Computer Simulation Designs Low Carbon Rail Systems
Efforts to support decarbonisation in rail encompass a wide range of activities, such as new components and assets that use power; changing operational practices; and designing and configuring the infrastructure that delivers power.
Individual components and subsystems can deliver benefits which can be amplified when coupled at a system level to maximise energy efficiency. Therefore, system-level computer simulation presents a cost-effective opportunity for the rail industry to accurately predict interactions, which can be used to improve design and inform operational decision making.
However, this approach is not without its challenges, particularly in terms of integrating models from different tools often designed for specific purposes and in diverse formats. Furthermore, suppliers may also be concerned that work aimed at integrating the models, and the data associated with them, might reveal their Intellectual Property.
The Digital Environment for Collaborative Intelligent De-carbonisation (DECIDe) research project assessed the feasibility of a collaborative simulation environment for rail decarbonisation and power optimisation. The environment was underpinned by two key capabilities:
- a multi-model approach using the Functional Mockup Interface (FMI) standard for integrating diverse simulation models and INTO-CPS, a co-simulation environment for running FMI-compliant models
- a model marketplace approach, providing an accessible, secure, cloud-based environment for stakeholders to share and integrate models
The research found FMI can be successfully used to analyse the impact of different configurations of driver strategy and rolling stock design in an urban rail example and adapted for mainline rail applications.
Overall, DECIDe has the potential to lower the barrier to entry and the effort of systems modelling in rail. This is relevant not only to decarbonisation but to other industry challenges, including performance, timetable planning and resource planning.