Brief communication: A momentum-conserving superposition method applied to the super-Gaussian wind turbine wake model

Accurate wind farm flow predictions based on analytical wake models are crucial for wind farm design and layout optimization. In this regard, wake superposition methods play a key role and remain a substantial source of uncertainty. Recently, new models based on mass and momentum conservation have been proposed in the literature. In the present work, such methods are extended to the superposition of super-Gaussian-type velocity deficit models, allowing the full wake velocity deficit estimation and design of closely packed wind farms.