Data-driven Model Updating of an Offshore Wind Jacket Substructure
| Autor: | Ursula Smolka, Martin Dalgaard Ulriksen, John Dalsgaard Sørensen, Dawid Jakub Augustyn, Ulf T. Tygesen |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Estimation theory
Computer science 020101 civil engineering Ocean Engineering 02 engineering and technology White noise Operational modal analysis 01 natural sciences Turbine Digital twin 010305 fluids & plasmas 0201 civil engineering Weighting Condition monitoring Offshore wind power Operational Modal Analysis Jacket substructure Modal Model updating Control theory 0103 physical sciences Wind turbines Sensitivity (control systems) |
| Zdroj: | Augustyn, D J, Smolka, U, Tygesen, U T, Ulriksen, M D & Sørensen, J D 2020, ' Data-driven Model Updating of an Offshore Wind Jacket Substructure ', Applied Ocean Research, vol. 104, 102366 . https://doi.org/10.1016/j.apor.2020.102366 |
| DOI: | 10.1016/j.apor.2020.102366 |
| Popis: | The present paper provides a model updating application study concerning the jacket substructure of an offshore wind turbine. The updating is resolved in a sensitivity-based parameter estimation setting, where a cost function expressing the discrepancy between experimentally obtained modal parameters and model-predicted ones is minimized. The modal parameters of the physical system are estimated through stochastic subspace identification (SSI) applied to vibration data captured for idling and operational states of the turbine. From a theoretical outset, the identification approach relies on the system being linear and time-invariant (LTI) and the input white noise random processes; criteria which are violated in this application due to sources such as operational variability, the turbine controller, and non-linear damping. Consequently, particular attention is given to assess the feasibility of extracting modal parameters through SSI under the prevailing conditions and subsequently using these parameters for model updating. On this basis, it is deemed necessary to disregard the operational turbine states---which severely promote non-linear and time-variant structural behaviour and, as such, imprecise parameter estimation results---and conduct the model updating based on modal parameters extracted solely from the idling state. The uncertainties associated with the modal parameter estimates and the model parameters to be updated are outlined and included in the updating procedure using weighting matrices in the sensitivity-based formulation. By conducting the model updating based on in-situ data harvested from the jacket substructure during idling conditions, the maximum eigenfrequency deviation between the experimental estimates and the model-predicted ones is reduced from 30 % to 1 %. |
| Databáze: | OpenAIRE |
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