Investigating wake reproduction of a model-scale wind turbine: experimental measurements versus large eddy simulation with actuator line

Accurate modeling of wind turbine wakes is essential for understanding turbine performance, wake interactions, and structural loading in wind energy applications. This work presents a combined experimental and numerical investigation addressing key gaps in model-scale wind turbine testing and high-fidelity wake modeling. A major contribution of this study is the design and characterization of a new model-scale wind turbine, TWIST (Turbine for Wind-tunnel Investigation and Scaled Testing), developed to enable high-resolution measurements of near-wake velocity fields and blade deformation. The resulting dataset provides detailed experimental information that remains scarce in the literature, particularly in the near-wake region. In parallel, a continuous actuator line method (cALM) is developed, introducing a novel force-distribution strategy that avoids spanwise averaging along the blade. This approach improves physical consistency and allows for better resolution of loading variations near the blade tip and hub regions. Inflow conditions in the simulations are carefully matched to experimental profiles of mean velocity and turbulence intensity, ensuring that discrepancies primarily reflect modeling assumptions. A systematic comparison between numerical and experimental results is performed to assess the model's ability to reproduce key wake features. Two sensitivity studies are also conducted. First, the influence of blade pitch angle on wake development is evaluated for two operating conditions and compared directly with experimental measurements. Second, blade deformation is analyzed both numerically and experimentally to investigate aeroelastic coupling and its role in observed deviations. Results demonstrate that the cALM captures key near-wake features at 1.41 D , including velocity deficit and wake boundary, and remains consistent with experiments at 4.35 D , with discrepancies mainly near the ground. Overall, the framework improves ALM fidelity and provides a reference dataset for controlled wind turbine testing and wake modeling.