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In the present work, we consider multi-fidelity surrogate modelling to fusethe output of multiple aero-servo-elastic computer simulators of varyingcomplexity. In many instances, predictions from multiple simulators for thesame quantity of interest on a wind turbine are available. In this type ofsituation, there is strong evidence that fusing the output from multipleaero-servo-elastic simulators yields better predictive ability and lower modeluncertainty than using any single simulator. Hierarchical Kriging is amulti-fidelity surrogate modelling method in which the Kriging surrogate modelof the cheap (low-fidelity) simulator is used as a trend of the Krigingsurrogate model of the higher fidelity simulator. We propose a parametricapproach to Hierarchical Kriging where the best surrogate models are selectedbased on evaluating all possible combinations of the available Krigingparameters candidates. The parametric Hierarchical Kriging approach isillustrated by fusing the extreme flapwise bending moment at the blade root ofa large multi-megawatt wind turbine as a function of wind velocity, turbulenceand wind shear exponent in the presence of model uncertainty andheterogeneously noisy output. The extreme responses are obtained by two widelyaccepted wind turbine specific aero-servo-elastic computer simulators, FAST andBladed. With limited high-fidelity simulations, Hierarchical Kriging producesmore accurate predictions of validation data compared to conventional Kriging.In addition, contrary to conventional Kriging, Hierarchical Kriging is shown tobe a robust surrogate modelling technique because it is less sensitive to thechoice of the Kriging parameters and the choice of the estimation error.

Hierarchical Kriging for multi-fidelity aero-servo-elastic simulators - Application to extreme loads on wind turbines