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

Tutkimustuotos: Lehtiartikkelivertaisarvioitu

Tutkijat

Organisaatiot

Kuvaus

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.

Yksityiskohdat

AlkuperäiskieliEnglanti
Sivut14-28
JulkaisuCold Regions Science and Technology
Vuosikerta112
TilaJulkaistu - 2015
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

    Tutkimusalat

  • Bayesian networks, Machine learning, Ship beset in ice, Ship performance in ice

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ID: 2018225