Vortex-model-based Multi-objective Optimization of Winglets for Wind Turbines using Machine Learning
conference paper
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.
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.
Topics
TNO Identifier
973366
Source
Journal of Physics; Conference Series, pp. 1-13.
Publisher
IOP Publishing
Source title
The Science of Making Torque from Wind (TORQUE 2022)
Pages
1-13
Files
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