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

School of Engineering

About the Project

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 physical models of wind turbine tower, foundation and rotor blades. Full-scale, in-situ monitoring will also be considered.

There is a strong interest from the industry in coming up with design and maintenance practices that would maximize the reliability and longevity of wind turbines in a cost-efficient way. A sub-objective of this project is to develop a methodology for optimization of the trades-off between maximizing the structural reliability and minimizing the costs related to initial construction, inspections, monitoring and maintenance throughout the life-cycle of wind turbines and entire wind farms.

The project will also explore how optimisation of the layout and application of control to entire wind farms can yield further savings to the costs of electricity production by reducing the initial construction costs and life cycle maintenance expenditures by choosing the locations of individual turbines and the efficiency of extracting energy from the air flow by actively adjusting the position of rotors considering interactions between multiple turbines.

This highly industry-relevant research will pave the way for industry uptake of the automated damage monitoring technologies.

Candidates should have (or expect to achieve) a UK honours degree at 2.1 or above (or equivalent) in Civil (Structural), Mechanical, Aerospace, Mechatronic, Electronic, Control Engineering, Applied Physics, Mathematical Physics, Applied Mathematics.

Candidates must have strong academic background in engineering, applied physics or applied mathematics. 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 research project described above.


• Apply for Degree of Doctor of Philosophy in Engineering
• State name of the lead supervisor as the Name of Proposed Supervisor
• State ‘Self-funded’ as Intended Source of Funding
• State the exact project title on the application form

When applying please ensure all required documents are attached:

• All degree certificates and transcripts (Undergraduate AND Postgraduate MSc-officially translated into English where necessary)
• Detailed CV

Informal inquiries can be made to Dr P Omenzetter (), with a copy of your curriculum vitae and cover letter. All general enquiries should be directed to the Postgraduate Research School ()

It is possible to undertake this project entirely by distance learning. Interested parties should discuss this with Dr Omenzetter.

Funding Notes

This project is advertised in relation to the research areas of the discipline of structural and mechanical engineering. The successful applicant will be expected to provide the funding for Tuition fees, living expenses and maintenance. Details of the cost of study can be found by visiting View Website. THERE IS NO FUNDING ATTACHED TO THIS PROJECT


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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