Vortex-model-based Multi-objective Optimization of Winglets for Wind Turbines using Machine Learning

Nick Leenders*, Wei Yu, Mac Gaunaa, Marco Caboni, Carlos Simão Ferreira

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

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Abstract

Different Design Driving Load constraints (DDLs), are explored in this work to determine under which constraints and conditions a winglet can have an added value to the wind turbine blade design. Multi-objective Bayesian optimization is used to maximize the rotor's power production while minimizing the flapwise DDLs. Surrogate models, created using machine learning techniques such as Gaussian Processes and Bayesian Neural Networks, are used in combination with an acquisition function, to determine what designs should be evaluated by the lifting line model AWSM, with the goal to obtain designs that lie on the Pareto front of two or more objectives. The recent Bayesian Neural Networks as surrogate model were able to find the Pareto-front most effectively in this work. Furthermore, the results show that different DDL constraints led to different winglet designs, with noticeable differences between upwind and downwind winglet designs. Winglet designs were found to be able to increase power without increasing the thrust, root flapwise bending moment and flapwise bending moment at radial locations on the blade. A noticeable increase in power was found when introducing sweep to the winglet design.

Original languageEnglish
Article number032056
Number of pages12
JournalJournal of Physics: Conference Series
Volume2265
Issue number3
DOIs
Publication statusPublished - 2022
Event2022 Science of Making Torque from Wind, TORQUE 2022 - Delft, Netherlands
Duration: 1 Jun 20223 Jun 2022

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