The paper presents a true digital twin concept, which is a general and novel methodology that significantly improves the fatigue prediction models of existing marine structures. The actual structural condition of existing marine platforms can often change after several years in operation due to degradation mechanisms and/or other structural changes. It is within this context, the true digital twin concept has been developed and the general idea is to create a coupling between the digital twin and measurements. The measurements are performed by Structural Health Monitoring Systems (SHMS). This coupling facilitates a direct performance evaluation of the digital twin against measurements and most importantly creates the basis for improving the performance of the digital twin to accurately capture the actual condition of the structure, and thus become a true digital twin. The full concept of creating a true digital twin encompass novel advanced analysis methods ranging from linear system identification, expansion processes, Bayesian FE model updating, wave load calibration, quantification of uncertainties from measured data, and Risk- and Reliability Based Inspection Planning (RBI) analysis, [1]. This paper presents the first 3 levels for establishing a true digital twin. The levels are illustrated by 3 case stories.
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%.
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