Research at the Vitoria-Gasteiz Faculty of Engineering of the UPV/EHU has used convolutional neural networks to predict airflow characteristics in the aerodynamic profiles of high-power wind turbines, and has shown that flow control devices can be studied using these neural networks, with tolerable errors and a reduction in computational time of four orders of magnitude. The study has been published in Scientific Reports.Research at the Vitoria-Gasteiz Faculty of Engineering of the UPV/EHU has used convolutional neural networks to predict airflow characteristics in the aerodynamic profiles of high-power wind turbines, and has shown that flow control devices can be studied using these neural networks, with tolerable errors and a reduction in computational time of four orders of magnitude. The study has been published in Scientific Reports.
Neural networks being used to improve the performance of high-power wind turbines
