Welcome to The Centre for Intelligent Transport
The Centre for Intelligent Transport (CIT) combines complementary strengths in Mechanical and Aeronautical Engineering, Power Systems, Robotics and AI, Digital Design and Manufacturing, and Advanced Materials to drive future transport and mobility technologies that better our world.
The Centre takes pride in being particularly strong in merging world-leading theoretical research with applied, industry-oriented experimental work, creating systems and software tools with high market potential and benefit for society. Our mission is to conduct research of the utmost excellence and to translate it into products with wide and beneficial applications, in close collaboration with industry, international universities and governmental bodies.
Research within the Centre focuses on various scientific and engineering problems relevant to future transport systems and spans from Green Propulsion, Robotics, and Autonomous vehicles to Future Mobility with Environmental and Climate technologies, such as those which help reduce aircraft noise and emissions. We also carry out extensive research in Advanced Materials and Manufacturing for Future Transport.
A cross-cutting theme, which is relevant for many our applications is Data Centric Systems Engineering, which includes Numerical Methods and Simulation and AI. Our research has been supported by EPSRC, EU, Innovate UK, BEIS, Royal Academy of Engineering, and other agencies and companies.
Staff within the Centre for Intelligent Transport are active in delivering research-informed education for our undergraduate and postgraduate students in these broad areas.
News
Recent Publications
- Nanjangud A, Underwood C, Rai CM, Eckersley S, Sweeting M and Bianco P (2024). Towards robotic on-orbit assembly of large space telescopes: Mission architectures, concepts, and analyses. Acta Astronautica, Elsevier BV vol. 224, 379-396.
01-11-2024 - Boscagli L, Sabnis K, MacManus DG, Babinsky H, Tejero F and Sheaf C (2024). Numerical and experimental investigations of diffusion-induced boundary layer separation on aero-engine nacelles. International Journal of Heat and Fluid Flow, Elsevier vol. 109
01-10-2024 - Cheng L, Qi H, Ma R, Kong X, Zhang Y and Zhu Y (2024). FS-PTL: A unified few-shot partial transfer learning framework for partial cross-domain fault diagnosis under limited data scenarios. Knowledge-Based Systems
01-10-2024
Recent Grants
James Busfield
£3,999 Bridgestone Corporation (01-10-2024 - 30-09-2025)
Kshitij Sabnis
£20,000 Royal Society (01-10-2024 - 30-09-2025)
Kaspar Althoefer
£75,000 Ocado Innovation Ltd (01-10-2024 - 30-09-2027)