Fatigue Analysis of Wind Turbine Blade Coating Considering Uncertainty Due to Voids Subjected to Impact Fatigue

Abstract

Wind Turbine Blade (WTB) coating is a protective layer that experiences repetitive raindrop impact. The impacts cause cyclic stresses, fatigue, and erosion of the leading edge of the blade coating which leads to the damage of the WT blade. The presence of voids (manufacturing defects) in the coating leads to local stress concentration and enhances erosion. This erosion can cause an increase in the drag coefficient and reduction in lift coefficient resulting in reduced performance of the WT. In India, the annual monsoon with high-intensity rain makes the problem more critical. FE model was developed using Coupled Eulerian and Lagrangian (CEL) method to calculate stress field due to raindrop impact. In the present case, stress-time histories are random in nature as observed. The stress histories are transformed into respective spectra. The spectrum obtained is converted into simpler cycles using the rain flow cycle counting method. The method is well suited for the fatigue life assessment due to random stresses in the coating of the wind turbine blades under raindrop impact. The cycles thus obtained from the spectrum can be used to estimate the cumulative fatigue life using the Palmgren-Miner rule. This rule assumes that the damage carried out by the sections of a stress signal having a particular range can be estimated and aggregated to the total damage separately from that done by other ranges. Damage evaluation in the coating with the randomly distributed void due to stochastic raindrop impacts is computed from fatigue analysis. The main novelty of this work is fatigue assessment of WTB coating, taking explicit account of uncertainties due to the presence of voids in the erosion phenomenon. Simultaneously reliability analysis is carried out considering the probabilistic distribution of droplet diameter, the velocity of impact, and occurrence of maximum stress due to raindrop impacts in the coating. This model predicts the accurate and reliable life of the coating.