Multi-­Disciplinary Design And Analysis Of Wind Turbines Under Uncertainty

The performance of wind turbines can be negatively affected by the presence of uncertainties. We introduce a comprehensive multi-physics computational model EOLO that enables the estimation of aerodynamic and structural characteristics assiociated with horizontal axis wind turbines, and use it to study the impact of uncertainties on the aerodynamic performance and noise. We consider variability in the wind conditions, manufacturing tolerances and roughness induced by insect contamination as sources of uncertainties and treat them within a probabilistic framework using Latin Hypercube Sampling and Stochastic Simplex Colocation. The results demonstrate that these two methods lead to a statistical characterization of the quantity of interest which is considerably faster than classical Monte Carlo methods. In addition, we demonstrate how the uncertainties impact the aerodynamics and noise leading to a largely inferior performance compared to the nominal design. Finally a multi-disciplinary shape optimization of the wind turbine blade is proposed.