The main work presented within this thesis is a numerical investigation of the effects of crosswind yaw angles upon the aerodynamic characteristics of a high-speed passenger train. The applicability of Computational Fluid Dynamic (CFD) approaches to the simulation of external flow around a passenger train were investigated. Results showed the Delayed DES (DDES) approach produced the highest correlation to experimental results. Ballast heights of 0m, 0.3m and 0.75m were simulated and compared to existing experimental results. Comparisons between cases found that a vortex from the train’s underbody follows the ballast profile. Crosswind simulations were conducted at yaw angles of 0o, 5o, 10o and 15o. The results obtained showed the formation of a large vortex upon the leeward side at yaw angles of 10o and above, this caused a region of low pressure which increased the overturning forces acting upon the train. Generally, crosswinds increased pressures upon the windward side of the train and decreased them upon the leeward side. Slipstream velocities on the windward side were seen to decrease whilst leeward side velocities increased. Results were compared to regulations currently in place for the operation and testing of trains. This enabled comments and suggestions to be made upon existing regulations.

The work presented in this thesis was motivated by the desire of the railway industry to capitalise on new technology developments promising seamless integration of distributed data. This includes systems that generate, consume and transmit data for asset decision support. The primary aim of the research was to investigate the limitations of previous syntactic data integration exercises, creating a foundation for semantic system development. The objective was to create a modelling process enabling domain experts to provide the information concepts and semantic relationships between those concepts. The resulting model caters for the heterogeneity between systems supplying data that previous syntactic approaches failed to achieve and integrate data from multiple systems such that the context of data is not lost when centralised in a repository. The essence of this work is founded on two characteristics of distributed data management; the first is that current Web tools, like XML, are not effective for all aspects of technical interoperability because they do not capture the context of the data; and second, there is little relationship between conventional database management systems and the data structures that are utilised in Web based data exchange which means that a different set of architecture components are required.

Railways worldwide have experienced unprecedented growth over the last decade. The infrastructure of many European railways is becoming increasingly saturated, while the operators face strong demands to increase services and carry more passengers and freight. There has been a high demand for performance improvement, to provide better service to customers. This problem is drawing increasing attention from many parties within and beyond the railway industry. In this research, the author first reviewed and analysed the existing performance measures used in railway transportation. A new generic framework for the measurement and improvement of railway network performance has been proposed, based on the concept of quality of service. The key factors affecting quality of service are identified and analysed. Secondly, the quantification of performance has been identified based on the Quality of Service framework. Multi criteria decision making has been applied to determine the weights of each Key Performance Indicator in the framework. Finally, factors within the railway system have been analysed for their impact on performance with a sensitivity analysis, to support the development of performance improvement plans. A case study has been conveyed to show the influence of the system properties on the performance measured by quality of service.

The technical suitability of hydrogen for railway traction is evaluated in the research. World-wide, diesel fuel is currently the main source of energy for railways but relies on petroleum and emits greenhouse gases. Hydrogen as a secondary energy can be produced from fossil fuels or renewables. Thus, a decrease or elimination of emissions is possible, and reliance on petroleum reduced. A well-to-wheel analysis for diesel, electricity, and hydrogen was conducted to establish overall efficiencies and emissions. The results show that hydrogen fuel cell traction has a similar efficiency to electric traction and reduces carbon emissions compared to diesel and in some case electric railways, if the hydrogen feedstock is natural gas. Renewable hydrogen would reduce emissions further. A prototype hydrogen-powered locomotive, the first in the UK, was constructed and employed for a performance evaluation; both demonstrated that hydrogen is suitable for railway traction. Computer modelling of a diesel-electric regional train, a hydrogen-powered vehicle, and a hydrogen-hybrid version was conducted. The journey time and the range of all three options was the same while energy reductions of 34 % for the hydrogen-only and 55 % for the hydrogen-hybrid, compared to the diesel were achieved.

Point machines are the key actuator used in railways to provide a means of moving a switch blade from one position to the other. Failure in the point actuator has a significant effect on train operations. Condition monitoring systems for point machines have been therefore implemented in some railways, but these condition monitoring systems have limitations for detecting incipient faults. Furthermore, the majority of condition monitoring systems which are currently in use cannot diagnose faults. The ability to diagnose faults is useful to maintenance staff who need to fix problems immediately. This thesis proposes a methodology to detect and diagnose incipient faults using an advanced algorithm. In the main body of this thesis the author considers a new approach using Wavelet Transforms and Support vector machines for fault detection and diagnosis for railway electrical AC point machines operated in Japan. The approach is further enhanced with more data sets collected from railway electrical DC point machines operated in Great Britain. Furthermore, a method to express the qualitative features of healthy and faulty waveforms was proposed to test the transferability of the specific algorithm parameters from one instance of a point machine to another, which is tested on railway electrical DC point machines used in Great Britain. Finally, an approach based on Wavelet Transforms and Neural networks is used to predict the drive force when the point machine is operating. The approach was tested using electrical DC point machines operated in Great Britain. It is shown through the use of laboratory experimentation that the proposed methods have potential to be used in a real railway system.

The optimisation algorithm has been further investigated to understand the influence of the weight coefficients that affect the solution of all the optimisation problems and it is very often overlooked in the traditional approach. In fact, the choice of weight coefficients leading to the optimum among different optimal solutions also presents a challenge and this specific problem does not give any a priori indications. This challenge has been tackled using both genetic algorithms and particle swarm optimisations, which are the best methods when there are multiple local optima and the number of parameters is large. The results show that, when the optimal set of coefficients are used and the optimal positions and capacitances of EDLCs are selected, the energy savings can be up to 42%. The second problem of the control of the storage has been tackled with a linear state of charge control based on a piece-wise linear characteristic between the current and the voltage deviation from the nominal voltage of the supply at the point of connection of the storage. The simulations show that, regardless of the initial state of charge, the control maintain the state of charge of EDLCs within the prescribed range with no need of using the on-board braking resistor and, hence, dissipating braking energy. The robustness of the control algorithm has been verified by changing the characteristics of the train loading and friction force, with an energy saving between 26-27%.

This thesis presents a new experimental technique to determine the structure of train slipstreams. The highly turbulent, non stationary nature of slipstreams make their measurement difficult and time consuming as in order to identify the trends of behaviour several passages of the train have to be made. The new technique has been developed in order to minimise considerably the measuring time. It consists of a rotating rail rig to which a 1/50th scale model of a train is attached. Flow velocities are measured using two multi-hole Cobra probes, positioned close to the sides and top of the model. Tests were carried out at different model speeds. Velocity time histories for each configuration were obtained from ensemble averages of the results of a large number of runs (of the order of 80). From these it was possible to define velocity and turbulence intensity contours along the train as well as the displacement thickness of the boundary layer, allowing a more detailed analysis of the flow. Wavelet analysis was carried out on the experimental data to reveal details of the unsteady flow structure around the vehicle. It is concluded that, although this methodology introduces some problems the results obtained with this technique are in good agreement with previous model and full scale measurements.

Abrupt changes in track stiffness can occur where the ballasted track transitions from soft foundations onto bridges and viaducts, onto hard foundations and into tunnels, or at the interface between bal¬lasted track and slab track. Such interfaces between areas of different track stiff¬ness are defined as critical zones and can cause passenger discomfort, unpredictable track degradation and, potentially, track support failures. Given their nature, main¬taining critical zones is more expensive than maintaining plain line track and measuring and assessing the variations in track stiffness are critical activities in track main¬tenance and in ascertaining the quality of newly designed and built railways. Existing methods of track stiffness measurement are not sufficiently accurate and depen¬dable. Therefore, the aim of this research was to develop an innovative but rapid method of assessing track stiffness, so as to identify problems in transition zones. During her doctoral research, the author designed and built a sleeper deflection measurement system as a means of quickly assessing track stiffness. The laser-based digital system developed uses position sensitive detectors (PSDs) and a line laser. The performance of the new measurement system was verified on a private railway at Long Marston, which showed that the system was able to capture the displacement data of individual sleepers and thus the continuous movement of rails supported by 18 sleepers. Ob¬serving the train-induced dynamic deflection patterns across multiple sleepers allows a rapid assessment of the track stiffness and provides the information that is necessary to manage the stiffness variation around transition zones.

At present, high speed dual purpose rail/road vehicles employing fixed non-destructive testing (NDT) sensors are used to inspect rails. Due to the uncertainties in characterisation of the defects when they are detected at high speed, manual re-visiting of the defects by expert operators is required before any decision regarding track maintenance is made. This research has been driven by a desire from the rail industry for a robotic system performing faster than human operators and being capable to both detect and characterise rolling contact fatigue (RCF) cracks in rails with the aim of automating the existing manual inspection and enhancing its accuracy and reliability. This thesis combines expert systems technologies with robotic NDT to fulfil this aspiration. A great deal of effort has been spent to develop a robotic inspection trolley which can automatically detect and characterise the RCF cracks in rails using an alternating current field measurement (ACFM) sensor. It uses a rule based expert system (RBES) proposed to control the robotic trolley and more importantly process ACFM data for both detecting and sizing defects. The developed system can detect the possible presence of defects in railway tracks at high speed pass (5-20 km/h) and can automatically return to an identified defect location to perform a slower and more detailed scan (up to 20 mm/s) across a rail section to determine the size, depth and number of cracks present in that section.

Rolling contact fatigue (RCF) cracks are a widespread problem that impairs the service life of railway rails and wheels, with an associated high cost of labour and capital expenditure for remediation. Severe RCF cracks cause serious safety issues as they can turn down at a larger propagation angle into the rail potentially leading to a rail break. Rail grinding can effectively eliminate RCF cracks if they are detected when they are small enough to be removed. Alternating current field measurement (ACFM) is one of the electromagnetic (EM) techniques that can be used for defect detection and sizing in the rail industry. ACFM has been reported to be more accurate in providing length and depth information than conventional UT for small RCF cracks and is less sensitive to lift-off than eddy current methods. The aim of the present research is to analyse the response of ACFM signals to single and multiple RCF cracks in railway rails using experimental measurements and FE based modelling tools, focusing on the influences of crack vertical angle and multiple cracks (number, spacing, size, uniformity) on the ACFM signal to improve the accuracy of dimension predictions. A novel method (using the Bz signal) is proposed to determine the vertical angle of the RCF cracks, which then allows the crack vertical depth to be determined from the pocket length (standard output from ACFM measurements) and therefore the appropriate amount of rail grinding to remove the RCF cracks. It was found that the vertical angle influences the pocket length determined from the measured ΔBxmax/Bx value when the cracks are shallow (vertical angles < 30°), therefore greater accuracy can be obtained when compensating the ΔBxmax/Bx value using the determined vertical angle. It is shown that the variations of crack surface length, crack inner spacing and crack number for multiple cracks also influence the ΔBxmax/Bx values determined for multiple cracks. The influences of asymmetrical crack shapes on crack sizing are discussed, in general it has been found that for accurate sizing of RCF cracks using a single ACFM scan the cracks should be regular, where the assumption of semi ellipse shapes is appropriate. The methods developed in the project were assessed using calibration samples (machined cracks with different sizes and vertical angles) and rails removed from service containing single and multiple RCF cracks. It was found that the new approach proposed in this work allowed the vertical angle to be predicted well for single and multiple RCF cracks (difference to measurements < 14.3 %). In addition the error in pocket length prediction is greatly decreased when using the sizing method including compensation determined from the crack vertical angle.