This repository provides an implementation of a diffusion-based turbine wake model based on (Ali et al. 2024):
Ali K, Stallard T, Ouro P. A diffusion-based wind turbine wake model. Journal of Fluid Mechanics. 2024;1001:A13. doi:10.1017/jfm.2024.1077
Bibtex entry:
@article{Ali_2024,
title={A diffusion-based wind turbine wake model},
volume={1001},
DOI={10.1017/jfm.2024.1077},
journal={Journal of Fluid Mechanics},
author={Ali, Karim and Stallard, Tim and Ouro, Pablo},
year={2024},
pages={A13}
}
Please cite the above article in case this model is used.
Key points:
- This model is based on the assumption that normal to the streamwise direction, the shape of a turbine's wake behaves similar to the diffusion of a passive scalar.
- The model naturally evolves from a radially uniform shape in the near wake to a Gaussian shape in the far wake.
- The wake length scale is adjusted to take into account the near-wake region.
- The model presents analytical solutions to integrals of the modified Bessel function within the context of enforcing the conservation of linear momentum.
For any enquiries, please contact: karim.ali@manchester.ac.uk
The Python script diffusion_wake.py contains two main functions:
- diffusion_model: which is the implementation of the diffusion-based wake model
- example: an example comparison of the diffusion-based wake model to experimental measurements in the wake of a turbine
The diffusion_model function is called as
diffusion_model(yds, ct, ti, xd, lnw)
where
- yds: a list of values for the lateral coordinate y normalised by the diameter of the turbine.
- ct: the thrust coefficient of the turbine.
- ti: the turbulence intensity of the free-stream flow.
- xd: the streamwise distance measured from the turbine location, normalised by the turbine's diameter.
- lnw: the streamwise extent of the near wake region, normalised by the turbine's diameter.
The function example can be called as
example()
which will create an image named example.png containing the wake comparison.