Model selection through robustness and fidelity criteria: Modeling the dynamics of the CX-100 wind turbine blade
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- 作者：Kendra L. Van Buren ; Sez Atamturktur ; Fran莽ois M. Hemez
- 关键词：Info-Gap decision theory ; Model selection ; Wind turbine blade ; Model complexity ; Prediction ; Test-analysis correlation
- 刊名：Mechanical Systems and Signal Processing
- 出版年：3 February, 2014
- 年：2014
- 卷：43
- 期：1-2
- 页码：246-259
- 全文大小：1873 K
文摘
Several plausible modeling strategies are available to develop numerical models for simulating the dynamics of wind turbine blades. While the modeling strategy is typically selected according to expert judgment, the 鈥渂est鈥?/em> modeling approach is unknown to the model developer. Thus, comparing plausible modeling strategies through a systematic and rigorous approach becomes necessary. This manuscript departs from the conventional approach that selects the model with the highest fidelity-to-data; and instead explores the trade-off between fidelity of model predictions to experiments and robustness of model predictions to model imprecision and inexactness. Exploring robustness in addition to fidelity lends credibility to the model, ensuring model predictions can be trusted even when lack-of-knowledge in the modeling assumptions and/or input parameters result in unforeseen errors and uncertainties. This concept is demonstrated on the CX-100 wind turbine blade in an experimental configuration with large masses added to load the blade in bending during vibration testing. The finite element model of the blade is built with shell elements and validated against experimental evidence, while the large masses are modeled according to two different, but plausible strategies using (i) a combination of point-mass and spring elements, and (ii) solid elements. These two modeling strategies are evaluated considering both the fidelity of the natural frequency predictions against experiments, and the robustness of the predicted natural frequencies to uncertainties in the input parameters. By considering robustness during model selection, the authors determine the extent to which prediction accuracy deteriorates as the lack-of-knowledge increases. The findings suggest the model with solid elements offers a higher degree of fidelity-to-data and robustness to uncertainties, thus providing a superior modeling strategy than the model with point masses and stiffening springs.