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基于图神经网络的船舶液舱晃荡数值仿真

Numerical Simulations of Ship Liquid Tank Sloshing Based on Graph Neural Networks
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摘要 针对利用计算流体力学仿真方法进行船舶液舱晃荡计算资源消耗大的问题,提出了一种数据驱动的数值仿真模型。以图神经网络为基础构建而成,该网络采用编码器-处理器-解码器架构。编码器提取前5个时间步的流体粒子特征,处理器学习流体潜在的运动规律并更新特征,解码器预测下一时刻粒子特征。处理器结合自注意力机制,使模型能够动态分配邻接权重并突出不规则舱壁区域的影响。利用移动粒子半隐式法生成模型所需训练数据,针对缩尺压载液舱横摇工况开展数值仿真。结果表明:该模型的仿真精度以平均绝对误差为评价指标相较传统图神经网络提升了50%以上,计算效率较移动粒子半隐式法提升了2个数量级,为船舶液舱晃荡数值仿真提供了兼顾精度与效率的新方法。 To address the high consumption of computational resources in simulating ship liquid tank sloshing using computational fluid dynamics simulation methods,a data-driven numerical simulation model was proposed based on graph neural networks.An encoder-processor-decoder framework was employed in the proposed model.The encoder extracted features of fluid particles from the first five time steps.The processor learnt latent motion patterns of fluid and updated features,and the decoder predicted features of particles at subsequent time steps.The processor incorporated a self-attention mechanism to enable dynamic adjacency weight allocation and emphasize the influence of irregular tank wall regions.Training data for the model were generated through the moving particle semi-implicit(MPS)method.Numerical simulations were conducted for rolling conditions of scaled ballast tanks.The results have demonstrated that the proposed model achieves over 50%improvement in simulation accuracy measured by MAE compared to conventional graph neural networks,while maintaining two orders of magnitude higher computational efficiency than the MPS method.This approach provides a novel numerical simulation for ship tank sloshing analysis that effectively balances precision and efficiency.
作者 张文康 孙霄峰 钟一平 尹勇 Zhang Wenkang;Sun Xiaofeng;Zhong Yiping;Yin Yong(Key Laboratory of Marine Simulation and Control,Dalian Maritime University,Dalian 116026,China)
出处 《系统仿真学报》 北大核心 2025年第12期3087-3098,共12页 Journal of System Simulation
基金 国家重点研发计划(2022YFB4301402,2022YFB4300803) 工信部项目(CBG3N21-3-3)。
关键词 船舶液舱晃荡 数值仿真 自注意力机制 图神经网络 移动粒子半隐式法 ship liquid tank sloshing numerical simulation self-attention mechanism graph neural network moving particle semi-implicit(MPS)method
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