Digital twins of wind turbine structures in the era of artificial intelligence: Evolution, applications, and future perspectives

The continuous upscaling of wind turbines and their deployment into increasingly challenging operational environments necessitates a fundamental evolution in structural asset management. In this context, the rise of Digital Twins in Engineering marks a pivotal moment, bringing a new era of technological innovation. This review traces the historical development of the Digital Twin concept and its diverse roles in engineering, critically synthesizes previously reported Digital Twin frameworks for wind turbine structures, and subsequently proposes a comprehensive, structure-centric Digital Twin framework tailored for offshore wind turbine support structures. It critically analyzes the enabling technologies that form the backbone of the system, including the integration of data acquisition from supervisory control and data acquisition (SCADA) and structural health monitoring (SHM) systems, advanced multi-physics simulation, and Artificial Intelligence (AI) technologies. A key component of this work is an in-depth categorization of Digital Twins applications, which encompass virtual sensing, dynamic performance monitoring, and advances in prognostics that facilitate a shift from corrective maintenance to preventive and condition-based maintenance. The present work also addresses global strategic initiatives and discusses primary technical and organizational bottlenecks, including persistent challenges in model credibility, computational efficiency, and data interoperability. The work concludes that the continued integration of these technologies provides the foundation for realizing fully lifecycle-aware Digital Twins, capable of transforming the management of this critical renewable energy infrastructure towards sustainability and resilience.