Blockage Effects In Large Scale Wind Farms

Lead Research Organisation: University of Oxford
Department Name: Engineering Science

Abstract

EPSRC Project Description:
With the increasing demand for sustainable and renewable energy sources, it is clear offshore tidal and wind energy sources will play a pivotal role in reaching the net zero target. To meet the high demand for renewable energy, new large-scale tidal and wind farms are soon to be constructed. These arrays of multiple machines exhibit several complex interactions that drastically alter their efficiency. One such effect is known as the blockage effect. The blockage effect is the change in efficiency of a turbine due to neighbouring turbines slowing and deflecting the flow field around them. This can work to both increase and decrease turbine efficiency and is most prevalent in large scale arrays with many machines. The blockage effect depends on a number of factors such an inter-turbine spacing and atmospheric stability. Understanding blockage effects poses a complex fluid dynamic problem and will require novel use of computational methods and analytical modelling. This project has a number of aims:
1. Quantify blockage effects in arbitrary size offshore wind farms.
2. Simulate these effects using novel methodologies.
3. Develop mathematical and computational models for predicting the magnitude of the blockage effect.
4. Use this work to inform and optimise wind farm design.
Achieving these aims will be key in maximising renewable energy output.
To achieve these aims, a number of methods must be used. Computational fluid dynamics is a well-researched methodology and will play a crucial role in the simulation of these large-scale dynamic structures. Large eddy simulations (LES) and Reynolds averaged Navier-Stokes (RANS) simulations have seen great success in this field but have high computational complexity putting restraints on the scale of the simulations performed. For this reason, it is vital to explore alternative novel methods. One such promising method is the use of physics-informed neural networks (PINNs). These networks rely on statistical techniques while ensuring physical properties, such as conservation laws, remain unaltered. Training such a model allows for fast computation of otherwise costly simulations. In addition, such techniques can be used to enhance existing simplified models such as the actuator disk model.
The project's impact extends to academia, industry, and the public. The public are becoming increasingly concerned with the environmental impact of their energy supply. In response, governmental entities are formulating ambitious plans for expansion to address these concerns. The execution of such plans requires collaboration with industrial partners, who play a crucial role in producing efficient and financially viable products. Thus, optimal design in energy production is heavily sought after by many parties and will be invaluable in the transfer to net zero emissions.
This project falls within the EPSRC engineering theme as well as the EPSRC energy and decarbonisation theme and is part of the EPSRC Wind & Marine Energy Systems & Structures (WAMESS) Centre for Doctoral Training (CDT).

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S023801/1 01/04/2019 30/09/2027
2887694 Studentship EP/S023801/1 01/10/2023 30/09/2027 Dylan Green