Session: 06-04-04 Marine Engineering and Technology IV

Paper Number: 127986

127986 - Impact of Ship Performance Model on Voyage Optimization Algorithm for Energy Efficient Sailing 

A voyage optimization system has become a common technique for seagoing vessels to achieve safe and intelligent operations. And the ship performance model is an essential component in the system, since the voyage optimization requires the evaluation of the associated sailing cost for decision-making, to suggest a feasible voyage with the optimized cost. For energy-efficient sailings, the voyage optimization system needs a ship performance model to estimate the corresponding fuel cost of each move, based on the sailing speed and environmental conditions. Thus, the accuracy and the robust assessment of the ship performance model would have a great influence on the voyage optimization result. The estimation of fuel consumption is dependent on many parameters such as engine parameters and ship resistance. And various approaches can be used to construct the ship performance model, such as a theoretical model based on empirical knowledge, or machine learning models using advanced artificial techniques. However, their behaviors have different impacts on voyage optimization, which can further influence the reliability of the optimization result especially for complex algorithms. In this study, five different ship performance models developed by theoretical and machine learning methods are integrated and validated in the voyage optimization algorithm. Furthermore, a chemical tanker with full-scale measurement is used in the case study with actual ocean crossing voyages collected in the North Atlantic. Finally, the impacts of different ship performance models on voyage planning are investigated through the comparison with the voyage optimization results, followed by the discussion of the benefits of different methods in detail. 

Presenting Author: Yuhan Chen Chalmers University of Technology

Presenting Author Biography: Yuhan Chen is a doctoral student at the Division of Marine Technology for Marie Skłodowska-Curie ITN project AutoBarge, and the research focuses on real-time multi-objective voyage optimization algorithms based on online machine learning for efficient autonomous navigation. The aim of the project is to build an online autonomous ship navigation platform to develop sophisticated multi-objective voyage optimization algorithms, that can remotely optimize, update/adjust, monitor unmanned ship operations in inland waters in real-time, and communicate with various inland water infrastructures.

Authors:

Yuhan Chen Chalmers University of Technology
Wengang Mao Chalmers University of Technology