have been identified using roller rig measurements and dynamic track measurements as well to validate the simulation models. The calculated wear is then validated against measurements by calibrating the wear rate coefficients. The influence of initial conicity on the lateral wear distribution is analyzed. Wheel profiles with lower initial conicities result in significantly less wear but more vibrations which possibly worsen the ride comfort. Increasing the roll-stiffness shows to be an effective way to damp the low frequency vibrations for the low conicity wheel while resulting in low wear. The suspension parameters and initial conicity which give the most stable equivalent conicity evolution and best ride comfort are selected for field tests.

A successful predictive maintenance strategy for wheels and rails depends on an accurate and robust modelling of damage evolution, mainly uniform wear and Rolling Contact Fatigue (RCF). In this work a life prediction framework for wheels and rails is presented. The prediction model accounts for wear, RCF, and their interaction based on the output from MBS simulations to calculate the remaining life of the asset, given in mileage for wheels and MGTs for rails. Once the model is calibrated, the proposed methodology can predict the sensitivity of the maintenance intervals against changes in operational conditions, such as changes in contact lubrication, track gauge, operating speeds, etc. The prediction framework is then used in two operational cases on the Swedish Iron-Ore line. The studied cases are, the analysis of wheel life for the locomotives, and the analysis of rail life for gauge widening scenarios. The results demonstrate the capabilities of the MBS-based damage modelling for predictive maintenance purposes and showcase how these techniques can set the path towards Digital Twins of railway assets.

A methodology that combines simulations of long-term mechanical degradation including maintenance interventions with an assessment of the associated socio-economic impact is proposed. This development responds to an urgent need within the railway sector to enable the evaluation of maintenance strategies from a LCC perspective. The functionality of the methodology is demonstrated in an investigation of rail grinding strategies for a curve on the Swedish Iron-ore line. The results indicate a reduction in LCC of 50% when using a harder rail material (R400HT) combined with annual rail grinding as compared to a softer rail material (R350LHT) and two grinding campaigns per year.

A new multiscale computational method predicts RCF crack formation in rails by integrating multi-body simulations, finite-element methods, and critical plane approaches. Multi-variable modal value sampling is introduced to reduce the computational expense of large-scale FE studies by capturing multiple wheel passes in a single loading profile. The Smith-Watson-Topper (SWT) critical plane fatigue damage parameter is selected to capture the non-proportional multiaxial load history drivers for RCF formation. The critical plane fatigue damage parameter matches the experimentally observed location and orientation of RCF cracks, highlighting the applicability of this new method for future RCF assessments.

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.

In this work, the authors present a detailed ’train-track’ interaction model of a long freight train operation topredict long-term rail surface damage. In addition to vehicles and track, intermediate maintenance actions inthe form of cyclic grinding passes have also been modelled according to EN standards to effectively representthe evolving wheel-rail interface. The influence of longitudinal train dynamics in the form of traction, braking,gradients, etc are also accounted in the method to reflect their effect on damage evolution. The two-stepmethodology consists of modules each modelling longitudinal train dynamics and long-term rail surface damagerespectively. The multi-disciplinary integrated numerical framework comprising of train, real operational casesand track attributes has been built based on principles of vehicle dynamics, tribology, and fatigue analysis. Themodel has been demonstrated for a heavy haul freight train operation on a 120 km long track section in thenorth of Sweden for which the results have been presented. Also, additional scenarios that can be expected ina real time operation with varying traction/braking, gradients etc have been considered. In the end, thisintegrated numerical framework comprising of 4 T’s namely train, track, tractive strategies, and trackmaintenance can be tuned into a digital twin to guide infrastructure managers regarding the condition of railassets as a function of tonnage passage. This can in turn facilitate predictive maintenance of track depending ontraffic and operation.

In this work, the authors present a detailed train-track interaction model of a long freight train operation to predict long-term rail surface damage. In addition to vehicles and track, intermediate maintenance actions in the form of cyclic grinding passes have also been modelled according to European standards to realistically represent the evolving wheel-rail interface. The influence of longitudinal train dynamics in the form of inter-vehicle interactions, traction, braking, gradients, etc is also included in this method to reflect their effect on damage evolution. The authors demonstrate that the novel ‘Train-track interaction’ formulation is more complete and therefore better suited to study long-term rail surface damage as opposed to existing ‘vehicle-track’ formulations since the former brings the system dynamics at play, significantly altering the wheel-rail interaction. A key highlight of this work is that the rail surface damage is expressed in the form of evolving rail profiles over a large tonnage passing and by depicting RCF-affected zones. This framework can be tuned into a digital twin to guide infrastructure managers regarding the condition of rail surface as a function of tonnage passage. This can in turn facilitate predictive maintenance of track depending on traffic and operation.

In this paper, a modified wear calculation method is developed, which can give less precise but faster results compared to the classic wear calculation method. Besides, a precise contact point detection program is developed to cooperate with this modified method.