Machine Learning of Wind Plant Turbulence Anisotropy Fields

Machine Learning of Wind Plant Turbulence Anisotropy Fields

Luan, Yuyang (TU Delft Aerospace Engineering)

Dwight, Richard (mentor)

Delft University of Technology

Aerospace Engineering | Aerodynamics and Wind Energy

2020-01-17

In studies of wind plant designs, wake dynamics are of great interests as wakes affect downstream turbine loading that impacts wind plant efficiency. Recent developments of Tensor Basis Decision Tree (TBDT) based machine learning (ML) models in reconstructing the turbulence anisotropy fields of simple flow cases prompt the motivation in applying such models to the more complex wind plant simulations. A significantly more efficient TBDT framework has been developed to tackle large scale flow domains. By training on the Large Eddy Simulation data of a one-turbine case, the ML models can reconstruct the free shear layers in wind plants of various turbine layouts and atmospheric conditions while predicting the correct shape and orientation of turbine wakes. Subsequently, a data-driven augmentation to Reynolds-averaged Navier-Stokes simulations of wind plants has been employed that results in more accurate turbulent fields and turbine outputs, which demonstrates the potential of efficient wind plant simulations of tomorrow.

Machine Learning
CFD
Wind Farm
Turbulence Modelling
LES
RANS

http://resolver.tudelft.nl/uuid:52b5c560-62c6-4e0a-8da7-64928f211b97

Student theses

master thesis

© 2020 Yuyang Luan