Automated Modal Parameters Identification During Ice-Structure Interactions

• Published in: Volume 2: Structures, Safety, and Reliability
• Main Authors: Wang, Chunlin, Nord, Torodd Skjerve, Li, Guoyuan
• Format: Book Part
• Language: English
• Published: The American Society of Mechanical Engineers (ASME) 2022
• Subjects: Arctic
• Online Access: https://hdl.handle.net/11250/3047446; https://doi.org/10.1115/OMAE2022-81075

Offshore structures are prone to damage caused by ice-induced vibrations. It is presently unknown to what extent different ice conditions change the properties of the structure, such as natural frequency, damping ratio, and mode shape. Understanding the dynamic interaction between ice and structures are important for the operational ability of offshore structures. In this study, the covariance-driven stochastic subspace identification algorithm (SSI-cov) is introduced to identify modal parameters of a scale-model structure during ice-structure interactions. In order to reduce the number of user interactions and inherent bias to the identified modal parameters, we therefore introduce an automated parameter identification approach. First, SSI-cov is used to obtain poles that describe the information: damping ratio, mode shape, etc. After that, a stable criterion is used to pick up stable poles. Finally, Hierarchical clustering is used to cluster poles to identify the natural frequency. The proposed method is able to reduce the many user-intervenes and enables efficient automatic parameter identification. The results show that Hierarchical clustering can render more successful identifications than the slack value-based method among different ice speeds. The results also show changes in the system frequencies for different ice conditions. submittedVersion