PhD: Data-Driven Wind Turbine Wake Dynamics

DTU Wind; Kongens Lyngby; On-site; DKK 360,000 - 480,000; Full time

Job summary

DTU Wind and Energy Systems in Denmark invites applications for a PhD student position focusing on forcing and nonlinear interactions in wind turbine wakes. The project combines turbulence, high-fidelity simulations, and data-driven reduced-order modelling to advance wake dynamics under realistic conditions.

The PhD is conducted with Imperial College London and Professor Oliver Buxton, including an extended research stay in London.

Qualifications

• Background in engineering, physics or similar.
• Understanding of turbulence and wake aerodynamics.
• Experience with data-driven modelling and large datasets.
• Proficient in scientific programming, e.g. Python.

Responsibilities

• Perform CFD simulations of wind turbine wake aerodynamics under varied conditions.
• Investigate data-driven methods for accuracy and efficiency to model wake dynamics.
• Conduct uncertainty quantification and validate against high-fidelity results.
• Provide physical interpretation of constructed models and present results at conferences.

Skills

Data-driven methods

POD

Autoencoders

Python

Uncertainty quantification

Large data handling

Turbulence knowledge

Education

Master's degree in engineering/physics (or equivalent)

Tools

OpenFOAM

Python libraries (NumPy/SciPy)

Job description

DTU Wind and Energy Systems in Denmark invites applications for a PhD student position focusing on forcing and nonlinear interactions in wind turbine wakes. The project combines turbulence, high-fidelity simulations, and data-driven reduced-order modelling to advance wake dynamics under realistic conditions.

The PhD is conducted with Imperial College London and Professor Oliver Buxton, including an extended research stay in London.