Stochastic subspace identification of modal parameters during ice–structure interaction

Torodd Skjerve Nord, Oyvind Petersen, Hayo Hendrikse

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Abstract

Identifying the modal parameters of structures located in ice-infested waters may be challenging due to the interaction between the ice and structure. In this study, both simulated data from a state-of-the-art ice–structure interaction model and measured data of ice–structure interaction were both used in conjunction with a covariance-driven stochastic subspace identification method to identify the modal parameters and their corresponding variances. The variances can be used to assign confidence to the identified eigenfrequencies, and effectively eliminate the eigenfrequencies with large variances. This enables a comparison between the identified eigenfrequencies for different ice conditions. Simulated data were used to assess the accuracy of the identified modal parameters during ice–structure interactions, and they were further used to guide the choice of parameters for the subspace identification when applied to measured data. The measured data consisted of 150 recordings of ice actions against the Norströmsgrund lighthouse in the Northern Baltic Sea. The results were sorted into groups defined by the observed ice conditions and governing ice failure mechanisms during the ice–structure interaction. The identified eigenfrequencies varied within each individual group and between the groups. Based on identified modal parameters, we suggested which eigenmodes play an active role in the interaction processes at the ice–structure interface and discussed the possible sources of errors.

This article is part of the theme issue ‘Environmental loading of heritage structures’.
Original languageEnglish
Article number20190030
Number of pages29
JournalPhilosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Volume377
Issue number2155
DOIs
Publication statusPublished - 2019

Keywords

  • Ice-structure interaction
  • Subspace methods
  • System identification
  • Uncertainty quantification

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