Problems, assumptions and solutions in locomotive design, traction and operational studies

Abstract

Locomotive design is a highly complex task that requires the use of systems engineering that depends upon knowledge from a range of disciplines and is strongly oriented on how to design and manage complex systems that operate under a wide range of different train operational conditions on various types of tracks. Considering that field investigation programs for locomotive operational scenarios involve high costs and cause disruption of train operations on real railway networks and given recent developments in the rollingstock compliance standards in Australia and overseas that allow the assessment of some aspects of rail vehicle behaviour through computer simulations, a great number of multidisciplinary research studies have been performed and these can contribute to further improvement of a locomotive design technique by increasing the amount of computer-based studies. This paper was focused on the presentation of the all-important key components required for locomotive studies, starting from developing a realistic locomotive design model, its validation and further applications for train studies. The integration of all engineering disciplines is achieved by means of advanced simulation approaches that can incorporate existing AC and DC locomotive designs, hybrid locomotive designs, full locomotive traction system models, rail friction processes, the application of simplified and exact wheel-rail contact theories, wheel-rail wear and rolling contact fatigue, train dynamic behaviour and in-train forces, comprehensive track infrastructure details, and the use of co-simulation and parallel computing. The co-simulation and parallel computing approaches that have been implemented on Central Queensland University’s High-Performance Computing cluster for locomotive studies will be presented. The confidence in these approaches is based on specific validation procedures that include a locomotive model acceptance procedure and field test data. The problems and limitations presented in locomotive traction studies in the way they are conducted at the present time are summarised and discussed.

Digital Traction Force Meter for Model Trains

“…The minimum value can be interpreted as a "worst-case scenario" when the adhesion is completely lost and only the coefficient of sliding friction wheels-to-rails is still present. The traction power of a locomotive depends on many factors, such as its weight, its motor torque, its number of axles (driven or not, fitted with rubber traction tires or not, sprung or not), the weight distribution over the driven axles etc., and it is important to assess this parameter in order to safely drive trains over the railway system, either real [6] or model. The authors measured the traction power of some locomotives from their collections (table 1).…”

Digital Twin for Railway: A Comprehensive Survey

“…Used/Recommended Technique Key findings [42] Inspection and maintenance of railway bridges Digital Transformation, IoT, predictive maintenance, and DTs Improved maintenance efficiency, accuracy, and reliability through real-time data analysis and predictive maintenance [18] Health and safety training for Austrian railway company AR and VR immersive learning environments Improved health and safety training through realistic simulations [92] Locomotive design and operational studies…”

Investigating the stick-slip vibration behavior of a locomotive with adhesion control in a curve

“…At present, the adhesion control method is often used in railway transportation to realize the utilization of high adhesion of wheel-rail, Common adhesion control methods such as combination correction method, 17,18 PID control and fuzzy control, 19 sliding mode control, 20 and artificial neural network method, 21 particle swarm optimization method. 22 A co-simulation model was established by Spiryagin et al 23,24 including the locomotive traction system and locomotive dynamics system, which based on a TCP/IP data exchange interface to analyze the performance of the locomotive traction control system and the train dynamics, offering guidance for large-scale train marshaling operations. A co-simulation model of the locomotive electric drive subsystem and the vehicle-track mechanical subsystem was established by Zhou et al 25 to analyze the dynamic response under complex excitations, such as time-varying gear mesh stiffness and track irregularity.…”