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

Aleksandar Saša Milaković*, Fang Li, Mohamed Marouf, Sören Ehlers

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

40 Downloads (Pure)

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%.

Original languageEnglish
Pages (from-to)974-980
Number of pages7
JournalShips and Offshore Structures
Volume15
Issue number9
Early online date27 Nov 2019
DOIs
Publication statusPublished - 20 Oct 2020
MoE publication typeA1 Journal article-refereed

Keywords

  • Artificial neural network
  • machine learning
  • ship ice transit simulations
  • ship resistance in ice
  • ship speed profile in ice

Fingerprint

Dive into the research topics of 'A machine learning-based method for simulation of ship speed profile in a complex ice field'. Together they form a unique fingerprint.

Cite this