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A novel energy conservation method for wind-assisted propulsion ships based on sails thrust optimization

Author

Listed:
  • Zhang, Rui
  • Huang, Lianzhong
  • Chen, Jijun
  • Peng, Guisheng
  • Ma, Ranqi
  • Cao, Jianlin
  • Wang, Cong
  • Wu, Jianyi
  • Li, Xiaowu

Abstract

The interference and aerodynamic performance of the sails are affected by changes in wind direction. Investigating the thrust optimization potential of multiple-sail systems is essential for enhancing wind energy utilization. A full-scale three-dimensional numerical simulation model of the multiple-sail system is established using validated computational fluid dynamics methods. The sail's interference and aerodynamic characteristics are analyzed across 30°–150° apparent wind direction, and found that these are closely related to wind direction. A multiple-stage optimization framework is then proposed, integrating simulation data, Kriging surrogate models, Genetic algorithms, and Expectation improvement strategies. The results show that the thrust coefficient (CT) of the sail system is effectively improved during the optimal operation, with an improvement range from 0.4 % to 10.6 %. Under bow and side winds, the aerodynamic performance of the downstream sail is enhanced through optimization, resulting in an increased CT, while the upstream sail's CT is improved at a quartering wind direction. Finally, a modified fuel consumption model is employed to evaluate the energy-saving effects of sail thrust optimization. The optimal operation resulted in a 5.4 % reduction in fuel consumption compared to the traditional synchronized approach during the Sunda Strait to Lomé, clearly demonstrating substantial energy conservation through thrust optimization.

Suggested Citation

  • Zhang, Rui & Huang, Lianzhong & Chen, Jijun & Peng, Guisheng & Ma, Ranqi & Cao, Jianlin & Wang, Cong & Wu, Jianyi & Li, Xiaowu, 2025. "A novel energy conservation method for wind-assisted propulsion ships based on sails thrust optimization," Energy, Elsevier, vol. 340(C).
  • Handle: RePEc:eee:energy:v:340:y:2025:i:c:s0360544225046675
    DOI: 10.1016/j.energy.2025.139025
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    References listed on IDEAS

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    1. Chunchang Zhang & Jia Zhu & Huiru Guo & Shuye Xue & Xian Wang & Zhihuan Wang & Taishan Chen & Liu Yang & Xiangming Zeng & Penghao Su, 2024. "Technical Requirements for 2023 IMO GHG Strategy," Sustainability, MDPI, vol. 16(7), pages 1-16, March.
    2. Ruan, Zhang & Huang, Lianzhong & Wang, Kai & Ma, Ranqi & Wang, Zhongyi & Zhang, Rui & Zhao, Haoyang & Wang, Cong, 2024. "A novel prediction method of fuel consumption for wing-diesel hybrid vessels based on feature construction," Energy, Elsevier, vol. 286(C).
    3. Dawei Zhan & Huanlai Xing, 2020. "Expected improvement for expensive optimization: a review," Journal of Global Optimization, Springer, vol. 78(3), pages 507-544, November.
    4. Bartosz Kawecki & Michal Kulak & Michal Lipian, 2024. "Wing Sails: Numerical Analysis of High-Performance Propulsion Systems for a Racing Yacht," Energies, MDPI, vol. 17(3), pages 1-16, January.
    5. Shipeng Fang & Cunwei Tian & Yuqi Zhang & Changbin Xu & Tianci Ding & Huimin Wang & Tao Xia, 2024. "Aerodynamic Analysis of Rigid Wing Sail Based on CFD Simulation for the Design of High-Performance Unmanned Sailboats," Mathematics, MDPI, vol. 12(16), pages 1-15, August.
    6. Marcin Kolodziejski & Mariusz Sosnowski, 2025. "Review of Wind-Assisted Propulsion Systems in Maritime Transport," Energies, MDPI, vol. 18(4), pages 1-33, February.
    7. Bai, H.L. & Chan, C.M. & Zhu, X.M. & Li, K.M., 2019. "A numerical study on the performance of a Savonius-type vertical-axis wind turbine in a confined long channel," Renewable Energy, Elsevier, vol. 139(C), pages 102-109.
    8. Yongxu Jiang & Chenze Cao & Ting Cui & Hao Yang & Zhengjun Tian, 2024. "Numerical Study on Auxiliary Propulsion Performance of Foldable Three-Element Wingsail Utilizing Wind Energy," Energies, MDPI, vol. 17(15), pages 1-19, August.
    9. Wang, Kai & Li, Zhongwei & Zhang, Rui & Ma, Ranqi & Huang, Lianzhong & Wang, Zhuang & Jiang, Xiaoli, 2025. "Computational fluid dynamics-based ship energy-saving technologies: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 207(C).
    10. Wang, Kai & Xue, Yu & Xu, Hao & Huang, Lianzhong & Ma, Ranqi & Zhang, Peng & Jiang, Xiaoli & Yuan, Yupeng & Negenborn, Rudy R. & Sun, Peiting, 2022. "Joint energy consumption optimization method for wing-diesel engine-powered hybrid ships towards a more energy-efficient shipping," Energy, Elsevier, vol. 245(C).
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