基于RAdam Bi-LSTM的LNG动力船舶上甲板储罐泄漏后果预测方法

Consequence prediction of deck tank leakage on LNG powered ship using RAdam Bi-LSTM

针对液化天然气(Liquid Natural Gas,LNG)动力船舶上甲板储罐泄漏后果预测难度大、预测时间慢和预测成本高等问题,提出一种基于修正自适应矩估计(Rectified Adaptive Moment Estimation,RAdam)优化算法的双向长短期记忆模型循环神经网络(Bi-Directional Long Short-Term Memory,Bi-LSTM)对泄漏后果进行预测。利用FLACS软件对LNG动力船舶上甲板储罐泄漏过程进行数值模拟,并将数值模拟结果作为神经网络的数据集,使用决定系数(R-Square,R2)作为评价预测性能指标。为提高Bi-LSTM网络模型的预测精度和适应性,分别对其激活函数修正线性单元(Rectified Linear Unit,Relu)、Sigmoid、Tanh与优化器RAdam、自适应矩估计(Adaptive Moment Estimation,Adam)和随机梯度下降(Stochastic Gradient Descent,SGD)进行对比分析计算,发现基于Relu激活函数的RAdam Bi-LSTM网络模型的R2均值可达到0.97。为验证Bi-LSTM网络模型的优越性,对循环神经网络(Recurrent Neural Networks,RNN)、长短期记忆模型循环神经网络(Long Short-Term Memory,LSTM)和Bi-LSTM的预测结果进行对比,发现Bi-LSTM网络模型的R2较其他两个模型分别提高4.5%和1.5%,确定使用Bi-LSTM作为所提出的预测方法的网络模型。因此,基于Relu激活函数的RAdam Bi-LSTM网络模型为所提出预测模型中的最优模型,可作为LNG储罐泄漏后果的快速预测方法,以解决事故后果预测速度的问题。

To Predict the consequences of deck storage tank leakage on a LNG powered ship is difficult,time consuming and costly.The RAdam-LSTM is introduced to handle the problem.A set of data for network training is produced by means of software FLACS.The predictions are evaluated with their determination coefficient.The parameters with different Bi-LSTM networks are calculated and compared,including the following parameters:rectified linear unit(Relu),sigmoid,tanh,Adam,and SGD.Based on the calculation and comparison,the most accurate and suitable Bi-LSTM network is selected.The advantage of the Relu activated Bi-LSTM model is verified through comparing it to RNN,ordinary LSTM and ordinary Bi-LSTM.Experiment shows that the R^(2)achieved by the Bi-LSTM network is 4.5%and 1.5%higher than that from the other two models,respectively.