Predicting traction return current in electric railway systems through physics-informed neural networks

This paper addresses the problem of determining the distribution of the return current in electric railway traction systems. The dynamics of traction return current are simulated in all three space dimensions by informing the neural networks with the Partial Differential Equations (PDEs) known as telegraph equations. In addition, this work proposes a method of choosing optimal activation functions for training the physics-informed neural network to solve higher-dimensional PDEs. We propose a Monte Carlo based framework to choose the activation function in lower dimensions, mitigating the need for ensemble training in higher dimensions. To further strengthen the applicability of the Monte Carlo based framework, experiments are presented under two loss functions governed by L2 and L∞ norms. The presented method efficiently simulates the traction return current for electric railway systems, even for three-dimensional problems.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Railway Engineerin

Rail wear rate on the Belgian railway network: a big-data analysis

This paper presents a big data-based analysis of the rail wear of the whole Belgian railway network measured in 2012 and 2019. Wear rates are reported, discussed, and quantitatively formulated as functions of critical factors in terms of curve radius, annual tonnage (rail age), high rail in curves, an average from both rails in straight tracks at rail top (vertical wear) and gauge corner (45° wear) and for steel grade R200 and R260. The influence of preventive grinding is also analysed. The wear rates are derived in an aggregated manner for the whole network. The wear rates do not show significant change with changes in rolling stock over the years, implying that the wear rates could also hold for other networks. It is found that R200 shows, on average, a 34% higher wear rate than R260. Also, the wear rate per tonnage is lower for high-loaded tracks. Thus, time is a relevant factor in explaining the wear evolution of low-loaded tracks; for instance, the effect of corrosion may have an important role. The paper provides statistically significant information that can be used for wear modelling, understanding and treating rolling contact fatigue based on the wear rate and developing tailored rail maintenance strategies.Railway Engineerin

Optimal Management of Railway Perturbations by Means of an Integrated Support System for Real-Time Traffic Control

Automatic real-time control of railway traffic perturbations has recently received the attention of practitioners. The aim is to make use of mathematical algorithms to maintain the required service availability during unplanned disturbances to operations. In the literature many tools for real-time traffic control are proposed, but their effects on traffic have never been studied neither in real life nor in realistic simulation environments. We can mention only a few pilot tests and a unique installation in the Lötschberg Base tunnel in Switzerland, which is in any case an ad-hoc implementation not extendible to other case studies. In this paper we present the ON-TIME framework for the real-time management of railway traffic perturbations. The main innovation is a standard web service-oriented architecture that ensures scalability and flexibility. A standard RailML interface is used for the input/output data of the modules, allowing immediate applicability of the framework to any network having a RailML representation. The scalability makes the framework independent from the number of modules and the amount of data exchanged. The flexibility permits any module to be replaced with others having similar features. The framework is tested in a closed-loop with the simulation environment HERMES for a perturbed traffic scenario on the Swedish Iron Ore line. Tests are performed for two different replanning algorithms (ROMA and RECIFE) used as conflict detection and resolution modules of the framework. The analysis represents a proof-of-concept to confirm the effectiveness of our framework in automatically solving conflicts and deadlocks during perturbed traffic conditions.Transport & PlanningCivil Engineering and Geoscience

Integrated Decision Support Tools for Disruption Management

During railway operations unexpected events can require railway operators and infrastructure managers to adjust their schedules. In this research we investigate the disruption management process. More specifically, we come up with an architecture and algorithmic framework which railway operators could use for decision support during disruptions. The use of this framework results in a fully feasible timetable, rolling stock plan, and crew schedule to deal with the disruption, while minimizing the number of delayed and/or (partially) cancelled trains. We demonstrate the effectiveness of our framework on a disruption case on the Dutch Railway network, which is introduced within the EU FP7 project ON-TIME.Transport & PlanningCivil Engineering and Geoscience