Joao Pombo | Associate Professor
Heriot-Watt University | United Kingdom

João Pombo is an Associate Professor in Railway Engineering at Heriot-Watt University, UK, with a PhD in Mechanical Engineering by the University of Lisbon, Portugal. He is a specialist in railway engineering with 20 years’ experience and has been involved in Research and Development (R&D) activities involving train & track dynamics, vehicle-infrastructure interaction, condition monitoring, predictive maintenance, structural analysis, wear and optimization. He has led and participated in several projects in close collaboration with leading international research centres and industry players. He also has more than three years’ working experience at BOMBARDIER and ALSTOM. João Pombo’s R&D work has been awarded four innovation prizes and he is author of more than 100 papers in refereed journals and international conferences. He is the editor-in-chief of the “International Journal of Railway Technology” and chairman of the "International Conference on Railway Technology" series.

Lecture Summary:
Due to their multidisciplinary, all the issues involving railway systems are complex. Furthermore, the requirements for higher operational speeds, greater loads per axle and the quest for interoperability of different trainsets in existing and projected networks put an extra level of demand on the ability to study such problems. Therefore, the use of accurate, reliable and efficient computational tools that represent the state-of-the-art and that are able to characterize the modern designs and to predict the vehicles’ performance, by using validated mathematical models, is essential.
In this work, the development of computational tools that are able to model with detail the vehicle, the infrastructure, including the track and the overhead power line (catenary) and the interaction among them, is presented. Instead of using the traditional approach, in which these subsystems are handled independently, here they are integrated in common, reliable and user friendly modules that allow studying the vehicle and track performance and to access their long-term behavior in order to predict their maintenance intervals and life-cycle costs. The methodologies are validated in close collaboration with the railway industry using real data or experimental testing.