An Inverse Design and Optimization Framework for Offshore Wind Turbine Modeling from In Situ Measurements with Uncertainty Characterization

This study presents a framework for developing, emulating, and validating offshore wind turbine models when proprietary blade designs are unavailable. The methodology addresses a critical industry challenge by demonstrating that aero-servo-hydro-elastic models reproducing the measured operational behavior can be constructed using only publicly available reference designs and operational measurements. An inverse design approach based on differential evolution optimization reconstructs blade aerodynamic characteristics from field data, enabling the creation of models that replicate operational behavior without requiring access to proprietary geometries. The framework incorporates statistical error characterization through machine learning techniques to predict simulation errors based on environmental and operational conditions. Validation against extensive field measurements from an operational offshore wind turbine demonstrates the effectiveness of the methodology. The machine-learning models predict the simulation-error distributions (bias and variability). The prediction fidelity is highest for the fore–aft response, which is thrust driven, and lower for the side–side response, for which several influencing factors remain unmodeled. This approach offers a practical pathway for model calibration and error prediction for offshore wind turbines, particularly when complete design documentation is unavailable.