The key to achieving the wide-scale deployment of wind energy is enabling a new understanding of, and the ability to predict, the fundamental flow physics and coupled structural dynamics that govern whole wind plant performance, including wake formation, complex-terrain impacts, and turbine-turbine interactions through wakes. Current methods for modeling wind plant performance fall short due to insufficient model fidelity and the inadequate treatment of key phenomena combined with the lack of computational power needed to address the wide range of relevant length scales associated with wind plants. Thus, the ExaWind challenge problem is a predictive simulation of a wind farm with tens of megawatt-scale wind turbines dispersed over an area of many square kilometers with complex terrain that involves simulations with tens to hundreds of billions of grid points. These predictive, physics-based, high-fidelity computational models validated with targeted experiments will drive innovation in the blade, turbine, and wind plant design processes by providing a validated “ground truth” foundation for new turbine design models, wind plant siting, and operational controls, as well as by reliably integrating wind energy into the grid.
The efficiency and reliability of renewable energy will continue to grow in importance as the U.S. energy grid transitions from reliance on fossil fuels. Wind power is the largest source of renewable energy in the U.S., and one of the fastest growing energy sources in the country: According to the U.S. Office of Efficiency and Renewable Energy, wind energy made up more than 10% of U.S. power in 2022, and supplied more than 40% of new U.S. energy capacity in 2020, and more than 20% of new capacity in 2022. High performance computing is poised to greatly improve the efficiency of wind power generation by using advanced multiphysics simulations to model and optimize key operational parameters such as airflow and turbine structural dynamics.
The Exascale Computing Project’s ExaWind application provides the software architecture needed to apply the power of next-generation supercomputers to the burgeoning wind energy sector. The project simulates the complex physics of an entire windfarm under various geographic and weather conditions. These simulations help determine the impacts of turbine placement, wind farm location, and blade design, driving improvements in efficiency and reducing the cost per megawatt hour of energy produced.
High performance computers have been used to improve wind turbine efficiency for more than a decade, but simulation fidelity and duration has been heavily limited by processing speed, resulting in simulations which could only yield general trends in efficiency with low degrees of confidence. To improve the accuracy of turbine and wind farm simulations, researchers needed to capture the physical airflow processes at the micron-scale boundary layer around turbine blades and to couple these processes to larger interactions occurring at the scales of turbines and entire wind farms. This coupling spans roughly eight orders of magnitude, ballooning the computational cost and forcing less powerful computational systems to sacrifice accuracy for simulation speed.
The ExaWind team has addressed these shortcomings for exascale machines, successfully integrating a whole-wind-farm simulation at scales ranging from microns to kilometers, and has reduced the duration of these simulations from days to hours. The integrated simulations span up to 38 billion grid points and simultaneously model multiple turbines at high fidelity, allowing for high-confidence numerical predictions of efficiency and energy loads, as well as accurate models of structural stability and deformation under operational and storm conditions.
Using ExaWind’s large scale, high fidelity multiphysics simulations, researchers can model new turbine and wind farm design approaches without the slow and cost-intensive processes of real-world development and testing. This new tool will greatly improve the speed and cost-efficiency of wind energy innovation while supporting “longshot” designs which were previously not financially viable, and which may rapidly accelerate progress in the field.
The ExaWind project’s scientific goal is to advance fundamental understanding of the flow physics that govern whole wind plant performance, including wake formation, complex terrain impacts, and turbine-turbine interaction effects. The greater use of the nation’s abundant wind resources for electric power generation—reaching 30% of US electrical supply—will have profound societal and economic impacts, such as strengthening US energy security through greater diversity in its energy supply, providing cost-competitive electricity to key regions across the country, reducing greenhouse gas emissions, and reducing water used in thermoelectric power generation.
This multidisciplinary project embodies the systematic development of the modeling capability and the computational performance and scalability required for effective exascale simulations. The project plan builds progressively from predictive petascale simulations of a single turbine for which the detailed blade geometry is resolved, meshes rotate and deform with blade and tower motions, and atmospheric turbulence is realistically modeled to a multi-turbine array in complex terrain. This new modeling and simulation capability will establish a virtual wind farm test bed that will revolutionize the design and control of wind farms and significantly advance the scientific community’s ability to predict the response of wind farms to a wide range of atmospheric conditions.
The project objective is to capture crucial phenomena that are under-resolved in today’s models, including wake formation, complex-terrain impacts, wake-atmosphere interaction, turbine-turbine interaction, and blade-boundary-layer dynamics. This target requires a modeling and simulation capability that resolves turbine geometry and uses adequate grid resolution (down to micron scales within the blade boundary layers). The resolution must capture the upstream chord-scale atmospheric turbulent eddies, the generation of near-blade vorticity, and the propagation and breakdown of this vorticity within the turbine wake to a distance of many rotor diameters downstream. This application uses the ExaWind software stack, which has three physics-solver components: Nalu-Wind, an unstructured-grid computational fluid dynamics (CFD) code; AMR-Wind, a structured-grid CFD background solver built on AMReX; and OpenFAST, a turbine simulation code. The challenge-problem simulation will require a hybrid Reynolds-averaged-Navier-Stokes/large-eddy-simulation (RANS/LES) turbulence model, fluid-structure interaction, and turbulent atmospheric flow.
The challenge simulation will contain at least nine megawatt-scale turbines (e.g., NREL 5 MW reference turbines) organized in a 3 × 3 array and residing in a 4 × 4 km2 domain with a height of at least 1 km. A hybrid-RANS/LES model will be employed, wherein an unsteady RANS model will be used near turbine surfaces and an LES model will be used in the wake region. The simulation will have a mean wind speed at the turbines’ rated speed (e.g., 11.4 m/s for the NREL 5 MW reference turbine). The model will require at least 30 billion grid points and 150 billion degrees of freedom to resolve the system, and near-blade grid spacing will be such that the viscous sublayer within the RANS region is resolved. A successful simulation will require an optimized solver stack that minimizes time per time step. A scientifically meaningful simulation duration will be for at least one domain transit time (about 500 s for the 4 × 4 km domain at 11.4 m/s). The project will demonstrate that such a simulation is feasible within 4 weeks of system time.