NREL-Led ExaWind Project Demonstrates New Capability for Simulating Modern Wind Turbines

Dec. 4, 2018 | Contact media relations
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NREL and partners have created a new blade-resolved model of a large modern wind turbine, enabling scientists and engineers to begin understanding the complex flow physics in multi-turbine wind farms that will take advantage of future exascale modeling and simulation capability.

This animation shows the geometry-resolved large-eddy simulation of the NREL 5-megawatt wind turbine created through the ExaWind project.

The new simulation from the NREL-led ExaWind project represents a major step toward the goal of creating a predictive wind energy simulation capability that runs on an exascale-class computer by 2022. An exascale system will be capable of at least one-billion-billion calculations per second—approximately 50 to 100 times faster than the nation's most powerful supercomputers in use today.

The ExaWind project is aimed at tackling a key challenge for wide-scale deployment of wind power: plant-level inefficiencies. Plant-level performance losses can be as high as 20%–30% due to complex terrain, unique atmospheric flow phenomena, and the complex flow interactions that occur in large wind farms. Reducing these losses requires more knowledge of their dynamics. However, a fully blade-resolved model of a single wind turbine would require the full computing capability of today's fastest computers. Detailed models of entire wind plants are beyond current capabilities.

The new blade-resolved model created by the ExaWind team demonstrates progress toward future exascale capability. The ExaWind team recently performed large-eddy simulation (LES) of multiple revolutions of the NREL 5-megawatt (MW) reference turbine with an open-source computational fluid dynamics (CFD) code called Nalu-Wind. LES is a well-known mathematical CFD approach to capturing the turbulent flow structures in engineering applications. The NREL 5-MW reference turbine is a notional turbine fully defined in the open domain that has the key features of large modern wind turbines.

The simulations were performed on the National Energy Research Scientific Computing Center (NERSC) Cori Haswell high-performance computing system with multiple resolutions, including a model with 6 billion grid points. Using a coarse mesh, a simulation of multiple turbine revolutions was demonstrated. The model exercised a sliding-mesh interface that enables rotation of the fluid mesh surrounding the turbine rotor within a fixed background mesh. This simulation capability establishes a key baseline for future model improvements.

When validated by targeted experiments, these and other predictive physics-based high-fidelity computational models at the center of the ExaWind project—and the new knowledge derived from their solutions—provide an effective path to optimizing wind plants.

About ExaWind

The ExaWind project is a close collaboration between NREL, Sandia National Laboratories, Oak Ridge National Laboratory, and the University of Texas at Austin. ExaWind is funded by the U.S. Department of Energy’s (DOE) Exascale Computing Project, a joint collaboration of two DOE sponsoring organizations: the Office of Science and the National Nuclear Security Administration.

The ExaWind project also collaborates closely with the Atmosphere to electrons High Fidelity Modeling (HFM) project, which is funded by the DOE Wind Energy Technologies Office. Whereas ExaWind is largely focused on developing and optimizing the simulation software stack for exascale-class simulations, the HFM project is directed at the physics-based models, community modeling environment, and model validation. The partnership between the HFM and ExaWind project teams is critical to ultimately achieving predictive exascale simulations of wind farms.

For additional details on this development, read a more in-depth project highlight on the Exascale Computing Project website. For more information about ExaWind, please visit the Wind Energy tools page or contact the ExaWind Principal Investigator, Michael Sprague.

Tags: Computational Science,Wind