Project Title

Description

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

Supervisor:
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.

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Building better batteries: modelling and optimisation of electrode filling

Supervisors:
Ferran Brosa Planella (Maths), Radu Cimpeanu (Maths)

Manufacturing not only has a significant impact on battery performance and lifetime, but also on cost and environmental impact. A key process (yet not a well-studied one) is the filling, in which a liquid electrolyte is incorporated into the battery, occupying the pores in the electrodes. It requires keeping the battery at high temperatures for days, becoming a very expensive process both in terms of time and energy usage. In this project, you will have the opportunity to build exciting new capabilities for modelling and optimisation of electrode filling, with a potential to energise our understanding of battery manufacturing.

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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.

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Developing the capability to forecast extreme Space Weather events

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.

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

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.

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Probabilistic learning for time-parallel solvers of complex models

Supervisors:
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.

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Wastewater reuse for agricultural irrigation is an option to deal with water scarcity and associated threats to food security. Investigations show that treated wastewater may contain micro and nano-pollutants. What is to be explored is the capacity of the soils to act as a filter for purifying wastewater from pollutants. Predicting the filtering capacity of the soils requires a firm scientific foundation for the characterization of basic mechanisms associated with reactive pollutant transport in porous media, and a robust understanding of all relevant processes and properties across the spectrum of relevant length and time scales. This project aims to develop and use machine learning algorithms that quantify the rate of interaction between the micropollutants and soil termed sorption rates.

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