Cycle-Consistent Framework for Mooring Tension Prediction in Floating Offshore Wind Turbines
The accurate prediction of mooring and structural tension is essential for efficient structural health monitoring (SHM) of floating offshore wind turbines (FOWTs), which are exposed to extreme harsh and dynamic sea conditions. This paper presents a data-driven framework inspired by the Deep Operator Network (DeepONet) architecture to directly anticipate time-dependent mooring tension responses from time-varying platform motion. A novel cycle-consistency constraint is integrated into the traditional forward network to improve the model's physical consistency, stability, and robustness. Within this framework, the forward network acquires the mapping from the three translatory platform's motion and temporal inputs to the appropriate tension responses, while an auxiliary inverse network is developed to rebuild the original motion sequences from the predicted tensions. The cycle-consistency loss guarantees aggrement between the original and reconstructed signals, thus regularizing the training process and guiding the network towards physically consistent predictions. This approach is particularly advantageous for sparse or noisy datasets, as conventional data-driven models frequently experience overfitting and provide non-physical predictions. The framework is validated using OpenFAST's numerically simulated FOWT datasets generated under realistic environmental loading conditions. The sensing configuration consists of platform-mounted motion sensors measuring the three translational degrees of freedom (surge, sway, and heave) together with tension sensors installed at the fairlead locations of the mooring lines. Results demonstrate that the proposed cycle-consistent architecture improves prediction accuracy and stability compared to independently trained forward networks, particularly in sparse and noisy data regimes. This framework signifies a significant advancement in physics-informed, data-efficient SHM methods for FOWTs. It also provides a basis for the development of sophisticated digital twins and facilitates real-time monitoring and control of FOWTs.