A machine learning-based method for simulation of ship speed profile in a complex ice field

Computational methods for predicting ship speed profile in a complex ice field have traditionally relied on mechanistic simulations. However, such methods have difficulties capturing the entire complexity of ship–ice interaction process due to the incomplete understanding of the underlying physical phenomena. Therefore, data-driven approaches have recently gained increased attention in this context. Hence, this paper proposes a concept of a first machine learning-based simulator of ship speed profile in a complex ice field. The developed approach suggests using supervised machine learning to trace a function mapping several ship and ice parameters to the ship acceleration/deceleration between the two adjacent points along the route. The simulator is trained and tested on a dataset obtained from the full-scale tests of an icebreaking ship. The results show high accuracy of the developed method, with an average error of the simulated ship speed against the measured one ranging from 2.6% to 9.4%.

Subjects

Artificial neural network

machine learning

ship ice transit simulations

ship resistance in ice

ship speed profile in ice

MLE@TUHH

DDC Class

600: Technik

More Funding Information

The financial support by the Barents 2020 project (NOR-13/0080) funded by Norwegian Ministry of Foreign Affairs, as well as the support from the project partners, namely: ABB, CHNL, DNV GL, HiÅ (now part of NTNU), Marintek (now part of SINTEF Oceans), Northenergy and NTNU, are greatly appreciated.

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https://creativecommons.org/licenses/by-nc-nd/4.0/

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A machine learning based method for simulation of ship speed profile in a complex ice field.pdf

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A machine learning-based method for simulation of ship speed profile in a complex ice field