Division of Aerospace Engineering and Fluid Mechanics
Our research is organised in 3 themes:
1. Aerospace technologies
QMUL has been a European research leader in spacecraft electric propulsion for nano- and micro satellites. On the propulsion theme, our research topics also involve plasma- and hydrogen - powered actuators and thrusters in aerospace, including feedback control of actuators and thrusters for regulated (controlled) delivery of thrust, energy, or power in application to aircraft actuators and thrusters. In addition, our research interests include high temperature superconducting devices: actuators, motors and energy storage devices (batteries) and high altitude space planes for long distance civilian travel.
Our Division also has a strong track record in aeroacoustics in the area of community noise modelling (jet noise, jet installation noise, and aerofoil noise) with our projects being supported at the national and international level. Most recently, our research topis have included aerodynamics and aeroacoustics of Urban Air Mobility. The key enabling technology for aeroacoustic simulations is based on high-resolution numerical algorithms and massively parallel computing (e.g. Graphics Processing Units) with combining tricks of metamodel-based optimisation and Machine Learning.
2. Optimisation and operational research
The core expertise of the division is in the development of new optimisation algorithms and coupling them with extremely demanding physics-based simulations (e.g. optimisation coupled with CFD that often results in a very large number of design parameters), as well as in software engineering for integrating complex system components, e.g. design, improvement and installation of integrated systems of humans, materials, information, equipment and energy.
Digital Twins for all pervasive use of simulation in the whole lifecyle of products from design to operation, and decomissioning. All aspects of life, not just 'planes, trains and automobiles', but also e.g. buildings, factories.
Methods: (1) Robust multi-fidelity modelling using Machine Learning (ML): pervasive use of multi-fidelity modelling with accurate error estimation to have ML trade-off between fidelity and cost and (2) Modelling for uncertainty: move away from the current static analysis model (geometry and parameters are assumed to be known exactly) to include their stochastic variation / evolution over time)
3. Multiscale fluid and solid mechanics
We have broad expertise in single-phase and multi-phase mechanics across scales, with unique strengths in aero- and hydrodynamics, fluid dynamics of printing, fluid-structure-interaction, nano-fluidics including flows around nanomaterials, and mesh-less methods.