Centre for Intelligent Transport
Research Themes
- Energy Systems (including Green Propulsion and Power Trains, Simulation and High-Resolution Imaging)
- Future Mobility and Environmental and Climate technologies (including Urban Air Mobility, Intelligent systems, Multimodal Transportation, External Aerodynamics, and Aeroacoustics)
- Robotics and Smart Machines (including autonomous vehicles and AI)
- Advanced Materials and Structures for Future Transport (including Smart and Light Weight Materials and Digital Technology, Structures and Mechanics)
- Next-generation manufacturing systems (including digital fabrication and additive manufacturing)
- Digital Twins (including Data Centric Systems Engineering, Numerical Methods and Simulation, and Optimisation)
Energy Systems
Our research topics involve spacecraft electric propulsion for nano- and micro- satellites, plasma- and hydrogen-powered actuators and thrusters in aerospace, energy, or power in application to aircraft actuators and thrustersFuture Mobility
CIT has a strong track record in aeroacoustics in the area of community noise modelling and traffic management in the area of routing, scheduling and control. Research topics include Urban Air Mobility and learning combinatorial optimisation algorithms over graphs.Robotics and Smart Machines
Research in Robotics covers robot design and mechatronics, human-robot interaction, control and systems engineering, autonomous systems, robotic locomotion, sensing and biomedical mechatronics.Advanced Materials and Structures
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The focuses of this research theme are mechanics and modelling with a coordinated endeavour of solid mechanics, computational modelling and materials science for future transport.Digital Manufacturing
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Advanced additive manufacturing process particularly multi-scale multi-physics approaches to metallic materials for aerospace applications and 3D printing using autonomous robots.Digital Twins
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Optimisation algorithms coupled with extremely demanding physics-based simulations; Robust multi-fidelity modelling using Machine Learning in the whole lifecycle of products.