School of Computing, Engineering and Technology

Atmospheric Boundary Layer Stratification Effects on Floating Offshore Wind Farm Performance

This research will investigate the impact of stratified atmospheric boundary layer on aerodynamics and power output of floating offshore wind farms. By integrating advanced numerical tools with hybrid machine learning models, the project will improve prediction of real atmospheric conditions, enabling smarter wind farm design and operation in complex environments.

Award

MRes | MPhil | PhD | MSc by Research

Start Date

Usually February and October - at individual School's discretion

Research Opportunities

Summary

Offshore wind turbines operate within atmospheric boundary layers that exhibit complex stratification driven by thermal stability, turbulence intensity, and marine meteorological conditions. Stable and unstable stratified flows significantly alter wake persistence, turbulence recovery, and ultimately wind farm power output. This PhD will investigate how atmospheric boundary layer stratification influences floating offshore wind farm aerodynamics and performance prediction.

The project will develop advanced CFD models to simulate stratified atmospheric boundary layers under realistic offshore conditions. Large-Eddy Simulation (LES) and stability-dependent turbulence modelling approaches will be employed to examine wake behaviour under varying thermal and shear profiles. Coupled simulations will incorporate floating platform motion to assess the combined influence of stratification and structural dynamics.

To enhance predictive capability, the research will integrate hybrid CFD–AI approaches, including Physics-Informed Neural Networks, to construct reduced-order models capable of rapid performance forecasting across a range of atmospheric scenarios. These models will support improved wind farm layout design, power prediction, and operational optimisation.

The student will acquire advanced skills in atmospheric flow modelling, turbulence simulation, offshore wind aerodynamics, and machine learning integration. The project is expected to produce publishable outcomes in leading renewable energy and fluid mechanics journals, and to contribute to improved forecasting tools for offshore wind developers.

Applicants should have a strong background in fluid mechanics, numerical modelling, or renewable energy engineering. Programming experience and interest in turbulence modelling or AI-based methods will be advantageous.