基于CNN-LSTM方法的液环泵非稳态流场预测分析

Prediction and Analysis of Unsteady Flow Field in Liquid-ring Pump Based on CNN-LSTM

摘要:

为实现对液环泵内非稳态气液两相流场的快速预测，提出了一种基于深度学习的非定常周期性流场预测方法，可以实现样本集之后未来一定时间段内流场的高精度快速预测。通过对液环泵非稳态CFD结果获取的各时间步上的流场快照建立流场数据集，利用卷积神经网络(CNN)对流场快照进行特征提取，并结合长短期记忆神经网络(LSTM)构建时间序列神经网络预测模型，预测结果与CFD数值模拟结果进行对比，分析表明，CNN-LSTM模型能够实现对未来时刻非稳态流场的高精度预测；相态场、压力场、温度场的预测结果平均相对误差分别为1.37%、1.28%、1.78%；在利用LSTM预测壳体及进口压力脉动时，在样本集之后叶轮旋转360°时间上平均相对误差分别为1.61%、0.09%、0.20%。在样本空间外的预测集上，CNN-LSTM的预测性能优于本征正交分解(POD)方法，尽管在外延时间序列上的预测精度随时间增加逐渐下降，但在整个时间历程上保持了较好的预测精度，在预测内流场结果方面具有显著优势。

Abstract:

Aiming to achieve rapid prediction of the unsteady gas-liquid two-phase flow field in liquid ring pumps, an unsteady periodic flow field prediction method was proposed based on deep learning. This method can realize high-precision and fast prediction of the flow field within a certain period in the future after the sample set. A flow field dataset was established using flow field snapshots at each time step obtained from unsteady CFD results of liquid ring pumps. The features of these flow field snapshots were extracted by convolutional neural network (CNN), and a time series neural network prediction model was constructed by combining long short-term memory neural network (LSTM). The prediction results were compared with CFD numerical simulation results. The analysis showed that the CNN-LSTM model can realize high-accuracy prediction of unsteady flow fields in the future. The average relative errors of the prediction results for the phase field, pressure field, and temperature field were 1.37%, 1.28%, and 1.78%, respectively. When LSTM was used to predict the pressure pulsation of the shell and inlet, the average relative errors on the impeller rotation time of one week after the sample set were 1.61%, 0.09%, and 0.20%, respectively. The prediction performance of CNN-LSTM was better than that of the proper orthogonal decomposition (POD) method on the prediction set outside the sample space. Although the prediction accuracy of the extrapolated time series gradually decreased with the increase of time, it maintained good prediction accuracy throughout the entire time history and had a significant advantage in predicting internal flow field results.