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Data-Driven Modeling of Ship Maneuvers in Waves via Dynamic Mode Decomposition
Conference proceeding   Open access

Data-Driven Modeling of Ship Maneuvers in Waves via Dynamic Mode Decomposition

MATTEO Diez, ANDREA Serani, EMILIO F. Campana and FREDERICK Stern
The 9th Conference on Computational Methods in Marine Engineering (Marine 2021), Vol.1(1)
01/31/2022
DOI: 10.2218/marine2021.6852
url
https://doi.org/10.2218/marine2021.6852View
Published (Version of record) Open Access

Abstract

A data-driven and equation-free approach is proposed and discussed to modelships maneuvers in waves, based on the dynamic mode decomposition (DMD). DMD is a dimensionality-reduction/reduced-order modeling method, which provides a linear finite-dimensional representation of a possibly nonlinear system dynamics by means of a set of modeswith associated oscillation frequencies and decay/growth rates. DMD also allows for short-termfuture estimates of the system’s state, which can be used for real-time prediction and control.Here, the objective of the DMD is the analysis and forecast of the trajectories/motions/forces ofships operating in waves, offering a complementary efficient method to equation-based systemidentification approaches. Results are presented for the course keeping of a free-running navaldestroyer (5415M) in irregular stern-quartering waves and for the free-running KRISO ContainerShip (KCS) performing a turning circle in regular waves. Results are overall promising and showhow DMD is able to identify the most important modes and forecast the system’s state withreasonable accuracy upto two wave encounter periods.

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