Predicting ice-induced load amplitudes on ship bow conditional on ice thickness and ship speed in the Baltic Sea

Mikko Kotilainen, Jarno Petteri Vanhatalo, Mikko Suominen, Pentti Kujala

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Transportation in ice prone waters is a timely topic due to the pursuit for arctic natural resources and sea routes. One important safety aspect in designing ships that enter ice prone waters is to determine the ice-induced loads on ships. However, ice is a particularly inconsistent material; therefore it is difficult to predict the occurring loads when the ship hull breaks the ice. We propose a novel probabilistic, Bayesian, method for modeling and predicting ice load distributions in different ice and operational conditions. We assume the ice loads to be generated from a random process whose parameters change as a function of ice thickness and ship speed. We test four alternative hierarchical Gaussian Process models. The best model shows good performance in predictive validation tests. According to the results the probability of high ice loads increases with increasing ice thickness and increasing speed. The model can be used to predict continuously ice loads in different ice thickness and speed conditions and, with further development, has potential to be utilized in determining the safe way to operate ships in different conditions.
Original languageEnglish
JournalCold Regions Science and Technology
Volume135
Pages (from-to)116–126
Number of pages11
ISSN0165-232X
DOIs
Publication statusPublished - Mar 2017
MoE publication typeA1 Journal article-refereed

Fields of Science

  • 112 Statistics and probability
  • 1172 Environmental sciences

Cite this

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title = "Predicting ice-induced load amplitudes on ship bow conditional on ice thickness and ship speed in the Baltic Sea",
abstract = "Transportation in ice prone waters is a timely topic due to the pursuit for arctic natural resources and sea routes. One important safety aspect in designing ships that enter ice prone waters is to determine the ice-induced loads on ships. However, ice is a particularly inconsistent material; therefore it is difficult to predict the occurring loads when the ship hull breaks the ice. We propose a novel probabilistic, Bayesian, method for modeling and predicting ice load distributions in different ice and operational conditions. We assume the ice loads to be generated from a random process whose parameters change as a function of ice thickness and ship speed. We test four alternative hierarchical Gaussian Process models. The best model shows good performance in predictive validation tests. According to the results the probability of high ice loads increases with increasing ice thickness and increasing speed. The model can be used to predict continuously ice loads in different ice thickness and speed conditions and, with further development, has potential to be utilized in determining the safe way to operate ships in different conditions.",
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author = "Mikko Kotilainen and Vanhatalo, {Jarno Petteri} and Mikko Suominen and Pentti Kujala",
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journal = "Cold Regions Science and Technology",
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Predicting ice-induced load amplitudes on ship bow conditional on ice thickness and ship speed in the Baltic Sea. / Kotilainen, Mikko; Vanhatalo, Jarno Petteri; Suominen, Mikko; Kujala, Pentti.

In: Cold Regions Science and Technology, Vol. 135, 03.2017, p. 116–126.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Predicting ice-induced load amplitudes on ship bow conditional on ice thickness and ship speed in the Baltic Sea

AU - Kotilainen, Mikko

AU - Vanhatalo, Jarno Petteri

AU - Suominen, Mikko

AU - Kujala, Pentti

PY - 2017/3

Y1 - 2017/3

N2 - Transportation in ice prone waters is a timely topic due to the pursuit for arctic natural resources and sea routes. One important safety aspect in designing ships that enter ice prone waters is to determine the ice-induced loads on ships. However, ice is a particularly inconsistent material; therefore it is difficult to predict the occurring loads when the ship hull breaks the ice. We propose a novel probabilistic, Bayesian, method for modeling and predicting ice load distributions in different ice and operational conditions. We assume the ice loads to be generated from a random process whose parameters change as a function of ice thickness and ship speed. We test four alternative hierarchical Gaussian Process models. The best model shows good performance in predictive validation tests. According to the results the probability of high ice loads increases with increasing ice thickness and increasing speed. The model can be used to predict continuously ice loads in different ice thickness and speed conditions and, with further development, has potential to be utilized in determining the safe way to operate ships in different conditions.

AB - Transportation in ice prone waters is a timely topic due to the pursuit for arctic natural resources and sea routes. One important safety aspect in designing ships that enter ice prone waters is to determine the ice-induced loads on ships. However, ice is a particularly inconsistent material; therefore it is difficult to predict the occurring loads when the ship hull breaks the ice. We propose a novel probabilistic, Bayesian, method for modeling and predicting ice load distributions in different ice and operational conditions. We assume the ice loads to be generated from a random process whose parameters change as a function of ice thickness and ship speed. We test four alternative hierarchical Gaussian Process models. The best model shows good performance in predictive validation tests. According to the results the probability of high ice loads increases with increasing ice thickness and increasing speed. The model can be used to predict continuously ice loads in different ice thickness and speed conditions and, with further development, has potential to be utilized in determining the safe way to operate ships in different conditions.

KW - 112 Statistics and probability

KW - 1172 Environmental sciences

U2 - 10.1016/j.coldregions.2016.12.006

DO - 10.1016/j.coldregions.2016.12.006

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SN - 0165-232X

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