AI-based Rigid-Flexible Coupled Dynamic Performance Analysis of Floating Offshore Wind Turbines


   School of Engineering

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  Prof Zhiqiang Hu  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

On purpose of achieving Net-zero target, offshore wind turbines are having to be built and operate in deeper waters and further offshore which allows for harvesting more abundant and consistent wind resources, and Floating Offshore Wind Turbines (FOWTs) is a promising solution. However, reliability and survivability of FOWTs under complex sea states and cost issues have made accurate dynamic performance predictions for FOWTs are of crucial importance for offshore wind industry. 

The aim of this PhD project is to improve the accuracy of dynamic performance analysis of FOWTs and to develop an AI-based numerical programme to solve the aero-hydro-elastic-mooring-servo coupled equations of FOWTs. FOWTs are typical rigid-flexible coupled multi-body systems, and their dynamic performances show high-nonlinear and complicated characters. The combination using of theoretical models and AI technology is a promising solution to this challenge. The programme will be applied for code-to-code comparison via international benchmark project such as OC7 project, as well as with published basin experimental data or full-scale measured data (supported by ORE Catapult).  

The supervisor’s research team has built a solid connection with offshore wind industry and the research outcomes will be validated via industrial engineering practices.  

Newcastle University is committed to being a fully inclusive Global University which actively recruits, supports and retains colleagues from all sectors of society.  We value diversity as well as celebrate, support and thrive on the contributions of all our employees and the communities they represent.  We are proud to be an equal opportunities employer and encourage applications from everybody, regardless of race, sex, ethnicity, religion, nationality, sexual orientation, age, disability, gender identity, marital status/civil partnership, pregnancy and maternity, as well as being open to flexible working practices. 

References:   

  • Software-in-the-Loop Combined Reinforcement Learning Method for Dynamic Response Analysis of FOWTs. Frontiers in Marine Science, Volume 7, Article 628225, January 2021. 
  • Coupled aero-hydro-servo-elastic methods for floating wind turbines. Renewable Energy 130 (2019) 139-153. 
  • Application of SADA method on full-scale measurement data for dynamic responses prediction of Hywind floating wind turbines. Ocean Engineering 239 (2021) 109814 

Application enquiries: 

Leading supervisor: Prof Zhiqiang Hu 

Email: [Email Address Removed] 

Weblink: https://www.ncl.ac.uk/engineering/staff/profile/zhiqianghu.html  

Computer Science (8) Engineering (12)
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 About the Project