Reconstruction and Prediction of Ship Maneuvering Motion Based on Dynamic Mode Decomposition

Abstract

In order to reveal the dynamic characteristics and achieve fast prediction of ship maneuvering motion, dynamic mode decomposition (DMD) is applied to reconstruct and predict the turning circle and zig-zag maneuvers. Case study is conducted for a KVLCC2 tanker by utilizing its free-running model test data. First, all DMD modes are extracted from the test data, and the dominant DMD modes are selected according to their contributions to the dynamical systems of ship maneuvering motion. The dynamic characteristics inside the dynamical systems are revealed according to the growth rates and frequencies of the dominant DMD modes. Then, the dynamical systems of maneuvering motions are reconstructed and predicted by reduced-order and full-order DMD algorithms. The effects of the truncation rank and input sample are analyzed by a parametric and feasibility study, which indicates that the truncation rank and input sample are crucial to the reconstruction and prediction accuracy. The dynamical systems reconstructed by the dominant DMD modes are consistent with those reconstructed by full-order DMD algorithm or all DMD modes. The results with proper truncation rank and input sample agree well with the test data, and input samples with more snapshots do not necessarily bring higher reconstruction and prediction accuracy.

Keywords: ship maneuvering, dynamic mode decomposition, reduced-order model, data-driven model, reconstruction and prediction