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首页> 外文期刊>Journal of marine science and technology >A STUDY ON HYDRODYNAMIC FORCES INDUCED BY LIQUID TANK OF LNG CARRIER IN WAVES BASED ON EXPERIMENT AND RECURRENT NEURAL NETWORK
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A STUDY ON HYDRODYNAMIC FORCES INDUCED BY LIQUID TANK OF LNG CARRIER IN WAVES BASED ON EXPERIMENT AND RECURRENT NEURAL NETWORK

机译:基于实验和经常性神经网络的波浪液箱液箱诱导的流体动力学力研究

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摘要

The sloshing of liquid in a partially filled tank is of great concern to aerospace vehicles, road vehicles and ships due to complex nonlinear motion involving the free surface of the liquid. Sloshing is a type of violent liquid motion that is created by forces such as an impact and breaking waves. Oscillation of the liquid in the vessel may threaten navigation safety. This paper attempts to predict the hydrodynamic forces induced by the motion of liquid inside a tank based on experiment and Recurrent Neural Network (RNN). The experiment of sloshing test is carried out using the Stewart platform at Changwon National University (CWNU). The motions of the LNG carrier are inputted into the Stewart platform, then the hydrodynamic forces induced by the liquid in the tank are analyzed. Then, an RNN is used to model the complex dynamics of the sloshing load in the liquid tank and the RNN predictions are compared with the experimental results. This new approach, which offers robust modeling of the complex dynamics of a sloshing load in an LNG carrier, can be applied to simulate the ship maneuvering in waves.
机译:由于涉及液体的自由表面的复杂的非线性运动,部分填充罐中的液体晃动对航空航天车辆,道路车辆和船舶的晃动非常关注。晃动是一种剧烈的液体运动,由诸如冲击和破碎波的力产生。血管中液体的振动可能会威胁到导航安全性。本文试图通过基于实验和复发性神经网络(RNN)来预测液体内液体运动诱导的流体动力力。沉积试验的实验在昌原国立大学(CWNU)的斯图尔特平台进行了。 LNG载体的运动被输入到斯图尔特平台中,然后分析罐中的液体诱导的流体动力力。然后,RNN用于模拟液体罐中的晃动负荷的复杂动态,并将RNN预测与实验结果进行比较。这种新方法提供了一种稳健建模的LNG载体中的晃动负载的复杂动态,可以应用于模拟波浪中操纵的船舶。

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