Towards probabilistic models for the prediction of a ship performance in dynamic ice

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en Montewka, Jakub Goerlandt, Floris Kujala, Pentti Lensu, Mikko 2017-05-11T08:29:50Z 2017-05-11T08:29:50Z 2015
dc.identifier.citation Montewka , J , Goerlandt , F , Kujala , P & Lensu , M 2015 , ' Towards probabilistic models for the prediction of a ship performance in dynamic ice ' COLD REGIONS SCIENCE AND TECHNOLOGY , vol 112 , pp. 14-28 . DOI: 10.1016/j.coldregions.2014.12.009 en
dc.identifier.issn 0165-232X
dc.identifier.other PURE UUID: 7cea38f2-40c4-448e-8bbb-b1f91f2c4496
dc.identifier.other PURE ITEMURL:
dc.identifier.other PURE LINK:
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dc.description VK: T20404
dc.description.abstract For safe and efficient exploitation of ice-covered waters, knowledge about ship performance in ice is crucial. The literature describes numerical and semi-empirical models that characterize ship speed in ice. These however often fail to account for the joint effect of the ice conditions on ship's speed. Moreover, they omit the effect of ice compression. The latter, when combined with the presence of ridges, can significantly limit the capabilities of an ice-strengthened ship, and potentially bring her to a halt, even if the actual ice conditions are within the design range for the given ship. This paper introduces two probabilistic, data-driven models that predict a ship's speed and the situations where a ship is likely to get stuck in ice based on the joint effect of ice features such as the thickness and concentration of level ice, ice ridges, rafted ice, moreover ice compression is considered. To develop the models, two full-scale datasets were utilized. First, the dataset about the performance of a selected ship in ice is acquired from the automatic identification system. Second, the dataset containing numerical description of the ice field is obtained from a numerical ice model HELMI, developed in the Finnish Meteorological Institute. The collected datasets describe a single and unassisted trip of an ice-strengthened bulk carrier between two Finnish ports in the presence of challenging ice conditions, which varied in time and space. The relations between ship performance and the ice conditions were established using Bayesian networks and selected learning algorithms. The obtained results show good prediction power of the models. This means, on average 80% for predicting the ship's speed within specified bins, and above 90% for predicting cases where a ship may get stuck in ice. en
dc.format.extent 14-28
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries COLD REGIONS SCIENCE AND TECHNOLOGY en
dc.relation.ispartofseries Volume 112 en
dc.rights openAccess en
dc.subject.other 214 Mechanical engineering en
dc.title Towards probabilistic models for the prediction of a ship performance in dynamic ice en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Applied Mechanics en
dc.subject.keyword Bayesian networks
dc.subject.keyword Machine learning
dc.subject.keyword Ship beset in ice
dc.subject.keyword Ship performance in ice
dc.subject.keyword 214 Mechanical engineering
dc.identifier.urn URN:NBN:fi:aalto-201705114022
dc.identifier.doi 10.1016/j.coldregions.2014.12.009
dc.type.version publishedVersion

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