Investigating aeroelastic and aeroacoustic responses of wind turbines using large-eddy simulations

This thesis investigates the aeroelastic and aeroacoustic behavior of large-scale wind turbines through high-fidelity numerical simulations, offering new insights into their aerodynamic performance, wake dynamics, and noise emissions. A detailed aeroelastic study is conducted on the NREL 5-MW and IEA 15-MW wind turbines, comparing their structural responses under different inflow conditions using a coupled Computational Fluid Dynamics (CFD) and Computational Structural Dynamics (CSD) approach and benchmarking the results against OpenFAST. The fluid model is based on the incompressible Navier-Stokes equations solved using Large-Eddy Simulation (LES), with the Actuator Line Model (ALM) employed to represent the aerodynamic forces exerted by the turbine blades. The structural model relies on a geometrically exact nonlinear beam formulation, ensuring an accurate representation of the blade deformations under aerodynamic and inertial loads. The study highlights significant discrepancies between high-fidelity and Blade Element Momentum (BEM)-based solvers, particularly in local incidence and blade deformation, with OpenFAST underestimating the impact of tower shadowing effects. Additionally, preliminary results assess the response of the IEA 15-MW turbine under a turbulent inflow, providing initial observations on the role of turbulence intensity in modifying aerodynamic loads and structural deflections. To further explore the influence of turbine size on wake dynamics, a Dynamic Mode Decomposition (DMD) analysis is performed under identical turbulent Atmospheric Boundary Layer (ABL) conditions. The results demonstrate that larger turbines exhibit lower-frequency wake structures, confirming that rotor size acts as a filter on coherent structures while also amplifying tip vortex-related high-frequency modes, thereby influencing wake evolution and recovery. In parallel, the aeroacoustic characteristics of the NREL 5-MW turbine are examined by coupling LES with the Ffowcs Williams-Hawkings (FWH-P) acoustic analogy, allowing for the identification of dominant aerodynamic modes contributing to tonal and broadband noise. The application of Sparsity-Promoting Dynamic Mode Decomposition (SPDMD) further enables a low-dimensional representation of the flow field, revealing key frequencies responsible for noise generation. The findings of this thesis contribute to the advancement of wind turbine modeling by improving the accuracy of predictive tools for aeroelastic response, wake development, and noise emissions, supporting the design of more efficient and quieter wind energy systems.