Analysis of the Aeroelastic Dynamics of Wind-Turbine Blades

Reducing the uncertainties related to blade dynamics by the improvement of the quality of numerical simulations of the fluid structure interaction process is a key for a breakthrough in windturbine technology. A fundamental step in that direction is the implementation of aeroelastic models capable of capturing the complex features of innovative prototype blades, so they can be tested at realistic full-scale conditions with a reasonable computational cost. We make use of a code based on a combination of two advanced numerical models implemented in a parallel HPC supercomputer platform: First, a model of the structural response of heterogeneous composite blades, based on a variation of the dimensional reduction technique proposed by Hodges and Yu. This technique has the capacity of reducing the geometrical complexity of the blade section into a stiffness matrix for an equivalent beam. The reduced 1-D strain energy is equivalent to the actual 3-D strain energy in an asymptotic sense, allowing accurate modeling of the blade structure as a1-D finite-element problem. This substantially reduces the computational effort required to model the structural dynamics at each time step. Second, a novel aerodynamic model based on an advanced implementation of the BEM (Blade Element Momentum) Theory; where all velocities and forces are re-projected through orthogonal matrices into the instantaneous deformed configuration to fully include the effects of large displacements and rotation of the airfoil sections into the computation of aerodynamic forces. This allows the aerodynamic model to take into account the effects of the complex flexo-torsional deformation that can be captured by the more sophisticated structural model mentioned above. In this presentation, we report some recent results we have obtained applying our code to full-scale composite laminate wind-turbine blades, analyzing the fundamental vibrational modes and the stress load in normal operational conditions.