CgWind is a new, high-ﬁdelity simulation tool designed to meet the modeling requirements of advanced wind energy resources. These new resources, targeting 20% of the US electrical supply by 2030, require the development of larger and lighter wind turbines as well as more accurate estimates for the performance of turbines in realistic terrain and atmospheric conditions.
To model such systems, CgWind couples large eddy simulation (LES) models, based on the incompressible Navier-Stokes equations, with moving grid techniques that resolve the ﬂow near the turbine blades. Both LES and detached eddy simulation methods will be available in CgWind. In particular, CgWind is incorporating nonlinear LES models that capture anisotropy at the sub-grid-scale and are well-suited for atmospheric boundary layer ﬂows. The new modeling framework enables the use of advanced numerical methods to design and predict the performance of individual wind turbines and large-scale wind parks.
CgWind’s technology exploits the composite grid approach, which leverages the computational beneﬁts of overlapping, structured grids to represent complex geometry. These grids are ideal for the high-order accurate compact discretizations used by CgWind as well as the matrix-free geometric multi-grid algorithm that enables large-scale, high-resolution computations with realistic geometry. The composite grid approach, also known as overlapping or Chimera grids, provides a natural and efﬁcient mechanism for modeling bodies in relative motion. Each turbine blade and tower is meshed independently with high-quality, structured grids and assembled automatically into a collection of overlapping grids. When the geometry moves (e.g., the blades rotate or deform), the new configuration undergoes local regridding, which is orders of magnitude faster than the global remeshing methods used in many unstructured mesh approaches.
Overlapping grids also provide a natural framework for structured adaptive mesh reﬁnement that will allow CgWind to automatically enhance resolution in regions of the computational domain with important ﬂow features (e.g., wake vortices). The memory footprint and CPU performance advantages of high-order accurate methods on structured grids allows CgWind to perform simulations at spatial resolutions currently unobtainable by other approaches.
CgWind will also interface to the Weather Research and Forecasting (WRF) meso-scale model to simulate wind-farm scale problems for siting studies and wind park performance analysis. In particular, WRF can provide time varying inﬂow conditions to CgWind, thereby incorporating the local weather conditions. Terrain data can be imported from GIS sources, meshed, and incorporated into the model. For large-scale park models, CgWind provides an interface for wake models thereby obviating the need to highly resolve hundreds of turbines.
While currently under development at Lawrence Livermore National Laboratory, CgWind is intended to be community tool for use by turbine manufacturers, wind park designers, and wind energy researchers. The software is modular and contains well-deﬁned interfaces for custom or proprietary wake and turbulence models. Built upon the openly available Overture software framework, CgWind will be freely downloadable.
This work was done by Kyle K. Chand, William D. Henshaw, Katherine A. Lundquist, and Michael A. Singer of Lawrence Livermore National Laboratory.
This Brief includes a Technical Support Package (TSP).
Accurate Wind Simulation Tool for Wind Turbines and Wind Farms
(reference GDM0010) is currently available for download from the TSP library.
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