Computing Sciences masthead Berkeley Lab Computing Sciences Berkeley Lab logo

Exascale for Energy

Renewable Electricity: Wind Energy

When it comes to generating electricity, wind power is the technology closest to being cost competitive with fossil-fuel-driven power generating plants. Although its use by utilities is limited by its intermittent nature, there are sufficient wind energy resources in the continental United States to meet a substantial portion of national energy needs at a competitive cost. The goal of generating 20% of the total U.S. electrical supply from wind energy by 2030, while feasible, is highly challenging. Turbine installations are growing dramatically, but they still provide less than 1% of U.S. electricity.

Current wind plants often under-perform predicted performance by more than 10 percent, and wind turbines often suffer premature failures and reduced lifetimes from those predicted during design. Turbine downtimes and failures lead to a reduced return on investment, which lowers the confidence of investors and increases the cost of raising capital necessary to develop a wind plant. A key reason for these early failures is a lack of detailed knowledge about unsteady wind flows and how they interact with turbines.

Standard meteorological data sets and weather forecasting models do not provide the detailed information on the variability of wind speeds, horizontal and vertical shears, and turbulent velocity fields that are needed for the optimal design and operation of wind turbines and the exact siting of wind plants. For example, wind turbines are frequently deployed in regions of undulating topography to take advantage of the expected speed-up of wind as the atmosphere is forced up over the hill. But recent evidence suggests that the drag imposed by trees can create turbulence that can damage wind turbines on subsequent ridges. More research is needed on the planetary boundary layer (PBL, the lowest part of the atmosphere) and how it interacts with the shape and ground cover of the land in specific locations in order to understand unsteady wind flows.

Researchers at the National Center for Atmospheric Research (NCAR) have developed a new large-eddy simulation (LES) code for modeling turbulent flows in boundary layers. Running on as many as 16,384 processors of the Franklin Cray XT4 at NERSC, the NCAR-LES code enables fine mesh simulations that allow a wide range of large- and small-scale structures to co-exist and thus interact in a turbulent flow.

High-resolution flow visualizations in Figures 24 and 25 illustrate the formation of both large and small structures. In Figure 24, we observe the classic formation of plumes in a convective PBL. Vigorous thermal plumes near the top of the PBL can trace their roots through the middle of the PBL down to the surface layer. Closer inspection of the large-scale flow patterns in Figure 24 also reveals coherent smaller scale structures. This is demonstrated in Figure 25, which tracks the evolution of 105 particles over about 1000 seconds and shows how dust devil vortices form in convective boundary layers. Coarse-mesh LES hints at these coherent vortices, but fine-resolution simulations allow a detailed examination of their dynamics within a larger-scale flow.

Convection simulation

Figure 24. Visualization of the vertical velocity field in a convective PBL at heights of 100 m, 500 m, and 900 m, from a 5123 simulation. Plumes near the inversion can trace their origin to the hexagon patterns in the surface layer. Source: P. Sullivan and E. Patton, NCAR

Convective particles

Figure 25. Visualization of particles released in a convective PBL from a 10243 simulation of convection. The viewed area is ~3.8% of the total horizontal domain. Time advances from left to right beginning along the top row of images. Notice the evolution of the larger-scale line of convection into small-scale vortical dust devils. Source: P. Sullivan and E. Patton, NCAR

Petascale computing will permit simulation of turbulent flows over a wide range of scales in realistic outdoor environments, such as flow over tree-covered hills. This will allow researchers to resolve 1–10 meter surface features while still capturing 1–100 km energy scales of motion in the boundary layer. Exascale computing will allow simulation of mesoscale systems with resolved clouds and a host of important small-scale processes that are now parameterized.

Improved PBL and turbine inflow modeling capabilities will enable the design of wind turbines that optimize performance by getting more power out of lighter designs, but will also be more cost effective due to longer lifetimes and reduced operations cost.


<< Previous page