Reliability-based design optimization of wind turbine blades for fatigue life under dynamic wind load uncertainty
Weifei Hu, K. K. Choi, Hyunkyoo Cho.
Department of Mechanical and Industrial Engineering, The University of Iowa, Iowa City, USA
This paper studies reliability-based design optimization (RBDO) of a 5-MW wind turbine blade for designing reliable as well as economical wind turbine blades. A novel dynamic wind load uncertainty model has been developed using 249 groups of wind data to consider wind load variation over a large spatiotemporal range. The probability of fatigue failure during a 20-year service life is estimated using the uncertainty model in the reliability-based design optimization process and is reduced to meet a desired target reliability. Meanwhile, the cost of composite materials used in the blade is minimized by optimizing the composite laminate thicknesses of the blade.
In order to obtain the reliability-based design optimization optimum design efficiently, deterministic design optimization (DDO) of the 5-MW wind turbine blade is carried out first using the mean wind load obtained from the wind load uncertainty model. The reliability-based design optimization is then initiated from the DDO optimum. During the reliability-based design optimization iterations, fatigue hotspots for reliability-based design optimization are identified among the laminate section points.
For an efficient reliability-based design optimization process, surrogate models of 10-min fatigue damages D10 at the hotspots are accurately created using the Kriging method. Using the wind load uncertainty model and surrogate models, probability of fatigue failure during a 20-year lifespan at the hotspots and the design sensitivities are calculated at given design points. Using the probability of fatigue failure and design sensitivity, reliability-based design optimization of the 5-MW wind turbine blade has been successfully carried out, satisfying the target probability of failure of 2.275 %.Go To Structural and Multidisciplinary Optimization