Norwich Research Park Featured PhD Programmes
University of Southampton Featured PhD Programmes
University College London Featured PhD Programmes

Developing high-fidelity digital twins to advance the design of next generation floating offshore wind turbines

   Department of Engineering

This project is no longer listed on and may not be available.

Click here to search for PhD studentship opportunities
  Dr S Campobasso, Dr A Nixon  No more applications being accepted  Competition Funded PhD Project (European/UK Students Only)

About the Project

This cross-disciplinary project aims to substantially reduce Floating Offshore Wind Turbine (FOWT) development uncertainty, which limits the full exploitation of global wind energy resources.
Unlike fixed-bottom turbines, which cannot be installed deeper than 50 meters, multi-megawatt FOWTS can be used in deeper waters, and can tap into greater and higher-quality (e.g. less turbulent) wind resources further away from the coastline. However, further from the coast is a more extreme environment, with FOWT stability, efficiency and durability determined by the interaction of aerodynamic loads on rotor and tower, and wave- and current-induced loads on floater. Reliably assessing these loads in harsh metocean states is paramount to designing the system control tasked with guaranteeing power generation with minimal fluctuations, fatigue load alleviation, and overall system stability achieved by controlling aerodynamic damping. The current uncertainty affecting low-fidelity engineering codes (originally designed for fixed-bottom turbines) for FOWT development presents a significant challenge for the long-term success of FOWTs. Nobody has investigated this uncertainty using FOWT control functionalities in Navier-Stokes Computational Fluid Dynamics (NS-CFD) simulations required for conclusive reliability assessment and improvement of modern FOWT engineering codes.
The project addresses this challenge by developing novel NS-CFD functionalities in the FOWT finite volume NS-CFD code COSA, featuring efficient high-performance-computing (HPC) functionalities. The key research and development tasks are to: a) implement accurate and parallelizable sliding mesh functionalities in COSA to simulate individual blade pitch control responding to wind gusts and wave-induced turbine motion, b) develop Graphic-card-unit (GPU) computing into the existing distributed-memory COSA. The key research objectives are to: 1) assess the correlation of engineering codes and high-fidelity predictions by analysing design-driving FOWT regimes using state-of-the-art engineering codes and the new GPU-supported and pitch-control-enabled COSA code, 2) analyse key FOWT physics and advance the knowledge in this area, 3) assess shortfalls of engineering codes and correct their low-fidelity models to provide the FOWT industry with more reliable FOWT design capabilities.
This research may also involve using OpenFOAM, is linked to and will interact with a 3-year UK Engineering and Physical Sciences Research Council (EPSRC) research project on numerical/experimental analysis of FOWT extreme loads (EPSRC grant EP/T004274/1), will be carried out in collaboration with Politecnico di Milano, and will make use of state-of-the-art national and international HPC facilities, including computer clusters of standard CPUs and new clusters of GPUs. The project will be supervised and coordinated by Dr. M.S. Campobasso (Department of Engineering), and have key supervisory contributions also by Dr. A. Nixon (Department of Mathematics and Statistics), and Dr. P. Garraghan (School of Computing and Communications).
The research project consists of four key stages. Stage 1 regards primarily training/literature survey. It may include attending ARCHER UK-supercomputing-service programming and HPC courses, and Dr. Campobasso’s Postgraduate course Renewable Energy, and may also include attending Erasmus-supported Wind Energy courses abroad. Stage 2 focuses on developing parallelisable sliding mesh functionalities, a task requiring advanced discrete geometry, mathematical modelling and programming skills. Stage 3 consists of developing and testing the GPU parallelisation of the new COSA code. This work will also benefit from ongoing research collaborations with the UK supercomputing service at EPCC, University of Edinburgh, who will also provide support and advice on this task. Stage 4 consists of assessing special design-driving aspects of FOWT physics with the developed high-fidelity functionalities and engineering codes, and improving low-fidelity codes. This activity will also be supported by Politecnico di Milano, and visiting that institution is also planned to support this activity.
The Scholarship is available to UK and EU students. The successful candidate will have at least a second higher UK honours undergraduate degree at the level of MEng etc, or international equivalent, in Mechanical or Aeronautical Engineering, Applied Mathematics, Computer Science or other related scientific areas. Previous experience with and/or development of CFD, and some programming skills and expertise are essential. Highly desirable are knowledge in aerodynamics, turbomachinery and/or mathematical modelling. The candidate will also have very good and certified (e.g. IELTS) written and spoken English skills, will be willing to work independently and propose and explore new ideas, and be also a good team player, working in small groups when required.
To apply please send to Dr. Campobasso ([Email Address Removed]) your CV, and a brief statement of why you want to pursue this PhD.

Funding Notes

This 3.5-year Scholarship is a Lancaster University Natural Sciences PhD Project, and is funded by Lancaster’s Faculty of Science and Technology. Funds cover tuition fees and a maintenance grant of about £15,000 per year in line with UKRI rates. The PhD is due to commence in October 2020.


Please send two Academic references to Dr. Campobasso (
Search Suggestions
Search suggestions

Based on your current searches we recommend the following search filters.

PhD saved successfully
View saved PhDs