Research Themes

Mathematical physics-based engineering flow models

As many wind turbines in wind farms have to operate in the wakes of upwind turbines, they are exposed to incoming wind velocities that are smaller than those under unperturbed (unwaked) conditions. As a result, turbine wake flows are responsible for important power losses in wind farms. Despite the rapid growth of flow measurement technologies and numerical simulation techniques over the last few decades, fast-running engineering models are still the most popular tools in industry to characterise and predict flows within wind farms. This is mainly due to their low computational costs and ease of use.  The main aim of our research is to employ fluids mechanics and turbulence research to systematically develop simple and yet reliable physics-based wind farm  flow models.  High-fidelity experimental and numerical data are used to validate and calibrate these models.  

The engineering wake models that we have developed have been widely used in industry to predict and optimise the efficiency of wind farms (i.e., layout optimisation). They are implemented in several wind software packages such as FLORIS (NREL, US), and PyWake (DTU, Denmark), among others.

Experimental analysis of wind energy aerodynamics

A deep understanding of wind turbine aerodynamics and their interaction with adjacent turbines is essential for designing optimised wind energy projects. Laboratory experiments under fully-controlled conditions are valuable tools that help us achieve this goal. Our research objectives are: (i) design optimum miniature wind turbines and analyse their power and load response to atmospheric turbulent flows in laboratory environments, (ii) detailed study of wind flow distribution around wind turbines and wind farms using advance measurement techniques such as PIV and LDV.

PIV measurements downwind of a miniature wind turbine

Vorticity distribution downwind of wind turbines (Bastankhah and Porte-Agel 2017, POF)

Graphical summary of the results published in Bastankhah and Porte-Agel 2017, POF

Wind farm control (WFC) strategies

The need for more efficient energy production from wind farms has dominated research in the industry. A strong interest has been given to the possibility of using Wind Farm Control (WFC) strategies to optimise the production by controlling the whole wind farm as an integrated system, rather than just controlling single turbines. Among different approaches, wake steering is currently regarded as the most promising: it consists of intentionally yawing some wind turbines in order to deflect the wake away from the downstream turbines. The objective of our research is to develop robust modelling tools that can increase our understanding and confidence in using novel WFC strategies. Our developed models have been already used in industry to implement wake steering in real operating conditions. 

Wind farm power increase via wake steering (Bastankhah and Porte-Agel 2019)

Energy conversion in a miniature wind turbine (Bastankhah and Porte-Agel 2017, Energies)

Schematic figure of the designed miniature wind turbine (Bastankhah and Porte-Agel 2017, Energies)

Funded Research Projects

CONFLOWS - CONtrol of FLOating wind farms with Wake Steering 

Duration: 2021-2023

Funded by: Innovate UK  -  Bilateral UK and US offshore wind R&D programme

Partners: DNV, Durham University, Marine Power Systems (MPS), NREL, Cornell University, and Equinor

The main objectives of this research project include:

This research will be carried out in cooperation with a US consortium, led by NREL, which has been formed to investigate similar control strategies for fixed offshore wind farms. Sharing of expertise and wind farm data will lead to improved wake modelling techniques which will help bring the technical and economic benefits of wake steering to the growing US and UK offshore wind farm markets. 

Systematic prediction of wind farm wakes: an emerging challenge in offshore wind sector 

Duration: 2020-2022

Funded by: Uppsala - Durham joint Seedcorn Fund

Partners: Durham University, Uppsala University

Offshore wind has grown exponentially over the past decade and is expected to become one of the major sources of renewables in both UK and Sweden within the next decade. To meet this target, many new offshore wind farms will be installed in finite offshore areas with favourable conditions. This will arise an important question about the interaction of neighbouring wind farms with each other, which is far from being well understood. In this collaborative project, we aim at pooling our knowledge and resources to systematically study and model this problem using our cutting-edge experimental, numerical and theoretical tools. Outcomes of this project will improve our capabilities to predict and optimise performance of offshore wind plants.