Optimisations of draft gear designs for heavy haul trains

This thesis developed a parallel multiobjective optimisation methodology to enable fast optimisations of draft gear designs for heavy haul trains. Improvements were achieved in the development of deterministic white-box draft gear models to enable direct use of the results in product design. Draft gear model parameters such as spring stiffness, wedge angles, and preloads were used as optimisation variables. Two optimisation algorithms were used: Genetic Algorithm and Particle Swarm Optimisation. All draft gear designs in the optimisations were constrained by impact tests to ensure the optimised designs also comply with current draft gear acceptance standards. Draft gear performance was assessed using whole-trip Longitudinal Train Dynamics (LTD) simulations and coupler fatigue damage calculations. Each simulation covered about 640 km of track and had about 10 hours of operational time. Three optimisation objectives were considered: minimal fatigue damage for wagon connection systems of loaded trains, minimal in-train (coupler) forces for loaded trains, and minimal longitudinal wagon accelerations for empty trains

Computing schemes for longitudinal train dynamics : sequential, parallel and hybrid

Conventionally, force elements in longitudinal train dynamics (LTD) are determined sequentially. Actually, all these force elements are independent from each other, i.e., determination of each one does not require inputs from others. This independent feature makes LTD feasible for parallel computing. A parallel scheme has been proposed and compared with the conventional sequential scheme in regard to computational efficiency. The parallel scheme is tested as not suitable for LTD; computing time of the parallel scheme is about 165% of the sequential scheme on a four-CPU personal computer (PC). A modified parallel scheme named the hybrid scheme was then proposed. The computing time of the hybrid scheme is only 70% of the sequential scheme. The other advantage of the hybrid scheme is that only two processors are required, which means the hybrid scheme can be implemented on PCs

A dynamic model of friction draft gear

Friction draft gears are the most widely used draft gears. Modeling and prediction of their dynamic behavior are of significant assistance in addressing various concerns. Longer, heavier and faster heavy haul trains mean larger in-train forces and more complicated force patterns, which require further improvements of dynamic modeling of friction draft gears to assess the longitudinal train dynamics. In this paper a force displacement characteristics model named "base model" was described. The base model was simulated after the analyses of a set of field-test data. Approaches to improve the base model to a full advanced draft gear model were discussed; preliminary simulation results of an advanced draft gear model were also presented

Preface to special issue on parallel computing and co-simulation in railway research

Due to the high cost of physical testing and the increasing acceptance of computer simulation modelling, railway research has become more and more reliant on computer simulations to advance the state-of-the-art. With the collective efforts of researchers from all around the world, computer models and simulation techniques in railway research have been developed to advanced levels incorporating unprecedented amounts of detail. For example, three-dimensional wheel-rail contact models, traction models with consideration of gear transmission and mechatronic control, discrete element ballast models and multibody draft gear models are now available. However, these advanced models are often developed from the perspective of different research interests and by various researchers using different programming tools. In real-world railway engineering, computing models that require multiple detailed model components are sometimes needed. There are also motivations from a theoretical research perspective to integrate multiple detailed model components as such a combination does provide more accurate simulation results. In these cases, there is a research question about how to connect legacy models that were developed by different researchers and often using different programming tools so as to avoid developing new models from scratch. The reason is obvious as new developments are time consuming. Under these circumstances, co-simulation is an effective approach to resolve the question. Co-simulations can be achieved in various ways, such as shared memory and communication protocols. Also, the co-simulations can be achieved among different software packages no matter whether the packages are in-house or commercial

Applications of particle swarm optimization in the railway domain

This paper provides a comprehensive review regarding the applications of particle swarm optimization (PSO) in the railway domain. One hundred and thirty nine (139) publications in the railway domain are listed and summarized. The review indicates that PSO has seen more and more applications in the railway domain in recent years; scheduling, active controls, and network layout planning represent the three largest application areas. PSO variants such as genetic PSO, chaotic PSO, and quantum-behaved PSO are also used in the railway domain. The inertial weight has been widely accepted and used in railway applications, while the contraction coefficient and variable velocity limit have seen fewer applications. Optimization of vehicle mechanical systems dynamics has been identified as an area that has the potential for more applications. From this paper, researchers from other areas of the railway domain can identify many other potential applications. Parallel PSO was not found in previous railway applications; it can be one direction to leverage the PSO applications by improving the computational speed. Two new application cases of parallel PSO for railway vehicle designs were presented. © 2016 Informa UK Limited, trading as Taylor & Francis Group

A method to improve draft gear designs for heavy haul trains

This paper introduces a method to improve draft gear designs for heavy haul trains. Improved designs are expected to achieve smaller coupler fatigue damage and lower in-train forces. The method used Longitudinal Train Dynamics (LTD) simulations, coupler fatigue damage calculations and a Genetic Algorithm (GA). An advanced draft gear model was used in this method. The advanced draft gear model has considered all components in the draft gear and their geometries. It is directly related to the structures of the real draft gear design; the developed method can therefore be used to guide future designs. A case study was conducted to show the feasibility and effectiveness of the method. The case study shows that there area number of different designs that can achieve both lower in-train forces and smaller coupler fatigue damage when compared to the original design. The resulting optimised design can double the fatigue life of coupler system components and achieve a 13% reduction in terms of the maximum in-train forces

Experimental prototyping of the adhesion braking control system design concept for a mechatronic bogie

The dynamic parameters of a roller rig vary as the adhesion level changes. The change in dynamics parameters needs to be analysed to estimate the adhesion level. One of these parameters is noise emanating from wheel–rail interaction. Most previous wheel–rail noise analysis has been conducted to mitigate those noises. However, in this paper, the noise is analysed to estimate the adhesion condition at the wheel–rail contact interface in combination with the other methodologies applied for this purpose. The adhesion level changes with changes in operational and environmental factors. To accurately estimate the adhesion level, the influence of those factors is included in this study. The testing and verification of the methodology required an accurate test prototype of the roller rig. In general, such testing and verification involve complex experimental works required by the intricate nature of the adhesion process and the integration of the different subsystems (i.e. controller, traction, braking). To this end, a new reduced-scale roller rig is developed to study the adhesion between wheel and rail roller contact. The various stages involved in the development of such a complex mechatronics system are described in this paper. Furthermore, the proposed brake control system was validated using the test rig under various adhesion conditions. The results indicate that the proposed brake controller has achieved a shorter stopping distance as compared to the conventional brake controller, and the brake control algorithm was able to maintain the operational condition even at the abrupt changes in adhesion condition

Train energy simulation with locomotive adhesion model

© 2020, The Author(s). Railway train energy simulation is an important and popular research topic. Locomotive traction force simulations are a fundamental part of such research. Conventional energy calculation models are not able to consider locomotive wheel–rail adhesions, traction adhesion control, and locomotive dynamics. This paper has developed two models to fill this research gap. The first model uses a 2D locomotive model with 27 degrees of freedom and a simplified wheel–rail contact model. The second model uses a 3D locomotive model with 54 degrees of freedom and a fully detailed wheel–rail contact model. Both models were integrated into a longitudinal train dynamics model with the consideration of locomotive adhesion control. Energy consumption simulations using a conventional model (1D model) and the two new models (2D and 3D models) were conducted and compared. The results show that, due to the consideration of wheel–rail adhesion model and traction control in the 3D model, it reports less energy consumption than the 1D model. The maximum difference in energy consumption rate between the 3D model and the 1D model was 12.5%. Due to the consideration of multiple wheel–rail contact points in the 3D model, it reports higher energy consumption than the 2D model. An 8.6% maximum difference in energy consumption rate between the 3D model and the 1D model was reported during curve negotiation

Parallel computing enables whole-trip train dynamics optimizations

Due to the high computing demand of whole-trip train dynamicssimulations and the iterative nature of optimizations, whole-triptrain dynamics optimizations using sequential computing schemesare practically impossible. This paper reports advancements inwhole-trip train dynamics optimizations enabled by using the parallelcomputing technique. A parallel computing scheme forwhole-trip train dynamics optimizations is presented and discussed.Two case studies using parallel multiobjective particleswarm optimization (pMOPSO) and parallel multiobjectivegenetic algorithm (pMOGA), respectively, were performed to optimizea friction draft gear design. Linear speed-up was achievedby using parallel computing to cut down the computing time from18 months to just 11 days. Optimized results using pMOPSO andpMOGA were in agreement with each other; Pareto fronts wereidentified to provide technical evidence for railway manufacturersand operators

Full-Scale 3D Heavy Haul Train-Track Dynamics Modelling Method

Full-scale 3D train-track dynamics simulations for long heavy haul trains have not been reported. Successful implementations of such simulations can unlock a series of research topics such as vehicle derailments in train operational environment and track dynamics behaviour under train forces. This paper introduces a method that can be used to develop 3D train-track dynamics models for long heavy haul trains. The method uses parallel computing for 3D train dynamics simulations, in which one computer core is used to compute each vehicle of the train. Rails are modelled using the Finite Element Method and then decomposed into shorter sections by using the Domain Decomposition Method. Parallel computing is then used to simulate individual track sections by using one computer core per track section. Individual computer cores exchange information about coupler forces, vehicle statuses, wheel-rail contact forces and track domain boundary conditions. Hundreds of computer cores are required, therefore, a High Performance Computer or cluster is required to perform such modelling and simulation