This research investigates near-wall turbulence, the chaotic fluid motion responsible for much of aerodynamic drag in transportation systems. Using high-resolution computational simulations and predictive modelling, the work aims to better understand turbulence near surfaces, enabling more efficient aerospace designs, reduced fuel consumption, and potentially major reductions in greenhouse gas emissions.

This research investigates why supersonic aircraft engines fail under turbulent atmospheric conditions. Using high-performance supercomputer simulations, the study models airflow disruptions around supersonic engines to identify early warning signs of instability. The work aims to improve engine reliability and help revive safe, efficient supersonic passenger air travel.

This research addresses the challenge of building stable quantum computers by modelling superconducting qubits. It develops simulation tools to predict behaviour, optimise design, and reduce errors caused by environmental disturbances. By improving qubit reliability, the work supports scalable quantum computing capable of solving complex problems beyond classical computational limits.