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A novel cooperative optimization method of course and speed for wing-diesel hybrid ship based on improved A* algorithm.

Authors :
Wang, Cong
Huang, Lianzhong
Ma, Ranqi
Wang, Kai
Sheng, Jinlu
Ruan, Zhang
Hua, Yu
Zhang, Rui
Source :
Ocean Engineering. Jun2024, Vol. 302, pN.PAG-N.PAG. 1p.
Publication Year :
2024

Abstract

The wing-diesel hybrid ship, utilizes a wind propulsion system, which is significantly influenced by weather conditions. Therefore, a cooperative optimization method for course and speed is proposed based on an improved A* algorithm, considering weather changes and the ship characteristics, to achieve further improvement in the ship operational energy efficiency. Firstly, a meteorological and hydrological dataset is utilized to establish a dynamic maritime area model with complex weather conditions. Based on this model, the paper considers the actual navigation circumstances to define prohibited and navigable areas. Secondly, the improved A* algorithm incorporates the impact of weather on wing-sails. The improved A* algorithm considers the ship operating position and enhances the evaluation function by variables normalization of fuel consumption and voyage time. This cooperative optimization ensures the maximization of wind energy utilization, thereby achieving the goal of reducing fuel consumption while meeting the overall voyage time requirement. Experimental validation is conducted with the ship "New Aden" in the Indian Ocean region, and the results show that, compared to the original route, the optimized route reduces total fuel consumption by 5.48%, increases the navigation distance by up to 2.18% and increases the navigation time by 5.74%. • A cooperative optimization method of wing-diesel hybrid ship is proposed. • Improved A* algorithm is designed considering wing-sail and meteorological data. • Interpolation method is adopted to align meteorological data with ship data. • The algorithm evaluation function is calculated by variables normalization. • Optimized route reduces total fuel consumption by 5.48%. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00298018
Volume :
302
Database :
Academic Search Index
Journal :
Ocean Engineering
Publication Type :
Academic Journal
Accession number :
176866830
Full Text :
https://doi.org/10.1016/j.oceaneng.2024.117669