Research Assistant in Risk-based Offshore Wind Life Extension

UCL Mechanical Engineering has been pioneering the development of engineering education, having taught the core discipline for over 170 years. In the latest UK Research Excellence Framework (REF 2021) 93% of our faculty’s research submitted was judged “world leading” or “internationally excellent”. As a large multi-specialisation department, the diversity of our research is one of our strengths, leading to many interdisciplinary research opportunities.

Dr Duffour and Prof Fromme are the lead academics of the UCL Wind Turbine Dynamics Group that has expertise in the modelling and simulation of onshore and offshore wind turbines. The rapid expansion of offshore wind electricity generation capacity over the past decades means that many existing offshore wind farms (OWF) will soon come to the end of their design life. In the UK, approximately 14 OWF, accounting for 1.2GW of power generation, are approaching this point by 2030. This research project aims to develop of a risk-based methodology to assess the life extension of the fixed support structures of OWF. This project is funded by the EPSRC through the Supergen Offshore Renewable Energy Hub. The project has two strands: developing and testing a guided wave testing methodology to interrogate wind turbine monopile foundations and (2) Develop a risk-based framework integrating the structural monitoring data from (1) to an overall probabilistic approach to help assess end of life sc enarios.

About the role

The Research Assistant will contribute to developing the risk-based methodology (Strand 2) and assist in the measurement campaigns planned on partner test sites (Strand 1). The position is available for 6 months. The successful applicant will be subject to the usual UCL probationary procedures.

Duties and responsibilities:

- To assist in the guided ultrasonic wave (GUW) monitoring on a scaled laboratory experiments in wave tank and Finite Element Analysis (FEA).

• To assist in the full scale GUW tests to be carried out at the partner sites
• To develop the probabilistic assessment of RUL for structural and foundation subsystems.
• To develop the risk-based decision-making framework for OWT foundation life extension.

- To closely and actively liaise with project partners in UK and overseas concerning different tasks.
- To lead/contribute to publications and progress reports on the research for peer-reviewed international journals and funding bodies as required.
- To attend relevant workshops/training and conferences as necessary. –
- To contribute to the overall activities of the research team and department as required.

About you

Applicants should hold a PhD degree in Mechanical/Civil/Structural Engineering or relevant subject area (e.g. general engineering or renewable engineering with mechanical focus) and an Undergraduate qualification graded at equivalent First Class level in mechanical, civil, or structural engineering or relevant subject area. Programming skills (e.g. Matlab, Python, FEM (e.g. Abaqus)) and a track record in publishing high-quality research, excellent written and verbal communication skills, including the ability to present complex or technical information effectively to a range of audiences are required.

Your application will be enhanced if you are experienced in using, developing or designing Structural Health Monitoring Systems and/or experience in probabilistic framework (reliability) and Bayesian updating.

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