GANs enabled super-resolution reconstruction of wind field
Peer reviewed, Journal article
Published version
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Åpne
Permanent lenke
https://hdl.handle.net/11250/2734989Utgivelsesdato
2020Metadata
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- Institutt for matematiske fag [2474]
- Institutt for teknisk kybernetikk [3742]
- Publikasjoner fra CRIStin - NTNU [38278]
Originalversjon
10.1088/1742-6596/1669/1/012029Sammendrag
Atmospheric flows are governed by a broad variety of spatio-temporal scales, thus making real-time numerical modeling of such turbulent flows in complex terrain at high resolution computationally unmanageable. In this paper, we demonstrate a novel approach to address this issue through a combination of fast coarse scale physics based simulator and a family of advanced machine learning algorithm called the Generative Adversarial Networks. The physics-based simulator generates a coarse wind field in a real wind farm and then ESRGANs enhance the result to a much finer resolution. The method outperforms state of the art bicubic interpolation methods commonly utilized for this purpose.