Scientists in the Computational Science Center at the National Renewable Energy Laboratory (NREL) are performing wind-farm computational fluid dynamics (CFD) and structural dynamics simulations that will provide a better understanding of the interactions of wind turbine wakes with one another, with the surrounding winds, and with the loads they impose on turbine blades and other components.
Large-scale wind power generation deployment is a realistic and largely inevitable proposition as energy security, supply uncertainties, and global climate concerns drive the U.S. to develop diverse sources of domestic, clean, and renewable energy. The U.S. is currently on a path to produce 20% of its electricity from wind energy by 2030, which is a 10-fold increase over the current value of roughly 2%.
Although wind energy holds immense promise, there remain critical scientific questions that must be answered about the physical interactions within and between large-scale wind farms, or between wind farms and the environment. These questions arise, in large part, from the complex interaction between the wake that forms behind the turbine and the turbulent wind found in real-world operating environments.
Utility-scale wind turbine blades are large enough that they pass through significantly higher speed winds as they travel through the top of their rotation than nearer to the ground at the bottom, which causes substantial cyclic loading of the blades.
In addition, each turbine in a wind farm creates a turbulent wake that can affect the performance and mechanical loads experienced by the wind turbines downstream.
Turbine wakes interact with each other and are affected by the amount of turbulence in the surrounding winds. These effects reduce power produced by wind turbines and mechanically load their parts in ways that are not fully understood, reducing revenue and increasing operations and maintenance costs of wind farms.
A better understanding of turbine wake and turbulent wind behavior will lead to improved wind farm designs that capture more energy while reducing the mechanical loading on the turbines, generating more profit over the life of the wind farm.
This knowledge can also lead to improved lower-order, wind-farm planning tools that better predict farm performance, which leads to a more accurate prediction of wind farm revenue over its lifetime.
NREL researchers: From the National Wind Technology Center: Patrick Moriarty (PI), Julie Lundquist (joint with the University of Colorado-Boulder), Matthew Churchfield, Michael Lawson, and Sang Lee
From the Computational Science Center: John Michalakes, Kenny Gruchalla, Avi Purkayastha, and Michael Sprague