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Continuum: Available Projects

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


The University of Warwick has been awarded £11m to train PhD students in computational modelling.

Available Projects for a September 2024 start

For guidance on how to apply, student funding, the integrated HetSys training programme and what life is like in the HetSys CDT, please visit the Study with Us page.

Note: We are still accepting applications from UK students. The application window for overseas students has now closed.

For details of all our available projects click here.

Project Title Description
Alpha energy deposition simulations for direct-drive laser fusion

The recent successes at the National Ignition Facility in achieving fusion gain from laser-driven implosions is major step forward for fusion research. For UK based researchers the next step is to work towards a direct-driven laser fusion facility. Such a step requires predictive models for laser system and fuel pellet design. Such codes are also needed now to interpret existing experimental data. Warwick has developed a dedicated fluid code for this work but it is missing some key physics packages. The code needs to include fusion reactions and alpha particle energy deposition. This involves coupling particle based MC codes to the fluid code with quantifiable estimates of uncertainty. The project is part funded and supervised by the Central Laser Facility at the Rutherford laboratory and is part of a much wider UK coordinated fusion programme.

Building better batteries: modelling and optimisation of electrode filling 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 so-called 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.

Frozen In: Predicting Microstructure in Solidifying Droplets

This project is a pioneering study into the microstructural development inside spreading and solidifying droplets (Fig.1e), to solve 21st century challenges such as efficiency-reducing ice accretion on wind turbines (Fig.1f) and poor bonding in the 3D printing of metals (‘MetalJet’ Fig.1b,c). Guided by experts in the latest scientific techniques, you will predict the complex dendritic growth of crystals within a droplet (Fig.1a,d), connect this to engineering-scale mechanical properties and have the opportunity to apply machine learning image processing techniques to guide theory with experimental analyses. Your research will lead to new discoveries and a close interaction with our industrial collaborators.

Hybrid modelling approaches for moving fluid-fluid interfaces around solid obstacles

Interfacial fluid flows around obstacles and through porous materials are key to numerous applications, including filtration, decontamination and manufacturing. For instance, resin must be injected into a porous mesh, without trapping air bubbles, to manufacture composite materials. Interfacial flows are difficult to model and simulate accurately, and in porous media the multiple disparate lengthscales further complicates matters. However, this multi-scale setting also provides beautiful mathematical modelling opportunities. In this project we will develop and use hybrid modelling approaches for moving fluid-fluid interfaces around obstacles, incorporating analytical and computational techniques, to investigate questions such as minimising defects in composite manufacturing.

Laser-plasma interaction modelling for direct drive fusion

The recent successes at the National Ignition Facility in achieving fusion gain from laser-driven implosions is major step forward for fusion research. For UK based researchers the next step is to work towards a direct-driven laser fusion facility. Such a step requires predictive models for laser system and fuel pellet design. Such codes are also needed now to interpret existing experimental data. Warwick has developed a dedicated fluid code for this work but it is missing some key physics packages. The code needs to include surragte ML codes to model the 3-wave laser-plasma interactions which reflect laser energy and generate super-thermal electrons both of which are detrimental to performance. We can model these processes precisely but not fast enough to be inlined into the fluid code. The project is part funded and supervised by the Central Laser Facility at the Rutherford laboratory and is part of a much wider UK coordinated fusion programme.

Machine learning accelerated design of composite materials for hydrogen economy

Hydrogen is a zero-carbon emission fuel with the potential to decarbonise automotive and aerospace industries. Design of super-durable composite materials that can sustain harsh hydrogen environments is critical to achieving decarbonisation goals for the benefit of our planet. Multi-scale modelling methodologies that integrate modelling concepts from chemistry, physics, and engineering, and are accelerated with Machine Learning (ML), are crucial for accurate and efficient design of composites. This project will develop a radically-new predictive platform by combining mechanistic and data-driven approaches within the Bayesian framework. The platform will generate new knowledge and computer design tools, enabling wider exploitation of composite materials in hydrogen economy.

Modelling extreme magnetosphere-atmosphere interactions 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 disrupt communication signals. Accurately modelling magnetosphere-atmosphere energy transfers is important to understanding the evolution of planetary atmospheres as well as developing real-world space weather forecasts. Through collaboration with QinetiQ, this project will develop state-of-the-art plasma simulations to probe magnetosphere-atmosphere interactions during solar storms, with application to characterising their technological impacts as well as to understanding Earth-like exoplanets subjected to more extreme star-planet interactions.

Unlocking their potential: Modelling Accelerated Degradation in Ni-rich Li-ion Batteries

Electric vehicles employ Ni-rich layered oxides for their Li-ion batteries that offer high energy densities but also accelerated degradation. To avoid this degradation, <3/4 of the available lithium is used.To reach electric vehicle targets for the next decade,design strategies are needed to increase battery cycle lifetimes. Recent battery studies have revealed the Li-ions can get trapped behind atomically thin surface layers formed by the oxygen loss. Modelling the transport properties across these boundaries is critical for identifying and evaluating engineering solutions. This PhD project will have access to unique battery studies at Warwick to test their models.

Filled Projects:

Artificial Intelligence driven multi-physics phase field fracture simulations for composites

Composites are widely adopted by automotive, aeronautical, and structural engineering due to their enhanced properties, yet their complex heterogeneous structure presents several challenges. Fracture is recognised as the main one, as it impacts composite safety, and when coupled with other physics, can lead to complex thermo-mechanical damage/failure scenarios. Commercially viable composite structures demand numerical methods adept at handling such complexities. This research aims to utilise the latest computational material modelling techniques to predict complex cracking patterns in composites, followed by creating an AI-driven multi-physics model for fast structural assessments. Outcomes will include enhanced understanding of damage processes, a new approach for investigating damage processes via phase-field fracture simulations, and a method to accelerate simulations using scientific machine learning.

Something in the air: predicting the behaviour of nanoparticle aerosols

The World Health Organisation recently classified air pollution as “the single biggest environmental threat to human health”. The airborne particulate matter thought to be most harmful is at the nanoscale – particles so small that they can evade our respiratory defence systems. Evidence indicates that the shape of nanoparticles has a big influence on health outcomes, but currently there are no ways to detect this property in isolation. This project will combine continuum fluid-mechanics models with probabilistic particle simulators to train a predictive tool capable of inferring shape, and other properties, from measurable quantities and limited experimental data.

Are you interesting in applying for this project? Head over to our Study with Us page for information on the application process, funding, and the HetSys training programme

At the University of Warwick, we strongly value equity, diversity and inclusion, and HetSys will provide a healthy working environment, dedicated to outstanding scientific guidance, mentorship and personal development.

HetSys is proud to be a part of the Engineering Department which holds an Athena SWAN Silver award, a national initiative to promote gender equality for all staff and students.