Investigation of main bearing fatigue estimate sensitivity to synthetic
turbulence models using a novel drivetrain model implemented in OpenFAST
Abstract
A coupled medium-fidelity drivetrain model is developed and
implemented in OpenFAST for a 10-MW land-based reference turbine. The
implementation is verified against a fully coupled multibody wind
turbine model, including a detailed drivetrain. The new model can
simultaneously and accurately estimate main bearing loads and represent
elastic bending of the drivetrain. It has low computational cost, useful
for early design phases, sensitivity analyses and complex systems like
wind farms (where computational expense must be expended elsewhere).
Here, the model is extended to a monopile offshore wind turbine and used
to investigate sensitivity of predicted main bearing rolling contact
fatigue to different synthetic turbulence models. Large eddy simulations
(LES) intentionally targeting stable, neutral, and unstable atmospheric
conditions at below-, near- and above-rated wind speeds, were used as a
reference. The turbulence models recommended by IEC, the Mann spectral
tensor model and the Kaimal spectral model with exponential coherence,
were fitted to the LES data. Additionally, a constrained turbulence
generator, PyConTurb, based on LES data, was applied in the
aero-hydro-servo-elastic simulations. Taking PyConTurb as the baseline,
the Kaimal model significantly underestimates fatigue of the downwind
main bearing, with between 10 and 40% less damage. The Mann model also
underestimates the downwind main bearing fatigue with up to 30%. The
upwind main bearing damage is driven by mean loads, and differences
between models are less significant, although the trends are similar.
Reasons for these discrepancies are investigated and attributed to
differences in spatial and temporal variations among the turbulence
models.
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