Modelling Ship Performance in Ice Using Bayesian Networks
Modelling Ship Performance in Ice Using Bayesian Networks
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发表时间：
2013
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通讯作者：
J. Montewka;H. Sinclair;P. Kujala;J. Haapala;M. Lensu
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作者：
J. Montewka;H. Sinclair;P. Kujala;J. Haapala;M. Lensu
近几十年来，冰中航行受到了极大的关注。这种日益增加的关注导致了数值和半经验模型的发展，描述船舶在冰中的性能。这些模型考虑了许多参数，如：水平冰厚度，脊冰厚度和冰浓度添加剂的方式。然而，它们没有考虑到上述冰特征对船速的联合影响。此外，冰压缩对船舶性能的影响通常被忽略。本文介绍了概率模型，根据现场观测，预测船舶的速度和船舶可能陷入冰的情况下，选定的冰功能，如厚度和浓度的水平冰，冰脊，漂流冰，冰压缩的联合作用的基础上。为了开发模型，使用贝叶斯置信网络。本文提出的案例研究认为，一个单一的和独立的行程的冰加强散货船在芬兰的两个港口之间的存在具有挑战性的冰条件下，所获得的结果显示出很好的预测能力的模型。
Navigation in ice has received substantial attention over recent decades. This increased attention has led to the development of numerical and semi-empirical models that characterize ship performance in ice. These models consider numerous parameters such as; level ice thickness, ridged ice thickness and ice concentration in additive fashion. However, they fail to account for the joint effect of the above ice features on ship speed. Moreover, the effect of ice compression on ship performance is usually omitted. This paper introduces probabilistic models, based on field observations, that predict a ship’s speed and the situations where a ship is probable to get stuck in ice based on the joint effect of selected ice features, such as the thickness and concentration of level ice, ice ridges, rafted ice, and ice compression. To develop the models a Bayesian Belief Network is used. The case study presented in this paper considers a single and unassisted trip of an ice-strengthened bulk carrier between two Finnish ports in the presence of challenging ice conditions and the obtained results show very good prediction power of the models.