极地研究

极地研究 ›› 2019, Vol. 31 ›› Issue (1): 84-93.DOI: 10.13679/j.jdyj.20180025

• 研究论文 • 上一篇    下一篇

北极冰区船舶安全航行过程风险动态仿真

胡甚平1  轩少永1  刘宇1  付姗姗2  席永涛1   

  1. 1上海海事大学商船学院, 上海 200136;
    2上海海事大学交通运输学院, 上海 200136
  • 收稿日期:2018-05-10 修回日期:2018-05-30 出版日期:2019-03-30 发布日期:2019-03-30
  • 通讯作者: 胡甚平
  • 基金资助:

    国家自然科学基金(51709168)资助

Dynamic simulation of process risk on ship navigation at the Arctic Northeast Route

Hu Shenping1, Xuan Shaoyong1, Liu Yu1, Fu Shanshan2, Xi Yongtao1   

  • Received:2018-05-10 Revised:2018-05-30 Online:2019-03-30 Published:2019-03-30

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摘要：

近年来随着北极冰区航道的开辟与成功通航, 船舶航行安全广受重视, 因冰区通航条件对船舶交通安全影响因素特殊且变化大, 有必要分析极地冰区船舶航行时航路过程中的风险。探讨低温、浮冰、高纬度等特殊属性对船舶极地冰区航行安全的影响, 结合船舶交通风险的特点, 提出北极冰区东北航道船舶航行风险状态概率转移的过程风险, 构建北极冰区东北航道船舶航行过程风险模型。基于马尔科夫过程假设, 建立基于马尔科夫链-蒙特卡洛方法的云仿真模型, 利用该模型对北极冰区东北航道船舶航行进行时间连续的过程风险动态仿真。研究表明: 夏季时期船舶在北极冰区东北航道航行过程风险整体上处于可通航状态, 离散风险程度随时间而连续变化, 总体呈现“M”型趋势, 风险波动明显。

关键词: 北极航路, 船舶航行, 过程安全, 马尔科夫链-蒙特卡洛方法, 云模型

Abstract:

In recent years, with the opening and successful navigation of the Arctic ice waterway, the safety of ship navigation in polar waters has been highly valued. Because there are many variable factors affecting the safety of ship traffic in the ice area, it is necessary to analyze the process risk for en-route navigation in the Arctic ice waterway. Combining the characteristics of maritime traffic risks, the effects of special attributes, such as low temperature, ice drift, and high latitude, were evaluated on the navigation safety of ships. The navigation risk on the Northeast Route in the Arctic was analyzed and then the navigational risk transition model for ships in the Arctic northeast route was constructed. The Markov process theory was introduced to establish a cloud simulation model based on the Markov Chain Monte Carlo (MCMC) Algorithm, thereby simulating the risk of ship navigation in the Arctic with continuous time. This study indicates that during the summer season, the risk of navigation in the northeastern route of the Arctic is as low as reasonably navigable. The risk level continuously changes with time, showing an overall M-type curve trend, with significant risk fluctuations.

Key words: Arctic route, ship navigation, process safety, Markov Chain Monte Carlo Algorithm, cloud model