A predictive model of energy savings from top of rail friction control

In this paper the authors present a predictive model of train energy requirements due to the application of a top of rail friction modifier (TOR-FM) versus dry wheel / rail conditions. Using the VAMPIRE® Pro simulation package, train energy requirements are modeled for two sets of TOR-FM frictional conditions, one using full Kalker coefficients and the other by using a Kalker factor of 18%. Both scenarios use a top of rail saturated coefficient of friction of 0.35. Under both TOR-FM frictional conditions, train energy savings are shown for complete laps of the Transportation Technology Center Inc.’s (TTCI) Transit Test Track (TTT) loop, and also when isolating only the tangent section of the loop. However, the magnitude of energy savings varies greatly depending on the Kalker coefficient factor used, highlighting the need to model this relationship as accurately as possible. These simulation results are compared with data obtained from a field study, in which train energy savings of 5.3% (lap) and 7.8% (tangent) are shown due to the application of TOR-FM

Assessing railway vehicle derailment potential using neural networks

Current methods for ensuring the safe running of railway vehicles assess the track and vehicle condition against fixed limits. Any exceedence of these limits requires remedial action to be taken. The setting of these limits is based on past experience or on computer modelling of vehicle track interaction. This paper describes the initial results of a novel method aimed at predicting vehicle behaviour from track measurements using an artificial neural network. The speed of the neural network method would allow quick analysis of all the data measured by the track recording coach and would also allow maintenance decisions to be based on the effect of track condition on the vehicle behaviour rather than on simple limits

Opportunities for improving interfaces between railway engineering analysis tools

Railways are complex systems, which utilize many engineering disciplines in order to ensure their safe and efficient operation. Railway engineers make use of ever improving analysis tools to control the performance of various parts of the railway system. Often these tools have become efficient but highly specialized. For example, vehicle engineers use powerful dynamic simulation packages but the information these provide are not always fully utilized in work carried out by infrastructure engineers who are using their own software tools. The EPSRC funded TRAINS project is studying the Railway System as a whole. As part of this the Rail Technology Unit at MMU is investigating the links between tools used by different engineering disciplines in the railway field. An overview of the TRAINS project is given in [1]. A database of tools has been set up and is being used to establish links between tools and, more importantly, gaps where tools are not interfacing as they could. The structure of the database is outlined in this paper. This project provides an opportunity to investigate the interaction between design, maintenance and operation of the vehicle and the effect on the track. In this paper we show how a sample system model is being set up which links some of the identified tools to demonstrate the interfaces. This system tool is then used to establish the effects of changes in one part of the system (such as the wheel-rail interface) on other parts of the system (such as interaction with the infrastructure)

Simulation and testing of a wheelset with induction motor driven independent wheels

Independently rotating wheels (IRW) for railway vehicles have been under serious consideration at a theoretical and experimental level for many years. This paper presents dynamic and control simulations of a rail vehicle wheelset with induction motors for independently rotating wheels. Simulation models have been developed for both the mechanical and electrical aspects of the system. The simulation and experimental results have demonstrated that the proposed control strategy has good dynamic performance in term of response time and controllability. A test implementation on a 1/5 scale roller rig has validated the simulation results and shown that good stabilization can be achieved by the proposed wheel motor driven configuration

A systems approach to evaluating rail life

Corus Rail Technologies (CRT) has undertaken a major project involving track studies of Rolling Contact Fatigue (RCF), with support from Manchester Metropolitan University (MMU). The work takes a systems approach and involves the development and application of a suite of numerical models used to investigate the in-service conditions of track components with particular emphasis on RCF. The suite of models, called the Track System Model (TSM), comprises of vehicle dynamics models, developed by MMU, a Global Track model and a rail-wheel Contact model, developed by CRT. The modelling is being complemented through the monitoring of a number of RCF affected sites on the UK network to provide essential empirical data. A total of seven vehicle models have been developed using ADAMS/Rail, including two locomotives, two DMUs, two passenger coaches, and a freight wagon. Vehicle simulations were conducted for a range of UK sites and provided results such as wheel-rail contact forces and contact patch positions. The vehicle models have been validated using track measurements. The results were then used as inputs for the CRT Global Track and Contact models. The Global Track model is a finite element (FE) model that represents a length of railway track and includes the rails, sleepers and ballast. Forces from the vehicle simulations were applied to the Global model in order to predict the bending stresses in the rail head. This was conducted for a number of vehicles at seven sites and the predicted values showed good comparison with track measurements. The Contact FE model is a 3 dimensional (3D) representation of a wheel section rolling on a short length of rail. Wheel loads calculated from the vehicle dynamics simulations were applied to the contact model in order to predict surface and subsurface stresses, including directional and shear, in the rail head. Subsurface stress distribution is of primary importance for understanding the development of RCF and crack growth. The TSM successfully integrates the vehicle and track aspects of the railway system and provides an accurate method of predicting stresses in rails. When used in conjunction with the practical understanding of RCF, through site monitoring, it will enable the development of analytical fatigue life models that can be used by the track engineer to support future decision making for an optimum rail grinding strategy and rail renewal programme

Future trends in railway engineering

This article reviews the current state of railway engineering with a focus on the steady development of conventional technology that has led to the 575 km/h run of the French TGV train in 2007. Several key engineering areas are explored and predictions made for future technological developments in vehicle and track design including active suspension, improved aerodynamic performance, and novel track systems such as slab track. Non-conventional technologies such as magnetic levitation are discussed but the conclusion arrived at is that considerable improvements are still possible through optimization and incremental development of conventional engineering solutions and that, in the short to medium term, implementation of radically novel systems is less likely

Optimisation of railway wheel profiles using a genetic algorithm

This paper presents the procedures and preliminary results of a novel method for designing wheel profiles for railway vehicles using a genetic algorithm. Two existing wheel profiles are chosen as parents and genes are formed to represent these profiles. These genes are mated to produce offspring genes and then reconstructed into profiles that have random combinations of the properties of the parents. Each of the offspring profiles are evaluated by running a computer simulation of the behaviour of a vehicle fitted with these wheel profiles and calculating a penalty index. The inverted penalty index is used as the fitness value in the genetic algorithm. The method has been used to produce optimised wheel profiles for two variants of a typical vehicle, one with a relatively soft primary suspension and the other with a relatively stiff primary suspension