Aerodynamic and wake characteristics for full-scale and model-scale 5 MW wind turbines using data-driven modal decomposition

This paper examines the aerodynamic performance and wake characteristics for full-scale and 1:50 model-scale wind turbines using data-driven mode decomposition methods. Simulations are performed on a 5 MW horizontal axis wind turbine from NREL, utilizing the SST k−ω turbulence model along with overset mesh and adaptive mesh refinement techniques. The study analyzes performance coefficients at different blade tip speed ratios (TSRs) and wake velocity losses at different vertical profiles to compare aerodynamic performance and wake characteristics between the full-scale and model-scale turbines. Additionally, the evolutionary characteristics and frequency properties of the wake flows are examined by identifying the wake patterns based on proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD). Findings reveal that Reynolds number primarily influences the aerodynamic performance, while turbulence intensity affects the wake characteristics. Both wake flows undergo vortex generation, shedding, growth, development and breakdown. POD modes extract the highest energy structures in the wake, which accurately describe the generation and development of wake vortices. DMD uncovers the dynamic behaviors of various frequency components through frequency-domain decoupling, capturing the time scales of the wake vortex evolution and its stability, and it offers a more detailed understanding of tail vortex development.