In this work Artificial Neural Networks (ANN) are used for a multi-target optimization of the aerodynamics of a wind turbine blade. The Artificial Neural Network is used to build a meta-model of the blade, which is then optimized according to the imposed criteria. The neural networks are trained with a data set built by a series of CFD simulations and their configuration (number of neurons and layers) selected to improve performances and avoid over-fitting. The basic configuration of the airfoil is the profile S809, which is commonly used in horizontal axis wind turbines (HAWT), equipped with a Coanda jet. The design position and momentum of the jet are optimized to maximize aerodynamic efficiency and minimize the power required to activate the Coanda Jet.
Neural network topology for wind turbine analysis / Gaymann, A.; Schiaffini, G.; Massini, M.; Montomoli, F.; Corsini, A.. - (2019). (Intervento presentato al convegno 13th European Turbomachinery Conference on Turbomachinery Fluid Dynamics and Thermodynamics, ETC 2019 tenutosi a Losanna, Svizzera).
Neural network topology for wind turbine analysis
Massini M.;Corsini A.
2019
Abstract
In this work Artificial Neural Networks (ANN) are used for a multi-target optimization of the aerodynamics of a wind turbine blade. The Artificial Neural Network is used to build a meta-model of the blade, which is then optimized according to the imposed criteria. The neural networks are trained with a data set built by a series of CFD simulations and their configuration (number of neurons and layers) selected to improve performances and avoid over-fitting. The basic configuration of the airfoil is the profile S809, which is commonly used in horizontal axis wind turbines (HAWT), equipped with a Coanda jet. The design position and momentum of the jet are optimized to maximize aerodynamic efficiency and minimize the power required to activate the Coanda Jet.File | Dimensione | Formato | |
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Gaymann_neural_2019.pdf
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Note: https://www.euroturbo.eu/paper/ETC2019-174.pdf
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