Computational fluid dynamics and digital twins for wind turbines: a review

Wind energy is one of the fastest-growing sources of renewable power and a key contributor to decarbonised energy systems. Ensuring the efficiency, reliability, and lifetime performance of wind farms is therefore critical to meeting future energy demand and climate targets. A central challenge lies in accurately modelling the complex aerodynamic behaviour of wind turbines, particularly under wake interactions and atmospheric turbulence in large wind farms. Digital Twin (DT) technologies have emerged as a promising approach for improving wind turbine monitoring, control, and maintenance. In parallel, high-fidelity Computational Fluid Dynamics (CFD) provides detailed insight into flow physics that govern energy capture and fatigue loading. However, the direct integration of CFD into real-time DT frameworks remains limited by computational cost and modelling complexity. This review examines the current state of research on incorporating CFD within DTs for wind turbine applications. We show that, despite significant advances in aerodynamic simulation fidelity, practical deployment within dynamic, data-driven digital frameworks is still constrained. Particular emphasis is placed on reduced-order and hybrid modelling strategies that balance physical accuracy with computational efficiency, and are therefore most compatible with real-time operation and large-scale wind-farm applications.