Incorporating stochastic analysis in wind turbine design: data-driven random temporal-spatial parameterization and uncertainty quantification
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Wind turbines reliability is affected by stochastic factors in the turbulent inflow and wind turbine structures. On one hand, the variability in wind dynamics and the inherent stochastic structures result in random loads on wind turbine rotor and tower. On the other hand, the inherent structural uncertainties caused by imperfect control of manufacturing process introduce unpredictable failures and decreases wind generators availability. Therefore, to improve reliability, it is important to incorporate the variability in wind dynamics, and the inherent stochastic structures in analyzing and designing the next generation wind-turbines.
In order to perform stochastic analysis on wind turbine, there are several improvements need to be made. Current stochastic wind turbine analyses are mostly based on incomplete turbulence input models. These models either failed to account for temporal variation of the stochastic wind field or unable to preserve spatial coherence which is a very important property that describes turbulence structure. On the subject of modeling wind turbine, most commonly used wind turbine design code is based on stead, lumped component blade models which lack the ability to describe the complex 3D fluid-structure interaction (FSI); which is essential to provide precise blade stress distribution and deformation details. Finally, when it comes to analyzing simulation results, most of existing work are done by analyzing the time response of wind turbine, without looking at the stochastic nature of performance of wind turbines, and its relationship between stochastic sources in turbulent inflow and turbine structure.
In this work, we first develop a data driven temporal and spatial decomposition (TSD), which is capable of modeling any given large wind data set, to construct a low-dimensional yet realistic stochastic wind flow model. Results of several numerical examples on the TSD model show good consistency between given measured data and simulated synthetic turbulence. After that, a stochastic simulation based on TSD simulated full-field turbulence and a simplified wind turbine model is performed. The result of this analysis shows the adequacy of using TSD as turbulence simulation tool as well as the random nature of wind turbines' performance. Finally, a stochastic analysis on a full scale 3D rich-structural wind turbine model with stochastic composite material properties is performed. With a given steady wind load, the model gives the deformation and the stress distribution of the blades. Critical regions that are most likely to have stress larger than design strength of the material were identified. Failure analysis is then performed based Tsai-Wu failure criterion.