Fatigue reliability analysis of floating offshore wind turbines under the random environmental conditions based on surrogate model

Fatigue reliability analysis is essential for ensuring the safe operation of floating offshore wind turbines (FOWTs) under random wind and wave loads. Traditionally, fatigue assessments are computationally expensive due to the need for numerous numerical simulations. To reduce computational costs, a fatigue reliability analysis method is proposed in the present study by implementing the surrogate model, C-vine copula, and Monte Carlo simulation. The multivariate distribution of environmental conditions is modeled using the C-vine copula and marginal mixed distribution models, while short-term fatigue damages are estimated by the surrogate model. Finally, Monte Carlo simulation is employed to assess the fatigue reliability. The proposed method is applied to evaluate fatigue reliability at three critical locations on a FOWT. Results show that both the back propagation neural network (BPNN) and the Kriging model can accurately predict short-term fatigue damage at various locations. However, the BPNN-based surrogate model is recommended for its lower computationally cost. Furthermore, the proposed method not only assesses the probability of fatigue failure at individual locations but also evaluates system-level fatigue reliability by accounting for correlation between fatigue damage at different locations.