减少船舶航次油耗对航运企业提高船舶营运经济性以及减少温室气体排放均有重要意义。为提高船舶的营运能效，减少碳排放，有必要对船舶航速进行优化。本文以一艘超大型油轮VLCC为研究对象，基于采集的营运数据进行了气象数据融合以及数据预处理。本文提出了机理模型和数据学习耦合的船舶主机油耗预估半机理模型，采用FISHER最优分割算法对航线进行时序性航段划分，构建了以主机油耗最低的航速优化数学模型，并基于经典遗传算法进行航速优化。实验结果表明，提出的半机理模型对转速和轴功率的预测R2系数均大于0.98，预测精度较高；航速优化后目标船舶能节省燃油102.28 t。
Reducing fuel consumption during shipping voyages is of significant importance for shipping companies to improve the operational efficiency of vessels and to decrease greenhouse gas emissions. In order to enhance the operational energy efficiency of ships and reduce carbon emissions, it is necessary to optimize ship speed. This paper takes a very large crude carrier (VLCC) as a research object and performs weather data integration and data preprocessing based on collected operational data. A semi-mechanistic model for estimating ship main engine fuel consumption, coupled with data learning and domain knowledge, is proposed. The FISHER optimal segmentation algorithm is used to divide the route into segments, and a mathematical model for optimizing speed at minimal main engine fuel consumption is constructed, with speed optimization performed using a classical genetic algorithm. Experimental results show that the proposed semi-mechanistic model has R2 coefficients greater than 0.98 for predicting propeller rotational speed and shaft power, indicating high prediction accuracy. Following speed optimization, the target vessel is able to save 102.28 tons of fuel.
2026,48(1): 165-173 收稿日期：2025-4-28
DOI：10.3404/j.issn.1672-7649.2026.01.024
分类号：U694
作者简介：陈映彬(1998-),男,硕士,研究实习员,研究方向为船舶节能优化
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