Abstract 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%.

中文翻译：

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海上风导管下部结构的数据驱动模型更新

摘要 本文提供了海上风力发电机导管架下部结构的模型更新应用研究。更新是在基于灵敏度的参数估计设置中解决的，其中表示实验获得的模态参数与模型预测的参数之间差异的成本函数被最小化。物理系统的模态参数是通过随机子空间识别 (SSI) 来估计的，随机子空间识别 (SSI) 应用于为涡轮机的空转和运行状态捕获的振动数据。从理论上讲，识别方法依赖于线性和时不变 (LTI) 系统和输入白噪声随机过程；由于操作可变性、涡轮控制器和非线性阻尼等原因，在此应用中违反了这些标准。最后，特别注意评估在主要条件下通过 SSI 提取模态参数并随后使用这些参数进行模型更新的可行性。在此基础上，认为有必要忽略运行中的涡轮机状态——这会严重促进非线性和时变结构行为，因此，参数估计结果不精确——并根据仅从模型中提取的模态参数进行模型更新。空转状态。与模态参数估计和要更新的模型参数相关的不确定性被概述并包含在更新程序中，使用基于灵敏度的公式中的加权矩阵。通过基于在空转条件下从导管架子结构采集的现场数据进行模型更新，