High-Precision Identification Method for Distributed Ice Loads Exerted on Hull Structures Using an Enhanced Model

Abstract

The random dynamic ice loads encountered during icebreaking operations pose a typical dynamic loading problem. Monitoring hull structure ice loads is crucial in ship structural research, with frequency-domain load identification providing a feasible solution for obtaining ice load spectral characteristics. However, severe ill-posedness in initial mathematical models frequently causes significant identification errors. Current ill-posedness mitigation methods remain limited owing to frequency-domain model complexities. This study proposes a dimension-reduction, optimization-enhanced model using the multiple combination retrieval algorithm (MCRA) to address existing problems. First, the initial mathematical model construction is clarified by analyzing the ship–ice collision process, including establishing an equivalent mechanical model for large-area multiple ice loads, determining 133 ice load points for identification, and proposing an initial sensor arrangement scheme. Subsequently, the initial frequency response function matrix is optimized using C-optimal and D-optimal methods, and a targeted improvement to the MCRA optimization method is proposed. Results show that the MCRA successfully reduced the condition number from 3 954.66 to 102.84, significantly decreasing matrix ill-posedness compared with existing methods. The global maximum condition number obtained by the MCRA is 278.04, considerably lower than other optimization results, making it more advantageous. Subsequently, Tikhonov regularization, truncated singular-value decomposition regularization, and the Moore–Penrose generalized inverse method were employed to comparatively analyze inverse ice load spectrum results. The identification accuracy of enhanced models using C-optimal, D-optimal, and MCRA methods was evaluated to verify the accuracy advantages of the MCRA enhanced model. Finally, the applicability and stability of the model were verified through robustness analyses under five different noise levels and through ice load identification results at different sailing speeds. The mean relative error of all identified results under different working conditions remained stable at approximately 9%.

Keywords

Icebreaker / Ice load identification / Model ill-posedness / Enhanced model / Optimal sensor placement

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