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Understanding the impact of high traction hybrid locomotive designs on heavy haul train performance
conference contributionposted on 04.09.2020, 00:00 by Maksym Spiryagin, Qing Wu, Christopher Bosomworth, Colin Cole, Peter Wolfs, M Hayman, I Persson
The analysis of hybrid traction with its application to heavy haul application tasks is a very complex problem and is not well studied. Considering that any locomotive field tests for the verification, validation and adaptation of new locomotive designs involve high costs and disruption to commercial operations; the development of the methodology that is based on the advanced simulation approaches and takes into consideration a whole train dynamic behaviour looks very adequate for this purpose. The methodology is built on a system engineering approach that requires knowledge from a range of disciplines (mechanical engineering, civil engineering, computer science and electrical engineering) and is strongly oriented on how to design and manage complex systems and predict behaviour of locomotive traction systems under different heavy haul train operational conditions. As a result, the developed methodology will increase knowledge of high traction locomotive design requirements in order to deliver better performances in rolling stock and train efficiency operations. In the developed methodology, the integration of the disciplines mentioned the above is achieved through a novel, advanced co-simulation and parallel simulation technique. Advanced locomotive modelling principles have been developed and a significant number of components have been included in order to evaluate locomotive behaviours. The components considered include existing AC and hybrid locomotive designs, full locomotive traction system models, the application of exact wheel-rail contact theory, train dynamic behaviour and in-train forces, comprehensive track infrastructure modelling, rail friction processes and parallel computing. This new simulation methodology represents a Software-In-the-Loop (SiL) testing technique which has been implemented on CQUniversity’s High Performance Computing (HPC) cluster. Confidence in the results delivered through simulations is based on specific validation approaches that include a locomotive model acceptance procedure, laboratory and field test data.