Research projects
- Research area
Big data and sensors and digitalisation for the offshore environment
- Institution
University of Hull
- Research project
Digital Twin Logistics of Operations and Maintenance for Offshore Wind Farms
- Lead supervisor
Dr Xinhui Ma (Computer Science Lecturer, University of Hull)
- PhD Student
- Supervisory Team
Prof Nishikant Mishra (Head of Management Systems Subject Group, University of Hull)
Dr Nina Dethlefs (Senior Lecturer in Computer Science, University of Hull)
Project Description:
Operations and maintenance (O&M) of offshore wind turbines play an important role in the development of offshore wind farms. There are a number of challenges offshore. For example, offshore wind turbines are subject to different loads that are not often experienced on shore. More importantly, challenging wind and wave conditions limit the operability of the vessels needed to access offshore windfarms. As the power generation capacity improves constantly, advanced planning of O&M activities has gained vital importance to support achieving reduced downtime, optimised availability and maximised revenue. Considering the above background, this research will focus on developing the most cost-effective approach to allocate O&M resources of service vessels, using the emerging technology of Digital Twins.
This project aims to develop Digital Twins of Operations and Maintenance for Offshore Wind Farms to achieve the most cost-effective allocation of O&M resources. Practical physics and virtual models are paired to predict when maintenance should be performed. All the data flow and processes between the digital models and the physical models will be automatically bi-directionally synchronised via various sensory data. By combining measured data and virtual models, failures can be predicted before they occur. This method can be applied to both offshore wind turbines and service vessels.
The project covers three main aims:
- To develop a virtual digital model of the physical offshore wind turbines to predict future performance and possible failures (type and frequency).
- To develop the digital models of the physical service vessels, which may include helicopter, crew transfer vessels, offshore access vessels, and jack-up vessels, to manage the resources via operational analysis of transportation systems.
- To develop the Digital Twins for O&M to plan cost-effective allocation of resources, simulating repairs by considering short-term weather and long-term climate conditions.
