Long freight trains and long-term rail surface damage
Abstract: Track damage due to progressively increasing tonnage, especially due to longer and heavier freight trains, is one of the major problems faced in the European rail sector. In this context, to stay competitive, optimal track maintenance practices, track-friendly vehicles and safe operations of long freight trains assume prominence.This PhD thesis studies long freight train operations and the long-term rail surface damage that they cause, to build a computer simulation-based framework for maintenance planning and assessment of running safety. The framework is formulated with four parts: long freight train operations, vehicle dynamics, rail surface damage and track maintenance. This is followed by a literature survey on each of the subtopics and how they are linked to each other.Safe operation of long freight trains in infrastructure bottlenecks such as S-curves is studied using three-dimensional multi-body simulations. Based on this, guidelines to build long freight trains and driving scenarios that can keep longitudinal in-train forces within acceptable limits have been provided. Multi-body simulation models of various freight bogies, including a novel design, are built and their dynamic running behaviour studied according to EN standards. The key focus is on track-loading and to this effect, methodologies for simulations-based assessment of `track-friendliness' of various bogie designs are studied. Various approaches to quantify rail surface damage using multi-body simulations in the form of wear and Rolling Contact Fatigue (RCF) are studied. Based on this, measures to ascertain similarities and differences in results from different approaches have been put forward. The impact of track maintenance, in the form of periodic rail reprofiling activities in different networks, on the evolution of rail surface damage is studied. It is found that optimal maintenance planning can be tailored depending on the type of traffic on the network.Finally, various parts of the framework have been brought together to form a `train-track interaction' approach to facilitate optimal maintenance planning.
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