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A Framework of LSTM Neural Network Model in Multi-Time Scale Real-Time Prediction of Ship Motions in Head Waves

长短期记忆神经网络在船舶迎浪运动多时间尺度实时预测中的应用研究
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摘要 Ship motions induced by waves have a significant impact on the efficiency and safety of offshore operations.Real-time prediction of ship motions in the next few seconds plays a crucial role in performing sensitive activities.However,the obvious memory effect of ship motion time series brings certain difficulty to rapid and accurate prediction.Therefore,a real-time framework based on the Long-Short Term Memory(LSTM)neural network model is proposed to predict ship motions in regular and irregular head waves.A 15000 TEU container ship model is employed to illustrate the proposed framework.The numerical implementation and the real-time ship motion prediction in irregular head waves corresponding to the different time scales are carried out based on the container ship model.The related experimental data were employed to verify the numerical simulation results.The results show that the proposed method is more robust than the classical extreme short-term prediction method based on potential flow theory in the prediction of nonlinear ship motions. 波浪引起的船舶运动对海上作业的效率和安全有着重大影响。未来几秒钟内船舶运动的实时预测在执行敏感活动中起着至关重要的作用。然而,船舶运动时间序列具有明显的记忆效应,这给快速准确的预测带来了一定的困难。因此,本文提出了一种基于长短期记忆(LSTM)神经网络模型的实时方法来预测规则/不规则波迎浪中的船舶运动响应。采用15000 TEU集装箱船作为算例模型,对不同时间尺度的不规则波迎浪中船舶运动响应进行了数值预报和实时预测,并利用相关试验数据对结果进行了验证。结果表明,在非线性船舶运动预测中,所提出的方法比基于势流理论的经典极值短期预报更具可信度。
作者 CHEN Zhan-yang ZHAN Zheng-yong CHANG Shao-ping XU Shao-feng LIU Xing-yun 陈占阳;占正勇;常绍平;徐绍峰;刘兴云(哈尔滨工业大学(威海),山东威海264209;大连理工大学工业装备结构分析优化与CAE软件全国重点实验室,辽宁大连116024;航空工业西安飞行自动控制研究所,西安710065)
出处 《船舶力学》 EI CSCD 北大核心 2024年第12期1803-1819,共17页 Journal of Ship Mechanics
基金 山东省自然科学基金面上项目(ZR2024ME139) 航空科学基金(2024M074189001) 工业装备结构分析国家重点实验室开放基金资助项目(GZ23112)。
关键词 deep learning LSTM ship motion real-time prediction irregular waves 深度学习 LSTM 船舶运动 实时预报 不规则波
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