Wind Turbine: Uncertainity Quantification and Stochastic Optimization

Current-generation wind turbine blades are tailor-built with composite materials and are structurally complex with varying material, chord, and twist distribution along the span of the blade. In fact, design and manufactory of the wind turbine blades to exact specifications is a challenging task due to the stochastic nature of loading they experience in operation and due to various uncertainties in composite manufacturing. These uncertainties can have adverse effects on the performance and reliability of wind turbine blades, therefore their influence should be quantified.

Uncertainty quantification while considering a large number of random parameters as in wind turbine blades is a computationally-intensive process often demanding thousands of finite element analysis (FEA) calls. To tackle this problem, a computationally efficient approach using l1 regularized polynomial chaos expansion (PCE) is used in this work. This approach allows to perform a global sensitivity analysis, thus giving an estimate of the influence of random parameters on the stochastic responses. This approach is tested on a NREL 5MW wind turbine blade.

In addition to the reliability concerns, the power generated by a wind turbine is also affected by the stochastic nature of wind and the uncertainties in modeling parameters. To this end, a stochastic optimization of a wind turbine is performed considering the influence of randomness in wind speed and blade material properties.

References: 

Thapa, M., and Missoum, S., “High-Dimensional Uncertainty Quantification and Global Sensitivity Analysis of a Composite Wind Turbine Blade”, Proceedings of the ASC 35th Annual Technical Conference, Virtual Conference: American Society for Composites, 2020.

Thapa, M., and Missoum, S., “Stochastic Optimization of a Horizontal-Axis Composite Wind Turbine Blade”, Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Virtual Conference: American Society of Mechanical Engineers, 2020. En yüksek deneme bonusu veren siteler 2023 listesine tinfishgaslamp.com adresinden ulaşabilirsiniz.