Abstract. We define and demonstrate a procedure for quick assessment of site-specific
lifetime fatigue loads using simplified load mapping functions (surrogate
models), trained by means of a database with high-fidelity load simulations.
The performance of five surrogate models is assessed by comparing
site-specific lifetime fatigue load predictions at 10 sites using an
aeroelastic model of the DTU 10 MW reference wind turbine. The surrogate
methods are polynomial chaos expansion, quadratic response surface, universal
Kriging, importance sampling, and nearest-neighbor interpolation. Practical
bounds for the database and calibration are defined via nine environmental
variables, and their relative effects on the fatigue loads are evaluated by
means of Sobol sensitivity indices. Of the surrogate-model methods,
polynomial chaos expansion provides an accurate and robust performance in
prediction of the different site-specific loads. Although the Kriging
approach showed slightly better accuracy, it also demanded more computational
resources.
Abstract. We define and demonstrate a procedure for quick assessment of site-specific lifetime fatigue loads, using surrogate models calibrated by means of a database with high-fidelity load simulations. The performance of six surrogate models is assessed by comparing site-specific lifetime fatigue load predictions at ten sites. The surrogate methods include polynomial-chaos expansion, quadratic response surface, universal Kriging, importance sampling, and nearest-neighbor interpolation. Practical bounds for the database and calibration are defined via nine environmental variables, and their relative effects on the fatigue loads are evaluated by means of Sobol sensitivity indices. Of the surrogate-model methods, polynomial-chaos expansion provided an accurate and robust performance in prediction of the different site-specific loads. Although the Kriging approach showed slightly better accuracy, it also demanded more computational resources. Taking into account other useful properties of the polynomial chaos expansion method within the performance comparisons, we consider it to generally be the most useful for quick assessment of site-specific loads.
A novel validation methodology allows verifying a CFD model over the entire wind turbine induction zone using measurements from three synchronized lidars. The validation procedure relies on spatially discretizing the probability density function of the measured free-stream wind speed. The resulting distributions are reproduced numerically by weighting steady-state Reynolds averaged Navier-Stokes simulations accordingly. The only input varying between these computations is the velocity at the inlet boundary. The rotor is modelled using an actuator disc. So as to compare lidar and simulations, the spatial and temporal uncertainty of the measurements is quantified and propagated through the data processing. For all velocity components the maximal difference between measurements and model are below 4.5% relative to the average wind speed for most of the validation space. This applies to both mean and standard deviation. One rotor radius upstream the difference reaches maximally 1.3% for the axial component.
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