In-situ, continuous monitoring of offshore wind turbine blades
PhDs and postgraduate research
Funded PhD Project (UK and EU students only)
School of Mechanical and Design Engineering
4 May 2021 (12pm GMT)
Candidates applying for this project may be eligible to compete for one of a small number of bursaries available; these cover tuition fees at the UK rate for three years and a stipend in line with the UKRI rate (£15,609 for 2021/22). Bursary recipients will also receive a £1,500 p.a. for project costs/consumables.
The work on this project could involve:
- Experimental investigation of optimum sensory strategy on the sample blade
- Post-processing of already existing operation data and signal processing techniques
- Development of a Finite Element (FE) model to simulate and predict the blade’s performance under dynamic loading
The structural health monitoring of offshore wind farms and their maintenance are major challenges for the renewable energy sector due to the large scale, the high cost and the extreme environments that are involved. Various unpredictable and random events, such as lightning strikes, foreign object impact, ice and moisture intrusion can give rise to damage on the wind turbine blades, which is mainly dealt with during the annual inspection of the blade, in which case the cost from the downtime can be very significant. This cost could be remarkably mitigated if an in-situ monitoring system of the state of the blade existed, which would be able to identify the damage at its infancy.
The goal of this project is to design an efficient structural health monitoring (SHM) system for wind turbine blades that uses blended sensory approaches, such as Acoustic Emission (AE), Ultrasonic guided waves (e.g. phased array ultrasonic techniques) and/or fibre Bragg grating (FBG) sensors. The aim of this project is to work closely with industrial partners into developing the necessary framework that will enhance the Operational and Maintenance aspects of existing wind-turbine installations in the short-term, where operational data from existing systems will be supplied by Insensys Ltd and, will set the foundation of real time SHM on new stock in the medium to long term.
The physical experimentation work will be conducted using laboratory scale model testing measuring performance of the SHM framework. In that effect, small scale “blade” specimens, of known characteristics will be manufactured, both with and without defects to test and analyse the detection capabilities of the different SHM arrangements measuring the system’s repeatability and validity under controlled conditions.
This highly industry-relevant research will pave the way for industry uptake in the renewables energy sector. This project aligns with the Future and Emerging Technologies Research Theme of University of Portsmouth, which aims to develop ground-breaking solutions and innovations for the practical applications in wind turbine blades design.
You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a Master’s degree in an appropriate subject. In exceptional cases, we may consider equivalent professional experience and/or qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.
You should have a solid background in structural mechanics and an interest in composite materials. Good knowledge and experience in programming (ideally Matlab) and in Finite Element analysis (ideally Ansys or Abaqus) is desirable.
How to apply
We’d encourage you to contact Dr Antigoni Barouni (firstname.lastname@example.org) to discuss your interest before you apply, quoting the project code.
When you are ready to apply, you can use our online application form. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘How to Apply’ page offers further guidance on the PhD application process.
If you want to be considered for this funded PhD opportunity you must quote project code SMDE6060521 when applying.