A Reduced Order Model to Predict Transient Flows around Straight Bladed Vertical Axis Wind Turbines
1
ETSIAE-UPM (School of Aeronautics), Universidad Politécnica de Madrid, Pza Cardenal Cisnero 3, 28040 Madrid, Spain
2
Center for Computational Simulation (CCS), Universidad Politécnica de Madrid, Boadilla del Monte, 28660 Madrid, Spain
*
Author to whom correspondence should be addressed.
Energies 2018, 11(3), 566; https://doi.org/10.3390/en11030566
Received: 22 January 2018 / Revised: 15 February 2018 / Accepted: 1 March 2018 / Published: 7 March 2018
(This article belongs to the Special Issue Data Science and Big Data in Energy Forecasting)
We develop a reduced order model to represent the complex flow behaviour around vertical axis wind turbines. First, we simulate vertical axis turbines using an accurate high order discontinuous Galerkin–Fourier Navier–Stokes Large Eddy Simulation solver with sliding meshes and extract flow snapshots in time. Subsequently, we construct a reduced order model based on a high order dynamic mode decomposition approach that selects modes based on flow frequency. We show that only a few modes are necessary to reconstruct the flow behaviour of the original simulation, even for blades rotating in turbulent regimes. Furthermore, we prove that an accurate reduced order model can be constructed using snapshots that do not sample one entire turbine rotation (but only a fraction of it), which reduces the cost of generating the reduced order model. Additionally, we compare the reduced order model based on the high order Navier–Stokes solver to fast 2D simulations (using a Reynolds Averaged Navier–Stokes turbulent model) to illustrate the good performance of the proposed methodology.
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Keywords:
reduced order model; vertical axis wind turbine; high order discontinuous Galerkin; turbulent flow; flow prediction; high order dynamic mode decomposition
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MDPI and ACS Style
Le Clainche, S.; Ferrer, E. A Reduced Order Model to Predict Transient Flows around Straight Bladed Vertical Axis Wind Turbines. Energies 2018, 11, 566.
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