基于RC模型的KVLCC2船六自由度运动机器学习预测

IF 13 1区工程技术 Q1 ENGINEERING, MARINE

Journal of Ocean Engineering and Science Pub Date : 2025-02-01 DOI:10.1016/j.joes.2022.08.004

Ling Liu , Yu Yang , Tao Peng

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Machine learning prediction of 6-DOF motions of KVLCC2 ship based on RC model

This study uses a machine learning technique based on the Reservoir Computing (RC) model to predict the surge, sway, heave, roll, pitch, and yaw (6-DOF) motions of the KVLCC2 ship in an irregular wave environment. The trained RC model can predict the 6-DOF motions and give the predicted length of 2–5 wave cycles ahead with good accuracy. This work shows the strong ability of machine learning to predict vessel wave-excited motions. It implies that machine learning has important guiding significance in real-time forecasting for motions of both manned and unmanned ships.

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来源期刊

Journal of Ocean Engineering and Science Multiple-

CiteScore

11.50

自引率

19.70%

发文量

224

审稿时长

29 days

期刊介绍： The Journal of Ocean Engineering and Science (JOES) serves as a platform for disseminating original research and advancements in the realm of ocean engineering and science. JOES encourages the submission of papers covering various aspects of ocean engineering and science.