End-to-end wind turbine wake modelling with deep graph representation learning
File(s)
Published version
Author(s)
Li, Siyi
Zhang, Mingrui
Piggott, Matthew D
Type
Journal Article
Abstract
Wind turbine wake modelling is of crucial importance to accurate resource assessment, to layout optimisation, and to the operational control of wind farms. This work proposes a surrogate model for the representation of wind turbine wakes based on a state-of-the-art graph representation learning method termed a graph neural network. The proposed end-to-end deep learning model operates directly on unstructured meshes and has been validated against high-fidelity data, demonstrating its ability to rapidly make accurate 3D flow field predictions for various inlet conditions and turbine yaw angles. The specific graph neural network model employed here is shown to generalise well to unseen data and is less sensitive to over-smoothing compared to common graph neural networks. A case study based upon a real world wind farm further demonstrates the capability of the proposed approach to predict farm scale power generation. Moreover, the proposed graph neural network framework is flexible and highly generic and as formulated here can be applied to any steady state computational fluid dynamics simulations on unstructured meshes.
Date Issued
2023-06-01
Date Acceptance
2023-03-01
Citation
Applied Energy, 2023, 339
ISSN
0306-2619
Publisher
Elsevier
Journal / Book Title
Applied Energy
Volume
339
Copyright Statement
© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000965483100001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
Subjects
CFD MODEL
Computational fluid dynamics
Energy & Fuels
Engineering
Engineering, Chemical
FRAMEWORK
Geometric deep learning
Graph neural networks
Science & Technology
SIMULATIONS
Technology
Wind farm power
Wind turbine wake modelling
Publication Status
Published
Article Number
120928
Date Publish Online
2023-03-21