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  Enhancing the performance, condition, safety and reliability of offshore wind energy infrastructure systems using optimal inspections and structural health monitoring


   School of Engineering

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

About the Project

These projects are open to students worldwide, but have no funding attached. Therefore, the successful applicant will be expected to fund tuition fees at the relevant level (home or international) and any applicable additional research costs. Please consider this before applying. 

Wind turbines are exposed to frequent structural damage increasing the cost of wind energy generation. To drive these costs down, this project will develop new automated methods for detection of structural damage to wind turbines by examining vibration responses measured by sensors attached to the structure. Dynamic responses will be used to detect differences between healthy and damage structural components (foundations, tower and rotor blades). Numerical structural models (‘digital twins’) will also be built to simulate the degradation across several scales (macro, meso and micro). Data for the research will come from computer simulations and experimental validation will be undertaken using laboratory physical models of wind turbine. This highly industry-relevant research will pave the way for industry uptake of the automated damage monitoring technologies.

Essential Background:

Decisions will be based on academic merit. The successful applicant should have, or expect to obtain, a UK Honours Degree at 2.1 (or equivalent) in Civil (Structural), Mechanical (Structural, Materials), Aerospace, Mechatronic, Electronic, Control Engineering, Applied Physics, Mathematical Physics, Applied Mathematics or similar, is a necessary requirement along with a strong academic background in engineering, applied physics or applied mathematics, with emphasis on mathematics/physics-based components of the degree. Enthusiasm, can-do attitude and exceptional skills in structural mechanics, dynamics or control and mathematical and computer modelling (or strong motivation and clear, evidence-based potential to learn these quickly, efficiently and independently), as well as willingness to engage in experimental work are all must-haves. Preference will be given to applicants who can demonstrate both a clear potential for research excellence and their suitability for the advertise research topic. 

Application Procedure:

Formal applications can be completed online: https://www.abdn.ac.uk/pgap/login.php.

You should apply for Engineering (PhD) to ensure your application is passed to the correct team for processing.

Please clearly note the name of the lead supervisor and project title on the application form. If you do not include these details, it may not be considered for the studentship.

Your application must include: A personal statement, an up-to-date copy of your academic CV, and clear copies of your educational certificates and transcripts.

Please note: you DO NOT need to provide a research proposal with this application.

If you require any additional assistance in submitting your application or have any queries about the application process, please don't hesitate to contact us at

Engineering (12) Mathematics (25)

Funding Notes

This is a self-funding project open to students worldwide. Our typical start dates for this programme are February or October.

Fees for this programme can be found here Finance and Funding | Study Here | The University of Aberdeen (abdn.ac.uk)

Additional research costs / bench fees may also apply and will be discussed prior to any offer being made.


References

Hoell, S., Omenzetter, P. (2016). Optimal selection of autoregressive model coefficients for damage detectability with an application to wind turbine blades. Mechanical Systems and Signal Processing, 70-71, pp. 557–577, doi: 10.1016/j.ymssp.2015.09.007.
Shabbir, F., Omenzetter, P. (2015). Particle swarm optimization with sequential niche technique for dynamic finite element model updating. Computer Aided Civil and Infrastructure Engineering, 30(5), pp. 359–375, doi: 10.1111/mice.12100.
Park, J., Law, K. (2015). A Bayesian optimization approach for wind farm power maximization. Smart Sensor Phenomena, Technology, Networks, and Systems Integration 2015, Proc. of SPIE Vol. 9436.
Wang, L., Shahriar, R., Tan, A., Gu, Y. (2015). Optimal design of wind farm layout and control strategy. Proc. 11th World Congress on Structural and Multidisciplinary Optimisation.

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