Uncertainty propagation on wind turbine blade loads

Minimizing the cost and enhancing the lifespan of wind turbines entails the optimization of the material distribution of wind turbine components (blades, tower, etc.) without compromising their structural safety. Wind turbines are often design using the IEC 61400-1 standard to provide an appropriate level of protection against damage from all hazards during the planned lifetime. Typically, aero-elastic simulations codes are used to determine loads and displacements time history in the wind turbine. To predict the fatigue damage limit of the wind turbine blade, it is important to quantify and model all relevant uncertainties but it requires a considered amount of simulation time, and a surrogate model can substitute this simulation, to decrease this time consuming part of the problem. In this study, Monte Carlo simulation and FAST code are used to simulate different wind conditions. Here, 10-min of effective simulations generate a time history for all forces and moments acting in 10 selected gages of the blade. Subsequently, we quantify the uncertainty of their maximum value using a Gaussian process (Kriging) and Deep Neural Network (DNN), fitting this maximum output values with their correspondent input values. For Kriging and DNN a good fitting was found for almost all output variables.