PhD Studentship - Non-contact Measurements of Thin Catalytic Membranes with Ultrasonics x1

Join a project that will combine physics, machine learning, and ultrasonics to design new sensors for the digital revolution in industry. Ultra-thin membranes are produced in many high tech industries. They are the basis of flexible solar panels and electronics, as well as biosensors for medical diagnostics. To automate the production of these membranes we need a real-time virtual representation of them. The focus of this project is to create this virtual representation by transmitting small elastic waves along them, and then measuring these waves with lasers. The speed and amplitude of the waves are key to creating a virtual representation of the membranes.

In a thin solid membrane, if you shake the membrane you can transmit a vibration that can propagate over a metre or more (depending on the material). If you use a laser to measure this wave, the measured signal will contain information about the integrity of the membrane between the transmitter and measured point. These vibrations are known as membrane or lamb waves, which produce small displacements throughout the membrane thickness, so the entire thickness of the membrane is interrogated, including surface changes, and changes within such as debonding. Using Lamb waves forms a practical and quantitative way to measure thin structures and has had a significant impact in finding defects in aerospace structures. We plan to adapt these methods for thin coated membranes.

As part of a team, you will develop mathematical and computational models, as well as lead the experimental work. You will work closely with our industrial partner. You will also be part of a world leading Dynamics group at the University of Sheffield, part of the prestigious Russell Group.

This project is about high throughput characterization and testing of a range of materials which are essential to reach net zero. The project will both use a data based approach, with machine learning, as well modelling and simulations to link the material make up with the Lamb wave characteristics. Finally, measurement (as proposed by this project) is the first step towards digitalisation in materials manufacturing.

Supervisors: Artur Gower & Kirill Horoshenkov

A good degree or Masters in Engineering, Applied Mathematics, or Physics, is required from candidates. Desirable skills: mathematical modelling, numerical methods, good programming skills in any language, self-motivation and a passion for the subject, and excellent communication skills.

This project is open to applicants who are eligible for the Home fee tuition rate only. The studentship covers Home tuition fees, a tax-free stipend at the UKRI PGR rate (£20,780 in 25-26), and a research training support grant. We strongly encourage applications from female candidates.

For application-related queries, please contact Sharon Brown (sharon.brown@sheffield.ac.uk).

If you have specific technical or scientific queries about this PhD, we encourage you to contact the lead supervisor, Artur Gower (a.l.gower@sheffield.ac.uk).

Application Guidance: select 'Doctoral Training Course', and 'Developing National Capability for Materials 4.0'.

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