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Three-dimensional spatiotemporal wind field reconstruction based on physics-informed deep learning
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Zhang, Jincheng and Zhao, Xiaowei (2021) Three-dimensional spatiotemporal wind field reconstruction based on physics-informed deep learning. Applied Energy, 300 . 117390. doi:10.1016/j.apenergy.2021.117390 ISSN 0306-2619.
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WRAP-three-dimensional-spatiotemporal-wind-field-reconstruction-based-physics-informed-deep-learning-Zhao-2021.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (25Mb) | Preview |
Official URL: https://doi.org/10.1016/j.apenergy.2021.117390
Abstract
In this work, a physics-informed deep learning model is developed to achieve the reconstruction of the three-dimensional (3-D) spatiotemporal wind field in front of a wind turbine, by combining the 3-D Navier–Stokes equations and the scanning LIDAR measurements. To the best of the authors’ knowledge, this is for the first time that the full 3-D spatiotemporal wind field reconstruction is achieved based on real-time measurements and flow physics. The proposed method is evaluated using high-fidelity large eddy simulations. The results show that the wind vector field in the whole 3-D domain is predicted very accurately based on only scalar line-of-sight LIDAR measurements at sparse locations. Specifically, at the baseline case, the prediction errors for the streamwise, spanwise and vertical velocity fields are 0.263 m/s, 0.397 m/s and 0.361 m/s, respectively. The prediction errors for the horizontal and vertical direction fields are 2.84° and 2.58° which are important in tackling yaw misalignment and turbine tilt control, respectively. Further analysis shows that the 3-D wind features are captured clearly, including the evolutions of flow structures, the wind shear in vertical direction, the blade-level speed variations due to turbine rotation, and the speed variations modulated by the turbulent wind. Also, the developed model achieves short-term wind forecasting without the commonly-used Taylor’s frozen turbulence hypothesis. Furthermore it is very useful in advancing other wind energy research fields e.g. wind turbine control & monitoring, power forecasting, and resource assessments because the 3-D spatiotemporal information is important for them but not available with current sensor and prediction technologies.
Item Type: | Journal Article | ||||||||||||
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Subjects: | T Technology > TA Engineering (General). Civil engineering (General) T Technology > TJ Mechanical engineering and machinery |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||||||
Library of Congress Subject Headings (LCSH): | Computational fluid dynamics, Wind turbines -- Aerodynamics, Machine learning | ||||||||||||
Journal or Publication Title: | Applied Energy | ||||||||||||
Publisher: | Elsevier BV | ||||||||||||
ISSN: | 0306-2619 | ||||||||||||
Official Date: | 15 October 2021 | ||||||||||||
Dates: |
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Volume: | 300 | ||||||||||||
Article Number: | 117390 | ||||||||||||
DOI: | 10.1016/j.apenergy.2021.117390 | ||||||||||||
Status: | Peer Reviewed | ||||||||||||
Publication Status: | Published | ||||||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||||||
Date of first compliant deposit: | 26 July 2021 | ||||||||||||
Date of first compliant Open Access: | 13 July 2022 | ||||||||||||
RIOXX Funder/Project Grant: |
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