E. Y. K. Ng

As wind energy continues to be a crucial part of sustainable power generation, the need for precise and efficient modeling of wind turbines, especially under yawed conditions, becomes increasingly significant. Addressing this, the current study introduces a machine learning-based symbolic regression approach for elucidating wake dynamics. Utilizing WindSE’s actuator line method (ALM) and Large Eddy Simulation (LES), we model an NREL 5-MW wind turbine under yaw conditions ranging from no yaw to 40 degrees. Leveraging a hold-out validation strategy, the model achieves robust hyper-parameter optimization, resulting in high predictive accuracy. While the model demonstrates remarkable precision in predicting wake deflection and velocity deficit at both the wake center and hub height, it shows a slight deviation at low downstream distances, which is less critical to our focus on large wind farm design. Nonetheless, our approach sets the stage for advancements in academic research and practical applications in the wind energy sector by providing an accurate and computationally efficient tool for wind farm optimization. This study establishes a new standard, filling a significant gap in the literature on the application of machine learning-based wake models for wind turbine yaw wake prediction.

Drag-dominant tidal turbine energy holds tremendous clean energy potential but faces significant hurdles as unsuitability of the actuator disc model due to the varying swept blockage area, unaccounted bypass flow downstream interaction, and rotor parasitic drag, whereas blade element momentum theory is computably effective for majorly 3-blade lift-dominated aerofoil. This study validates a novel method to find the optimal TSR of any turbine with a cost-effective and user-friendly moment balancing algorithm to support robust tidal energy development. Performance analysis CFD study of Pinwheel and Savonius tidal turbines in a Biffis canal hydrodynamic system was carried out. Thrust and idle moment are analyzed as functions of only inlet fluid velocity and rotational speed, respectively. These relationships were verified through regression analysis, and the turbines’ net moment equations were established based on these parameters. In both simulation models, rotational speed and inlet velocity were proved excellent predictor variables (R2 value ≈ 1) for idle and thrust moments, respectively. The optimal TSR values for Pinwheel and Savonius turbines were 2.537 and 0.671, respectively, within an acceptable error range for experimental validation. The optimal basin efficiency (ηopt, TSR) values for Pinwheel and Savonius in the 12% blockage channel were (29.09%, 4.0) and (25.67%, 2.87), respectively. The trade-off between TSRopt and ηopt is the key instruction concerning electricity generation and environmental impact.