Session: 12-07-01 Blue Economy VII: Floating Offshore Wind and Green Shipping Technologies

Submission Number: 181244

Data-Driven Dynamic Modeling and Power Demand Prediction of Wind-Assisted Ships Based on Measured Data 

In the context of green and low-carbon shipping, wind-assisted propulsion has emerged as a promising and effective approach to harness renewable wind energy for reducing both fuel consumption and carbon emissions in the maritime industry. This study proposes a comprehensive data-driven dynamic modeling method for wind-assisted ships in real operational conditions, which integrates traditional ship dynamics theory with extensive multi-region voyage measurement data. The methodology involves detailed steps of data preprocessing, time-series resampling, and the extraction of key hydrodynamic and aerodynamic parameters to ensure the accuracy and robustness of the model. Based on these processes, a power demand evaluation model adaptable to various sea and wind conditions is established, enabling a quantitative assessment of the energy-saving performance of different wind-assisted propulsion configurations. The analysis results indicate that the deployment of wind-assist devices, such as rigid sails or rotor sails, can significantly reduce the propulsion power demand and improve overall voyage efficiency under favorable wind conditions. Furthermore, the proposed framework provides valuable insights for optimising ship operation strategies, evaluating energy efficiency across different routes, and supporting policy development toward sustainable and green maritime transportation. This research contributes to advancing practical applications of wind-assisted technologies in achieving decarbonization goals in the global shipping sector.

Presenting Author: Yunjie Li Shanghai Jiao Tong University

Presenting Author Biography: Yunjie is a master student at Shanghai Jiao Tong University.

Authors:

Yunjie Li Shanghai Jiao Tong University
Xindong Liu Shanghai
Jiankun Lou Shanghai Jiao Tong University
Hongdong Wang Shanghai Jiao Tong University
Mingyang Zhang Shanghai Jiao Tong University