Multiscale multiphysics structured interfaces

Whilst research in metamaterials (structured materials that can control waves and energy) has undergone a renaissance, the majority of work has focused on controlling a single physical domain - usually light. In contrast, this project seeks to develop a mathematical framework to study, design, and create multiphysical metasurfaces (Maradudin, 2011) capable of manipulating waves across many physical phenomena. Metasurfaces are structured interfaces where two or more physical systems interact, such as solids and fluids. Structured interfaces arise in many real-world applications - anywhere two media meet, from antenna designs, to the interface between the ocean and sea-bed, the foundations of modern buildings, medical implants, and safety-critical power generation components.

Machine Learning for Data Driven Sound Propagation Modelling

This project will develop a series of high-fidelity digital twins capable of encapsulating a number of critical dynamic phenomena, which affect the propagation of sound waves through ocean environments, including internal waves, multi-scale structural thermal and temporal variations and fluctuations, scattering by non-smooth interfaces and boundaries (e.g. semi-submerged structures, sea bed, surface), currents, eddies, and fronts. The resulting sound propagation models, and advanced understanding embodied within these models, will enable the Royal Navy and other users of the ocean, the means to improve the effectiveness of their sonar systems and achieve the best results from sonar deployments and operations.

The developed models will be capable of intelligently adapting their approach and selecting the best solution method based on the input data, computational and operational constraints, desired outputs, and physical configuration. These models will incorporate advances in Finite Element Methods, via GPU parallel computing capability that has been largely untapped for underwater acoustics, along with hybrid semi-analytic coupling methods. Stochastic analysis of physical scattering models, incorporating real-world data, will provide a major step forward in the capability available to investigate dynamic ocean mechanisms. Analysis of simulated and measured oceanographic and acoustic data, including the use of artificial intelligence and machine learning techniques, will be supported by a parallel PhD project in the Department of Mathematical Sciences on Advances in mathematical modelling to study complex sound propagation in an inhomogeneous moving ocean. This will help to identify the properties and behaviour of different mechanisms. New ways of representing the specified mechanisms will be developed, and environment-specific modelling tools and innovative mathematical representations will be implemented.

MUSICA: Modelling UltraSonic Inspection of Challenging defects for Automated analysis

We will develop automated ultrasonic data analysis tools to improve the reliability of detection, sizing, and characterisation of defects which occur in high safety significance industrial plant. The project will develop capability for defects of critical industrial importance, targeting two species: Thermal Fatigue (a service induced species) and Hydrogen cracking (a manufacturing/welding defect). Both species are known to occur and often materially impact plant availability/longevity. This project will also enable capability for other challenging defect species going forward, such as stress corrosion cracks.

Using machine learning and artificial intelligence to improve the tracking of vessels in sonar spectrograms

This PhD project explores creating an AI model that can correctly classify quiet targets in waterfall (sonar) data. Currently, waterfall data is analysed by human operators; however, this is time-consuming and expensive; these human operators outperform traditional automated passive contact follower algorithms, such as the Kalman and Alpha-Beta filters: these filters are susceptible to the abundant underwater noise and struggle with crossing tracks and quiet contacts. In contrast, humans can use their experience to learn how to mitigate the challenging aspects of the task. An automatic detection and tracking model that is more accurate and robust than traditional methods would reduce the human operator’s workload.