Wind turbine life-cycle management involves disparate timescales, including real-time control for operational stability and long-term prognostics for structural integrity. Current digital twin applications typically treat these aspects as isolated domains. This study proposes and validates a unified, multi-scale digital twin framework tailored for the support structure, integrating subsystems for high-frequency operational control and low-frequency health prognostics. The proposed framework is validated through a case study on the NREL 5-MW reference wind turbine. The real-time control module, employing a Model Predictive Control (MPC) strategy for a magnetorheological (MR) damper, achieved a 37.3% reduction in the root mean square (RMS) tower top-displacement compared with the uncontrolled structure under turbulent wind conditions. Subsequently, the long-term prognostics module predicted a baseline remaining useful life (RUL) of 6.7 years at the tower base for the uncontrolled structure under an aggressive corrosion-fatigue scenario. The integrated framework demonstrated that the implementation of real-time control extends the predicted RUL to 19.8 years, representing a 198% improvement in service life. Furthermore, probabilistic assessment quantified the sensitivity of RUL to environmental and loading uncertainties, enabling risk-informed maintenance planning. The synergistic value of the proposed architecture lies in its bi-directional data exchange: prognostic data maintains control model fidelity, while operational data refines RUL predictions. By validating these capabilities on a representative case, the framework provides an effective solution for optimizing performance and ensuring the structural integrity of wind turbine support systems throughout their life-cycle. • Proposes a unified multi-scale digital twin for wind turbines. • Integrates real-time control with magnetorheological dampers. • Reduces tower displacement by over thirty-seven percent. • Predicts remaining life under corrosion and fatigue damage. • Enables two-way synergy between control and health prognosis.
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Elgammal et al. (Fri,) studied this question.
www.synapsesocial.com/papers/69fd7eb0bfa21ec5bbf06efc — DOI: https://doi.org/10.1016/j.jei.2026.100003
Ahmed Elgammal
Yasmin Ali
Junlin Heng
University of Birmingham
Sichuan University
Leibniz University Hannover
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