Robust Model Calibration With Load Uncertainties

The ultimate goal of this work is to propose a model calibration strategy for an industrial problema consisting in a MW class geared wind turbine power train subjected to uncertain loads. Lack of knowledge is commonplace in this kind of engineering system and a realistic model calibration cannot be performed without taking into account this type of uncertainty. The question at stake of this study is how to build a robust predictive model of a dynamic system given that the excitations are poorly known. The uncertainty in the latter will be represented with an info-gap model. The tradeoff between fidelity to data and robustness to uncertainty are investigated in order to minimize the maximum level of discrepancy for a given horizon of uncertainty. Different system configurations and different uncertain excitations are then tested and the predictive capability of the calibrated model is studied in detail. This methodology is illustrated on a simple academic model and on a more complex engineering system representing a wind turbine geared power train