Wind turbine siting by using mesoscale model data assimilation and computational fluid dynamics

Accurately predicting energy production for wind turbines in realistic environmental conditions can be challenging due to terrain and uncertainty in weather conditions. To enhance predictive capability we have developed an approach in which a high-fidelity Computational Fluid Dynamics (CFD) computer code is coupled to a Mesoscale numerical weather prediction model. The CFD model captures local terrain and environmental conditions while the Mesoscale model defines the inflow boundary conditions as well as interior wind profiles for the CFD model. The novel approach of data assimilation is incorporated that uses either meteorological data or Mesoscale model predictions to obtain a flow solution that is closer to observations. A case study in a valley near Rock Springs, PA is analyzed.