PhD Studentship: Accelerating Offshore Renewable Energy Deployment Through AI Models of the Ocean

Application deadline: 31/07/2024

Research theme: Offshore renewable energy; water waves

How to apply: Please click the 'Apply' button, above.

The design of offshore renewable energy systems should consider realistic ocean extremes which can be complex and highly nonlinear. However, linear models are often used for design due to their low cost, resulting in uncertainty. This project will develop AI models for nonlinear water wave problems, primarily aiming to learn the spatio-temporal mapping from linear (easy to model, widely used) to fully nonlinear wave fields. Both fully nonlinear potential flow models (e.g. OceanWave3D), and smoothed particle hydrodynamics (SPH) models that capture wave breaking, will be used to train the model, covering a wide range of realistic extreme conditions.

The outcome will be an open-source model which will give fast yet accurate fully nonlinear extreme kinematics based on a simplified linear, which can subsequently be used to drive fast models for offshore system design. Findings comes at a critical time for the offshore renewable energy sector as we look to accelerate the design and deployment of floating offshore wind turbines globally.

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline.

Interviews will be on a rolling basis until the position is filled, so applicants are encouraged to apply early.

We strongly recommend that you contact the supervisors for this project before you apply.

The supervisors are:

Dr Samuel Draycott (Samuel.Draycott@manchester.ac.uk),

Dr Alex Skillen (Alex.Skillen@manchester.ac.uk) and

Prof Benedict Rogers (Benedict.Rogers@manchester.ac.uk)

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