Abstract
The goal of this work is to propose a model calibration strategy for an industrial problem 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 in this study is how to perform 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 is then investigated in order to maximize the robustness of the prediction error at a given horizon of uncertainty. This methodology is illustrated on a simple academic model and on a more complex engineering system representing a wind turbine geared power train.
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Acknowledgements
The authors would like to thank Professor Yakov Ben-Haim for his insightful support in the advancement of this study.
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Pereiro, D., Cogan, S., Sadoulet-Reboul, E., Martinez, F. (2013). Robust Model Calibration with Load Uncertainties. In: Simmermacher, T., Cogan, S., Moaveni, B., Papadimitriou, C. (eds) Topics in Model Validation and Uncertainty Quantification, Volume 5. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6564-5_10
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DOI: https://doi.org/10.1007/978-1-4614-6564-5_10
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