Optimizing dimension selection for rain flow counting in fatigue assessment of large-scale lattice wind turbine support structures: a comprehensive study and design guidance

The selection of dimension (d) for the rain flow counting matrix significantly influences the reliable prediction of fatigue life in wind turbine support structures. However, there are limited public reports on the impact of dimensions on the fatigue assessment of wind turbine structures. This study explores the influence of dimensions on the fatigue assessment process and outcomes, focusing on a large-scale ultra-high lattice wind turbine support structure from an engineering project. Through integrated load simulation, fatigue load time series for different design load cases (DLCs) are obtained for the overall structure. The rain flow counting method is utilized to derive the overall rain flow counting matrix (Markov_n) with varying dimensions. Subsequently, employing a multi-scale fatigue assessment method, the damage matrix (Markov_D), cumulative fatigue damage (D), and fatigue life are computed. A detailed exploration examines the impact of rain flow counting matrix dimensions on Markov_n, Markov_D, D, fatigue life, and calculation time (t). Furthermore, guidance is provided for selecting the optimal dimension for engineering design. The findings indicate that selecting dimensions that are too small results in the loss and merging of smaller mean and amplitude load values, thereby failing to accurately represent the load history. Moreover, a small dimension yields an excessively cautious estimation of cumulative fatigue damage (D) and underestimates the fatigue life, consequently inflating construction expenses.