A machine learning-based method for simulation of ship speed profile in a complex ice field
Loading...
Access rights
openAccess
URL
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
Date
2020-10-20
Major/Subject
Mcode
Degree programme
Language
en
Pages
7
974-980
974-980
Series
Ships and Offshore Structures, Volume 15, issue 9
Abstract
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%.Description
Keywords
Artificial neural network, machine learning, ship ice transit simulations, ship resistance in ice, ship speed profile in ice
Other note
Citation
Milaković, A S, Li, F, Marouf, M & Ehlers, S 2020, ' A machine learning-based method for simulation of ship speed profile in a complex ice field ', Ships and Offshore Structures, vol. 15, no. 9, pp. 974-980 . https://doi.org/10.1080/17445302.2019.1697075