Estimating operability of ships in ridged ice fields
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Journal Title
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Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2017-03
Department
Major/Subject
Mcode
Degree programme
Language
en
Pages
11
51–61
51–61
Series
Cold Regions Science and Technology, Volume 135
Abstract
A method for estimating ship's resistance caused by sea ice ridge keels is revised and used as a part of a method for predicting performance of ships in ridged ice conditions. The resistance method is based on a continuum plasticity model of ridge rubble and is simple to compute. The performance prediction method combines deterministic simulations of ship motion with probabilistic modelling of ridged ice fields. Performance estimates given by the model are distribution of attainable mean speeds for given ice conditions and probability of the ship being able to operate independently. A comprehensive sensitivity analysis was performed to gain insight into the model and identify possible problematic parameters. The sensitivity analysis covered both the ice conditions and modelling assumptions. Two data-sets were used to test the simulation method. One set included the depth profile of sea ice, machinery data and the speed of a ship operating in ridged ice. The resistance method was able to predict the mean speed over 3km well. The second data-set consisted of a history of ship's speeds and positions from AIS data and ice conditions estimated by a numerical ice model HELMI, developed in the Finnish Meteorological Institute. Observed mean speeds were mostly well within the distributions of mean speeds simulated by the transit simulation model. Predictions of independent operation were also promising.Description
Keywords
Winter navigation, Ridged ice, Ship's performance, Transit simulation, Statistical simulation, Sensitivity analysis
Other note
Citation
Kuuliala, L, Kujala, P, Suominen, M & Montewka, J 2017, ' Estimating operability of ships in ridged ice fields ', Cold Regions Science and Technology, vol. 135, pp. 51–61 . https://doi.org/10.1016/j.coldregions.2016.12.003
