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

Research output: Contribution to journalArticleScientificpeer-review


  • Aleksandar Saša Milaković
  • Fang Li
  • Mohamed Marouf
  • Sören Ehlers

Research units

  • Norwegian University of Science and Technology
  • Hamburg University of Technology
  • Lufthansa Industry Solutions


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
JournalShips and Offshore Structures
Publication statusE-pub ahead of print - 27 Nov 2019
MoE publication typeA1 Journal article-refereed

    Research areas

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

ID: 39437703