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Continuum: Available Projects

New methods for fluids, plasma, porous media and composites for technological solutions.

Available Projects for Autumn 2023 entry

For further guidance on how to apply, student funding and the HetSys training programme, please visit the Study with Us page.

Project Title



Reliable quantum algorithms for plasma and fusion physics

Animesh Datta (Physics), Tom Goffrey (Physics)

The field of quantum computation and simulation seeks to develop efficient quantum algorithms for problems that are classically inefficient to solve and are therefore computationally expensive. Furthermore, a quantum-enhanced simulation must not only perform a hard classical simulation efficiently, but also correctly. The latter goal is particularly important as real-world quantum computers are noisy and error prone.

This project, in collaboration with IBM Research, will develop algorithms for efficient quantum simulation for plasma and fusion physics problems, and establishing their reliability in real-world quantum computers. The project is ideal for a student interested in a close interplay of quantum computation and simulation with plasma physics.


Quantum Information

Resisting the pressure: phase-field approach for composites under hydrogen environment

Lukasz Figiel (WMG)

When exposed to pressurized gaseous environments, composite materials can exhibit microscale damage phenomena such as micro-cavitation. Understanding of those damage phenomena in the presence of tiny gas molecules such as H2 is critical for future applications of composites for H2 storage. Here, we aim to develop a new chemo-mechanical phase field model that will predict onset and propagation of microscale damage as a function of material composition, hydrogen concentration/pressure, and loading conditions. The model will be experimentally informed (model parameters, microstructure) using a Bayesian approach.


Alternative protein sources: growing the next generation computational modelling framework
Radu Cimpeanu (Maths), Ferran Brosa Planella (Maths)

The alternative protein space represents one of the most dynamic scientific areas at present. Shrinking usable land mass, even accounting for agricultural advances, means a sustainable future is tightly linked with our ability to create and support clean food sources. Computational modelling has an immense (and largely untapped) potential to innovate a technological space still in its infancy. What about this HetSys project? We will use a combination of state-of-the-art techniques - multi-physics fluid mechanics, high performance computing and data-driven approaches - to design a versatile open-source modelling framework supported by fantastic experimental and industrial collaborators from around the world.


Mathematical and statistical foundations; Smart fluids

Developing the capability to forecast extreme Space Weather events

Ravindra Desai (Physics) Jeremie Houssineau (Statistics), David Jackson (UK MET Office)

Extreme Space Weather is driven by large-scale eruptions from the Sun called coronal mass ejections. Upon arrival at the Earth, these produce amazing auroral displays but also endanger satellites and astronauts, and disrupt communications and power grids. Forecasting these events is of primary importance to the UK MET Office, one of three centres world-wide providing round-the-clock space weather predictions. This project, in collaboration with the UK MET Office, will develop state-of-the-art plasma simulations to forecast extreme Space Weather and develop advanced statistical techniques to quantify the uncertainties in their prediction.


Mathematical and statistical foundations; space weather

Blending ultrasound data with physics-based models to predict damage in structural systems

Emmanouil Kakouris (Engineering), Rachel Edwards (Physics)

Monitoring of damage degradation in materials is vital as any cracks will reduce local stiffness, accelerating the ageing process of the physical assets. In-situ ultrasonic monitoring data is valuable for giving the inspectors information on the level of what may be wrong with the structural systems. However, purely data-driven representations do not always give inspectors enough information and the ability to predict how the system will behave in the future. This can be achieved by utilising some knowledge of the physics that underpins the system. This project will use the fusion of experimental data and physical models for failure prediction of materials, by taking measurements and developing models that are hybrid in nature.


Materials characterisation; Mathematical and statistical foundations; Alloys; Composites; data-driven computational modelling of materials

Probabilistic learning for time-parallel solvers of complex models

Massimiliano Tamborrino (Statistics), Tim Sullivan (Mathematics\Engineering)

Complex models in science often involve solving large systems of differential equations (DEs), whose study may be strongly limited by the wallclock time to numerically integrate them in time. For example, turbulent fusion plasma simulation can take 100-200 days to integrate over a time interval of 1s.
To tackle this, time-parallel integration methods have been proposed, but they: 1) do not account for the underlying uncertainty (e.g. model misspecification, numerical or observation errors); 2) do not scale well for high-dimensional DEs; 3) have not yet been embedded within parameter estimation algorithms. This PhD project aims to fill in one or more of these gaps.


Mathematical and statistical foundations; Uncertainty quantification; fluid plasma simulation; time-parallel solvers; simulation-based schemes