PhD Position Experimental Validation of Wind Farm Flow Control

The rapid development of large offshore wind farms is essential for the transition towards a sustainable energy system. However, as wind turbines are installed increasingly close together, they interact through turbulent wind wakes. These wakes reduce the energy production of downstream turbines and can increase structural loading, creating a major challenge for the efficient and reliable operation of large wind farms.

A novel approach developed within our research group, known as Helix, aims to mitigate these wake losses by actively manipulating the wake using individual blade pitch control. While promising results have been obtained, the effectiveness of Helix—and other advanced wake-mixing strategies—strongly depends on the accuracy of the wind farm models used to design and evaluate these control strategies. Improving the predictive capability of these models therefore remains a key scientific challenge.

In this PhD project, you will develop and apply data-fusion methods that combine physics-based wind farm models with wind tunnel and field data. A particular focus will be on exploiting the extensive experimental data generated within the HKN project, including measurements from wind-tunnel experiments and full-scale offshore wind turbines. These data will be used to improve and calibrate wind farm models, quantify their uncertainties, and experimentally assess the performance of Helix and potentially other wake-mixing and wind farm control strategies.

The project will bridge wind farm aerodynamics, advanced control, system identification, filtering, and data-driven modelling, with a strong emphasis on combining physical knowledge with measurements rather than relying on purely data-driven approaches. The ultimate goal is to develop more accurate and reliable models and methodologies for the design, validation, and deployment of advanced wind farm control strategies.

Teaching activities are part of your PhD trajectory and may include, for example: supervising work groups or lab sessions, assisting in courses, or mentoring BSc and MSc students. While teaching will not be your main responsibility, it offers valuable experience that supports your development and prepares you for future academic or professional roles. Teaching activities will not exceed 20% of your total appointment, averaged over the course of your PhD.

• An MSc. degree in systems and control, fluid dynamics wind energy, aeroelastics, mechatronics, applied mathematics, mechanical engineering, or a related field.
• The capacity to communicate effectively with peers, students and stakeholders in the application field.
• Good programming skills are a plus: MATLAB, Python, Git.
• Fluency in English.
• An open personality and good communication skills in written and spoken English.

Working at TU Delft means contributing to solutions that really make a difference. For over 180 years, we have been training engineers who make an impact worldwide in companies, government bodies, or as entrepreneurs. Our alumni turn knowledge into concrete solutions for the challenges of today and tomorrow.

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Working at TU Delft means join an…