Full title: "Prediction of the interaction between ocean waves and tidal turbine 
arrays through machine learning"

Tidal-stream turbines operate in harsh environmental conditions, with ocean 
waves likely leading to extreme conditions that can trigger large structural 
loads or modulate the turbine’s wake dynamics. This project will adopt and 
extend an in-house high-fidelity numerical simulation tool (DOFAS – Digital 
Offshore FArms Simulator) to represent realistic ocean conditions for bottom-
fixed and floating tidal turbines. This numerical model is essential to 
accurately quantifying the wave-current-turbine interaction, as they allow to 
control the variation of the wave’s characteristics, turbine operating point and 
environmental turbulent flow conditions. Understanding of all the former 
conditions is critical to inform industry about the performance of tidal 
turbines and to develop a machine learning model trained on flow and turbine 
loading data from a set of flow conditions that would allow to extrapolate to 
the any wave climate (wave height and period) to could be found at any tidal 
site.

High-fidelity simulation data will be used to build the machine-learning model 
to be then embedded into a GPU-accelerated blade-element momentum solver that 
will be made open-source to enable direct impact with world-leading industry, 
with whom the supervisory team has established collaborations for more than a 
decade. This multi-fidelity model integration is ground-breaking.

The project has two main objectives:

O1: Generation of a comprehensive dataset from large-eddy simulations using 
actuator line method which also needvalidation for laboratory- and full-scale 
tidal-stream turbines.

O2: Inform a machine-learning using both LES and blade-element momentum code to 
enable turbine loading calculations at unprecedented speed which will be 
expanded for a wider range of wave conditions.

Eligibility

Candidates must have a 1st or high 2i in a degree, ideally at Masters level, in 
an Engineering subject, Physics, Mathematics, or Atmospheric Sciences. Knowledge 
in fluid mechanics, numerical methods and computational modelling is necessary. 
The student is expected to have prior experience writing codes on Fortran or 
C/C++, and experience on Linux systems. No previous experience with machine 
learning is required, although it would be advantageous. The ideal candidate is 
expected to have a strong interest in renewable energy, be enthusiastic about 
physics-based computational modelling, be able to have a proactive attitude 
towards problem solving independently, and ability to work in multidisciplinary 
teams.

This project has to start between 1st July 2025 and 30th September 2025.

Before you apply

We strongly recommend that you directly contact the supervisor(s) for this 
project before you apply: Dr Pablo Ouro (pablo.ouro@manchester.ac.uk) and Prof 
Tim Stallard (tim.stallard@manchester.ac.uk). Please include details of your 
current level of study, academic background and any relevant experience and 
include a paragraph about your motivation to study this PhD project.

Funding Notes
This is a fully-funded Strategic Doctoral Landscape Award (EPSRC-DLA) 
Scholarships from the School of Engineering. Funding to Home / UKRI level 
(£19,237 for 2024/25) is preferred and enquiries from exceptional overseas 
candidates welcomed (which need to have an excellent MSc/MEng thesis in a 
related topic, and/or a track record of scientific publications and/or 
experience working in the offshore renewable energy industry).

This project is also eligible for the Osborne Reynolds top-up Scholarship which 
provides an additional £1500 per year top-up to other funding sources for 
outstanding candidates. Successful applicants will be automatically considered 
for this top-up.