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. We define and demonstrate a procedure for quick assessment of site-specificlifetime fatigue loads using simplified load mapping functions (surrogatemodels), trained by means of a database with high-fidelity load simulations.The performance of five surrogate models is assessed by comparingsite-specific lifetime fatigue load predictions at 10 sites using anaeroelastic model of the DTU 10 MW reference wind turbine. The surrogatemethods are polynomial chaos expansion, quadratic response surface, universalKriging, importance sampling, and nearest-neighbor interpolation. Practicalbounds for the database and calibration are defined via nine environmentalvariables, and their relative effects on the fatigue loads are evaluated bymeans of Sobol sensitivity indices. Of the surrogate-model methods,polynomial chaos expansion provides an accurate and robust performance inprediction of the different site-specific loads. Although the Krigingapproach showed slightly better accuracy, it also demanded more computationalresources.

From wind to loads: wind turbine site-specific load estimation with surrogate models trained on high-fidelity load databases