Identification of ice loads on shell structure of ice-going vessel with Green kernel and regularization method

Abstract

Ice loads are important environmental loads that can influence the structural safety of ships during navigation in ice-covered waters. The identification of ice loads on ship hulls is the core of ice load monitoring. In this study, a new ice load identification model based on Green kernel and regularization methods is established. First, the forward model for ice load identification is developed through the discretised convolution integral of ice loads. Next, three commonly used regularization methods, including Tikhonov, truncated singular value decomposition, and least square QR-factorization (LSQR) are adopted to reduce solution errors. The LSQR method is thereafter selected as the optimal regularization operator, and its regular property is proved by numerical cases with ice-induced strains that contain noise. Finally, two load identification cases are conducted on an experimental rig to evaluate the feasibility of the model in ice load identification. The identified loads can determine the signal features of applied loads in the time domain with good accuracy. This identification model provides new insights for full-scale ice load monitoring.