上海交通大学学报 ›› 2024, Vol. 58 ›› Issue (11): 1654-1664.doi: 10.16183/j.cnki.jsjtu.2023.101
收稿日期:
2023-03-24
修回日期:
2023-06-03
接受日期:
2023-06-25
出版日期:
2024-11-28
发布日期:
2024-12-02
作者简介:
侯先瑞(1986—),博士,讲师,从事船舶运动预报及控制、海洋波浪能开发应用研究.电话(Tel.):021-38284804;E-mail:xrhou@shmtu.edu.cn.
基金资助:
HOU Xianruia,b(), ZHOU Xingyua, HUANG Xiaochenga
Received:
2023-03-24
Revised:
2023-06-03
Accepted:
2023-06-25
Online:
2024-11-28
Published:
2024-12-02
摘要:
将物理信息神经网络(PINN)应用于船舶螺旋桨尾流场的重建.介绍了PINN的原理和基本框架;应用PINN求解Burgers方程,对PINN求解偏微分方程的可行性进行验证.利用计算流体力学(CFD)软件STAR CCM+对KVLCC2螺旋桨的敞水特性进行了数值模拟,得到了该桨在敞水中运动的流场信息.基于数值模拟得到的敞水桨流场特性信息,构造PINN训练样本集对PINN进行训练;训练后的PINN用于推断控制方程在任意时间和空间坐标的近似解.将PINN得到的速度和压力分布与STAR CCM+模拟的速度和压力分布进行了比较,对比结果验证了PINN在尾流场重建中的可靠性.研究结果表明,PINN可以应用于船舶螺旋桨尾流场的重建.
中图分类号:
侯先瑞, 周星宇, 黄骁骋. 基于物理信息神经网络的船舶螺旋桨尾流场重构[J]. 上海交通大学学报, 2024, 58(11): 1654-1664.
HOU Xianrui, ZHOU Xingyu, HUANG Xiaocheng. Reconstruction of Ship Propeller Wake Field Based on Physics-Informed Neural Networks[J]. Journal of Shanghai Jiao Tong University, 2024, 58(11): 1654-1664.
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