The ExaWind project strives to advance our fundamental understanding of the flow physics governing whole wind plant performance, including wake formation, complex terrain impacts, and turbine-turbine interaction effects. Ultimately, we aim to create a predictive wind energy simulation capability that runs on an exascale-class computer by 2022. An exascale system will be capable of at least 1018 or one-billion-billion calculations per second, or approximately 50 to 100 times faster than the nation's most powerful supercomputers in use today.
The National Renewable Energy Laboratory (NREL) and Sandia National Laboratories (SNL) co-lead the project in collaboration with scientists at Oak Ridge National Laboratory and the University of Texas at Austin.
ExaWind is part of the U.S. Department of Energy's (DOE's) Exascale Computing Project (ECP).
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 our current capabilities.
A key challenge to wide-scale deployment of wind energy in the utility grid without subsidies is predicting and minimizing plant-level, wind energy losses—such as those from turbine-turbine interactions—which remain significant, particularly in complex terrain. Current methods for modeling wind plant performance fall short due to insufficient model fidelity and inadequate treatment of key phenomena.
Thus, we need to develop a predictive simulation of a wind plant composed of O(100) multi-MW wind turbines sited within a 10 km x 10 km area with complex terrain, involving simulations with O(100) billion grid points.
We aim to develop exascale wind energy applications that can be ready to run once the exascale computing capability becomes available.
To meet the challenge, we'll develop predictive, physics-based, high-fidelity computational models that will drive innovation in the blade, turbine, and wind plant design process. They'll provide a validated "ground truth" foundation for new wind turbine design models, wind plant siting, operational controls, and reliable grid integration.
It involves developing a simulation capability that can be effectively scaled up to run on an exascale, high-performance computing (HPC) system. The approach will be to run more limited simulations on today's petascale computers, such as NREL's Peregrine and other DOE HPC systems, while validating the accuracy and demonstrating that the software can be scaled up to a next-generation exascale super computer.
We are building on the SNL-supported, open-source Nalu code for computational fluid dynamics, which is the numerical approach to modeling fluid flow. Model development is based on NREL’s previous work to develop 3-D models of wind energy flow through a simplified wind farm. That work has already yielded benefits, showing that coordinated wind farm controls can increase the total power output by as much as 4%-5%.
An advanced understanding of the complex flow physics of wind plants will further reduce electricity costs from wind energy, resulting in its increased use.
Greater use of the nation's abundant wind energy resources for electric power generation, reaching 30% of U.S. electrical supply, will profoundly strengthen U.S. energy security through greater diversity in its energy supply and reduce greenhouse gas emissions.