Offshore wind energy is growing rapidly in the UK and all over the world with thousands of wind turbines installed annually. With continuous investments in the ongoing and future development plans, the total global offshore wind capacity is expected to increase tenfold to 234 GW by 2030 and approaching 1000 GW by 2050 , . The high installation rate at the moment implies a high and increasing decommission quality in the future, and a solution based on reliability analysis and asset life-cycle management is critical for the offshore wind energy industry in the coming years.
This project will investigate the issues related to wind turbine reliability, asset management, and end-of-life assessment to evaluate offshore wind energy assets technical, economic and environmental impacts. In reliability modelling and analysis, the student is expected to collate and analyse reliability data to identify critical components and uncertainties. These data are, then, used to model the system failure and repair processes and to evaluate the wind turbine reliability and availability and to simulate the operation of the offshore wind turbines. In end-of-life assessment, the classification process of recycling, reuse, re-manufacture, and disposal will be discussed and assessed. The assessment can be transferred to environmental assessment over the entire wind turbine’s lifetime . The results from reliability modelling and analysis and end-of-life assessment are then integrated to investigate the feasibility of possible alternative solutions such as life extension and re-powering , . The outcomes of this project can help quantify the reliability and environmental impacts of different wind turbine technologies, which is vital for the future development of wind energy.
Methodologies related to data analysis, reliability modelling, Monte-Carlo simulation, and environmental life-cycle assessment will be used for this project –.
Students should have research experience, or be willing to develop knowledge and research skills in these topics. Students with a background in engineering, applied statistics or computer science are encouraged to apply.
Enquiries and applications
Informal enquiries are welcome; to apply for this PhD project, please submit a formal application through the University of Bradford web site.