PhD Studentship: Aeroelastic stability

Application deadline

22 May 2026

Start date

1 October 2026

This studentship is based in the College of Arts, Technology & Environment.

The drive to decarbonise electricity has accelerated the growth of offshore wind turbines with a particular focus on Floating Offshore Wind Turbines (FOWTs) to allow deployment in deeper waters, as they enable wind energy deployment with strong, steady and consistent winds. However, the structural performance of the wind turbine blades is expected to be strongly influenced by complex platform motions, unsteady aerodynamics, & structural flexibility. Existing design tools either rely on low‑ to mid‑fidelity models with limited predictive accuracy or on high‑fidelity numerical methods such as Computational Fluid Dynamics (CFD) that demand substantial computational resources. As a result, systematic exploration of next-generation floating offshore wind turbine blades remains challenging.

This PhD project will develop a physics-informed deep learning framework for the prediction of the aeroelastic stability of FOWTs that is fast, accurate, interpretable, and uncertainty-aware. The research includes the following key objectives:

1. Building a validated dataset based on high-fidelity CFD models with geometric nonlinearities & blade structural oscillations for various representative FOWT blades under different operating conditions. 
2. Developing, optimising and validating a Physics-informed Neural Networks (PINNs) architecture for rapid and accurate estimation of blade aerodynamic loads, flow fields, power output, aerodynamic damping, and stability margins, considering various sources of flow unsteadiness.
3. Design exploration with the surrogate model to support fast parametric studies, sensitivity analyses, and innovative blade designs for floating offshore wind turbine systems, to highlight the significance of accelerating early‑stage decision‑making and reducing reliance on repeated high-fidelity simulations.

The core methodology is the development of a PINNs architecture that incorporates the flow physics of unsteady aerodynamics, integrated with structural oscillation of the blade, directly into its loss function. The model learns physically consistent aeroelastic responses rather than relying solely on data-driven fitting. Training will be supported by a high-resolution CFD dataset with key design variables, including wind speed, turbulence intensity, yaw misalignment, and blade structural oscillation. The PINNs framework will output detailed aerodynamic and aeroelastic quantities such as unsteady flow fields, aerodynamic loads, aerodynamic damping and power output. The anticipated outcome is a computationally efficient, physics‑consistent surrogate capable of rapidly assessing flutter onset, divergence, & aeroelastic instabilities. The proposed AI-accelerated framework will bridge a key gap in the design of next-generation floating offshore wind turbine blades.

This project will be supervised by Dr. Shine Win Naung and Dr. Mehdi Rakhtala.

For more information about this studentship please contact Professor Yufeng Yao at Yufeng.Yao@uwe.ac.uk.

Funding

The studentship is available from 1 October 2026 for a period of 3 years, subject to satisfactory progress and includes a tax-exempt stipend, which is currently £20,780 (2025/26) pa.

In addition, full-time tuition fees will be covered for up to 3 years (Home).

How to apply

Please submit your application online. When prompted use the reference number 2627-OCT-CATE09. 

Application deadline

The closing date for applications is 22 May 2026.

Supporting documentation

You will need to upload your research proposal, all your degree certificates and transcripts and a recognised English language qualification is required.

You will need to provide details of 2 referees as part of your application.

Interview dates

It is expected that interviews will take place during June. If you have not heard from us by July, we thank you for your application but on this occasion you have not been successful.

Advert information