Time-Averaged Wind Turbine Wake Flow Field Prediction Using Autoencoder Convolutional Neural Networks

Time-Averaged Wind Turbine Wake Flow Field Prediction Using Autoencoder Convolutional Neural Networks

A convolutional neural network (CNN) autoencoder model has been developed to generate 3D realizations of time-averaged velocity in the wake of the wind turbines at the Sandia National Laboratories Scaled Wind Farm Technology (SWiFT) facility. Large-eddy simulations (LES) of the SWiFT site are conduc...

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Bibliographic Details
Main Authors: Zexia Zhang, Christian Santoni, Thomas Herges, Fotis Sotiropoulos, Ali Khosronejad
Format: Article
Language:English
Published: MDPI AG 2021-12-01
Series:Energies
Subjects:
convolutional neural network
wind turbine
wake flow predictions
large-eddy simulation
Online Access:https://www.mdpi.com/1996-1073/15/1/41

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https://www.mdpi.com/1996-1073/15/1/41

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