PhD Studentship - Condition Monitoring and Non-destructive Testing

Funding is available to pay the tuition fee and provide a tax-free (depending on circumstance) stipend at £28,000 pa for 3.5 years for candidates eligible for home-free paying status (UK applicants and EU applicants with settled status). Proposed start date 1^st October 2024. The advert will be removed once the project has been filled so please apply early.

Offshore wind energy is central to UK’s ambition of transitioning to a sustainable future electricity grid. The growth in power generation from floating wind turbines is increasingly further away from shore and utilising the floating wind turbine support structures. This increases the mechanical strain the cable is subjected to, which is enhanced further by in-service oscillatory movement caused by tidal and wave motion. It is unknown whether low frequency physical oscillations in dynamic cable structures pose an intrinsic reliability risk since conventional land cables are typically installed in relatively benign environments. Typically, cable assets contribute around 5-10% of the total investment costs for an offshore wind farm. However, cable failures cause the majority of the offshore power outages and account for approximately 80% of insurance claims in this industry. Hence, research and development of novel condition monitoring solutions are considered critical to safeguard the growth of green energy revolution.

This project will investigate the innovative techniques to continuously monitor the health of dynamic cables during live operation. The project is expected to generate thorough understanding from first principles the failure mechanisms and how they manifest in measurable dynamic cable properties. The research will then exploit this improved understanding to create laboratory demonstrators to validate the proposed techniques. This will pave the way for industrial scale up and a key enabling technology for the renewable energy industry.

Applicants should have, or expect to achieve, at least a first honours degree or a master’s in a relevant science or engineering related discipline. Candidates with 2.1 degree and relevant industrial experiences will also be considered.

Please contact the main supervisor before you apply: Dr. Chen - lujia.chen@manchester.ac.uk

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