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A study on a main engine power prediction model for a VLCC
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Yingbin CHEN1, 2, Guoxiang DONG1, 2, *, Sheng JI1, 2, Yanfei ZHANG1, 2
Navigation of China | 2026, 49(1) : 165 - 176
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Navigation of China | 2026, 49(1): 165-176
• Intelligent Shipping •
A study on a main engine power prediction model for a VLCC
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Yingbin CHEN1, 2, Guoxiang DONG1, 2, *, Sheng JI1, 2, Yanfei ZHANG1, 2
Affiliations
  • 1.State Key Laboratory of Maritime Technology and Safety, Shanghai Ship and Shipping Research Institute Co., Ltd., Shanghai 200135, China
  • 2.Key Laboratory of Marine Technology Ministry of Communications, Shanghai Ship and Shipping Research Institute Co., Ltd., Shanghai 200135, China
Published: 2026-02-25 doi: 10.3969/j.issn.1000-4653.2026.01.017
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Improving ship energy efficiency and reducing greenhouse gas emissions are major research priorities in the maritime industry. Accurate prediction of main engine power is fundamental to enhancing vessel energy efficiency. Using historical operational data collected from a Very Large Crude Carrier (VLCC), this study integrated and cleaned meteorological data to construct training and test datasets. Three models for main-engine power estimation are investigated and compared:a mechanistic model （SNNM), a non-mechanistic model based on Random Forest （RF), and a semi-mechanistic RF-based model. Simulation results indicate that while the mechanistic SNNM model can meet application requirements under specific engineering conditions, but R2 coefficient is relatively low. In contrast, both the non-mechanistic model based on RF and the semi-mechanistic RF-based model demonstrated excellent predictive accuracy for both main engine shaft rotational speed and power, with R2 values exceeding 0. 98.

VLCC  /  main engine power prediction model  /  SNNM  /  RF
Yingbin CHEN, Guoxiang DONG, Sheng JI, Yanfei ZHANG. A study on a main engine power prediction model for a VLCC[J]. Navigation of China, 2026 , 49 (1) : 165 -176 . DOI: 10.3969/j.issn.1000-4653.2026.01.017
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Year 2026 volume 49 Issue 1
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Article Info
doi: 10.3969/j.issn.1000-4653.2026.01.017
  • Receive Date：2025-02-25
  • Online Date：2026-05-19
  • Published：2026-02-25
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  • Received：2025-02-25
Affiliations
    1.State Key Laboratory of Maritime Technology and Safety, Shanghai Ship and Shipping Research Institute Co., Ltd., Shanghai 200135, China
    2.Key Laboratory of Marine Technology Ministry of Communications, Shanghai Ship and Shipping Research Institute Co., Ltd., Shanghai 200135, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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