Combining reinforcement learning and computational fluid dynamics for optimal kinematics of a robotic vertical-axis wind turbine

Vertical-axis wind turbines (VAWTs) have emerged as natural complements to their horizontal counterparts but suffer from performance limitations associated with complex aerodynamics, in particular dynamic stall. To address this challenge, we employ deep reinforcement learning to identify optimal blade pitching policies. A framework based on the twin-delayed deep deterministic policy gradient algorithm is coupled with a validated numerical simulation of a two-bladed VAWT, aimed at maximising power generation. The agent receives only onboard measurements of blade pressure and position, rendering the problem partially observable. From these inputs, it learns policies for three distinct flow cases: one uniform flow and two shear flows. The resulting policies outperformed the optimum fixed-pitch setting within 100 turbine revolutions across all cases. Ultimately, the agent found cycle-averaged power coefficients that are 3.1–5.6 times larger than those of a blade fixed at zero degrees, while simultaneously reducing variations in blade loads. Improvements originate equally from both halves of turbine rotation, despite the latter-half’s low absolute power contribution and depleted flow energy. Through flow field analysis using the power partitioning method, the underlying mechanisms responsible for power production are revealed. The presence of a rotational flow around the airfoil leading edge dominates. Its net power contribution is determined by the rotational intensity and the blade’s kinematics. Kinematics also play a central role in mitigating dynamic stall. Flow separation is not intrinsically detrimental, as proper blade positioning can suppress its harmful effects.