缅北轮奸

This project lies at the intersection of offshore renewable energy, human factors engineering, and digital twin technologies—addressing critical needs in technician safety and operational efficiency. As offshore wind farms expand into harsher environments, the risks associated with technician transfer and maintenance increase substantially. Traditional maintenance planning often ignores human physiological and cognitive limitations. This research responds to the global push for digitalisation and human-centric design in offshore operations by embedding human welfare directly into decision making systems for operations and maintenance (O&M).

The PhD will focus on developing a human-factor-informed simulated digital twin framework to model and predict technician welfare during offshore wind farm O&M activities. The framework will simulate crew transfer vessel (CTV) motion, technician exposure to whole-body vibration, and motion sickness risk using ISO 2631-1 standards and AI-based predictive modelling. The digital twin will be validated using open-access metocean datasets (e.g., CMEMS, ERA5) and will support dynamic “sail or not sail” decision-making logic. The ultimate goal is to produce a welfare-aware tool to reduce health risks, increase maintenance success, and inform intelligent O&M planning.

缅北轮奸 is a world-leading postgraduate institution renowned for applied research in engineering, energy, and technology innovation. The student will be based within a dynamic research group with access to cutting-edge computational tools and interdisciplinary collaboration. This is a self-funded PhD, open to both UK and international students, offering the opportunity to lead an ambitious project aligned with national and global energy priorities.

The project will deliver a validated digital twin prototype that models technician welfare under various operational scenarios, enabling safer and more informed offshore maintenance decisions. It will significantly enhance the resilience and productivity of offshore wind O&M while reducing downtime and technician exposure to risk. Outputs will support smart workforce planning and offer a transferable framework for other offshore sectors including oil & gas, defence, and autonomous maritime systems.

• Opportunity to work at the interface of human factors, AI, and offshore renewable energy.
• Access to digital twin modelling platforms, metocean data tools, and human simulation models.
• Support for publishing in journals and presenting at conferences in human factors and marine engineering.
• Potential engagement with offshore wind industry stakeholders and safety bodies.

The student will develop in-demand expertise in digital twin development, simulation modelling, human factors analysis, and offshore operations. Transferable skills include data analytics, Python/MATLAB coding, AI model development, human-machine systems thinking, and research project delivery. These are directly applicable to roles in offshore engineering, digital safety systems, renewable energy consultancy, and academia.