期刊
OCEAN ENGINEERING
卷 169, 期 -, 页码 457-468出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.oceaneng.2018.08.050
关键词
Ship energy efficiency; Speed optimization; Big data analysis; Parallel k-means algorithm; Hadoop
资金
- National Key Technology Support Program [2013BAG25B03]
- China Scholarship Council [201606950063]
- Hubei Provincial Leading High Talent Training Program [HBSTD [2012]86]
Energy efficiency of inland ships is significantly influenced by navigational environment, including wind speed and direction as well as water depth and speed. The complexity of the inland navigational environment makes it rather difficult to determine the optimal speeds under different environmental conditions to achieve the best energy efficiency. Route division according to the characteristics of these environmental factors could provide a good solution for the optimization of ship engine speed under different navigational environments. In this paper, the distributed parallel k-means clustering algorithm is adopted to achieve an elaborate route division by analyzing the corresponding environmental factors based on a self-developed big data analytics platform. Subsequently, a ship energy efficiency optimization model considering multiple environmental factors is established through analyzing the energy transfer among hull, propeller and main engine. Then, decisions are made concerning the optimal engine speeds in different segments along the path. Finally, a case study on the Yangtze River is performed to validate the present optimization method. The results show that the proposed method can effectively reduce energy consumption and CO2 emissions of ships.
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