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In-situ, continuous monitoring of offshore wind turbine blades


   School of Mechanical and Design Engineering

  Dr Antigoni Barouni,  Applications accepted all year round  Self-Funded PhD Students Only

Portsmouth United Kingdom Energy Technologies Environmental Engineering Mechanical Engineering Materials Science

About the Project

Applications are invited for a self-funded, 3 year full-time or 6 year part-time PhD project.  

The PhD will be based in the School of Mechanical and Design Engineering and will be supervised by Dr Antigoni Barouni, Dr Nikos Nanos and Dr David Begg

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

Project description

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.

General admissions criteria

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.

Specific candidate requirements

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 () 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.

Please quote project code SMDE6060521 when applying.


Funding Notes

Self-funded PhD students only.
Please View Website for tuition fee information and discounts.

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