CNN-based flow control device modelling on aerodynamic airfoils

Wind energy has become an important source of electricity generation, with the aim of achieving a cleaner and more sustainable energy model. However, wind turbine performance improvement is required to compete with conventional energy resources. To achieve this improvement, flow control devices are...

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Bibliographic Details
Published in:Scientific reports 2022-05, Vol.12 (1), p.8205-8205, Article 8205
Main Authors: Portal-Porras, Koldo, Fernandez-Gamiz, Unai, Zulueta, Ekaitz, Ballesteros-Coll, Alejandro, Zulueta, Asier
Format: Article
Language:English
Subjects:
639/166
639/705
639/766
Artificial intelligence
Computer applications
Energy resources
Energy sources
Flow characteristics
Flow control
Fluid dynamics
Humanities and Social Sciences
Hydrodynamics
multidisciplinary
Neural networks
Predictions
Science
Science (multidisciplinary)
Simulation
Sustainable energy
Turbines
Wind power
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Summary:Wind energy has become an important source of electricity generation, with the aim of achieving a cleaner and more sustainable energy model. However, wind turbine performance improvement is required to compete with conventional energy resources. To achieve this improvement, flow control devices are implemented on airfoils. Computational fluid dynamics (CFD) simulations are the most popular method for analyzing this kind of devices, but in recent years, with the growth of Artificial Intelligence, predicting flow characteristics using neural networks is becoming increasingly popular. In this work, 158 different CFD simulations of a DU91W(2)250 airfoil are conducted, with two different flow control devices, rotating microtabs and Gurney flaps, added on its Trailing Edge (TE). These flow control devices are implemented by using the cell-set meshing technique. These simulations are used to train and test a Convolutional Neural Network (CNN) for velocity and pressure field prediction and another CNN for aerodynamic coefficient prediction. The results show that the proposed CNN for field prediction is able to accurately predict the main characteristics of the flow around the flow control device, showing very slight errors. Regarding the aerodynamic coefficients, the proposed CNN is also capable to predict them reliably, being able to properly predict both the trend and the values. In comparison with CFD simulations, the use of the CNNs reduces the computational time in four orders of magnitude.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-12157-w