Jiannan Yang

This PhD project at the University of York focuses on developing advanced dimension-reduction techniques for digital twin (DT) models in the offshore wind industry. Digital twins are revolutionizing the design, operation, and predictive maintenance of dynamic engineering systems, offering enhanced accuracy and adaptability. However, their practical deployment is hindered by the 'curse of dimensionality,' as high-fidelity simulations of turbine blades and structures involve thousands of degrees of freedom, making real-time applications computationally intensive. The successful candidate will pioneer new mathematical and computational methods to jointly reduce both the parameter and state spaces of dynamic engineering models, enabling efficient, interpretable, and deployable digital twins for offshore wind applications. The research aims to construct parameter-to-state mappings that capture the influence of uncertain inputs on system dynamics, develop a unified framework for joint dimension reduction, and validate these methods on offshore wind turbine models, particularly for high-dimensional fatigue prediction and design optimization tasks. The project is closely aligned with international initiatives such as the IEA Wind Task 43 on Digitalisation, contributing to global efforts to enhance the cost efficiency, reliability, and longevity of renewable energy assets. A key feature of this PhD is its strong industrial collaboration with OWC, part of the ABL Group, a leading global renewable energy consultancy. The candidate will be co-supervised by industrial experts, gain access to exclusive operational datasets, advanced fatigue modelling workflows, and state-of-the-art simulation tools, and undertake industry placements in the UK and internationally. This collaboration provides unique exposure to real-world challenges and innovations in digital transformation for renewable energy, allowing the candidate to translate research into impactful industrial methodologies. The position is based in the School of Physics, Engineering and Technology at the University of York. Funding covers home tuition fees, an annual stipend, and a research training and support grant, with limited full studentships available for exceptional international candidates. Applicants should have a strong background in engineering, applied mathematics, physics, or a related field, with excellent mathematical and computational skills. The application deadline is January 31, 2026, for a September 2026 start.