Wind turbine design optimization is typically performed considering a given wind distribution. However, turbines so designed often end up being used at sites characterized by different wind distributions, resulting in significant performance penalties. This paper presents a probabilistic integrated multidisciplinary approach to the design optimization of multimegawatt wind turbines accounting for the stochastic variability of the mean wind speed. The presented technology is applied to the design of a 5 MW rotor for use at sites of wind power class from 3 to 7, where the mean wind speed at 50 m above the ground ranges from 6.4 to 11.9 m/s. Assuming the mean wind speed to vary stochastically in such range, the rotor design is optimized by minimizing mean and standard deviation of the levelized cost of energy (LCOE). Airfoil shapes, spanwise distributions of blade chord and twist, blade internal structural layup, and rotor speed are optimized concurrently, subject to structural and aeroelastic constraints. The probabilistically designed turbine achieves a more favorable probabilistic performance than the initial baseline turbine. The presented probabilistic design framework is portable and modular in that any of its analysis modules can be replaced with counterparts of user-selected fidelity.
Harvesting Renewable Energies through Innovative Kinetic Honeycomb Architectural Facades: The Mathematical & CFD Modeling for Wind Turbine Design Optimization
