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Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
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https://doi.org/10.5194/wes-2020-24
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-2020-24
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  25 Mar 2020

25 Mar 2020

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This preprint is currently under review for the journal WES.

Surrogate models for unsteady aerodynamics using non-intrusive Polynomial Chaos Expansions

Rad Haghi and Curran Crawford Rad Haghi and Curran Crawford
  • Institute for Integrated Energy Systems, University of Victoria, British Columbia, Canada

Abstract. In common industrial practice based on IEC standards, wind turbine simulations are computed in the time domain for each mean wind speed bin using six unsteady wind seeds. Different software such as FAST, Balded or HAWC2 can be used to this purpose, to capture the unsteadiness and uncertainties of the wind in the simulations. The statistics of these simulations are extracted and used to calculate fatigue and extreme loads on the wind turbine components. Having only six seeds does not guarantee an accurate estimation of the overall statistics. One solution might be running more seeds; however, this will increase the computation cost. Moreover, to move beyond Blade Element Momentum based tools toward vortex/potential flow formulations, a reduction in the computational cost associated with the unsteady flow and uncertainty handling is required. This study illustrates the stationary character of the unsteady aerodynamic statistics based on the standard turbulence models. Afterward, we propose a non-intrusive Polynomial Chaos Expansion to build a surrogate model of the loads' statistics at each time step, to estimate the statistics more accurately and efficiently.

Rad Haghi and Curran Crawford

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Status: final response (author comments only)
Status: final response (author comments only)
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Rad Haghi and Curran Crawford

Rad Haghi and Curran Crawford

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