PhD Studentship - Low Maintenance Reliable Main Bearings for Large Wind Turbines

Supervisor(s)

1. Dr. Yi Li, University of Sheffield, yili@sheffield.ac.uk
2. Dr. Charlie Lloyd, University of Hull
3. Dr. Ashley Willis, University of Sheffield

Enquiries email: yili@sheffield.ac.uk

Subject areas

• Engineering & Technology
• Mathematics & Statistics

Large scale wind farms often consist of hundreds of wind turbines with diameters going up to hundreds of metres. The wakes generated by these turbines interact with each other. The accurate modelling of the interaction between the wakes can have significant impact on our ability to optimise the operations of large wind farms and maximise their energy output.

Models of different levels of fidelity are developed in parallel to model wake-wake interactions. Novel semi-analytical wake models provide efficient estimate of the key mean features. High-fidelity simulations such as large eddy simulations (LES) can provide highly resolved three-dimensional turbulence, which are often used to understand the underlying physics of the flows and to provide detailed databases for the calibration of engineering models.

The scientific question behind these new challenges is how to model or parametrise the non-equilibrium features in the wakes (which in this case are introduced or amplified by the controls). This question has long been at the core of wind farm modelling and is one of the main questions being addressed.

This project intends to focus on a data driven approach, taking advantage of the availability of wind tunnel as well as field data that have been accumulated rapidly. The aim is to synthesise data assimilation (DA) techniques with LES to develop a modelling approach that will improve the understanding and prediction of wake-wake interactions. The application of data assimilation in the context of LES has received only limited research; many questions remain open.

Training & Skills

You will benefit from a taught programme, giving you a broad understanding of the breadth and depth of current and emerging offshore wind sector needs. This begins with an intensive six-month programme at the University of Hull, drawing on the expertise and facilities of the four academic partners in the EPSRC Offshore Wind CDT in Offshore Wind Energy Sustainability and Resilience. It is supplemented by Continuing Professional Development (CPD), which is embedded throughout your 4-year research scholarship.

In addition, the successful candidate will also develop skills in:

1. High performance computing
2. Numerical simulations
3. Modelling
4. Data analytics
5. Numerical optimisation
6. Fluid dynamics

Eligibility requirements

If you have received a First-class Honours degree, or a 2:1 Honours degree and a Masters, or a Distinction at Masters level with any undergraduate degree (or the international equivalents) in engineering, mathematics or statistics, we would like to hear from you.

If your first language is not English, or you require a Student Visa to study, you will be required to provide evidence of your English language proficiency level that meets the requirements of our academic partners. This course requires academic IELTS 7.0 overall, with no less than 6.0 in each skill.

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