Advanced Methods for Predicting 3D Unsteady Flows Around Wind Turbines

Wind power has an important role in satisfying the power needs of the United States. Since wind power is a clean renewable source of energy, it also serves an important role in reducing dependence on fossil fuels, in particular foreign oil supplies, as well as reducing greenhouse gas and carbon emissions. Unfortunately, significant maintenance costs, recently highlighted by a series of blade failures, can be a direct result of unsteady blade loading and wake interactions related to configuration, installation layout and off-design wind conditions. Much research has been performed to understand the aerodynamic loading on isolated wind turbines, but little has been done to understand and mitigate the fluid-structure-interactions (FSI) between wind turbines, atmospheric turbulence, and local terrain that contribute to structural fatigue and characteristic noise. This effort will develop an advanced methodology for accurately capturing the nonlinear FSI of the blade, long period wakes, and unsteady effects influencing wind turbine fatigue and noise-inducing FSI. This methodology will be capable of quantifying many of these phenomena so that modifications can be made to address these issues early in the design process of turbines and wind farms. In addition, as inflow models of the atmospheric boundary layer (ABL) under development through other funding mechanisms (by experts in that field) become available, they can be coupled with this methodology, via coupling mechanisms proposed in this effort. Thus a successful effort will pave the way for the development of quieter, more efficient wind turbines and wind farms with enhanced longevity and reduced maintenance costs. The proposed 9 month (36 week) effort seeks to build upon the mutually supporting experience of the team of Continuum Dynamics, Inc (CDI) and Georgia Institute of Technology (GIT) in wind turbine analysis, unsteady fluid dynamics, FSI, and noise prediction. It leverages prior and ongoing research rotorcraft aerodynamics and wake prediction to directly address the issue of wind turbine FSI. To address the inherent numerical diffusion of vorticity in RANS methods, this effort will apply CDI