Sort by
Refine Your Search
-
AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
engineers detect faults earlier, track system degradation, and make better-informed maintenance decisions. But how can we turn this complex information into something reliable, explainable, and actionable
-
of artificial intelligence (AI) nowadays, it has become possible to develop a fast-response AI-based condition monitoring system for gas turbine engines. The objective of the project is to develop novel AI-based
-
substrate, enabling the layer-by-layer construction of complex 3D objects. The temperature field created by the interaction between the electric arc and the material is a critical factor influencing the
-
involves feeding a metal filler wire, either coaxially or off-axis, into an electric arc to create a molten pool that solidifies on a substrate, enabling the layer-by-layer construction of 3D objects
-
lower orbit space debris). The increasing density of space objects in Lower Earth Orbit (LEO), including the proliferation of satellite constellations, further exacerbates the risk of collisions and the
-
-driving cars. This PhD research will be supervised by Professor Daniel J. Auger and Dr Abbas Fotouhi . The centre has a strong track record in battery systems research; members have pioneered state
-
to the development of digital twin technologies for sCO2 power generation systems. The Centre for Propulsion and Thermal Power Engineering has a key focus and a proven track record on gas turbine performance, gas path
-
publication-oriented, allowing you to build a strong academic and professional track record in energy-efficient and secure AI hardware innovation. By the end of this PhD, candidates will possess advanced
-
managed to allow for efficient solar energy harvesting. This project will deliver novel methods for modelling and controlling LGS structural dynamics in the extreme orbital environment. The objectives
-
of their machines is maximised, or machine downtime is minimised. The aim is to develop a smart sensor prototype and demonstrator for condition monitoring of low-speed bearings. The following objectives are defined