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 trains. 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.

The development of numerical models able to compute the wheel and rail profile wear is essential to improve the scheduling of maintenance operations required to restore the original profile shapes. This work surveys the main numerical models in the literature for the evaluation of the uniform wear of wheel and rail profiles. The standard structure of these tools includes a multibody simulation of the wheel–track coupled dynamics and a wear module implementing an experimental wear law. Therefore, the models are classified according to the strategy adopted for the worn profile update, ranging from models performing a single computation to models based on an online communication between the dynamic and wear modules. Nevertheless, the most common strategy nowadays relies on an iteration of dynamic simulations in which the profiles are left unchanged, with co-simulation techniques often adopted to increase the computational performances. Work is still needed to improve the accuracy of the current models. New experimental campaigns should be carried out to obtain refined wear coefficients and models, while strategies for the evaluation of both longitudinal and transversal wear, also considering the effects of tread braking, should be implemented to obtain accurate damage models.

In order to ensure safety and reliability of railway transportation the interest in monitoring systems is constantly increasing. These devices include sensors and electronic components, which need electric power supply for their proper working. However not all railway lines and vehicles are provided with electric power, furthermore, it can be complex to provide energy to sensors by cables in some location of the vehicle. The demand of a widespread distributed electrical energy can be fulfilled by energy harvesting techniques from primary sources. This paper analyses the different concepts of energy harvesters proposed in literature for the railway field, both for on board and wayside applications. The harvesters are classified according to the source from which energy is harvested and for each type of harvester advantages and disadvantages are critically discussed. The proposed review highlights the most promising solutions regarding energy harvesting in order to support future researches concerning this topic.

The paper shows the feasibility of building closed-form and fast-to-evaluate surrogate models via supervised kernel-based machine learning regressions behaving as digital twins for computationally expensive multibody simulations. The aforementioned surrogate models are adopted to predict the railway vehicle dynamics safety indexes defined in the international standards, depending on the wheel-rail forces, directly from the results of longitudinal train dynamics simulations. The digital twin models are trained with the outputs of Simpack multibody simulations of a reference freight wagon, to which the in-train forces calculated by an in-house MATLAB longitudinal train dynamics simulator are applied. Two machine learning techniques are considered: the support vector machine and the least-squares support vector machine regressions. Both techniques ensure a good accuracy even with a limited number of training samples. The derivation of the surrogate models can strongly enhance the modelling capabilities of common longitudinal train dynamics simulators, that cannot evaluate the wheel-rail contact forces. At the same time, the method shown in the paper allows to strongly reduce the total computational times, as the evaluation of the closed-form surrogate models allows to effectively estimate the safety indexes with no need for computationally demanding multibody simulations.

When high creep values occur on a braked/trailing wheelset running on a contaminated track, changes in the friction conditions take place. In fact, the work of the friction forces gradually removes the contaminant from the wheel and rail surfaces. Therefore, lower levels of adhesion are present on the leading wheelsets, while better conditions are restored on the following ones. A deep understanding of this phenomenon is needed to develop new algorithms for WSP and antiskid systems, in order to maximise the tractive/braking performances of railway vehicles. The research group of the Politecnico di Torino has recently designed an innovative multi-axle roller-rig to investigate the adhesion recovery phenomenon. First, the paper briefly shows the configuration of the new roller-rig, then focus is given to the experimental results obtained from adhesion recovery tests performed on the test bench.