Every two and a half hours, a new wind turbine rises in the U.S. In 2016, wind provided 5.6 percent of all electricity produced, more than double the amount generated by wind in 2010, but still a far cry from its potential.

A team of researchers from The University of Texas at Dallas (UT Dallas) has developed a new way to extract more power from the wind. This approach has the potential to increase wind power generation significantly with a consequent increase in revenue. Numerical simulations performed at the Texas Advanced Computing Center (TACC) indicate potential increases of up to six to seven percent.

According to the researchers, a one percent improvement applied to all wind farms in the nation would generate the equivalent of $100 million in value. This new method, therefore, has the potential to generate $600 million in added wind power nationwide.

The team reported their findings in Wind Energy in December 2017 and Renewable Energy in December 2017.

In the branch of physics known as fluid dynamics, a common way to model turbulence is through large eddy simulations. Several years ago, Stefano Leonardi and his research team created models that can integrate physical behavior across a wide range of length scales — from turbine rotors 100 meters long, to centimeters-thick tips of a blades — and predict wind power with accuracy using supercomputers.

“We developed a code to mimic wind turbines, taking into account the interference between the wake of the tower and the nacelle [the cover that houses all of the generating components in a wind turbine] with the wake of the turbine rotor,” said Leonardi, associate professor of mechanical engineering and an author on the Wind Energy paper, which was selected for the cover.

Beyond the range of length scales, modeling the variability of wind for a given region at a specific time is another challenge. To address this, the team integrated their code with the Weather Research and Forecasting Model (WRF), a leading weather prediction model developed at the National Center for Atmospheric Research.

Read more at University of Texas at Austin, Texas Advanced Computing Center