Gazing at clouds to understand turbulence on wind turbine airfoils

Poster (2016)

Authors

G.L. De Oliveira Andrade Wind Energy - Aerospace Engineering
R. Balbino dos Santos Pereira Wind Energy - Aerospace Engineering
W.A. Timmer Wind Energy - Aerospace Engineering
D. Ragni Wind Energy - Aerospace Engineering
F. Lau University of Lisbon
G.J.W. van Bussel Wind Energy - Aerospace Engineering
Research Group
Wind Energy (Aerospace Engineering) (TU Delft)
Copyright: © 2016 G.L. De Oliveira Andrade, R. Balbino dos Santos Pereira, W.A. Timmer, D. Ragni, F. Lau, G.J.W. van Bussel
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Published Date

2016

Language

English

Reuse Rights

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Faculty

Aerospace Engineering

Department

Flow Physics and Technology

Research Group

Wind Energy

Abstract

There are many ways to learn from data. Our first experiment consisted in reproducing the way aerodynamicists work [2] with a genetic optimizer. The data pool was too narrow and asymptotic tendencies were unreliable. Our 2nd Experiment, a simple version of [4], had a virtually unlimited data pool and used neural networks. Results were better, but computationally expensive. Data assimilation approaches used in EO [ 7] could yield better results..

Files

Poster_ESA_EO.pdf
(.pdf | 1.57 Mb)