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.

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.

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.

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.

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.