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Title Description Status Research Student Supervisor
Atomistically-informed continuum interface models for functional composites Functional composites are material candidates for high-energy density applications. Their overall energy density can be enhanced by tailoring constituent dielectric properties, breakdown strength, and interfacial polarisation. In Progress Aravinthen Rajkumar (Cohort 1) Lukasz Figiel
New foundations for electrochemical-mechanical coupling via multiscale simulations In-service performance of functional materials for energy storage-related applications depends on an intimate interplay between various physical-chemical processes, e.g. strain-dependent ionic transport in polymer electrolytes. Classical electrochemical-mechanical (ECM) models, used to predict the optimum performance of those materials, are based on ad hoc assumptions, omitting the origins of the ECM coupling at the nanoscale. Therefore, the ambition of this research project is to develop new foundations for a holistic computational modelling framework that rigorously captures the ECM coupling by bridging vastly different length scales via multiscale simulations of the material and machine learning. Particularly, the transport of charged species taking place in the presence of stress, electrostatic, and chemical potential gradients will be explored from nano to macro scale. Available TBC Lukasz Figiel, Livia Bartok-Partay
Statistics of porous media attributes and mixing processes state variables across scales Typical observations of porous media attributes arise from a variety of techniques which have their own spatial resolution and associated uncertainty. Therefore, key statistics of parameters driving transport processes in porous media vary across scales and a scientific foundation for the characterization of basic transport mechanisms requires understanding of all relevant processes across the relevant length and time scales. In this project, we develop a theoretical and computational framework to assimilate data associated with diverse variables collected at a range of scales and combine these to provide predictions of solute dynamics and associated uncertainties. In Progress Alisdair Soppitt (Cohort 2) Mohaddeseh Mousavi-Nezhad
(Truly) Multiscale Simulations of Polymer Crystallization Understanding the crystallisation of polymers is crucial to improve their functional properties. For instance, the strength-to-weight ratio of Kevlar depends on its degree of crystallinity, which impacts its usage in composite materials such as F1 chassis. However, the current theoretical frameworks for the crystallisation kinetics of polymers suffer from severe limitations when dealing with non-isothermal conditions or the presence of nucleating agent. This project seeks to break new ground by using multi-scale simulations (from molecular dynamic simulations to lattice models) to build a machine learning model for predicting the non-isothermal crystallisation of heterogeneous polymer mixtures. Available TBC Gabriele C. Sosso, Dr. Lukasz Figiel
Uncertainty Assessment of Solute Mixing in Heterogenous Porous Media The transport of chemical substances in the subsurface is relevant in many different applications. In Progress Matthew Harrison (Cohort 1) Mohaddeseh Mousavi Nezhad