Numerical simulation of wind turbine wakes

Abstract

In the context of global warming and climate change, wind energy emerges as an essential renewable source for global-scale energy transition. Much has been learned about the interaction between the atmosphere and wind farms through field experiments and computational simulation techniques. However, some processes are still not fully understood, such as the wake effect of wind turbines, which can potentially impact the efficiency (and consequently, the revenue) of wind farms. This project was conceived to gain a better understanding of this effect under different atmospheric conditions and wind characteristics. To achieve this, the Weather Research and Forecasting (WRF) Numerical Weather Prediction model was used in conjunction with Large Eddy Simulation (LES) techniques to simulate atmospheric turbulence. Additionally, a generalized actuator disk (GAD) model parameterized the behavior of a wind turbine. The validation of the simulations will be conducted by comparing the obtained results with data collected from the experimental SWiFT wind farm located at the National Renewable Energy Laboratory (NREL) in the USA. The SWiFT farm features two anemometer towers and three wind turbines, one of which is equipped with a nacelle lidar. The student will first be introduced to the foundational literature in the field and will then commence simulation work, utilizing remote Linux-based clusters and data processing in Python. At the conclusion of the project, research articles related to the study will be published in conference proceedings and events.