gans enabled super-resolution reconstruction of wind field · 2020. 1. 22. · gans enabled...
Post on 28-Jan-2021
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Duy Tan H. Tran1, Haakon Robinson1, Adil Rasheed1,2, Omer San3, Mandar Tabib2, Trond Kvamsdal2,41Department of Engineering Cybernetics, NTNU | 2Mathematics and Cybernetics, SINTEF Digital | 3Mechanical and Aerospace Engineering, OUS | 4Department of Mathematical Sciences, NTNU
Email: Haakon.robinson@ntnu.no, adil.rasheed@ntnu.no, osan@okstate.edu, mandar.tabib@sintef.no, trond.kvamsdal@sintef.no
GANs enabled super-resolution reconstruction of wind field
HIRLAM (2.5km X 2.5km)SIMRA (200mX200m)
G
DHIGH RESOLUTION
(200mX200m)
COARSE RESOLUTION(800mX800m)
GENERATED HIGH-RESOLUTION(200mX200m)
FAKE
REAL
17th Deep Sea Offshore Wind R&D Conference, EERA DeepWind’ 2020, 15-17 January 2020, Trondheim, Norway
Solution: Learn the flow characteristics using Generative Adversarial Networks (GANs) offline for efficient online predictions
RESULTS: Comparison between Bicubic Interpolation, GANS and Ground truth
Research challenge: Generating high resolution wind field in complex terrain without conducting computationallyexpensive high resolution numerical simulations
Generator competes withdiscriminator to generate more
realistic wind field
Discriminator competes withgenerator to find its fault
3 years hourly data from HARMONIE-SIMRA used to train the GANs
ACKNOWLEDGEMENT: The authors acknowledge the financial supportfrom the Norwegian Research Council and the industrial partners ofOPWIND: Operational Control for Wind Power Plants (Grant No.:268044/E20).
HIGHLIGHTS OF THE WORK
A novel approach of combing physics based computational fluiddynamics simulator with the power of advanced machine learningalgorithm like GANs is demonstrated for generating high resolutionwind field in complex terrain.The GANs network learns the main characteristics of the flow incomplex terrain and comfortably outperforms commonly usedinterpolation techniqueA significant improvement in computational efficiency (X100000)
L2-n
orm
Unseen image numbers
GANs reconstructedsuper-resolution wind
field reconstructionfrom coarse numerical
simulation
Offline GANs training with high resolutionarchived data from
numerical simulations
ZOO
MED
-IN V
IEW
BICUBIC GANs Truth
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