On the importance of wind turbine blade numerical modeling: A state-of-the-art review

As wind energy underpins the global trajectory toward carbon neutrality, the industry’s pivot toward the 15–20 MW era has exposed the limitations of conventional linear modeling assumptions under large blade deflections. This article provides a comprehensive synthesis of the technological trajectory in modeling to clarify the multi-scale logic that links microscopic damage to macroscopic structural failure. To organize the surveyed literature across operational regimes, the tip-deflection-to-rotor-radius ratio ( δ / R ≈ 10 % ) is adopted as an illustrative, heuristic flexibility screening indicator rather than a universal physical boundary. This benchmark delineates nominal operational envelopes from extreme-load regimes where heightened sensitivity to geometric nonlinearity and three-dimensional aerodynamic effects warrants rigorous multi-fidelity examination. Recognizing the boundaries and uncertainties of different modeling approaches supports reliable identification of structural failure risks during blade design. The review delineates the evolution from foundational 1D/2D models to high-fidelity Geometrically Exact Beam Theory and Absolute Nodal Coordinate Formulation coupling. We examine implementation strategies for Reduced-Order Models alongside the integration of Physics-Informed Neural Networks to bridge the gap between data sparsity and physical consistency. Finally, we propose a strategic roadmap focusing on hard-constraint physics-embedded AI and neural operators (e.g., Fourier Neural Operators) for rapid cross-condition generalization. These surrogate frameworks have the potential to better balance the trade-offs among real-time performance, fidelity, and computational data cost, establishing a paradigm for the design and maintenance of resilient next-generation rotors.