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GANs enabled super-resolution reconstruction of wind field

Abstract

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.
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Category

Academic article

Language

English

Author(s)

Affiliation

  • SINTEF Digital / Mathematics and Cybernetics
  • Norwegian University of Science and Technology
  • Oklahoma State University

Year

2020

Published in

Journal of Physics: Conference Series (JPCS)

ISSN

1742-6588

Volume

1669

Issue

012029

View this publication at Norwegian Research Information Repository