Reply on RC3

Abstract. Advanced wind energy technologies require predictions of the dynamic behaviour of wind turbine wakes. In this work, we present a dynamic wind turbine model PhyWakeNet, a physics-integrated generative adversarial network-convolutional neural network (GAN-CNN) model for wind turbines under aerodynamic force oscillations. The model combines three interconnected submodels for the time-averaged wake, wake meandering, and small-scale wake turbulence. The time-averaged wake model derives from mass and momentum conservation based on the concept of momentum entrainment, which is computed based on the wake meandering and small-scale wake turbulence models. The wake meandering is captured through conditional GAN-reconstructed spatial modes and neural network-enhanced dynamic system for temporal evolution, while the small-scale wake turbulence is generated via a CNN based on the time-averaged wake, wake meandering, and inflow turbulence. Validation on wind turbine wakes under active control demonstrates the model's capability to predict frequency-dependent wake responses, velocity deficits, and turbulence kinetic energy. The model accurately captures temporal variations of key characteristics like instantaneous wake centers and velocity deficits, enabling potential applications in wake management to mitigate aerodynamic loads and power fluctuations in wind farms.